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Supply Chain Management Thesis Topics- Top 30 Ideas

One of the most frequently asked question from SCMDOJO followers is, I am doing Supply Chain Management Masters from  Europe ,  UK  or  USA  and I need some Master Thesis ideas in Supply Chain?

Key academic research areas in SCM are offering robust and implementable supply chain management thesis that are transforming worldwide trends. The increasing strength of global Supply Chain Management (SCM) is one functional area that shows several students are seeking a good start, especially in solving significant problems in the form of  Masters  and  PhD thesis .

Nevertheless, with the changing trends in the industry, some students are likely to struggle with the early stages of academic writing. A significant reason for this problem is usually down to a lack of ideas or facing new topics with low research activity.

Old Industries and New Industries

The recent pattern shifts in academia, from the traditional research approach to other conventional methods, is taking a more student-centred view. Most of the supply chain management thesis is crafted by students, including dissertation, topic creation, research, and more with help of their supervisors.

With new industries, like Amazon and Apple, transforming old concepts with technological disruption, there are new trends to look out for to help narrow your supply chain management thesis.

The  7 Powerful Supply Chain Trends  (I also dubbed “Supply Chain 7.0”) have the potential to become a powerful influence over time. These trends, including Augmented reality (AR), Big Data, Gamification of the supply chain, moving supply chain to “Cloud,” and Internet of Things (IoT) – Industry 4.0. Also, Artificial Intelligence (AI) and machine learning in supply chain alongside 3D Printing are now needed to support the product life cycle.

Forbes also highlights the key  2020 Supply Chain Technology Trends  that are receiving lots of buzz in Supply Chain Management. In this regard, students seeking top-notch research areas for supply chain management thesis can consider new trends to help create adequate research content.

30  Supply Chain Management Thesis Topics for 2020

On these premises, any supply chain management thesis should be comprehensive. There several topics and areas to consider, and below are 30 Supply Chain Management Thesis Topics for 2020 that students can do research on towards an excellent postgraduate study in SCM.

Digital Transformation

• Digital Transformation Key Attributes; Challenges; enablers & Success Factors
• Smart Government Initiatives: How Governments are Driving Digital Change
• Digital Leadership linking to Virtual Teams or Self Organised Teams (Agile PM)
• COVID 19 impacted the implementation of Digital Transformation?
• Cross-functional collaboration in the decision-making process.
• The value of data and interdependencies in decision-making.
• Machine learning techniques in supply chain management

Sustainable Project Management (SPM)

• Can apply the SPM model or any of its dimensions to any type of project
• Can Blockchain help with Sustainable Project Management?
• Factors affecting the application of an efficient supply management system.

IoT- Industry 4.0 and Big Data

• Application of IoT in Logistics – Challenges; enablers & Success Factors
• The practicability of intertwined supply networks with IoT.
• Implementation of IoT in 3PL/4PL Industry – Challenges; enablers & Success Factors
• Big data and impact in DDMRP
• Evaluation of technology use in modern supply chain management.
• The extension of supply chain resilience through Industry 4.0
• The Impact of Industry 4.0 on supply chain management.
• Implementation of E-logistics in Supply Chain Operations.

Operations and Supply Chain Management

• Risk Evaluation and Management involved in a supply chain
• Partnerships Perspective in Supply Chain Management
• Assessing Supply Chain Risk Management Capabilities
• Implementation of Green Supply Chain Management Practices
• Supply Chain Management Practices and Supply Chain Performance Effectiveness
• The Impact of Supply Chain Management Practices on the Overall Performance of the org
• The Influence of Environmental Management Practices and Supply Chain Integration on Technological Innovation Performance
• The Relationship between Total Quality Management Practices and their Effects on Firm Performance
• Level of Commitment to Top Management regarding the TQM Implementation
• Impact of Mobility Solutions (transportation / latest technologies) on logistics.
• Study on the roles of supply chain management in corporate outsourcing.
• Evaluating strategies for cost reduction in SCM relating to exports and imports.

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The supply chain systems of today are more likely to see massive changes technologically in the coming years. Some selected supply chain management thesis topics may face limited data or access to real-time data in making proper research and forecast, including seasonality and trends. So, due diligence is necessary to ensure you not only pick an exciting supply chain management thesis, but you also have sufficient access to data, studies, and materials useful in such an area. The impact of these trends alongside technological advancement in the selected areas would certainly help your thesis stand out and unique.

If are looking for more articles in the education category,  you can visit this page

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About Dr. Muddassir Ahmed

Dr. Muddassir Ahmed is a global speaker, blogger and supply chain industry, expert. Dr Muddassir Ahmed has received a PhD in Management Science from Lancaster University Management school. Muddassir is a Six Sigma black belt and has founded scmdojo.com with the intention to enable supply chain professionals and supply chain teams to solve the problems they face in their jobs & business.

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About Zaragoza Logistics Center

Zaragoza Logistics Center is a research and educational institute affiliated to the  Massachusetts Institute of Technology  and the  University of Zaragoza .  Core research areas  in logistics and supply chain management at Zaragoza Logistics Center (ZLC) strive to be actionable and applied so that people and organizations can make decisions and take action.

Zaragoza Academic Partnership (ZAP)

The Zaragoza Academic Partnership (ZAP) Program  allows companies to sponsor ZLC students’ thesis projects for both masters  ZLOG , ZLOGb and MDSC . It is an initiative to enhance applied research and bring industry-academia relationships closer together in the field of supply chain management. Each year students are required to complete thesis projects and many of them work with our partner companies on challenging and innovative research projects through the ZAP Program.

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A research view of supply chain management: Developments and topics for exploration

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Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review

1 School of Business and Law, CQ University, Rockhampton North Campus, Sydney, Australia

Devika Kannan

2 SDU- Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, Odense, Denmark

The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field.

Introduction

Effective and efficient delivery of healthcare services is essential to counteract emergent diseases and promote healthy lives and the well-being of a population, particularly amid the COVID-19 outbreak. Many healthcare systems across the globe strive to efficiently utilise available resources and improve processes through more integrated operations involving all key stakeholders. As such, in the past few decades, research on healthcare has grown exponentially and has become a self-sustained discipline in operations and supply chain management (OSCM). Healthcare OSCM is a network of healthcare providers, pharmaceutical manufacturers, drug suppliers, distributors, and logistics service providers that are involved in the process of acquiring and transforming raw material into finished products, adding value, and facilitating the flow of products and services to the end-customer. With the rapid growth of research on healthcare OSCM, the extant studies have been conducted in various directions and expanded over a dozen of academic journals.

As the field has grown, a corresponding growth in systematic literature reviews in healthcare has occurred, especially in the past six years. For example, Narayana et al. ( 2014 ) reviewed 99 articles (published between 2000 to 2011) on the pharmaceutical supply chain and explore that most studies focus on efficiency improvement with growing interest in process analysis and technology implementation. Volland et al. ( 2017 ) presented a review of 145 articles (published between 1998 and 2014) on material logistics in hospitals and suggested further work on the concepts of supply and procurement, inventory management, and distribution and scheduling. Ali et al. ( 2018 ) conducted a review of 88 articles (published between 2009 and 2018) on cloud computing in hospitals and devised a framework on opportunities, issues, and applications of cloud computing-enabled healthcare systems. Malik et al. ( 2018 ) reviewed 22 articles (published between 2001 and 2015) on the application of data mining in healthcare service delivery and revealed that application of data mining is narrow-focused (workforce scheduling and quality of care), thereby suggesting further research on the broader application of data mining. Moons et al. ( 2019 ) presented a review of 56 articles (published between 2010 and 2016) on logistics performance measurement in the internal hospital supply chain, specifically inventory management and distribution activities in the operation theatre. Diwas Singh et al. ( 2020 ) offered a review of 70 empirical articles (published between 1999 and 2018) from three journals ( Management Science, Manufacturing and Service Operations Management, and Production and Operations Management ) and suggested the need for more empirical research on personalised medicine, value-based healthcare, and digitisation of healthcare. Keskinocak and Savva ( 2020 ) mapped the healthcare research published between 2009 and 2018 from a single journal ( Manufacturing and Service Operations Management ) and suggested the need to develop and apply new OM methods for the improvement of healthcare service.

The existing literature reviews have different scopes and emphases focusing on a specific topic (e.g., material logistics in hospitals, data mining, only empirical papers, etc.). In other words, they are not designed to capture latent topics (hidden topics) that reside in large volumes of scholarly data. Expanding upon some knowledge from the existing reviews, this paper seeks to advance our understanding of the key research topics that have been addressed in healthcare OSCM using a topic modelling-based literature review. Topic modelling with Latent Dirichlet Allocation (LDA) has been found as an effective method to algorithmically and automatically uncover the abstract topics that reside in a large and unstructured collection of articles (Antons et al., 2016 ; Blei, 2012 ; Zhang, 2012 ). LDA topic modelling builds upon rigorous statistical foundations which allows topic generation with little human intervention and/or manual processing (Blei, 2012 ; Zhang, 2012 ). Such an automatic method creates more meaningful and realistic topics and ensures the reliability and validity of outcomes as opposed to manual methods (Griffiths & Steyvers, 2004 ). LDA has been successfully applied for topic modelling in the information science field (Yan, 2014 ), marketing (Tirunillai & Tellis, 2014 ), statistics (De Battisti et al., 2015 ), tourism (Guo et al., 2017 ), decision sciences (Chae & Olson, 2018 ), computer science (Guo et al., 2017 ), and hydropower research (Jiang et al., 2016 ). However, to date, we have only seen a very limited application of topic modelling-base literature in OSCM fields (e.g., Chae & Olson, 2018 ; Lee & Kang, 2018 ), particularly in the healthcare context, to explore the significant topics hidden in a large sample of articles and to locate gaps for further research in each topic. As such, this study aims to: (1) uncover the latent topics residing in the articles published on healthcare OSCM, (2) examine the topic trend/evolution over time including hot, cold, and steady topics, and (3) find opportunities for more impactful research on each topic through a review of the literature around each topic. Driven by these core aims; the following research questions are posed:

• What are the emergent topics of research in the field of healthcare OSCM?
• How has research on each topic evolved over the past two decades?
• What are the opportunities for more impactful future research in the area?

Our study is among the few that offers a topic modelling-based review of literature, which allows us objectively to uncover hidden patterns of research that pervade in a large collection of studies and to explore the development of literature on each topic. The paper advances our understanding of how the literature on different topics in the field of healthcare OSCM has evolved over time and what are the recent trends, knowledge flow, and opportunities for insightful research on each topic. We find an increasing focus and need for further investigation on some emergent topics, such as patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. In terms of research methods, our analysis shows the increasing interest of researchers in the Markov method, mixed-integer programming, simulation, queueing model and stochastic programming in exploring multidimensional issues of healthcare OSCM. In addition, our analysis suggests that the past three years have also seen some empirical contributions in the area, including structural equation modelling, longitudinal study, and action research; thus, encouraging empirical researchers for more fruitful contributions on the topic.

Our study offers valuable research directions to researchers, journal editors, and policymakers in the field of healthcare OSCM. Researchers can observe whether their recent research topics are hot, cold, or steady, which will allow them to select appropriate topics for more productive forthcoming study in the area. Particularly, some early-career researchers find it difficult to keep up with the growing literature in their fields of research and familiarise themselves with a huge volume of literature in identifying the subject area on which they can focus or apply for research grants. The information from our topic modelling can be a useful tool for these researchers to understand recent developments in the field and then select more appropriate topics. Our findings can also be helpful to PhD students, who often face difficulties in topic selection due to time pressure. Journal editors can view if the extant research is consistent with their editorial policy, and they may even choose to launch a new editorial vision and direction. Our list of hot topics can be a valuable reference tool to launch special issues in the journals. Discovering underlying topics and tracking their evolution could also be of great interest to policymakers (e.g., government agencies) and industry. The information on which research themes and methods are growing or declining in popularity over time (Table ​ (Table3 3 and Fig.  4 ) can support governmental funding agencies for grants allocation to promising areas. Likewise, firm managers and the industry as a whole can use this information to formulate promising R&D strategies and more informed investment decisions.

Hot, cold, and steady topics/methods

Topic trend/dynamic over time

The rest of the paper is structured as follows. Section 2 elaborates on the research methods, covering data collection, screening and analysis procedures. Section 3 outlines results which include word cloud, key term matrix, topic labelling, and evolution of each topic over time. Section 4 provides discussion, while Sect. 5 offers a review of literature and opportunities for further research. The study is concluded in Sect. 6.

As mentioned earlier, this study employs the topic modelling method. The superiority of the topic modelling method has been evidenced in other fields, for example, Latent Semantic Analysis (LSA) (Deerwester et al., 1990 ), Non-Negative Matrix Factorization (NMF) (Lee & Seung, 1999 ), and probabilistic topic modelling. LSA uses singular value decomposition and a traditional matrix factorization technique to reduce the dimensionality of documents (Deerwester et al., 1990 ). Unlike singular value decomposition, in NMF, the transformed and reduced version of documents contains no negative elements (Lee & Seung, 1999 ). Probabilistic topic modelling is a recent development in topic modelling where words and documents are described through probabilistic topics, and a topic is described in terms of a probability distribution over a vocabulary of words (Hofmann, 2001 ; Lee & Kang, 2018 ). Latent Dirichlet Allocation (LDA) is the most popular probabilistic topic modelling algorithm. A comparison of LSA, NMF, and LDA suggests that LDA has the greatest ability to maintain topic coherence and to generate predominant topics that handle the issue of polysemy, the overlapping of words with multiple meanings (Stevens et al., 2012 ). LDA has been successfully applied in the information science field (Yan, 2014 ), marketing (Tirunillai & Tellis, 2014 ), statistics (De Battisti et al., 2015 ), tourism (Guo et al., 2017 ), decision sciences (Chae & Olson, 2018 ), computer science (Guo et al., 2017 ), and hydropower research (Jiang et al., 2016 ). Borrowing knowledge from adjacent areas, this study employs LDA in healthcare. The analysis is completed in four main steps: data collection; text pre-processing; word cloud; and topic modelling.

Data collection

The analysis aims to uncover the landscape of healthcare research published in good quality international journals in the field of operations and supply chains. The data were collected from Scopus on 25 January 2022, which is one of the largest abstracts and citation databases of academic papers (Ali & Gölgeci, 2019 ; Ferreira, 2018 ; Ali et al., 2021a , 2021b ). To retrieve a broad range of literature on the topic, a large set of keywords was developed based on the authors’ extensive knowledge of the field and review of the extant literature in the field. These keywords included: healthcare, AND operations AND “supply chain” OR hospital OR pharmaceutical OR drug OR medicine AND model OR framework OR Stochastic OR Simulation OR “linear programming” OR Heuristic OR mathematical OR optimization OR “numerical model” OR “nonlinear programming” OR “decision tool” OR “decision analysis” OR “discrete event” OR multi-objective OR multi-objective OR multi-attribute OR multi-criteria OR MCDM OR MCDA OR qualitative OR quantitative OR “case study” OR empirical. Quotation marks were placed on the keywords comprised of more than one word (e.g., “linear programming”) to ensure that search engines consider them as one word.

Initially, we did not limit the timespan because we sought to capture a wide range of publications. The search query generated 663 articles. We excluded the three articles published before 2000 to set the time interval between 2000 and 25 January 2022 (the date of data collection). Next, we carefully read the title and abstract of each article and excluded irrelevant articles. We also excluded articles in non-English languages. Extreme care was taken to include the articles that focus on healthcare OSCM. The inclusion and exclusion criteria resulted in the final set of 571 articles as shown in the supplementary file. Table ​ Table1 1 presents the distribution of these articles.

Distribution of articles across 20 selected journals

The articles selected from Scopus were validated using other databases, such as Web of Science, Google Scholar, ProQuest, and EBSCO to ensure that no important articles were missed in the review. The validation process also helped confirm that Scopus contains a greater number of related articles on the topic of healthcare OSCM compared to other databases, so it was used as a preferred database for this research.

Data pre-processing

Once the articles are collected from Scopus, some pre-processing of data collected is required to conduct further analysis. First, since our search result included the article’s title, keywords and abstract, we merged them as a single document. Second, we removed all punctuation and numbers and transformed all characters into lower-case. Third, we eliminated all “stop-words,” whose main role is to make a sentence grammatically correct, e.g., articles (a, an, the) and prepositions (of, by, from, etc.) and general words that repeatedly appear in most articles, e.g., “and,” “paper”, “find”, “effect”, and “discuss”. These words are not semantically meaningful, yet they create trouble in the analysis process. This step is important because without applying stop-word elimination, our analytical method could result in a big proportion of these unwanted words in the word cloud as well as in the topic matrix.

Word cloud with R-package is a text mining method to find the most frequently used keywords in sampled articles. A word cloud was designed to allow a visual interpretation of main themes and their trajectories in our review transcripts over the four different periods (2000–2004, 2005–2009, 2010–2014, 2015–2022). As mentioned earlier, we also included articles published until 25 January 2022. Data file for each period was imported into a popular R package, word cloud (Hornik & Grün, 2011 ) with default Dirichlet hyperparameters. The following functions in the R-package were applied to create word clouds: tm for text mining: snowballC for text stemming; Wordcloud2 for generating word cloud images; RColorBrewer for colour palettes.

Topics modelling

To extract the predominant topics from the 571 sampled articles, LDA’s generative process model was applied (Blei, 2012 ). Figure  1 demonstrates the process and its main variables where η is the parameter of the Dirichlet prior for the per-topic word distribution. K is the defined number of topics, while β k is a distribution of words over a topic. W represents the observable variable—the words in each article. W d,n is the n th word in article d . α is the parameter of the Dirichlet on the per-article topic distribution. θ d is a distribution of topics for the d th article. Z d,n is the topic assignment for the nth word in article d . Some variables are interdependent, that is, W (the observed word) depends on β (the distribution of topics) and Z (the topic assignment for the word), on θ (the topic proportion for the document). This process can be represented by the joint distribution of latent and observed variables through the following Figure and equations:

The graphical representation for LDA.

Adapted from Blei ( 2012 )

The generative process aims to explore the topic structure (θ, z , and β), which can explain W . Given the observed variable ( W ), the posterior distribution is estimated with the following equation:

The purpose of Fig.  1 and Eqs.  1 and 2 herein is to demonstrate the analysis process followed by LDA. To perform the analysis objectively and automatically, we used the R-package topic-models along with the tm package (Hornik & Grün, 2011 ). This analytical process produces a document term matrix for the desired number of topics.

Analysis and results

This section presents the analysis and the results of data processing, which consist of word cloud, topic modelling, and topic labelling.

As presented in Fig.  2 , the word clouds show some noticeable changes between 2000 and 2022 in the healthcare literature. In the first period (2000–2004), the popular terms include strategy, logistics, risk, simulation, integer, programming, operations, reengineering, information, inventory, and planning. While a few terms in the first period and second period (2005–2009) are similar (e.g., reengineering, outsourcing, supply, chains, healthcare, strategy, logistics), several new terms became popular in the second period which includes sustainability, warehousing, reverse, heuristic, stochastics, empirical, knowledge, humanitarian, among others.

Word cloud demonstrating changing pattern of terms over time (2000–2022)

The third period (2010–2014) shows some important changes, where several new terms (e.g., behavioural, literature, reviews, multilevel, action, structural, equation, Markov) emerged, and other terms (e.g., reengineering, outsourcing, strategy) disappeared. Some other terms remain similar to the second period (e.g., sustainability, warehousing, reverse, heuristic, stochastics, empirical, knowledge, humanitarian, waiting time).

While many terms of the third period continued to appear in the fourth period (2015–2022), several new terms are generated: circular, economy, climate, change, longitudinal. Terms such as warehousing, marketing, strategy disappeared in this period. Understandably, the terms “healthcare”, “operations”, “supply”, “chain” appeared consistently across the four periods.

Topic modelling

While the visualisation of word clouds (observed words) provides a broad picture of the research dynamics in the literature motivating towards further analysis, it does not provide quantitative information on the relationship between different terms that are generated. Without having quantitative information on each key term, it would be difficult to merge the related terms and assign them into specific topics.

To derive the quantitative information on the nexus between different terms, we used LDA topic modelling that explores the hidden or latent structure and generates different topics by merging interrelated terms from the data set. Generating an appropriate number of topics ( K value) from the given sample of articles is always challenging (Roberts et al., 2014 ). Selecting too few topics would not provide a full and meaningful understanding of the field, while selecting too many topics often results in meaningless outcomes. Therefore, to select the appropriate number of topics, two measures were undertaken: (1) a different number of topics (K value) were tried and accuracies of outcome were checked for each set of topics, and (2) brainstorming among researchers was conducted to ensure topic quality and relatedness to the context of the study (Mimno & McCallum, 2012 ; Silge & Robinson, 2016 ). As a result, 34 relevant topics were produced using LDA with R-package (Fig.  3 ). For each topic, the top five terms were selected, where each term represents a distribution weight (per-topic term probability).

Topic matrix generated in R-package using LDA principle

Topic labelling

We conducted topic labelling based on the ‘LDA topic matrix’ that produced 34 topics (see Fig.  3 ). Each topic constitutes five terms with different distribution weights. Since the term “healthcare,” “operations,” “supply,” and “chain” appeared in most topics, we decided not to use any of them in labelling the topics. Further, while topic labelling in Table ​ Table2, 2 , we used the numbering/sequence of topics exactly following the sequence of topics in the ‘LDA topic matrix’ (Fig.  3 ). To briefly illustrate the process of topic labelling, we discuss a few examples of topic labelling. From Fig.  3 , the top terms in Topic#1 are queuing and waiting, followed by hospital, patient, process; therefore, the topic is labelled the “patient’s waiting time”. Topic#2 includes top terms, such as RFID, big, data followed cloud, computing; therefore, we label the topic as “Industry 4.0”. Industry 4.0 refers to the fourth industrial revolution with the growing use of autonomous technologies such as cloud computing, big data analytics, internet supported RFID and others. Topic# 3 encompasses sustainability, economic, sustainable; hence, it is labelled as sustainability.

Likewise, Topic#8 is labelled “risk and resilience” since the top terms are risk, adaption, resilience supported by healthcare and readiness. Topic#18 is labelled “Stochastic programming” because its top terms are stochastics, programming, followed by model, method, and chain. We adopted a similar process for labelling all 34 topics as demonstrated in Table ​ Table2 2 .

Topic dynamic/evolution over time

In this section, we introduce the hot, cold, and steady topics (research themes and methods) to provide researchers with an idea of where trends are going (hot) and not going (cold). Exploring the dynamic changes in different topics over time can offer directions for more impactful research on healthcare. We identified three patterns of topic evolution including hot, cold, and steady. For this purpose, we employed a bivariate correlation between the frequency of keywords and time period using four different time periods, namely 2000–2004; 2005–2009; 2010–2014; 2015–2022 (Fig.  3 ). Scopus database was used to collect the information on the frequency of keywords (for each topic shown in Fig.  3 and Table ​ Table2) 2 ) in each time interval.

Hot, cold, and steady topics (which include ‘research themes’ and ‘research methods’ as shown in Table ​ Table3) 3 ) were identified based on the direction of the trendline on each topic and the value of slope (Fig.  4 ).

Hot topics show a trendline moving upward and a positive slope (x-value). Examples of the hot topics related to research themes include the Patient’s waiting time, Industry 4.0 technologies, Sustainability, Circular economy, Behavioural OSCM (OSCM), Risk and resilience, Climate change, and research methods that constitute Markov method, Simulation, Stochastic programming, among others.

The number of contributions on these topics (themes and methods) increased almost continuously from 2010 to 2014, with a dramatic increase on some topics (e.g., patient waiting time, sustainability, circular economy, etc.) between 2015 and 2022.

Cold topics, on the other hand, imply the opposite pattern to these hot topics; they show declining trendline and negative slope values, perhaps due to the relative lack of interest of researchers in recent times.

Examples of cold topics associated with research themes include warehousing, marketing, outsourcing, business process reengineering, strategy, operation strategy, and research methods of heuristics and game theory model among others. These topics enjoyed significant attention between 2000 and 2015, but their proportions have been steadily decreasing over the past five years (Fig.  4 ).

Finally, steady topics show a rise in the beginning but there is no rise or fall in trendlines over the past 5 or more years; perhaps, their importance has not been increased over time.

Overall, two topics depicted a steady growth over the past 5 or more years including goal programming and multi-level analysis. As can be seen in Fig.  4 , the trendline for goal programming modelling increased up to 2009, while no remarkable increase was observed since then. Likewise, the trendline for multi-level analysis shows an upward trend until 2014; however, no growth has been demonstrated since then.

While not investigated in this study, it can be speculated that these topics may lose the interest of research and likely become a cold topic in future. Drawing upon trendline and slope for each topic in Fig.  4 , we have presented all 34 topics under three main categories: hot, cold, and steady (Table ​ (Table3). 3 ). For better clarity of expression, we have segregated the topics into themes and methods. While both themes and methods are both important, our focus is on research themes in this analysis and discussion.

Over the last two decades, research on healthcare OSCM has seen spectacular growth. In light of the burgeoning literature on the topic, this study was motivated to explore recent developments, trends, and core topics of research in the area. The study was driven by three key research questions. Addressing the first research question, we explored 34 core topics on which extant research on the topic has been conducted over the past decades. For the second research question, our topic modelling analysis has resulted in the identification of hot, cold, and steady topics in empirical research healthcare OSCM (Table ​ (Table3 3 and Fig.  4 ). Thirteen topics of research themes show an upward trend, and nine of them (Patient waiting time, Industry 4.0, Sustainable OSCM, Behavioural OSCM, Risk and resilience, Humanitarian logistics, Climate change, Healthcare logistics, and Literature reviews) start with a relatively flat value (close to 0) before showing a rising trend from around 2010. The other four topics (Circular economy, Service ecosystem, Knowledge management, and Pharmaceutical OSCM) show a gradual upward trend from the early 2000s. Five research topics belong to cold topics. Outsourcing and operations strategy shows an almost linear declining trend, while both Marketing and Warehousing show a bell-shaped trend whose popularity rose in the early 2000s but fell after 2010 and 2015, respectively. Business process reengineering was a popular topic during the early 2000s but, since 2010, shows a declining trend. It is important to note, however, that being identified as a cold topic does not suggest that the topics are not important and should be abandoned. The results provide more of an indication about the interest (trends) of researchers’ interests rather than the importance of the topics themselves. It is also interesting to see that most of the changes of the trends (either upward or downward) of the topics took place around 2010.

Finally, in response to the third research question, we offered a review of the literature and identified numerous opportunities for further research (Sect. 5). Our findings show a shift of current research from traditional topics of operations management (Business process reengineering, Marketing, Outsourcing, Heuristics) to emergent topics such as Patient waiting time, Industry 4.0 technologies, Sustainable operations, Risk and resilience, Climate change, and Circular economy. In general, the shift in topics seems to show a move away from traditional, efficiency-focused research toward broader goals of superior customer service and system’s effectiveness. While each of the three groups of topics deserves discussion, in this section, we discuss the hot research topics as well as provide recommendations of avenues for fruitful future research. It should be noted that although the topic “literature reviews” was a hot topic, we do not include it in our discussion below as the extant research related to it has already been discussed in the introduction section.

Literature review and opportunities for further research

The following section provides a brief synthesis of current literature around emergent research topics that are recovered through topic modelling, and it offers numerous opportunities for further research in the field.

Patients’ waiting time and COVID-19

This research topic covers two interrelated and emerging issues: patient waiting time and COVID-19 outbreak. With a rapidly growing population of aged people, healthcare systems have been facing pressure for more health services resulting in patients’ long waiting times. Therefore, determining methods to ameliorate waiting time has been high on the agenda of most developing and developed economies. Correspondingly, in recent years, mostly building upon simulation and Markov as the primary method of research, considerable studies appeared around the issues of patients’ waiting time, such as patient’s admission process (Chae, 2019 ; Vissers et al., 2007 ); surgery procedures (VanBerkel & Blake, 2007 ); patient inflow and outflow in the emergency department (Abo-Hamad & Arisha, 2013 ); no-shows of patients and overbooking (Topuz et al., 2018 ); outpatient appointment scheduling (Shehadeh et al., 2019 ); time overestimation for the emergency procedure (Gartner & Padman, 2019 ); walk-in patients treatment (Pan et al., 2019 ); patient readmission and discharge planning model (Gu et al., 2019 ); resource planning model to reduce waiting time of patients (Hejazi, 2021 ); load smoothing of scheduled admission to the reduced number of beds required and probability of delay (Asgari & Asgari, 2021 ), and modelled association between length of appointment interval and no-shows of patients (Pan et al., 2021 ).

Overall, although the existing studies have captured several important issues related to patient waiting time, we identify the need for further research on this significant topic. For example, there is potential to develop an integrated model investigating how multiple factors such as general service time, equipment breakdown, high attrition of hospital staff, variability in patient’s arrival time, and unpunctuality of already booked patients cumulatively contribute to patients’ waiting time. Further research can also devise a model at the intersection of patients’ waiting list management, operations theatre scheduling, and patient waiting time for surgery. While the literature on simulation and mathematical modelling continues to grow, there is also an opportunity to further expand empirical research for testing causal links between various factors and their combined effect on patient waiting time. For example, empirical research can test links among customers’ heterogeneous behaviour and their service time in scheduling appointments. Further, a unified model capturing non-value-added practices at both micro and meso levels would be fruitful future research in reducing patient waiting time. While research on bedding and staffing is growing, there is a need to optimise bed and staff requirements amid the global pandemic. Another potential research area is to examine the longitudinal effect of hospital staff’s training on patients’ waiting time.

Likewise, given the outbreak of COVID-19, the recent literature of healthcare OSCM has been rapidly growing on the topic. Pamucar et al. ( 2022 ) proposed a model for supplier selection amid COVID-19. Fan and Xie ( 2022 ) generated an optimization model for COVID-19 testing facility design and planning. Ghaderi ( 2022 ) offered a framework for public health intervention in the wake of the pandemic. Govindan et al. ( 2020 ) developed a decision support system and fuzzy interface system to help with demand management in the healthcare supply chain amid the COVID-19 crises. Nagurney ( 2021a ) modelled labour shortage due to COVID-19, resulting in reduced food harvest and supply. Tavana et al. ( 2021 ) presented a mixed-integer programming model for the equitable distribution of COVID-19 vaccine in developing countries. Salarpour and Nagurney ( 2021 ) constructed a stochastic model to study competition among different countries for medical supplies amid COVID-19 crises. Nagurney ( 2021b ) offered a supply chain network optimization model to manage labour in electronic commerce during the COVID-19 pandemic. Thakur ( 2021 ) developed a model to manage hospital waste during the COVID-19 outbreak. Choi ( 2021 ) used a sense-and-response specific OR model to demonstrate the specific actions needed to deal with the COVID-19 outbreak.

To sum up, while the past research continues to grow on COVID, it would be interesting to explore how to extenuate potential risks and develop shockproof healthcare systems to deal with unanticipated outbreaks such as COVID-19 and similar future incidents. In this regard, it is worth exploring how such a topic as Industry 4.0 technologies could be applied across the healthcare system to capture data from wide sources and help the decision-making process in the event of a pandemic, including contact tracing, the identification of the outbreak location. Some potential areas of research amid COVID-19 include but are not limited to patient inflow and outflow in the emergency department; outpatient appointment scheduling model; models on time estimation for the emergency procedure; walk-in patients’ treatment model; patient admission and discharge planning model; resource planning model to reduce waiting time of patients; load smoothing of scheduled admission to the reduced number of beds required and probability of delay. Besides, pharmaceutical SCM would be the ultimate hope for the recovery and possibly eradication of the virus that allows us to go back to normal life. It would be insightful to examine how pharmaceutical supply chains were affected by COVID-19 and develop a recovery model dealing with such a situation. In particular, if/when a pharmaceutical solution to COVID-19 emerges, research is needed to help understand the best ways to rapidly scale up and distribute the materials on a scale that has never been seen before. It is also of interest to study whether synergy in logistics could be achieved between the public organisations and the growing number of private companies that have entered the healthcare business. Future research should continue to incorporate the healthcare and hospital view into operations management and transfer established concepts from other industries into healthcare while accounting for industry specifics. Again, the links between this and COVID-19 are both obvious and critical.

Industry 4.0 technologies

Industry 4.0 or the fourth industrial revolution refers to fully automated and in-connected technologies such as the Internet of Things (IoT), cloud computing, big data analytics, blockchain, artificial intelligence, among others (Ali & Govindan, 2021 ; Ali et al., 2021a , 2021b ). Industry 4.0 technologies can enhance the level of service in the industry through real-time information sharing, visibility, traceability, agility, and connectivity within a firm’s boundary and across the supply chain (Govindan et al., 2022 ). Given this, growth in research on the role of Industry 4.0 technologies in healthcare service provision is evident, such as Sultan ( 2014 ) developed a conceptual framework on the potential of cloud computing in the advancement of healthcare services. Chong et al. ( 2015 ) modelled variables that influence the adoption of IoT (e.g., RFID) in healthcare. Priya and Ranjith Kumar ( 2015 ) used big data analytics to predict the progression of atherosclerotic disease, the narrowing and hardening of arteries due to the accumulation of plaque on the artery wall. Fan et al. ( 2018 ) recognised the factors affecting the adoption of artificial intelligence-based medical diagnosis support systems. Kochan et al. ( 2018 ) modelled on how cloud computing improves visibility and responsiveness in hospital SC. Galetsi et al. ( 2020 ) developed a theoretical framework for the realisation of big data analytics.

The research on Industry 4.0 is growing with the advent of new technologies and provides fruitful opportunities for further research. The models on big data should incorporate data of all inter-connected functions in healthcare, instead of just focusing on a single function or silo approach. For example, the impact of Industry 4.0 on the entire healthcare supply chain can be modelled. This is important to understand how interaction with different functions/partners through Industry 4.0 technologies influence healthcare productivity. Most of the current research focuses on big data and cloud computing which needs to be expanded to other Industry 4.0 technologies, such as blockchain, cyber-physical systems and the Internet of Things in healthcare OSCM. There is a lack of a holistic model testing the combined impact of all Industry 4.0 technologies on the healthcare system and an investigation into if there is a trade-off for investing between various digital technologies would be helpful. Future research can also investigate how machine learning techniques impact diagnostic, maintenance, and prognostics systems in healthcare settings. Research highlights the intricate issues of human–machine interaction at the workplace (Arslan et al., 2021 ); we suggest further research on how to maintain trustworthiness and to harmonise productive human–machine interactions and reduce workers’ resistance to automated machines in the healthcare settings. We also highlight the need for further research on the application of artificial intelligence in healthcare OSCM.

Sustainable operations and supply chain

Today sustainability is a growing concern of most organisations and therefore the topic of sustainability has received increasing focus from researchers from operations management. Healthcare systems worldwide face pressing challenges of service quality and cost issues as well as safe disposal of waste. As such, in recent times considerable attempts have been made to realise sustainability in the healthcare system. Chauhan and Singh ( 2016 ) employed a hybrid model for the selection of sustainable locations to dispose of healthcare waste. Anuar et al. ( 2018 ) offered a conceptual framework demonstrating the link between lean practices and a sustainable healthcare system. Malekpoor et al. ( 2018 ) modelled a sustainable healthcare treatment plan. Thorsen and McGarvey ( 2018 ) investigated the effectiveness of fixed and mobile dentistry for the financial sustainability of the healthcare system. Mousa and Othman ( 2020 ) found a positive impact of green human resource management practices on a sustainable healthcare system.

While research on healthcare sustainability is growing, there is still space for further research. For instance, more studies are needed that simultaneously investigate the impact of all three dimensions (social, environmental, economic) of sustainability through a single model. Prospective studies can also investigate the combined impact of synchronous business activities including green human resource management, green marketing, and green production on healthcare sustainability. There is also an opportunity to develop a sustainable performance measurement and management model in healthcare.

Circular economy

The circular economy is a recent concept aimed to eliminate waste through the make, use, recycle and reuse approach. It helps to overcome a take-make-dispose linear pattern of production and consumption. The healthcare industry inevitably generates waste that may become hazardous to both public health and the environment. Therefore, the circular economy has recently gained significant attention in healthcare literature. For example, Kumar and Rahman ( 2014 ) suggested RFID supported process reengineering for waste management ensuring circular economy. Jensen et al. ( 2019 ) discussed the healthcare refurbish system as a means to reduce waste. Dehghani et al. ( 2019 ) suggested that transhipment can reduce waste and save substantial costs compared to no-transhipment. Viegas et al. ( 2019 ) identified the need for research on the circularity of end-of-use medicine in the pharmaceutical supply chain.

Although research circular economy has witnessed mounting attention, there is still room for further research. For instance, there is a need to develop a model that could make the circularity of outdated medicine more visible through all stages of reverse flow and re-manufacturing. Also, healthcare literature lacks studies modelling processes and decision-making for circular economy implementation at micro and meso levels. Prospective research can also explore the healthcare staff’s perception of the circular economy. It would be interesting to investigate how healthcare professionals are prepared to deal with the circular economy and how they might contribute to reducing wastage in reverse flow.

Service ecosystem

With its root in marketing, the service ecosystems concept is defined as “relatively self-contained, self-adjusting systems of resource-integrating actors connected by shared institutional logics and mutual value creation through service exchange” (Vargo & Akaka, 2012 , p. 207). That is, the service ecosystem generates value for all the actors in a network through resource integration, value co-creation, and co-innovation. The service ecosystem concept has received a great deal of attention in healthcare literature. Nudurupati et al. ( 2015 ) elaborate on the process of value co-creation through strategic alliance and collaboration with multi-stakeholders. Albarune et al. ( 2015 ) suggested a value-based supply chain for integrated hospital management. Chae ( 2019 ) suggested digital innovation as a promising source of resource sharing and value creation among actors in an ecosystem. Lee et al. ( 2020 ) showed a positive relationship between the previous year value-based purchasing penalty and the current year care process improvement in the hospital.

Service ecosystem research is a growing area of research in the healthcare context; thus, offering opportunities for further expansion. For example, the influence of contextual behavioural factors in value co-creation and unlocking innovation needs further investigation. Also, service ecosystem research needs to be expanded from the organisational level to the entire healthcare system. For example, how resource integration practice can be integrated into a multitude of value co-creation processes in the healthcare networks. Moreover, the role of advanced technologies and servitisation in the healthcare service ecosystem has yet to be investigated.

Knowledge management

Knowledge management refers to the process of generating, transferring, and managing knowledge and information in an organisation or network. Knowledge management becomes a major driver of performance and productivity where front-line workers are required to provide customised services tailored to the need of individual clients such as healthcare. As such, the literature has witnessed considerable growth in literature on knowledge management in healthcare settings. Gagnon et al. ( 2016 ) empirically corroborated a positive association between knowledge management on staff practices and quality of healthcare. Mura et al. ( 2016 ) found that knowledge exchange and assets have a positive impact on workers’ innovation, such as idea generation, idea promotion and idea implementation. Avgerinos and Gokpinar ( 2017 ) argued that knowledge transfer capability in a team positively influences healthcare productivity. Hiranrithikorn and Sutduean ( 2019 ) expound that access to information and knowledge has a positive influence on healthcare supply chain skills. Despite considerable contributions to knowledge management, there is still significant room for further research. A few models are investigating the impact of knowledge sharing on the quality and productivity of healthcare performance. Qualitative research can be conducted to explore the barrier and enablers of knowledge sharing in healthcare. Future research can use multi-method research investigating the relationship between social capital, culture, and knowledge management.

Behavioural operations

Research on behavioural operations is well-received, particularly from healthcare service perspectives. Healthcare systems have two main stakeholders: staff and patients. The research suggests that the findings of healthcare modelling may not be reliable without considering the behaviours of the main stakeholders (Brailsford et al., 2012 ). For example, a simulation model suggesting the effectiveness of a new drug will be unreliable if it does not consider patient behaviour; some patients may not complete the prescribed course of medication, and potential side effects of medicine should be shown. As such, healthcare literature has seen an emergence of interest in understanding both human behaviour in practice and how to capture it in operations research models. Brailsford et al. ( 2012 ) used a simulation model capturing behavioural factors of a patient while treating breast cancer. Based on a review of literature, Kunc et al. ( 2018 ) identified scarcity of research on behavioural aspects of healthcare. Harper ( 2019 ) used a queuing model to assess how healthcare workers behave and treat patients, especially when such professionals encounter changing workloads, service queues, and other factors that impact service quality.

To sum, behavioural operational research has become an important research topic in healthcare but significant room for further research in the area remains intact. Specifically, we suggest more research with detailed models on the nexus between behavioural interventions on healthcare practice. Future research can model workforce adaptive behaviours and productivity. We agree that the incorporation of behavioural factors is important to increase the reliability of the healthcare model because of the increasing influence of human behaviours in healthcare operations. Future research can also examine the behavioural consequences of inter-organisational interaction in complex healthcare systems.

Risk and resilience

Healthcare systems are susceptible to operational failures in services offered to patients when a shortage of medicine and equipment occurs, or when the execution of such systems is incorrectly managed. Given the divergent risks emerging from a variety of sources, there is an increasing focus on risk mitigation and the development of resilient healthcare. Nemeth et al. ( 2011 ) developed a model for healthcare crises management and resilience. Mahjoub et al. ( 2014 ) found risk mitigation in healthcare through a risk-sharing agreement between pharmaceutical companies and healthcare service providers. Saedi et al. ( 2016 ) suggested an optimal inventory policy to reduce drug shortage risks. Rubbio et al. ( 2019 ) examined the role of digital technologies in developing healthcare resilience. Rahimian et al. ( 2019 ) investigated an appropriate level of risk-aversion through robust optimization. Zhang et al. ( 2020 ) formulated a model which helps to reduce overtime risk during surgery in a hospital.

While research on risk and resilience in healthcare is growing, there is still substantial room for more impactful future research. Most models investigate risk and resilience in isolation. Others focus on particular functions such as hospital surgery. As such, a quantitative model that simultaneously investigates risk and resilience focusing on the key supply chain actors in healthcare is lacking. Future research can also use a mixed-methods approach where qualitative research can explore various risk factors and resilience strategies, while quantitative research can test the interrelationship between risk and resilience in explaining healthcare performance.

Humanitarian logistics

Humanitarian logistics refers to the transportation, delivery, and distribution of supplies in the event of natural disasters or another emergency to the affected area and people. Given the unprecedented incidents such as floods, cyclones, disease outbursts, the humanitarian logistics in healthcare (e.g., supplies of medicines, medical equipment, sterile items, linen, and food) has gained significant attention in recent times. For example, Naor et al. ( 2018 ) found strategies for effective humanitarian logistics in healthcare, such as quickly sending a team to the disaster area to gather firsthand information on the unique situations of disaster followed by the main delegation staff carrying effective disaster relief service. Prasad et al. ( 2018 ) suggested effective healthcare intervention to deal with disease outbreaks in a community. Cherkesly et al. ( 2019 ) assist with the design of a community healthcare network, increasing the service coverage for underserved areas in adverse situations.

Although research on humanitarian logistics has been progressing steadily, we highlight the need for further research on healthcare OSCM. A fruitful future research avenue is modelling the role of volunteers and NGOs in humanitarian logistics from a healthcare perspective. Also, interviews with disaster area patients can provide additional insights into the real-world phenomenon. Further research can also develop a model of budget allocation as the community service demand increases drastically due to a natural disaster. It would be interesting to investigate the nexus among service compliance rate, disease rate, and total cost by disaster type.

Pharmaceutical OSCM

A pharmaceutical supply chain involves firms, processes, and operations involved in the design, development, and distribution of life-saving drugs. It is one of the critical supply chains since it is related to the life and health of people. The success of healthcare services directly depends on well-managed pharmaceutical supplies. Therefore, the management of the pharmaceutical supply chain has become a popular topic in healthcare literature. Jia and Zhao ( 2017 ) highlighted the issue of drug shortage due to lack of collaboration between pharmacies and pharmaceutical manufacturing and supplier. Nematollahi et al. ( 2018 ) also found a positive link between hospital service level and efficient pharmaceutical supply chain. Viegas et al. ( 2019 ) discussed the significance of a coordinated pharmaceutical supply chain for the reverse flow of end-of-life medicine. Recently, Zandkarimkhani et al. ( 2020 ) formulated a model to redesign the pharmaceutical supply chain network in the wake of demand uncertainty.

Despite growth in literature on the pharmaceutical supply chain, significant room for further research still exists, particularly in the context of a recent pandemic. Most of the existing research lacks a holistic view that involves all key stakeholders of the supply chain. There is less focus on upstream networks and processes (Narayana et al., 2014 ). There is also limited research on integrated new product development in the pharmaceutical supply chain. There is also an opportunity for research to model examining the link between healthcare financing and pharmaceutical supply chain financing such as investment in R&D, production and distribution, between waste management, environmental and pharmaceutical supply chains. Future studies can also model the behaviour of individuals in the robustness of pharmaceutical supply chains. Given that supply chain design is critical in the planning process (Govindan et al., 2017 ), we suggest designing integrated models that encompass all stages from drug manufacturing to final consumer for robust decision-making in the pharmaceutical supply chain. Another interesting avenue is formulating a model at the intersection of supplier selection and sustainable pharmaceutical supply chain networks.

Climate change and healthcare

The increased intensity and frequency of disasters due to extreme weather conditions draw our attention to the unprecedented changes in climate which are mainly attributed to excessive greenhouse gas emissions and carbon footprints. To deal with the climate change challenges, greenhouse gas mitigation targets were set to a minimum level in the historic Paris Agreement (Bodansky, 2016 ). While a lot of research spotlights the emissions reduction in the automotive, energy, and mining industries, the carbon footprint and climate change research has recently received attention. Rezali et al. ( 2018 ) discussed the importance of a green healthcare supply chain to reduce the environmental impact of the healthcare industry. Belkhir and Elmeligi ( 2019 ) examined 15 pharmaceutical supply chains across different countries and found that the pharmaceutical industry is more emission-intensive than the automotive industry. Tanwar et al. ( 2019 ) identify the potential risks and risk mitigation measures in implementing a green pharmaceutical supply chain. Over the past five years, the topic of climate change has received increasing attention in healthcare settings. Most studies on the topic focus on factors contributing to climate change (greenhouse gas emissions, carbon footprint), thus leaving a gap for research models incorporating the business impact of climate risks when they unfold and examining specific risk mitigation measures.

Healthcare logistics

Healthcare logistics refer to the physical flow of healthcare materials from the pharmaceutical production facility to the healthcare service provider. Logistics cost has been identified as the second largest cost after personnel in the healthcare system (Ross & Jayaraman, 2009 ). As such, the importance of material flow in healthcare service is recognised important to add value and reduce the cost of services. Correspondingly, a growth in literature on healthcare logistics is evident. Oumlil and Williams ( 2011 ) suggested strategic alliance and consolidation of logistics in the healthcare sector as a strategy to reduced logistics costs. Volland et al. ( 2017 ) identified four main domains in which healthcare logistics research exists, including procurement, inventory management, distribution, and supply chain management. Al-Sharhan et al. ( 2019 ) expounded on the significance of hospital-supplier integration of inefficient material logistics. Fathollahi-Fard et al. ( 2019 ) developed a green home healthcare supply chain model to reduce emissions in logistics. Pohjosenperä et al. ( 2019 ) discussed the impact of service modularity in value-creating and improving healthcare logistics service. Zabinsky et al. ( 2020 ) developed a model through mixed-integer linear programming to address healthcare scheduling and routing problem. Further research could examine an item-level analysis of different product categories in the regional healthcare units, together with their replenishment models. Such a study is needed to show the true potential of modularity in service offerings of the logistics support organisation.

Over the last two decades, research on healthcare OSCM has been continuously advancing making it a self-sustained discipline in the OSCM field. We argue that with this unprecedented development in healthcare literature, the current status and trends in the field need to be mapped. The use of robust statistical and automatic methods for extracting key topics from a large set of articles has been increasingly used in other fields such as finance, marketing, statistics, tourism, and computer sciences. Borrowing the knowledge from these adjacent fields, this study employs probabilistic topic modelling with LDA as an automatic text analytics approach to unpack dominant topics and development trajectories in a large collection of research articles on healthcare OSCM. In doing so, we contribute to the literature on new and rigorous methods for systematic literature review in the field of OSCM. Our sample included 571 articles retrieved from 20 journals in the field of OSCM. The results produced 34 topics on healthcare research. Analysis of dynamic changes in the 34 over the past twenty years suggested 24 hot, 8 cold, and 2 steady topics (see Table ​ Table3 3 ).

Our findings show a shift of current research from traditional topics of operations management (Business process reengineering, Marketing, Outsourcing, Heuristics) to emergent topics such as Patient waiting for time, Industry 4.0 technologies, Sustainable operations, Risk and resilience, Climate change, and Circular economy. In general, the shift in topics seems to show a move away from traditional, efficiency-focused research toward broader goals of superior customer service and system’s effectiveness. The data on research methods shows a larger dominance of modelling research (e.g., Markov, mixed-integer programming, simulation, queueing model, and stochastic) in exploring multidimensional issues of healthcare operations. However, the past three years have also seen some empirical research, including Structural equation modelling, Longitudinal study, and Action research. This shows a positive change in operations research journals from purely quantitative modelling studies to empirical research as well. This recent shift in a strategy not only offers a great opportunity to empirical researchers for publication in some OR journals but also provides sound premises on which to expand the scope and subscription of these journals.

The findings of this research offer useful research directions to researchers, journal editors, and policymakers in the field of healthcare research. Researchers can observe whether their recent research topics are hot, cold, or steady, and accordingly select appropriate topics for more impactful future research in the area. Particularly, some early-career researchers find it difficult to keep up with the growing literature in their fields of research and familiarise themselves with a huge volume of literature in identifying the subject area on which they can focus or apply for a research grant. The information from our topic modelling can be a useful tool for these researchers to understand recent developments in the field and then select more appropriate topics. Our findings can also be helpful to PhD students, who often face difficulties in topic selection due to time pressure. Journal editors can view if the extant research is consistent with their editorial policy, and they may like to launch a new editorial vision and direction. Our list of hot topics can be a valuable reference tool to launch special issues in the journals. Discovering underlying topics and tracking their evolution could also be of great interest to policymakers (e.g., government agencies) and industry. The information on which research themes and methods are growing or declining in popularity over time (Table ​ (Table3 3 and Fig.  4 ) can support governmental funding agencies for grants allocation to promising areas. Likewise, firm managers and the industry as a whole can use this information to formulate promising R&D strategies and to make more informed investment decisions.

We believe that topic modelling is one of the most effective and reliable approaches in revealing the latent (hidden) structure and development of research topics in a field, and recommend that future research can combine the LDA-based models with other text analytics techniques from different text data (e.g., customer feedback, medical records, research reports, business reviews, company descriptions) and interconnection among various business areas (e.g., marketing, supply chain, information systems). For example, sentiment analysis (Pang & Lee, 2008 ) is deemed a popular text analytics tool in the field of finance (Sul et al., 2017 ), marketing (Liang et al., 2015 ), and operations/supply chain (Chae, 2015 ). The sentiment analysis can be integrated with topic modelling: a two-pronged method using topic modelling followed by sentiment analysis can offer more nuanced findings by revealing both positive and negative aspects of a product and/or service.

As with several studies, there are some limitations of our study. First, given the authors’ extensive knowledge of the field and review of the extant literature, full efforts were made to guarantee that all pertinent articles were included in the analysis. However, some articles might have been ignored. Nevertheless, two measures might help mitigate this limitation: (1) the topics are generated based on the frequencies of the keywords/key terms and, therefore, missing a few articles from such a large dataset (571 articles) will not have a significant impact on overall results; (2) the dataset is validated with other databases. Second, the research is limited to healthcare research published in OSCM journals. That is, we excluded the papers and related journals containing healthcare research but not from OSCM perspectives.

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50 Supply chain and logistic dissertation topics for 2024

The meaning of supply chain management

To look into supply chain management. There is a poor understanding of how the supply chains operate. Therefore, elaborating how these systems can be managed is necessary. The same issues are evident in the search for supply chain thesis topics.  Most interesting supply chain topics are already researched in detail. Therefore, getting a better topic that can be defended at ease is quite challenging. Despite having our well-researched topics online, you can also access supply chain management thesis topics pdf from our website. we have also compiled a list of Ph.D. topics.

1. Impact of Ukraine and Russia war on global logistics and supply chain

This topic tries to find the consequence of the ongoing war in Europe on the supply chain and logistics of the world. This is one of the best logistic dissertation topics for 2022 that is not yet covered in length. Therefore, one can easily develop a  great thesis from it.

2. Africa as a new heart for the global supply chain

This topic tries to explain why Africa is the new emerging market and manufacturing hub for global supply. Africa has untapped potential such as cheap labor that may be utilized to meet global supply needs.

3. Strategies used for cost reduction in supply chain management

 To find out how to minimize expenditure during supply. High costs tend to affect the outcome in the provision cycle.

4. Effects of E-logistics on supply chain

To determine the outcome of E-logistics on any given process during the distribution of goods or services. Failure to recognize better ways of reaching out to those involved in supply. This is one of the most diverse supply chain management research topics of 2022. It still has so much gap to be covered.

5. Mobility solutions for supply chain

To identify faster ways to efficiently move goods or services. The delay faced during supply has a negative impact on all the participants.

6. Importance of digital transformation on supply

To reveal how adapting to new technology impacts business outcomes. Some employees are unwilling to adapt to change and hence resist training.

7. An analysis of supply chain integration

Evaluating whether incorporation of many participants improves results. Getting onboard partners that are unable to meet deadlines.

8. Evaluating supply chain resilience

Assessing how adaptable the systems are to change. Maintaining the new changes can be challenging.

9. Critical analysis of supply chain agility

To discover how fast businesses can adjust to meet the market requirements. Some firms take a long time to remodel to be at par with current trends.

10. Evaluating risks involved in the supply chain

Gauging the dangers that one can encounter in the supply process. Some risks are man-made and hard to deal with.

11. Managing the supply chain globally

To figure out ways of staying afloat in the market in many regions. There is high competition affecting supply and demand globally which some producers fail to meet.

12. Optimizing costs of the supply chain

To work out methods of upgrading product value while retaining a reasonable price. Upgrading products leads to a rise in prices.

13. Supply chain progressive gradation

To determine how to maintain growth. Stagnation affects most production firms at some point.

14. Effects of leadership changeover in supply chain

To know how to keep the chief officers for longer periods. Getting new employers can affect businesses negatively since they need time to master the new roles.

15. How environmental changes affect the supply chain

To deduce whether the climate can affect the supply chain. It is hard to predict the changes that can occur within a locality.

16.  What does big data do to supply chain management?

To evaluate the impact of a variety of high-volume data in supply chain management. There is limited room for improvement. 

17. Impact of the Green project on the supply chain

To investigate how managers can integrate environmentally friendly processes during production. Establishment of firms with no consideration of their impact on the environment.

18. Importance of Total Quality Management

To evaluate the importance of every member of the team maintaining high standards in their various areas. Pulling in the same direction will always be a cutting edge for all organizations.

19. Effects of political factors on supply chain management

To look into issues related to the government that could influence how goods flow from one point to the next. Regional instabilities due to wars interrupt the normal flow of products.

20. How the law affects supply chain management

To prove that some laws can hinder business systems from normal operation. Businesses that do not meet the stipulated codes of conduct are not allowed to run. 

21. Studying how the IoT works in relation to the supply chain

To ascertain if supply chain management depends on IoT for its smooth operation. Some businesses are unable to keep up hence lose customers.

22. Fourth Industrial Revolution and supply chain management

To determine if 4IR is beneficial to supply chain management. Specialization and flexibility are costly as boundaries become more blurry.

23. Establishing the relationship between supply chains and the performance of firms

To show how a firm’s potential affects the movement of its products. Failing firms can hardly have sustainable supply chains.

24. Effects of Covid-19 on supply chain management

To determine how the pandemic affected businesses and their customers. The pandemic caused businesses to shut down while others had to change modes of operation.

25. Profit maximization in supply chain management

Identifying how to get the most out of the goods and services produced. Wrong pricing of products leads to massive losses that are damaging to the firms.

26. What roles do technological changes play in the supply chain?

To expose how technology is shaping the production of both goods and services. Rapid technological changes have had a huge impact on supply chains. 

27. Importance of business relationships in supply chain management

To plan how beneficial partnerships can be established. Poor consumer supplier relations disrupt efficiency.

28. How is manufacturing important in the supply business?

To establish how significant manufacturing is in the supply chain business. Expensive processes are involved due to a lack of proper planning.

29. Customer satisfaction in relation to supply chain management

To find out how best to meet consumer needs. Good or service production without customer participation.

30. Impact of supply chain management on business

To research the extent to which supply chain management affects any given organization. Inefficient managers lead to the collapse of supply chains. 

31. Supply chain efficiency versus effectiveness

To clarify what the two terms mean in the supply chain. Producers are incapable of differentiating the two concepts.

32. Significance of logistics

To know how to handle logistics to maximize output. Lack of qualified logisticians to handle the procedures involved. 

33. Does analytics in supply chain matter?  

To demonstrate the significance of analytics in supply. Insufficient information on analytics.

34. How supply chains can affect a country’s growth and development

To point out ways in which a disruption in supply chains can cripple progress. Inadequate resource allocation.

35. Use of AI in organizations

To make a comparison between artificial intelligence and human intelligence in firms. Laxity in embracing the use of machines in making decisions.

36. The future of supply chain management

To analyze what lies ahead in the product distribution sector. Inability to correctly predict the market trends.

37. Types of supply chains

To name and list the types of supply chains. Lack of clarity on the types of supply chains that exist.

38. How firms adhere to supply chain ethics

To determine how ethics affect organizations. Inadequate knowledge on the functions of these ethics.

39. Importance of supply chain management to the society

To give a detailed explanation of how the people benefit from proper regulations of supply chains. Few people understand what is involved in production and supply.

40. Supply chain visibility

To discuss how visibility can be maintained from the initial to the final stage. Few suppliers have the ability to trace their goods.

41. How an organization can ensure supply chain sustainability

To find out how companies deal with social, economic, and environmental issues in the process of supplying goods or services. There is disregard for issues that arise relating to the products being distributed.

42. How quality control works in supply chain management

To understand the role of quality control in supply chain management. Suppliers ignore matters related to quality controls resulting in poor outcomes.

43. Essential supply chain management tools

To find out which tools are used. Inability to recognize what is required for successful operations.

44. Lack of qualified personnel in the supply chain

To elaborate on the effect of fewer qualified professionals in supply chain management. Supply chain jobs were not considered to be as valuable.

45. Issues faced by supply chain managers

To state challenges that managers go through. Lack of experience in handling customers hence inability to solve the problems that arise.

46. Challenges that operation managers face currently

To discuss what operation managers experience in their line of work. Lack of the right skills to tackle problems.

47. How operation management is crucial in supply chain management

To bring out the significance of operation management.

48. The key elements in logistics

To describe the elements in logistics. Very few people understand the concept and how it works.

49. Logistics versus supply chain management

To bring out the difference between the two processes. Both deal with the end products in an organization.

50. The probability of supply chain managers being replaced by robots

To establish whether machines will take full control of the supply industry. It is difficult to tell really but it is a very interesting area to delve into.

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Areas of Research

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Students in the logistics doctoral program at Broad work closely with faculty from the onset of their program to ensure the best preparation for academic life – including working on supply chain management research projects that eventually lead to publications in top-tier academic journals. The program links the traditional areas of logistics research and development with faculty expertise in the areas of demand management and forecasting, logistics operations and modeling, logistics strategy and relationship management. Both empirical and analytical methods are examined in the program to provide students with the skill sets necessary to succeed in their chosen stream of research.

Publications

Below are selected publications by faculty who work in the logistics doctoral program.

Stanley E. Griffis

2016 Curt B. Moore, G. Tyge Payne, Chad W. Autry,  Stanley E. Griffis . (2016) “Project Complexity and Bonding Social Capital in Network Organizations.” Group & Organization Management . May, 1-35. 2015 Bell, John E. Autry, Chad W.,  Griffis, Stanley E.,  (2015) “Supply Chain Interdiction as a Competitive Weapon.” Transportation Journal . 54(1), 89-103. 2014 Griffis, Stanley E.,  Autry, Chad W., Thornton, LaDonna M., ben Brik, Anis (2014) “Assessing Antecedents of Socially Responsible Supplier Selection in Three Global Supply Chain Contexts.” Decision Sciences . 45(6), 1187-1215. Melnyk, Steven, Zobel, Christopher W., Macdonald, John,  Griffis, Stanley E.  (2014) “Making Sense of Transient Responses in Simulation Studies.” International Journal of Production Research . 52(3), 617-632, 2013 Whipple, Judith M., Griffis,  Stanley E.,  and Daugherty, Patricia J. (2013) “Conceptualizations of Trust: Can We Trust Them?”  Journal of Business Logistics . 34(2), 117-130 . Fleming, Christopher L.,  Griffis, Stanley E ., Bell, John E. (2013) “The Effects of Triangle Inequality on the Vehicle Routing Problem.”  European Journal of Operations Research . 224(1):1-7. 2012 Griffis, Stanley E ., Rao, Shashank, Goldsby, Thomas J., Voorhees, Clay M., Iyengar, Deepak, (2012) “Linking Order Fulfillment Performance to Referrals in Online Retailing: An Empirical Analysis.”  Journal of Business Logistics . 33(4), 278-292. Griffis, Stanley E ., Whipple, Judith, M., (2012) “A Comprehensive Risk Assessment and Evaluation Model: Proposing a Risk Priority Continuum.”  Transportation Journal . 51 (4), 428-451. Porterfield, Tobin E., Macdonald, John R.,  Griffis, Stanley E ., (2012) “An Exploration of the Relational Effects of Supply Chain Disruptions.”  Transportation Journal . 51 (4), 399-427. Griffis, Stanley E ., Bell, John E., Closs, David J., (2012) “Metaheuristics in Logistics and Supply Chain Management.”  Journal of Business Logistics.  33(2), 90-105. Griffis, Stanley E ., Rao, Shashank, Goldsby, Thomas J., Niranjan, Tarikere T., (2012) “The Customer Consequences of Returns in Online Retailing: An Empirical Analysis.”  Journal of Operations Management . 30 (4), 282-294. 2011 Rao, Shashank, Griffis, Stanley E., Goldsby, Thomas J. (2011) “Failure to Deliver?  Linking Online Order Fulfillment Glitches with Future Purchase Behavior.”  Journal of Operations Management . 29 (7-8), pp 693-703. Rao, Shashank, Goldsby, Thomas J., Griffis, Stanley E., Iyengar, Deepak (2011) “Electronic Logistics Service Quality (e-LSQ):  Its Impact on the Customer’s Purchase Satisfaction and Retention.”  Journal of Business Logistics . 32 (2), pp. 167-179. Payne, G. Tyge, Moore, Curt B.,  Griffis, Stanley E ., Autry, Chad W. (2011) “Multilevel Challenges and Opportunities in Social Capital Research.”  Journal of Management . 37 (2), 491-520. Wilcox, William, Horvath, Philip A.,  Griffis, Stanley E. , Autry, Chad W. (2011) “A Markov Model of Liquidity Effects in Reverse Logistics Processes: The Effects of Random Volume and Passage.”  International Journal of Production Economics . 129 (1), 86-101. Bell, John E.,  Griffis, Stanley E . Cunningham, William J., Eberlan, Jon (2011) “Location Optimization of Strategic Alert Sites for Homeland Defense.”  Omega, The International Journal of Management Science.  39 (2), 151-158. 2010 Bell, John E and  Stanley E. Griffis  (2010), “Swarm Intelligence: Application of the Ant Colony Optimization Algorithm to Logistics-Oriented Vehicle Routing Problems.”  Journal of Business Logistics.  31 (2), 157-175. 2008 Autry, Chad W.,  Stanley E. Griffis  (2008), “Supply Chain Capital: The Impact of Structural and Relational Linkages on Firm Execution and Innovation.”  Journal of Business Logistics.  29 (1), 157-173. 2007 Griffis, Stanley E ., Thomas J. Goldsby, Martha C. Cooper, and David J. Closs (2007), “Aligning Logistics Performance Measures to the Information Needs of the Firm.”  Journal of Business Logistics.  28 (2), 35-56. 2006 Goldsby, Thomas J.,  Stanley E. Griffis  and Anthony S. Roath (2006), “Modeling Lean, Agile, and Leagile Supply Chain Strategies.”  Journal of Business Logistics.  27 (1), 57-80.  Winner – The Bernard J. LaLonde Award for Best Paper in JBL 2006. 2005 Autry, Chad W., and  Stanley E. Griffis  (2005), “A Social Anthropology of Logistics Research: Exploring Productivity and Collaboration in an Emerging Science.”  Transportation Journal.  44 (4), 27-43. Autry, Chad W.,  Stanley E. Griffis , Thomas J. Goldsby, L. Michelle Bobbitt (2005), “Warehouse Management Systems: An Initial Assessment, Empirical Analysis and Research Agenda.”  Journal of Business Logistics.  26 (2), 165-183. 2004 Griffis, Stanley , Martha C. Cooper, Thomas J. Goldsby and David J. Closs (2004), “Performance Measurement: Measure Selection Based Upon Firm Goals and Information Reporting Needs.”  Journal of Business Logistics.  25 (2), 95-118. 2003 Griffis, Stanley E ., Thomas J. Goldsby, and Martha C. Cooper (2003), “Web-Based and Mail Surveys: A Comparison of Response, Data and Cost.”  Journal of Business Logistics.  24 (2), 237-258.

Stanley Lim

2022 Lim, S.F.W.T., Richards, T.J., Rabinovich, E. and Choi, M. 2022. Scan based trading and bargaining equilibrium: A structural estimation of supply chain profit. Manufacturing & Service Operations Management, forthcoming. 2021 Lim, S.F.W.T., Gao, F. and Tan, T.F. 2021. Slow and steady, or fast and furious? An empirical study about omnichannel demand sensitivity to fulfillment lead time. Management Science (Major revision). Lim, S.F.W.T., Rabinovich, E., Lee, S. and Park, S. 2021. Estimating stockout costs and optimal stockout rates to improve the management of ugly produce inventory. Management Science (Major revision). Lim, S.F.W.T., Rabinovich, E., Park, S. and Hwang, M. 2021. Shopping activity at warehouse club stores and its competitive and network density implications. Production and Operations Management, 30(1): 28-46. 2019 Lim, S.F.W.T. and Winkenbach, M. 2019. Configuring the last-mile in business-to-consumer e-retailing. California Management Review, 61(2): 132-154.

Jason W. Miller

2017 Miller, J. W . 2017. “Discrete Time Hazard Modeling of Large Motor Carriers’ Longitudinal CSA Performance.” Transportation Journal . Forthcoming. Miller, J. W . 2017. “A Multivariate Time Series Analysis of Motor Carrier Safety Behaviors.”  Journal of Business Logistics. Miller, J. W.  & Saldanha, J. P. 2017. “Do New Entrants Become Safer over Time?”  Transportation Journal. Miller, J. W ., Saldanha, J. P., Rungtusanatham, M., & Knemeyer, A. M. 2017. “How Does Driver Turnover Affect Motor Carrier Safety Performance and What Can Managers Do About It?”  Journal of Business Logistics. Miller, J. W ., Golicic, S., & Fugate, B. 2017. “Developing and Testing a Dynamic Theory of Motor Carrier Safety?”  Journal of Business Logistics.  In Press. Davis-Sramek, B., Fugate, B. S,  Miller, J. W , Germain, R., Izyumov, A., & Krotov, K. 2017. “Understanding the Present by Examining the Past: Imprinting Effects on Supply Chain Outsourcing in a Transition Economy.”  Journal of Supply Chain Management.  53(1): 65–86. 2016 Miller, J. W ., Fugate, B., & Golicic, S. 2016. “How Organizations Respond to Information Disclosure: Testing Alternative Longitudinal Performance Trajectories.” Academy of Management Journal . In Press. Miller, J. W ., & Saldanha, J. P. 2016. “A New Look at the Relationship between Financial Performance and Safety: A Longitudinal Growth Perspective.” Journal of Business Logistics 37(3): 284–306. Schwieterman, M. A.,  Miller, J. W . 2016. “Factor Market Rivalry: Towards an Integrated Understanding of Firm Action.” Transportation Journal . 55(2): 97–123. 2015 Mellat-Parast, M., Golmohammadi, D., McFadden, K.,  Miller, J. W . 2015. “Linking Business Strategy to Service Failures and Financial Performance: Empirical Evidence from the U.S. Domestic Airline Industry.” Journal of Operations Management . 38(1): 14–24. Stromeyer, W. R.,  Miller, J. W ., Murthy, R., & DeMartino, R. 2015. “The Prowess and Pitfalls of Bayesian Structural Equation Modeling: Important Considerations for Management Research.” Journal of Management . 41(2): 491–520. 2014 Rungtusanatham, M.,  Miller, J. W ., & Boyer, K. K. 2014. “Theorizing, Testing and Concluding for Mediation in SCM Research: What to Do, What Not to Do.”  Journal of Operations Management.  32(3): 99–113. Saldanha, J. P.,  Miller, J. W ., Hunt, C. S. & Mello, J. E. 2014. “Linking Formal Controls to Motor Carrier Performance: Curvilinear and Interaction Effects.”  Transportation Research Part E . 64(1): 28–47. Fawcett, S. E., Waller, M. A.,  Miller, J. W ., Schwieterman, M. A., Hazen, B. T., & Overstreet, R. E. 2014. “Editorial: A Trail Guide to Publishing Success: Tips on Writing Influential Conceptual, Qualitative and Survey Research.”  Journal of Business Logistics . 35(1): 1–16. 2013 Miller, J. W ., Saldanha, J. P., Hunt, S., & Mello, J. E. 2013. “Combining Formal Controls to Improve Firm Performance.”  Journal of Business Logistics . 34(4): 301–318.. Miller, J. W ., Stromeyer, W. R., & Schwieterman, M. A. 2013. “Extensions of the Johnson-Neyman Technique to Linear Models with Curvilinear Effects: Derivations and Analytical Tools.”  Multivariate Behavioral Research . 48(2): 267-300. Goldsby, T. J., Knemeyer, A. M.,  Miller, J. W ., & Wallenburg, C. M. 2013. “Measurement and Moderation: Finding the Boundary Conditions in Logistics and Supply Chain Research.” Journal of Business Logistics . 34(2): 109–116.

Judith M. Whipple

2016 Roh, Joseph, Virpi Turkulainen,  Judith M. Whipple , and Morgan Swink (forthcoming), “Understanding the Organizational Change Process for Supply Chain Management Organizations.”  International Journal of Logistics Management. 2015 Whipple, Judith M ., Robert Wiedmer, and Kenneth K. Boyer (2015), “A Dyadic Investigation of Collaborative Competence, Social Capital and Performance in Buyer-Supplier Relationships.”  Journal of Supply Chain Management.  51(2): 3-21 . 2013 Whipple, Judith M ., Stanley Griffis, and Patricia J. Daugherty (2013), “Conceptualizations of Trust: Can We Trust Them?”  Journal of Business Logistics.  34:2, 117-130. Roh, Joseph, Judith M. Whipple , Kenneth K. Boyer (2013), “The Effect of Single Rater Bias in Multi-Stakeholder Research: A Methodological Evaluation of Buyer-Supplier Relationships.”  Production and Operations Management Journal.  22:3, 711-725 . 2012 Griffis, Stanley and  Judith M. Whipple  (2012), “A Comprehensive Risk Assessment and Evaluation Model: Proposing a Risk Priority Continuum.”  Transportation Journal.  51:4, 428-451 . 2011 Nyaga, Gilbert and  Judith M. Whipple  (2011), “Relationship Quality and Performance Outcomes: Achieving a Competitive Advantage.”  Journal of Business Logistics.  32:4, 345-360. Speier, Cheri,  Judith M. Whipple , David J. Closs and M. Douglas Voss (2011), “Global Supply Chain Design Considerations: Mitigating Product Safety and Security Risks.”  Journal of Operations Management.  29:7-8, 721-736.  Winner, JOM Jack Meredith Best Paper of the Year Award and Stan Hardy Award. Allaway, Arthur W., Patricia Huddleston,  Judith M. Whipple , and Alexander E. Ellinger (2011), “Customer-Based Brand Equity, Equity Drivers and Customer Loyalty in the Supermarket Industry.”  The Journal of Product and Brand Management.  20:3, 190-204. 2010 Whipple, Judith M.  and Joseph Roh (2010), “Quality Fade and Agency Theory in Buyer-Supplier Relationships.”  The International Journal of Logistics Management.  21:3, 338-352. Richey, R. Glenn, Anthony S. Roath,  Judith M. Whipple , and Stanley E. Fawcett (2010), “Exploring a Governance Theory of Supply Chain Management: Barriers and Facilitators to Integration.”  Journal of Business Logistics.  31:1, 237-256 .  Bernard J. La Londe Best Paper Award in the Journal – 2010. Nyaga, Gilbert N., Judith M. Whipple , and Daniel F. Lynch (2010), “Examining Supply Chain Relationships: Do Buyer and Supplier Perspectives on Collaborative Relationships Differ?”  Journal of Operations Management.  28:2, 101-114.   Finalist, JOM Jack Meredith Best Paper of the Year Award based on 5-year impact. Whipple, Judith M.,  Daniel F. Lynch, and Gilbert N. Nyaga (2010), “A Buyer’s Perspective on Collaborative Versus Transactional Relationships.”  Industrial Marketing Management.  39:3, 507-518 . 2009 Voss, M. Douglas, Judith M. Whipple , and David J. Closs (2009), “The Role of Strategic Security: Internal and External Security Measures with Security Performance Implications.”  Transportation Journal.  48:2, 5-23 . Whipple, Judith M., M. Douglas Voss, and David J. Closs (2009), “Supply Chain Security Practices in the Food Industry: Do Firms Operating Domestically and Globally Differ?”  International Journal of Physical Distribution and Logistics Management.  39:7, 574-594. Huddleston, Patricia,  Judith M. Whipple , Rachel Nye Mattick, and So Jung Lee (2009), “Customer Satisfaction in Food Retailing: Comparing Specialty and Conventional Grocery Stores.”  International Journal of Retail and Distribution Management.  37:1, 63-80. 2007 Jones, Kraig, Kellie Curry Raper,  Judith M. Whipple , Diane Mollenkopf, and H. Christopher Peterson (2007), “Commodity-Procurement Strategies of Food Companies: A Case Study.”  Journal of Food Distribution Research.  XXXVIII:3, 37-53 . Whipple, Judith M.  and Dawn Russell (2007) “Building Supply Chain Collaboration: A Typology of Collaborative Relationship Strategies.”  The International Journal of Logistics Management.  18:2, 174-196. 2004 Sabath, Robert and  Judith M. Whipple  (2004), “Using the Customer/Product Action Matrix.”  Journal of Business Logistics.  25:2, 1-19.   Bernard J. La Londe Best Paper Award in the Journal – 2005. Huddleston, Patricia,  Judith Whipple , and Amy VanAuken (2004), “Food Store Loyalty: Application of a Consumer Loyalty Framework.”  Journal of Targeting, Measurement and Analysis for Marketing.  12:3, 213-230. 2002 Whipple, Judith M ., Robert Frankel, and Patricia J. Daugherty (2002), “Information Support for Alliances: Performance Implications.”  Journal of Business Logistics.  23:2, 67-82. Frankel, Robert, Thomas J. Goldsby, and  Judith M. Whipple  (2002), “Grocery Industry Collaboration in the Wake of ECR.”  International Journal of Logistics Management.  13:1, 57-72.  Accenture Award for Best Paper in the Journal – 2002. Anselmi, Kenneth, Robert Frankel, and  Judith Schmitz Whipple  (2002), “Performance in Product Versus Service Supplier Relationships.”  Journal of Business-to-Business Marketing.  9:1, 27-43. 2000 Whipple, Judith M.  and Robert Frankel (2000), “Strategic Alliance Success Factors: Do Both Parties Agree?”  The Journal of Supply Chain Management.  36:3 (Summer), 21-28 . Whipple, Judith Schmitz  and Julie J. Gentry (2000), “A Network Comparison of Alliance Motives and Achievements.”  Journal of Business and Industrial Marketing.  15:4/5, 301-322.

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Please note you do not have access to teaching notes, an analysis of supply chain management research by topic.

Supply Chain Management

ISSN : 1359-8546

Article publication date: 30 April 2018

Issue publication date: 9 May 2018

This paper aims to review logistics and supply chain management topics where theories have been applied to better understand the supply chain management (SCM) discipline identity. The purpose is threefold: to identify research topics in logistics and supply chain management where one or more theories have been examined; provide commentary on the theories that have been applied to the various logistics and SCM research topics; and to provide reference material and direction for future research.

Design/methodology/approach

This structured literature review (SLR) examines research papers in logistics and SCM from 1991 to 2015 published in eight leading academic journals. Papers in the data set are grouped by topic and further analyzed in terms of research method, purpose, year and journal.

This research categorizes papers by the topics that were studied to understand important insights about how these topics have been examined by researchers. Within each topic area, theories that researchers have used to investigate the topics are identified. This method exposes insights such as: how topics have evolved over time, which topics have lost prominence, which topics may be particularly promising for future research and how topics are treated in the literature.

Originality/value

Despite multiple calls for clarification regarding how theory has been used in logistics and SCM, the logistics and SCM disciplines continue to grow without adequate research on how theory has been used to examine SCM topics. This SLR therefore provides a broad compilation of logistics and SCM research that uses named theories and that is organized by SCM topic to better understand the SCM discipline.

• Supply-chain management

Swanson, D. , Goel, L. , Francisco, K. and Stock, J. (2018), "An analysis of supply chain management research by topic", Supply Chain Management , Vol. 23 No. 2, pp. 100-116. https://doi.org/10.1108/SCM-05-2017-0166

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Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review

• Original Research
• Published: 31 March 2022
• Volume 315 , pages 29–55, ( 2022 )

Cite this article

• Imran Ali 1 &
• Devika Kannan 2  

The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field.

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Humanitarian supply chain management: a thematic literature review and future directions of research.

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1 Introduction

Effective and efficient delivery of healthcare services is essential to counteract emergent diseases and promote healthy lives and the well-being of a population, particularly amid the COVID-19 outbreak. Many healthcare systems across the globe strive to efficiently utilise available resources and improve processes through more integrated operations involving all key stakeholders. As such, in the past few decades, research on healthcare has grown exponentially and has become a self-sustained discipline in operations and supply chain management (OSCM). Healthcare OSCM is a network of healthcare providers, pharmaceutical manufacturers, drug suppliers, distributors, and logistics service providers that are involved in the process of acquiring and transforming raw material into finished products, adding value, and facilitating the flow of products and services to the end-customer. With the rapid growth of research on healthcare OSCM, the extant studies have been conducted in various directions and expanded over a dozen of academic journals.

As the field has grown, a corresponding growth in systematic literature reviews in healthcare has occurred, especially in the past six years. For example, Narayana et al. ( 2014 ) reviewed 99 articles (published between 2000 to 2011) on the pharmaceutical supply chain and explore that most studies focus on efficiency improvement with growing interest in process analysis and technology implementation. Volland et al. ( 2017 ) presented a review of 145 articles (published between 1998 and 2014) on material logistics in hospitals and suggested further work on the concepts of supply and procurement, inventory management, and distribution and scheduling. Ali et al. ( 2018 ) conducted a review of 88 articles (published between 2009 and 2018) on cloud computing in hospitals and devised a framework on opportunities, issues, and applications of cloud computing-enabled healthcare systems. Malik et al. ( 2018 ) reviewed 22 articles (published between 2001 and 2015) on the application of data mining in healthcare service delivery and revealed that application of data mining is narrow-focused (workforce scheduling and quality of care), thereby suggesting further research on the broader application of data mining. Moons et al. ( 2019 ) presented a review of 56 articles (published between 2010 and 2016) on logistics performance measurement in the internal hospital supply chain, specifically inventory management and distribution activities in the operation theatre. Diwas Singh et al. ( 2020 ) offered a review of 70 empirical articles (published between 1999 and 2018) from three journals ( Management Science, Manufacturing and Service Operations Management, and Production and Operations Management ) and suggested the need for more empirical research on personalised medicine, value-based healthcare, and digitisation of healthcare. Keskinocak and Savva ( 2020 ) mapped the healthcare research published between 2009 and 2018 from a single journal ( Manufacturing and Service Operations Management ) and suggested the need to develop and apply new OM methods for the improvement of healthcare service.

The existing literature reviews have different scopes and emphases focusing on a specific topic (e.g., material logistics in hospitals, data mining, only empirical papers, etc.). In other words, they are not designed to capture latent topics (hidden topics) that reside in large volumes of scholarly data. Expanding upon some knowledge from the existing reviews, this paper seeks to advance our understanding of the key research topics that have been addressed in healthcare OSCM using a topic modelling-based literature review. Topic modelling with Latent Dirichlet Allocation (LDA) has been found as an effective method to algorithmically and automatically uncover the abstract topics that reside in a large and unstructured collection of articles (Antons et al., 2016 ; Blei, 2012 ; Zhang, 2012 ). LDA topic modelling builds upon rigorous statistical foundations which allows topic generation with little human intervention and/or manual processing (Blei, 2012 ; Zhang, 2012 ). Such an automatic method creates more meaningful and realistic topics and ensures the reliability and validity of outcomes as opposed to manual methods (Griffiths & Steyvers, 2004 ). LDA has been successfully applied for topic modelling in the information science field (Yan, 2014 ), marketing (Tirunillai & Tellis, 2014 ), statistics (De Battisti et al., 2015 ), tourism (Guo et al., 2017 ), decision sciences (Chae & Olson, 2018 ), computer science (Guo et al., 2017 ), and hydropower research (Jiang et al., 2016 ). However, to date, we have only seen a very limited application of topic modelling-base literature in OSCM fields (e.g., Chae & Olson, 2018 ; Lee & Kang, 2018 ), particularly in the healthcare context, to explore the significant topics hidden in a large sample of articles and to locate gaps for further research in each topic. As such, this study aims to: (1) uncover the latent topics residing in the articles published on healthcare OSCM, (2) examine the topic trend/evolution over time including hot, cold, and steady topics, and (3) find opportunities for more impactful research on each topic through a review of the literature around each topic. Driven by these core aims; the following research questions are posed:

What are the emergent topics of research in the field of healthcare OSCM?

How has research on each topic evolved over the past two decades?

What are the opportunities for more impactful future research in the area?

Our study is among the few that offers a topic modelling-based review of literature, which allows us objectively to uncover hidden patterns of research that pervade in a large collection of studies and to explore the development of literature on each topic. The paper advances our understanding of how the literature on different topics in the field of healthcare OSCM has evolved over time and what are the recent trends, knowledge flow, and opportunities for insightful research on each topic. We find an increasing focus and need for further investigation on some emergent topics, such as patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. In terms of research methods, our analysis shows the increasing interest of researchers in the Markov method, mixed-integer programming, simulation, queueing model and stochastic programming in exploring multidimensional issues of healthcare OSCM. In addition, our analysis suggests that the past three years have also seen some empirical contributions in the area, including structural equation modelling, longitudinal study, and action research; thus, encouraging empirical researchers for more fruitful contributions on the topic.

Our study offers valuable research directions to researchers, journal editors, and policymakers in the field of healthcare OSCM. Researchers can observe whether their recent research topics are hot, cold, or steady, which will allow them to select appropriate topics for more productive forthcoming study in the area. Particularly, some early-career researchers find it difficult to keep up with the growing literature in their fields of research and familiarise themselves with a huge volume of literature in identifying the subject area on which they can focus or apply for research grants. The information from our topic modelling can be a useful tool for these researchers to understand recent developments in the field and then select more appropriate topics. Our findings can also be helpful to PhD students, who often face difficulties in topic selection due to time pressure. Journal editors can view if the extant research is consistent with their editorial policy, and they may even choose to launch a new editorial vision and direction. Our list of hot topics can be a valuable reference tool to launch special issues in the journals. Discovering underlying topics and tracking their evolution could also be of great interest to policymakers (e.g., government agencies) and industry. The information on which research themes and methods are growing or declining in popularity over time (Table 3 and Fig.  4 ) can support governmental funding agencies for grants allocation to promising areas. Likewise, firm managers and the industry as a whole can use this information to formulate promising R&D strategies and more informed investment decisions.

The rest of the paper is structured as follows. Section 2 elaborates on the research methods, covering data collection, screening and analysis procedures. Section 3 outlines results which include word cloud, key term matrix, topic labelling, and evolution of each topic over time. Section 4 provides discussion, while Sect. 5 offers a review of literature and opportunities for further research. The study is concluded in Sect. 6.

As mentioned earlier, this study employs the topic modelling method. The superiority of the topic modelling method has been evidenced in other fields, for example, Latent Semantic Analysis (LSA) (Deerwester et al., 1990 ), Non-Negative Matrix Factorization (NMF) (Lee & Seung, 1999 ), and probabilistic topic modelling. LSA uses singular value decomposition and a traditional matrix factorization technique to reduce the dimensionality of documents (Deerwester et al., 1990 ). Unlike singular value decomposition, in NMF, the transformed and reduced version of documents contains no negative elements (Lee & Seung, 1999 ). Probabilistic topic modelling is a recent development in topic modelling where words and documents are described through probabilistic topics, and a topic is described in terms of a probability distribution over a vocabulary of words (Hofmann, 2001 ; Lee & Kang, 2018 ). Latent Dirichlet Allocation (LDA) is the most popular probabilistic topic modelling algorithm. A comparison of LSA, NMF, and LDA suggests that LDA has the greatest ability to maintain topic coherence and to generate predominant topics that handle the issue of polysemy, the overlapping of words with multiple meanings (Stevens et al., 2012 ). LDA has been successfully applied in the information science field (Yan, 2014 ), marketing (Tirunillai & Tellis, 2014 ), statistics (De Battisti et al., 2015 ), tourism (Guo et al., 2017 ), decision sciences (Chae & Olson, 2018 ), computer science (Guo et al., 2017 ), and hydropower research (Jiang et al., 2016 ). Borrowing knowledge from adjacent areas, this study employs LDA in healthcare. The analysis is completed in four main steps: data collection; text pre-processing; word cloud; and topic modelling.

2.1 Data collection

The analysis aims to uncover the landscape of healthcare research published in good quality international journals in the field of operations and supply chains. The data were collected from Scopus on 25 January 2022, which is one of the largest abstracts and citation databases of academic papers (Ali & Gölgeci, 2019 ; Ferreira, 2018 ; Ali et al., 2021a , 2021b ). To retrieve a broad range of literature on the topic, a large set of keywords was developed based on the authors’ extensive knowledge of the field and review of the extant literature in the field. These keywords included: healthcare, AND operations AND “supply chain” OR hospital OR pharmaceutical OR drug OR medicine AND model OR framework OR Stochastic OR Simulation OR “linear programming” OR Heuristic OR mathematical OR optimization OR “numerical model” OR “nonlinear programming” OR “decision tool” OR “decision analysis” OR “discrete event” OR multi-objective OR multi-objective OR multi-attribute OR multi-criteria OR MCDM OR MCDA OR qualitative OR quantitative OR “case study” OR empirical. Quotation marks were placed on the keywords comprised of more than one word (e.g., “linear programming”) to ensure that search engines consider them as one word.

Initially, we did not limit the timespan because we sought to capture a wide range of publications. The search query generated 663 articles. We excluded the three articles published before 2000 to set the time interval between 2000 and 25 January 2022 (the date of data collection). Next, we carefully read the title and abstract of each article and excluded irrelevant articles. We also excluded articles in non-English languages. Extreme care was taken to include the articles that focus on healthcare OSCM. The inclusion and exclusion criteria resulted in the final set of 571 articles as shown in the supplementary file. Table 1 presents the distribution of these articles.

The articles selected from Scopus were validated using other databases, such as Web of Science, Google Scholar, ProQuest, and EBSCO to ensure that no important articles were missed in the review. The validation process also helped confirm that Scopus contains a greater number of related articles on the topic of healthcare OSCM compared to other databases, so it was used as a preferred database for this research.

2.2 Data pre-processing

Once the articles are collected from Scopus, some pre-processing of data collected is required to conduct further analysis. First, since our search result included the article’s title, keywords and abstract, we merged them as a single document. Second, we removed all punctuation and numbers and transformed all characters into lower-case. Third, we eliminated all “stop-words,” whose main role is to make a sentence grammatically correct, e.g., articles (a, an, the) and prepositions (of, by, from, etc.) and general words that repeatedly appear in most articles, e.g., “and,” “paper”, “find”, “effect”, and “discuss”. These words are not semantically meaningful, yet they create trouble in the analysis process. This step is important because without applying stop-word elimination, our analytical method could result in a big proportion of these unwanted words in the word cloud as well as in the topic matrix.

2.3 Word cloud

Word cloud with R-package is a text mining method to find the most frequently used keywords in sampled articles. A word cloud was designed to allow a visual interpretation of main themes and their trajectories in our review transcripts over the four different periods (2000–2004, 2005–2009, 2010–2014, 2015–2022). As mentioned earlier, we also included articles published until 25 January 2022. Data file for each period was imported into a popular R package, word cloud (Hornik & Grün, 2011 ) with default Dirichlet hyperparameters. The following functions in the R-package were applied to create word clouds: tm for text mining: snowballC for text stemming; Wordcloud2 for generating word cloud images; RColorBrewer for colour palettes.

2.4 Topics modelling

To extract the predominant topics from the 571 sampled articles, LDA’s generative process model was applied (Blei, 2012 ). Figure  1 demonstrates the process and its main variables where η is the parameter of the Dirichlet prior for the per-topic word distribution. K is the defined number of topics, while β k is a distribution of words over a topic. W represents the observable variable—the words in each article. W d,n is the n th word in article d . α is the parameter of the Dirichlet on the per-article topic distribution. θ d is a distribution of topics for the d th article. Z d,n is the topic assignment for the nth word in article d . Some variables are interdependent, that is, W (the observed word) depends on β (the distribution of topics) and Z (the topic assignment for the word), on θ (the topic proportion for the document). This process can be represented by the joint distribution of latent and observed variables through the following Figure and equations:

Adapted from Blei ( 2012 )

The graphical representation for LDA.

The generative process aims to explore the topic structure (θ, z , and β), which can explain W . Given the observed variable ( W ), the posterior distribution is estimated with the following equation:

The purpose of Fig.  1 and Eqs.  1 and 2 herein is to demonstrate the analysis process followed by LDA. To perform the analysis objectively and automatically, we used the R-package topic-models along with the tm package (Hornik & Grün, 2011 ). This analytical process produces a document term matrix for the desired number of topics.

3 Analysis and results

This section presents the analysis and the results of data processing, which consist of word cloud, topic modelling, and topic labelling.

3.1 Word cloud

As presented in Fig.  2 , the word clouds show some noticeable changes between 2000 and 2022 in the healthcare literature. In the first period (2000–2004), the popular terms include strategy, logistics, risk, simulation, integer, programming, operations, reengineering, information, inventory, and planning. While a few terms in the first period and second period (2005–2009) are similar (e.g., reengineering, outsourcing, supply, chains, healthcare, strategy, logistics), several new terms became popular in the second period which includes sustainability, warehousing, reverse, heuristic, stochastics, empirical, knowledge, humanitarian, among others.

Word cloud demonstrating changing pattern of terms over time (2000–2022)

The third period (2010–2014) shows some important changes, where several new terms (e.g., behavioural, literature, reviews, multilevel, action, structural, equation, Markov) emerged, and other terms (e.g., reengineering, outsourcing, strategy) disappeared. Some other terms remain similar to the second period (e.g., sustainability, warehousing, reverse, heuristic, stochastics, empirical, knowledge, humanitarian, waiting time).

While many terms of the third period continued to appear in the fourth period (2015–2022), several new terms are generated: circular, economy, climate, change, longitudinal. Terms such as warehousing, marketing, strategy disappeared in this period. Understandably, the terms “healthcare”, “operations”, “supply”, “chain” appeared consistently across the four periods.

3.2 Topic modelling

While the visualisation of word clouds (observed words) provides a broad picture of the research dynamics in the literature motivating towards further analysis, it does not provide quantitative information on the relationship between different terms that are generated. Without having quantitative information on each key term, it would be difficult to merge the related terms and assign them into specific topics.

To derive the quantitative information on the nexus between different terms, we used LDA topic modelling that explores the hidden or latent structure and generates different topics by merging interrelated terms from the data set. Generating an appropriate number of topics ( K value) from the given sample of articles is always challenging (Roberts et al., 2014 ). Selecting too few topics would not provide a full and meaningful understanding of the field, while selecting too many topics often results in meaningless outcomes. Therefore, to select the appropriate number of topics, two measures were undertaken: (1) a different number of topics (K value) were tried and accuracies of outcome were checked for each set of topics, and (2) brainstorming among researchers was conducted to ensure topic quality and relatedness to the context of the study (Mimno & McCallum, 2012 ; Silge & Robinson, 2016 ). As a result, 34 relevant topics were produced using LDA with R-package (Fig.  3 ). For each topic, the top five terms were selected, where each term represents a distribution weight (per-topic term probability).

Topic matrix generated in R-package using LDA principle

3.3 Topic labelling

We conducted topic labelling based on the ‘LDA topic matrix’ that produced 34 topics (see Fig.  3 ). Each topic constitutes five terms with different distribution weights. Since the term “healthcare,” “operations,” “supply,” and “chain” appeared in most topics, we decided not to use any of them in labelling the topics. Further, while topic labelling in Table 2 , we used the numbering/sequence of topics exactly following the sequence of topics in the ‘LDA topic matrix’ (Fig.  3 ). To briefly illustrate the process of topic labelling, we discuss a few examples of topic labelling. From Fig.  3 , the top terms in Topic#1 are queuing and waiting, followed by hospital, patient, process; therefore, the topic is labelled the “patient’s waiting time”. Topic#2 includes top terms, such as RFID, big, data followed cloud, computing; therefore, we label the topic as “Industry 4.0”. Industry 4.0 refers to the fourth industrial revolution with the growing use of autonomous technologies such as cloud computing, big data analytics, internet supported RFID and others. Topic# 3 encompasses sustainability, economic, sustainable; hence, it is labelled as sustainability.

Likewise, Topic#8 is labelled “risk and resilience” since the top terms are risk, adaption, resilience supported by healthcare and readiness. Topic#18 is labelled “Stochastic programming” because its top terms are stochastics, programming, followed by model, method, and chain. We adopted a similar process for labelling all 34 topics as demonstrated in Table 2 .

3.4 Topic dynamic/evolution over time

In this section, we introduce the hot, cold, and steady topics (research themes and methods) to provide researchers with an idea of where trends are going (hot) and not going (cold). Exploring the dynamic changes in different topics over time can offer directions for more impactful research on healthcare. We identified three patterns of topic evolution including hot, cold, and steady. For this purpose, we employed a bivariate correlation between the frequency of keywords and time period using four different time periods, namely 2000–2004; 2005–2009; 2010–2014; 2015–2022 (Fig.  3 ). Scopus database was used to collect the information on the frequency of keywords (for each topic shown in Fig.  3 and Table 2 ) in each time interval.

Hot, cold, and steady topics (which include ‘research themes’ and ‘research methods’ as shown in Table 3 ) were identified based on the direction of the trendline on each topic and the value of slope (Fig.  4 ).

Topic trend/dynamic over time

Hot topics show a trendline moving upward and a positive slope (x-value). Examples of the hot topics related to research themes include the Patient’s waiting time, Industry 4.0 technologies, Sustainability, Circular economy, Behavioural OSCM (OSCM), Risk and resilience, Climate change, and research methods that constitute Markov method, Simulation, Stochastic programming, among others.

The number of contributions on these topics (themes and methods) increased almost continuously from 2010 to 2014, with a dramatic increase on some topics (e.g., patient waiting time, sustainability, circular economy, etc.) between 2015 and 2022.

Cold topics, on the other hand, imply the opposite pattern to these hot topics; they show declining trendline and negative slope values, perhaps due to the relative lack of interest of researchers in recent times.

Examples of cold topics associated with research themes include warehousing, marketing, outsourcing, business process reengineering, strategy, operation strategy, and research methods of heuristics and game theory model among others. These topics enjoyed significant attention between 2000 and 2015, but their proportions have been steadily decreasing over the past five years (Fig.  4 ).

Finally, steady topics show a rise in the beginning but there is no rise or fall in trendlines over the past 5 or more years; perhaps, their importance has not been increased over time.

Overall, two topics depicted a steady growth over the past 5 or more years including goal programming and multi-level analysis. As can be seen in Fig.  4 , the trendline for goal programming modelling increased up to 2009, while no remarkable increase was observed since then. Likewise, the trendline for multi-level analysis shows an upward trend until 2014; however, no growth has been demonstrated since then.

While not investigated in this study, it can be speculated that these topics may lose the interest of research and likely become a cold topic in future. Drawing upon trendline and slope for each topic in Fig.  4 , we have presented all 34 topics under three main categories: hot, cold, and steady (Table 3 ). For better clarity of expression, we have segregated the topics into themes and methods. While both themes and methods are both important, our focus is on research themes in this analysis and discussion.

4 Discussion

Over the last two decades, research on healthcare OSCM has seen spectacular growth. In light of the burgeoning literature on the topic, this study was motivated to explore recent developments, trends, and core topics of research in the area. The study was driven by three key research questions. Addressing the first research question, we explored 34 core topics on which extant research on the topic has been conducted over the past decades. For the second research question, our topic modelling analysis has resulted in the identification of hot, cold, and steady topics in empirical research healthcare OSCM (Table 3 and Fig.  4 ). Thirteen topics of research themes show an upward trend, and nine of them (Patient waiting time, Industry 4.0, Sustainable OSCM, Behavioural OSCM, Risk and resilience, Humanitarian logistics, Climate change, Healthcare logistics, and Literature reviews) start with a relatively flat value (close to 0) before showing a rising trend from around 2010. The other four topics (Circular economy, Service ecosystem, Knowledge management, and Pharmaceutical OSCM) show a gradual upward trend from the early 2000s. Five research topics belong to cold topics. Outsourcing and operations strategy shows an almost linear declining trend, while both Marketing and Warehousing show a bell-shaped trend whose popularity rose in the early 2000s but fell after 2010 and 2015, respectively. Business process reengineering was a popular topic during the early 2000s but, since 2010, shows a declining trend. It is important to note, however, that being identified as a cold topic does not suggest that the topics are not important and should be abandoned. The results provide more of an indication about the interest (trends) of researchers’ interests rather than the importance of the topics themselves. It is also interesting to see that most of the changes of the trends (either upward or downward) of the topics took place around 2010.

Finally, in response to the third research question, we offered a review of the literature and identified numerous opportunities for further research (Sect. 5). Our findings show a shift of current research from traditional topics of operations management (Business process reengineering, Marketing, Outsourcing, Heuristics) to emergent topics such as Patient waiting time, Industry 4.0 technologies, Sustainable operations, Risk and resilience, Climate change, and Circular economy. In general, the shift in topics seems to show a move away from traditional, efficiency-focused research toward broader goals of superior customer service and system’s effectiveness. While each of the three groups of topics deserves discussion, in this section, we discuss the hot research topics as well as provide recommendations of avenues for fruitful future research. It should be noted that although the topic “literature reviews” was a hot topic, we do not include it in our discussion below as the extant research related to it has already been discussed in the introduction section.

5 Literature review and opportunities for further research

The following section provides a brief synthesis of current literature around emergent research topics that are recovered through topic modelling, and it offers numerous opportunities for further research in the field.

5.1 Patients’ waiting time and COVID-19

This research topic covers two interrelated and emerging issues: patient waiting time and COVID-19 outbreak. With a rapidly growing population of aged people, healthcare systems have been facing pressure for more health services resulting in patients’ long waiting times. Therefore, determining methods to ameliorate waiting time has been high on the agenda of most developing and developed economies. Correspondingly, in recent years, mostly building upon simulation and Markov as the primary method of research, considerable studies appeared around the issues of patients’ waiting time, such as patient’s admission process (Chae, 2019 ; Vissers et al., 2007 ); surgery procedures (VanBerkel & Blake, 2007 ); patient inflow and outflow in the emergency department (Abo-Hamad & Arisha, 2013 ); no-shows of patients and overbooking (Topuz et al., 2018 ); outpatient appointment scheduling (Shehadeh et al., 2019 ); time overestimation for the emergency procedure (Gartner & Padman, 2019 ); walk-in patients treatment (Pan et al., 2019 ); patient readmission and discharge planning model (Gu et al., 2019 ); resource planning model to reduce waiting time of patients (Hejazi, 2021 ); load smoothing of scheduled admission to the reduced number of beds required and probability of delay (Asgari & Asgari, 2021 ), and modelled association between length of appointment interval and no-shows of patients (Pan et al., 2021 ).

Overall, although the existing studies have captured several important issues related to patient waiting time, we identify the need for further research on this significant topic. For example, there is potential to develop an integrated model investigating how multiple factors such as general service time, equipment breakdown, high attrition of hospital staff, variability in patient’s arrival time, and unpunctuality of already booked patients cumulatively contribute to patients’ waiting time. Further research can also devise a model at the intersection of patients’ waiting list management, operations theatre scheduling, and patient waiting time for surgery. While the literature on simulation and mathematical modelling continues to grow, there is also an opportunity to further expand empirical research for testing causal links between various factors and their combined effect on patient waiting time. For example, empirical research can test links among customers’ heterogeneous behaviour and their service time in scheduling appointments. Further, a unified model capturing non-value-added practices at both micro and meso levels would be fruitful future research in reducing patient waiting time. While research on bedding and staffing is growing, there is a need to optimise bed and staff requirements amid the global pandemic. Another potential research area is to examine the longitudinal effect of hospital staff’s training on patients’ waiting time.

Likewise, given the outbreak of COVID-19, the recent literature of healthcare OSCM has been rapidly growing on the topic. Pamucar et al. ( 2022 ) proposed a model for supplier selection amid COVID-19. Fan and Xie ( 2022 ) generated an optimization model for COVID-19 testing facility design and planning. Ghaderi ( 2022 ) offered a framework for public health intervention in the wake of the pandemic. Govindan et al. ( 2020 ) developed a decision support system and fuzzy interface system to help with demand management in the healthcare supply chain amid the COVID-19 crises. Nagurney ( 2021a ) modelled labour shortage due to COVID-19, resulting in reduced food harvest and supply. Tavana et al. ( 2021 ) presented a mixed-integer programming model for the equitable distribution of COVID-19 vaccine in developing countries. Salarpour and Nagurney ( 2021 ) constructed a stochastic model to study competition among different countries for medical supplies amid COVID-19 crises. Nagurney ( 2021b ) offered a supply chain network optimization model to manage labour in electronic commerce during the COVID-19 pandemic. Thakur ( 2021 ) developed a model to manage hospital waste during the COVID-19 outbreak. Choi ( 2021 ) used a sense-and-response specific OR model to demonstrate the specific actions needed to deal with the COVID-19 outbreak.

To sum up, while the past research continues to grow on COVID, it would be interesting to explore how to extenuate potential risks and develop shockproof healthcare systems to deal with unanticipated outbreaks such as COVID-19 and similar future incidents. In this regard, it is worth exploring how such a topic as Industry 4.0 technologies could be applied across the healthcare system to capture data from wide sources and help the decision-making process in the event of a pandemic, including contact tracing, the identification of the outbreak location. Some potential areas of research amid COVID-19 include but are not limited to patient inflow and outflow in the emergency department; outpatient appointment scheduling model; models on time estimation for the emergency procedure; walk-in patients’ treatment model; patient admission and discharge planning model; resource planning model to reduce waiting time of patients; load smoothing of scheduled admission to the reduced number of beds required and probability of delay. Besides, pharmaceutical SCM would be the ultimate hope for the recovery and possibly eradication of the virus that allows us to go back to normal life. It would be insightful to examine how pharmaceutical supply chains were affected by COVID-19 and develop a recovery model dealing with such a situation. In particular, if/when a pharmaceutical solution to COVID-19 emerges, research is needed to help understand the best ways to rapidly scale up and distribute the materials on a scale that has never been seen before. It is also of interest to study whether synergy in logistics could be achieved between the public organisations and the growing number of private companies that have entered the healthcare business. Future research should continue to incorporate the healthcare and hospital view into operations management and transfer established concepts from other industries into healthcare while accounting for industry specifics. Again, the links between this and COVID-19 are both obvious and critical.

5.2 Industry 4.0 technologies

Industry 4.0 or the fourth industrial revolution refers to fully automated and in-connected technologies such as the Internet of Things (IoT), cloud computing, big data analytics, blockchain, artificial intelligence, among others (Ali & Govindan, 2021 ; Ali et al., 2021a , 2021b ). Industry 4.0 technologies can enhance the level of service in the industry through real-time information sharing, visibility, traceability, agility, and connectivity within a firm’s boundary and across the supply chain (Govindan et al., 2022 ). Given this, growth in research on the role of Industry 4.0 technologies in healthcare service provision is evident, such as Sultan ( 2014 ) developed a conceptual framework on the potential of cloud computing in the advancement of healthcare services. Chong et al. ( 2015 ) modelled variables that influence the adoption of IoT (e.g., RFID) in healthcare. Priya and Ranjith Kumar ( 2015 ) used big data analytics to predict the progression of atherosclerotic disease, the narrowing and hardening of arteries due to the accumulation of plaque on the artery wall. Fan et al. ( 2018 ) recognised the factors affecting the adoption of artificial intelligence-based medical diagnosis support systems. Kochan et al. ( 2018 ) modelled on how cloud computing improves visibility and responsiveness in hospital SC. Galetsi et al. ( 2020 ) developed a theoretical framework for the realisation of big data analytics.

The research on Industry 4.0 is growing with the advent of new technologies and provides fruitful opportunities for further research. The models on big data should incorporate data of all inter-connected functions in healthcare, instead of just focusing on a single function or silo approach. For example, the impact of Industry 4.0 on the entire healthcare supply chain can be modelled. This is important to understand how interaction with different functions/partners through Industry 4.0 technologies influence healthcare productivity. Most of the current research focuses on big data and cloud computing which needs to be expanded to other Industry 4.0 technologies, such as blockchain, cyber-physical systems and the Internet of Things in healthcare OSCM. There is a lack of a holistic model testing the combined impact of all Industry 4.0 technologies on the healthcare system and an investigation into if there is a trade-off for investing between various digital technologies would be helpful. Future research can also investigate how machine learning techniques impact diagnostic, maintenance, and prognostics systems in healthcare settings. Research highlights the intricate issues of human–machine interaction at the workplace (Arslan et al., 2021 ); we suggest further research on how to maintain trustworthiness and to harmonise productive human–machine interactions and reduce workers’ resistance to automated machines in the healthcare settings. We also highlight the need for further research on the application of artificial intelligence in healthcare OSCM.

5.3 Sustainable operations and supply chain

Today sustainability is a growing concern of most organisations and therefore the topic of sustainability has received increasing focus from researchers from operations management. Healthcare systems worldwide face pressing challenges of service quality and cost issues as well as safe disposal of waste. As such, in recent times considerable attempts have been made to realise sustainability in the healthcare system. Chauhan and Singh ( 2016 ) employed a hybrid model for the selection of sustainable locations to dispose of healthcare waste. Anuar et al. ( 2018 ) offered a conceptual framework demonstrating the link between lean practices and a sustainable healthcare system. Malekpoor et al. ( 2018 ) modelled a sustainable healthcare treatment plan. Thorsen and McGarvey ( 2018 ) investigated the effectiveness of fixed and mobile dentistry for the financial sustainability of the healthcare system. Mousa and Othman ( 2020 ) found a positive impact of green human resource management practices on a sustainable healthcare system.

While research on healthcare sustainability is growing, there is still space for further research. For instance, more studies are needed that simultaneously investigate the impact of all three dimensions (social, environmental, economic) of sustainability through a single model. Prospective studies can also investigate the combined impact of synchronous business activities including green human resource management, green marketing, and green production on healthcare sustainability. There is also an opportunity to develop a sustainable performance measurement and management model in healthcare.

5.4 Circular economy

The circular economy is a recent concept aimed to eliminate waste through the make, use, recycle and reuse approach. It helps to overcome a take-make-dispose linear pattern of production and consumption. The healthcare industry inevitably generates waste that may become hazardous to both public health and the environment. Therefore, the circular economy has recently gained significant attention in healthcare literature. For example, Kumar and Rahman ( 2014 ) suggested RFID supported process reengineering for waste management ensuring circular economy. Jensen et al. ( 2019 ) discussed the healthcare refurbish system as a means to reduce waste. Dehghani et al. ( 2019 ) suggested that transhipment can reduce waste and save substantial costs compared to no-transhipment. Viegas et al. ( 2019 ) identified the need for research on the circularity of end-of-use medicine in the pharmaceutical supply chain.

Although research circular economy has witnessed mounting attention, there is still room for further research. For instance, there is a need to develop a model that could make the circularity of outdated medicine more visible through all stages of reverse flow and re-manufacturing. Also, healthcare literature lacks studies modelling processes and decision-making for circular economy implementation at micro and meso levels. Prospective research can also explore the healthcare staff’s perception of the circular economy. It would be interesting to investigate how healthcare professionals are prepared to deal with the circular economy and how they might contribute to reducing wastage in reverse flow.

5.5 Service ecosystem

With its root in marketing, the service ecosystems concept is defined as “relatively self-contained, self-adjusting systems of resource-integrating actors connected by shared institutional logics and mutual value creation through service exchange” (Vargo & Akaka, 2012 , p. 207). That is, the service ecosystem generates value for all the actors in a network through resource integration, value co-creation, and co-innovation. The service ecosystem concept has received a great deal of attention in healthcare literature. Nudurupati et al. ( 2015 ) elaborate on the process of value co-creation through strategic alliance and collaboration with multi-stakeholders. Albarune et al. ( 2015 ) suggested a value-based supply chain for integrated hospital management. Chae ( 2019 ) suggested digital innovation as a promising source of resource sharing and value creation among actors in an ecosystem. Lee et al. ( 2020 ) showed a positive relationship between the previous year value-based purchasing penalty and the current year care process improvement in the hospital.

Service ecosystem research is a growing area of research in the healthcare context; thus, offering opportunities for further expansion. For example, the influence of contextual behavioural factors in value co-creation and unlocking innovation needs further investigation. Also, service ecosystem research needs to be expanded from the organisational level to the entire healthcare system. For example, how resource integration practice can be integrated into a multitude of value co-creation processes in the healthcare networks. Moreover, the role of advanced technologies and servitisation in the healthcare service ecosystem has yet to be investigated.

5.6 Knowledge management

Knowledge management refers to the process of generating, transferring, and managing knowledge and information in an organisation or network. Knowledge management becomes a major driver of performance and productivity where front-line workers are required to provide customised services tailored to the need of individual clients such as healthcare. As such, the literature has witnessed considerable growth in literature on knowledge management in healthcare settings. Gagnon et al. ( 2016 ) empirically corroborated a positive association between knowledge management on staff practices and quality of healthcare. Mura et al. ( 2016 ) found that knowledge exchange and assets have a positive impact on workers’ innovation, such as idea generation, idea promotion and idea implementation. Avgerinos and Gokpinar ( 2017 ) argued that knowledge transfer capability in a team positively influences healthcare productivity. Hiranrithikorn and Sutduean ( 2019 ) expound that access to information and knowledge has a positive influence on healthcare supply chain skills. Despite considerable contributions to knowledge management, there is still significant room for further research. A few models are investigating the impact of knowledge sharing on the quality and productivity of healthcare performance. Qualitative research can be conducted to explore the barrier and enablers of knowledge sharing in healthcare. Future research can use multi-method research investigating the relationship between social capital, culture, and knowledge management.

5.7 Behavioural operations

Research on behavioural operations is well-received, particularly from healthcare service perspectives. Healthcare systems have two main stakeholders: staff and patients. The research suggests that the findings of healthcare modelling may not be reliable without considering the behaviours of the main stakeholders (Brailsford et al., 2012 ). For example, a simulation model suggesting the effectiveness of a new drug will be unreliable if it does not consider patient behaviour; some patients may not complete the prescribed course of medication, and potential side effects of medicine should be shown. As such, healthcare literature has seen an emergence of interest in understanding both human behaviour in practice and how to capture it in operations research models. Brailsford et al. ( 2012 ) used a simulation model capturing behavioural factors of a patient while treating breast cancer. Based on a review of literature, Kunc et al. ( 2018 ) identified scarcity of research on behavioural aspects of healthcare. Harper ( 2019 ) used a queuing model to assess how healthcare workers behave and treat patients, especially when such professionals encounter changing workloads, service queues, and other factors that impact service quality.

To sum, behavioural operational research has become an important research topic in healthcare but significant room for further research in the area remains intact. Specifically, we suggest more research with detailed models on the nexus between behavioural interventions on healthcare practice. Future research can model workforce adaptive behaviours and productivity. We agree that the incorporation of behavioural factors is important to increase the reliability of the healthcare model because of the increasing influence of human behaviours in healthcare operations. Future research can also examine the behavioural consequences of inter-organisational interaction in complex healthcare systems.

5.8 Risk and resilience

Healthcare systems are susceptible to operational failures in services offered to patients when a shortage of medicine and equipment occurs, or when the execution of such systems is incorrectly managed. Given the divergent risks emerging from a variety of sources, there is an increasing focus on risk mitigation and the development of resilient healthcare. Nemeth et al. ( 2011 ) developed a model for healthcare crises management and resilience. Mahjoub et al. ( 2014 ) found risk mitigation in healthcare through a risk-sharing agreement between pharmaceutical companies and healthcare service providers. Saedi et al. ( 2016 ) suggested an optimal inventory policy to reduce drug shortage risks. Rubbio et al. ( 2019 ) examined the role of digital technologies in developing healthcare resilience. Rahimian et al. ( 2019 ) investigated an appropriate level of risk-aversion through robust optimization. Zhang et al. ( 2020 ) formulated a model which helps to reduce overtime risk during surgery in a hospital.

While research on risk and resilience in healthcare is growing, there is still substantial room for more impactful future research. Most models investigate risk and resilience in isolation. Others focus on particular functions such as hospital surgery. As such, a quantitative model that simultaneously investigates risk and resilience focusing on the key supply chain actors in healthcare is lacking. Future research can also use a mixed-methods approach where qualitative research can explore various risk factors and resilience strategies, while quantitative research can test the interrelationship between risk and resilience in explaining healthcare performance.

5.9 Humanitarian logistics

Humanitarian logistics refers to the transportation, delivery, and distribution of supplies in the event of natural disasters or another emergency to the affected area and people. Given the unprecedented incidents such as floods, cyclones, disease outbursts, the humanitarian logistics in healthcare (e.g., supplies of medicines, medical equipment, sterile items, linen, and food) has gained significant attention in recent times. For example, Naor et al. ( 2018 ) found strategies for effective humanitarian logistics in healthcare, such as quickly sending a team to the disaster area to gather firsthand information on the unique situations of disaster followed by the main delegation staff carrying effective disaster relief service. Prasad et al. ( 2018 ) suggested effective healthcare intervention to deal with disease outbreaks in a community. Cherkesly et al. ( 2019 ) assist with the design of a community healthcare network, increasing the service coverage for underserved areas in adverse situations.

Although research on humanitarian logistics has been progressing steadily, we highlight the need for further research on healthcare OSCM. A fruitful future research avenue is modelling the role of volunteers and NGOs in humanitarian logistics from a healthcare perspective. Also, interviews with disaster area patients can provide additional insights into the real-world phenomenon. Further research can also develop a model of budget allocation as the community service demand increases drastically due to a natural disaster. It would be interesting to investigate the nexus among service compliance rate, disease rate, and total cost by disaster type.

5.10 Pharmaceutical OSCM

A pharmaceutical supply chain involves firms, processes, and operations involved in the design, development, and distribution of life-saving drugs. It is one of the critical supply chains since it is related to the life and health of people. The success of healthcare services directly depends on well-managed pharmaceutical supplies. Therefore, the management of the pharmaceutical supply chain has become a popular topic in healthcare literature. Jia and Zhao ( 2017 ) highlighted the issue of drug shortage due to lack of collaboration between pharmacies and pharmaceutical manufacturing and supplier. Nematollahi et al. ( 2018 ) also found a positive link between hospital service level and efficient pharmaceutical supply chain. Viegas et al. ( 2019 ) discussed the significance of a coordinated pharmaceutical supply chain for the reverse flow of end-of-life medicine. Recently, Zandkarimkhani et al. ( 2020 ) formulated a model to redesign the pharmaceutical supply chain network in the wake of demand uncertainty.

Despite growth in literature on the pharmaceutical supply chain, significant room for further research still exists, particularly in the context of a recent pandemic. Most of the existing research lacks a holistic view that involves all key stakeholders of the supply chain. There is less focus on upstream networks and processes (Narayana et al., 2014 ). There is also limited research on integrated new product development in the pharmaceutical supply chain. There is also an opportunity for research to model examining the link between healthcare financing and pharmaceutical supply chain financing such as investment in R&D, production and distribution, between waste management, environmental and pharmaceutical supply chains. Future studies can also model the behaviour of individuals in the robustness of pharmaceutical supply chains. Given that supply chain design is critical in the planning process (Govindan et al., 2017 ), we suggest designing integrated models that encompass all stages from drug manufacturing to final consumer for robust decision-making in the pharmaceutical supply chain. Another interesting avenue is formulating a model at the intersection of supplier selection and sustainable pharmaceutical supply chain networks.

5.11 Climate change and healthcare

The increased intensity and frequency of disasters due to extreme weather conditions draw our attention to the unprecedented changes in climate which are mainly attributed to excessive greenhouse gas emissions and carbon footprints. To deal with the climate change challenges, greenhouse gas mitigation targets were set to a minimum level in the historic Paris Agreement (Bodansky, 2016 ). While a lot of research spotlights the emissions reduction in the automotive, energy, and mining industries, the carbon footprint and climate change research has recently received attention. Rezali et al. ( 2018 ) discussed the importance of a green healthcare supply chain to reduce the environmental impact of the healthcare industry. Belkhir and Elmeligi ( 2019 ) examined 15 pharmaceutical supply chains across different countries and found that the pharmaceutical industry is more emission-intensive than the automotive industry. Tanwar et al. ( 2019 ) identify the potential risks and risk mitigation measures in implementing a green pharmaceutical supply chain. Over the past five years, the topic of climate change has received increasing attention in healthcare settings. Most studies on the topic focus on factors contributing to climate change (greenhouse gas emissions, carbon footprint), thus leaving a gap for research models incorporating the business impact of climate risks when they unfold and examining specific risk mitigation measures.

5.12 Healthcare logistics

Healthcare logistics refer to the physical flow of healthcare materials from the pharmaceutical production facility to the healthcare service provider. Logistics cost has been identified as the second largest cost after personnel in the healthcare system (Ross & Jayaraman, 2009 ). As such, the importance of material flow in healthcare service is recognised important to add value and reduce the cost of services. Correspondingly, a growth in literature on healthcare logistics is evident. Oumlil and Williams ( 2011 ) suggested strategic alliance and consolidation of logistics in the healthcare sector as a strategy to reduced logistics costs. Volland et al. ( 2017 ) identified four main domains in which healthcare logistics research exists, including procurement, inventory management, distribution, and supply chain management. Al-Sharhan et al. ( 2019 ) expounded on the significance of hospital-supplier integration of inefficient material logistics. Fathollahi-Fard et al. ( 2019 ) developed a green home healthcare supply chain model to reduce emissions in logistics. Pohjosenperä et al. ( 2019 ) discussed the impact of service modularity in value-creating and improving healthcare logistics service. Zabinsky et al. ( 2020 ) developed a model through mixed-integer linear programming to address healthcare scheduling and routing problem. Further research could examine an item-level analysis of different product categories in the regional healthcare units, together with their replenishment models. Such a study is needed to show the true potential of modularity in service offerings of the logistics support organisation.

6 Conclusion

Over the last two decades, research on healthcare OSCM has been continuously advancing making it a self-sustained discipline in the OSCM field. We argue that with this unprecedented development in healthcare literature, the current status and trends in the field need to be mapped. The use of robust statistical and automatic methods for extracting key topics from a large set of articles has been increasingly used in other fields such as finance, marketing, statistics, tourism, and computer sciences. Borrowing the knowledge from these adjacent fields, this study employs probabilistic topic modelling with LDA as an automatic text analytics approach to unpack dominant topics and development trajectories in a large collection of research articles on healthcare OSCM. In doing so, we contribute to the literature on new and rigorous methods for systematic literature review in the field of OSCM. Our sample included 571 articles retrieved from 20 journals in the field of OSCM. The results produced 34 topics on healthcare research. Analysis of dynamic changes in the 34 over the past twenty years suggested 24 hot, 8 cold, and 2 steady topics (see Table 3 ).

Our findings show a shift of current research from traditional topics of operations management (Business process reengineering, Marketing, Outsourcing, Heuristics) to emergent topics such as Patient waiting for time, Industry 4.0 technologies, Sustainable operations, Risk and resilience, Climate change, and Circular economy. In general, the shift in topics seems to show a move away from traditional, efficiency-focused research toward broader goals of superior customer service and system’s effectiveness. The data on research methods shows a larger dominance of modelling research (e.g., Markov, mixed-integer programming, simulation, queueing model, and stochastic) in exploring multidimensional issues of healthcare operations. However, the past three years have also seen some empirical research, including Structural equation modelling, Longitudinal study, and Action research. This shows a positive change in operations research journals from purely quantitative modelling studies to empirical research as well. This recent shift in a strategy not only offers a great opportunity to empirical researchers for publication in some OR journals but also provides sound premises on which to expand the scope and subscription of these journals.

The findings of this research offer useful research directions to researchers, journal editors, and policymakers in the field of healthcare research. Researchers can observe whether their recent research topics are hot, cold, or steady, and accordingly select appropriate topics for more impactful future research in the area. Particularly, some early-career researchers find it difficult to keep up with the growing literature in their fields of research and familiarise themselves with a huge volume of literature in identifying the subject area on which they can focus or apply for a research grant. The information from our topic modelling can be a useful tool for these researchers to understand recent developments in the field and then select more appropriate topics. Our findings can also be helpful to PhD students, who often face difficulties in topic selection due to time pressure. Journal editors can view if the extant research is consistent with their editorial policy, and they may like to launch a new editorial vision and direction. Our list of hot topics can be a valuable reference tool to launch special issues in the journals. Discovering underlying topics and tracking their evolution could also be of great interest to policymakers (e.g., government agencies) and industry. The information on which research themes and methods are growing or declining in popularity over time (Table 3 and Fig.  4 ) can support governmental funding agencies for grants allocation to promising areas. Likewise, firm managers and the industry as a whole can use this information to formulate promising R&D strategies and to make more informed investment decisions.

We believe that topic modelling is one of the most effective and reliable approaches in revealing the latent (hidden) structure and development of research topics in a field, and recommend that future research can combine the LDA-based models with other text analytics techniques from different text data (e.g., customer feedback, medical records, research reports, business reviews, company descriptions) and interconnection among various business areas (e.g., marketing, supply chain, information systems). For example, sentiment analysis (Pang & Lee, 2008 ) is deemed a popular text analytics tool in the field of finance (Sul et al., 2017 ), marketing (Liang et al., 2015 ), and operations/supply chain (Chae, 2015 ). The sentiment analysis can be integrated with topic modelling: a two-pronged method using topic modelling followed by sentiment analysis can offer more nuanced findings by revealing both positive and negative aspects of a product and/or service.

As with several studies, there are some limitations of our study. First, given the authors’ extensive knowledge of the field and review of the extant literature, full efforts were made to guarantee that all pertinent articles were included in the analysis. However, some articles might have been ignored. Nevertheless, two measures might help mitigate this limitation: (1) the topics are generated based on the frequencies of the keywords/key terms and, therefore, missing a few articles from such a large dataset (571 articles) will not have a significant impact on overall results; (2) the dataset is validated with other databases. Second, the research is limited to healthcare research published in OSCM journals. That is, we excluded the papers and related journals containing healthcare research but not from OSCM perspectives.

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Ali, I., Kannan, D. Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review. Ann Oper Res 315 , 29–55 (2022). https://doi.org/10.1007/s10479-022-04596-5

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DOI : https://doi.org/10.1007/s10479-022-04596-5

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