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Six Sigma Case Study: Everything You Need to Know

Explore the field of Six Sigma Case Studies in our comprehensive blog. From defining the methodology to real-world applications, our 'Six Sigma Case Study: Everything You Need to Know' blog sheds light on this powerful problem-solving tool. Uncover success stories and learn how Six Sigma can drive efficiency and quality improvements in various industries.

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By analysing such case studies, one can gain insights into the successful application of Six Sigma in various industries and understand its impact on process improvement. Read this blog on Six Sigma Case Study to learn how real-world businesses have achieved remarkable process improvement and cost savings. 

Table of Contents  

1) Understanding Six Sigma Methodology 

2) Six Sigma Case Study 

a) Improving customer service 

b) Improving delivery efficiency 

3) Conclusion 

Understanding Six Sigma Methodology

By applying statistical analysis and data-driven decision-making, Six Sigma helps organisations identify the root cause of problems and implement effective solutions. It emphasises the importance of process standardisation, continuous improvement, and customer satisfaction. With its focus on rigorous measurement and analysis, Six Sigma enables organisations to drive efficiency, reduce waste, and deliver exceptional products and services. The methodology follows a step-by-step process called Define, Measure, Analyse, Improve, and Control (DMAIC). These five phases are briefly explained below: 

a) Define: The project goals and customer requirements are clearly defined in this phase.  

b) Measure: In this phase, data is collected to understand the process's current state and identify improvement areas.  

c) Analyse: This phase focuses on analysing data to determine the root cause of defects or variations.  

d) Improve: This phase involves implementing solutions and making necessary changes to eliminate the identified issues.  

Six Sigma Case Study  

In this section we discuss two Six Sigma Case Study that will help you understand and use it better.  

Case Study 1: Improving customer service  

This Six Sigma Case Study will focus on a telecommunications company facing significant customer service challenges. The issues included long wait times, frequent call transfers, unresolved issues, and many more. The company decided to apply Six Sigma methodologies to enhance customer satisfaction.  

a) Define phase: Using the DMAIC approach, the team began by defining the problem: long wait times and inefficient call handling. They set a goal to reduce average wait time and increase first-call resolution rates.  

b) Measure phase: In this phase, data was collected to analyse call volume, wait times, and reasons for call transfers. This helped identify bottlenecks and areas for improvement.  

c) Analyse phase: During this phase, the team discovered that inadequate training and complex call routing were key contributors to the problems. They also found that certain product issues required better resolution protocols.  

d) Improve phase: In this phase, targeted solutions were introduced and implemented to address these issues. The team revamped the training program, ensuring agents were well-trained and equipped to handle customer inquiries. They simplified call routing and introduced automated prompts for quicker issue resolution.  

e) Control phase: Finally, monitoring systems were established in the control phase to track key metrics and ensure sustained improvements. Regular feedback loops were implemented to identify emerging challenges and make necessary adjustments.  

The results were exceptional. Average wait times were reduced by 40%, and first-call resolution rates increased by 25%. Customer satisfaction scores improved significantly, leading to increased loyalty and positive word-of-mouth.  

This Six Sigma Case Study highlights how Six Sigma methodologies can drive transformative improvements in customer service. By focusing on data analysis, process optimisation, and continuous monitoring, organisations can achieve outstanding outcomes and deliver exceptional customer experiences. 

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Case Study 2: Improving delivery efficiency

a) Define phase: The business used the Voice of the Customer (VoC) tool to understand customer needs and expectations. They identified prompt delivery, correct product selection, and a knowledgeable distribution team as crucial customer requirements. 

b) Measure phase: The team collected data to evaluate the problem of slow delivery. They discovered that their Order Fulfillment Cycle Time (OFCT) was 46% longer than competitors, leading to customer dissatisfaction.  

c) Analyse phase: The team brainstormed potential causes of slow delivery, including accuracy of sales plans, buffer stock issues, vendor delivery performance, and manufacturing schedule delays. They conducted a regression analysis, revealing that inadequate buffer stock for high-demand products was the main issue affecting delivery efficiency.  

d) Improve phase: The distributor implemented a monthly demand review to ensure that in-demand products are readily available. They emphasised ordering and providing customers with the specific products they desired.  

e) Control phase: The team developed plans to monitor sales of the top 20% of bestselling products, avoiding over or under-supply situations. They conducted annual reviews to identify any changes in demand and proactively adjust product offerings.  

By applying Six Sigma Principles , the plumbing product distributor significantly improved its delivery efficiency, addressing the root cause of customer dissatisfaction. Prompt action, data-driven decision-making, and ongoing monitoring allowed them to meet customer expectations, enhance its reputation, and maintain a competitive edge in the industry. This case demonstrates the power of Lean Six Sigma in driving operational excellence and customer-centric improvements. 

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Conclusion  

We hope this blog gives you enough insights into the Six Sigma Case Study. This blog showcased the effectiveness of its methodology in driving transformative improvements. By applying DMAIC and using customer insights and data analysis, organisations have successfully resolved delivery inefficiencies, improving customer satisfaction and operational performance. The blog highlights how Six Sigma can be a powerful framework for organisations seeking excellence and exceptional value. 

Learn the six-sigma methodology to achieve business objectives with our Six Sigma Certification Training today!  

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Six Sigma in Action: A Case Study at 3M

Introduction to 3m.

3M is the world’s 3 rd most innovative technology company that strives to create groundbreaking products. The goal of 3M is simple – create products that make positive differences in everyone’s lives. Six Sigma specializes management strategy method that has evolved and modernized since its origin in 1986. It focuses on proactively deterring issues that will arise in production and corporate operations. Like many companies have begun to do, 3M acquired the Six Sigma management strategy and has revolutionized its infrastructure. The “World’s Most Ethical Company” is now a leading innovator in technology, energy, and more due to the success of the method. Now, 3M offers an in-depth case study to show exactly how Six Sigma transformed their company.

Implementing Six Sigma

Gaining control of 3M in 2001, James McNerney placed the Six Sigma methodology into the backbone of the company. McNerney’s unique, considerate approach led to a four-year overhaul of the manufacturing and production processes. From eliminating waste to improving productivity, this methodology grew revenue faster than ever and continues to lead innovative technologies.

McNerney grew 3M other enterprises such as Global Souring, 3m Acceleration, eProductivity, and Indirect Cost Control. As a result, 3M began 2005 with over 30,000 employees Six Sigma certified, with a minimum Green Belt training for all technical and sales staff. Combining the Six Sigma methodology with a strong leadership, 3M consistently practices an ever-improving production process with increasing profits to match.

In addition to substantial revenue growth, this methodology continues to bring out massive savings and benefits. The 2003 Annual Report states that operating income was amplified by more than $500,000 in 2002 alone as a result of the Six Sigma initiatives. This figure is substantially larger than earlier predictions, and the forecast continues to remain high for the following year, sitting at $400,000, an estimate which was successfully met as reported by the Prudential Financial Conference in September 2004.

The Results

Alongside considerable financial growth, 3M enjoys significant corporate network growth. 3M’s network continues to expand by collaborating with numerous companies on over 250 projects such as Ford, Estee Lauder, Motorola, Wal-Mart, and Procter & Gamble. Mature, effective Six Sigma programs are easily spotted, sharing their knowledge with customers, suppliers, and other important personnel. Only Six Sigma has the tools necessary to transforming your business, with total process improvements and reducing defects. Six Sigma drives growth, reduces costs, increases revenue, and produces strong business relationships with customers that last a lifetime.

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Six-sigma application in tire-manufacturing company: a case study

• Original Research
• Open access
• Published: 20 September 2017
• Volume 14 , pages 511–520, ( 2018 )

Cite this article

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• Vikash Gupta 1 ,
• Rahul Jain 1 ,
• M. L. Meena 1 &
• G. S. Dangayach 1  

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Globalization, advancement of technologies, and increment in the demand of the customer change the way of doing business in the companies. To overcome these barriers, the six-sigma define–measure–analyze–improve–control (DMAIC) method is most popular and useful. This method helps to trim down the wastes and generating the potential ways of improvement in the process as well as service industries. In the current research, the DMAIC method was used for decreasing the process variations of bead splice causing wastage of material. This six-sigma DMAIC research was initiated by problem identification through voice of customer in the define step. The subsequent step constitutes of gathering the specification data of existing tire bead. This step was followed by the analysis and improvement steps, where the six-sigma quality tools such as cause–effect diagram, statistical process control, and substantial analysis of existing system were implemented for root cause identification and reduction in process variation. The process control charts were used for systematic observation and control the process. Utilizing DMAIC methodology, the standard deviation was decreased from 2.17 to 1.69. The process capability index ( C p ) value was enhanced from 1.65 to 2.95 and the process performance capability index ( C pk ) value was enhanced from 0.94 to 2.66. A DMAIC methodology was established that can play a key role for reducing defects in the tire-manufacturing process in India.

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Introduction

Tire has gone through many stages of evolution, since it was developed first time about 100 years ago. In the beginning, solid rubber tires were used mostly for bicycles and horse-driven carts. First, John Dunlop made a tire which consist a tube mounted on a spoked rim. Then, in 20th century with the arrival of motor vehicles, the use of pneumatic tires was started. The manufacturing process of tires begins with selection of rubber as well as other raw materials including special oils, carbon black, etc. These various raw materials are shaped with a homogenized unique mixture of black color with the help of gum. The mixing process is controlled by the computerized systems to insure uniformity of the raw materials. Furthermore, this mixture is processed into the sidewall, treads, or other parts of the tire. The tire bead wire is used as a reinforcement inside the polymer material of the tire. Bead wire is made up of high carbon steel and the main function of bead is to grasp the tire on the rim. The bead wire of functional tire can work at pressures of 30–35 psi (Palit et al. 2015 ). Bead wires help to transfer the load of vehicle to the tire through the rim. Due to the increase demand of tires, maintaining the quality and reliable performance becomes priority. In addition, there is need for maintaining the quality in the era of technological advancements in design of pneumatic tires.

The companies have to analyze, monitor, and make improvements of their existing manufacturing systems to comply with the market competition. Different companies use different methodologies, approaches, and tools for implementing programs for continuous quality improvement. Besides these, each company certainly required to use a proper selection and combination of different approaches, tools, and techniques in its implementation process (Sokovic et al. 2010 ). Variations are generally observed during the manufacturing process of any product. The prime objective of process management or process capability analysis in any organization is to investigate the variability during the manufacturing process of product (Pearn and Chen 1999 ) which helps organization to monitor and measure the potential of process (Wu et al. 2004 ). The process capability is determined when the process is under statistics control (i.e., the sample mean on X-bar and R-chart lies within three-sigma limits and varies in random manner). Sometimes, a process which is under statistical control may not produce the products within the specifications limits. The reason for this problem is the presence of common cause or this can be happened due to lack of centering of process mean (i.e., there is a significant different between mean value and specified nominal value). Process capability procedure uses control charts to detect the common causes of variation until the process not comes under statistical control (Boyles 1994 ; Chen et al. 2009 ). Process capability indices are used in many areas, i.e., continues measure of improvement, prevention of defects in process or products, to determine directions for improvement, etc. (Kane 1986 ). Process capability indices are measures of the process ability for manufacturing a product that meets specifications. Three basic characteristics (i.e., process yield, process expected loss, and process capability indices) had been widely used in measuring process potential and performance. Among various process, capability indices C p and C pk are easily understood and could be straightforwardly applied to the manufacturing industry (Chen et al. 2001 , 2002 ).

Literature review

The quality improvement tools and total quality management (TQM) are still used in modern industry. However, industries tried to incorporate strategic and financial issues with this kind of initiatives (Cagnazzo and Taticchi 2009 ). After inception of TQM in the early 1980s, six sigma came in picture as an element of TQM that could be seen as current state of evolution in quality management. Six sigma is a strategy that helps to identify and eliminate the defects which leads to customer dissatisfaction in tire industries (Gupta et al. 2012 ). An organization working on direction of implementing six sigma into practice or working to build six-sigma concepts with improvement in process performance and customer satisfaction is considered as six-sigma company (Kabir et al. 2013 ). General Electric and Motorola are two well-known companies who implemented six sigma successfully. For successful implementation of six sigma in organization, one must have to understand the barriers and motivating factors of the six sigma (Hekmatpanah et al. 2008 ). Six sigma aimed to achieve perfection in every single process of a company (Narula and Grover 2015 ). The term six sigma means having less than 3.4 defects per million opportunities (DPMO) or a success rate of 99.9997%. In six sigma, the term sigma used to represent the variation of the process (Antony and Banuelas 2002 ). If an industry works as per the concept of three-sigma levels for quality control, this means a success rate of 93% or 66,800 DPMO. Due to less rejections, the six-sigma method was a very demanding concept for quality control, where many organizations still working on three-sigma concept. In this regard, the six sigma is a methodology that enables the companies to review their existing status and guide them in making improvements by analyzing their status via statistical methods (Erbiyik and Saru 2015 ). For most of the industries, sigma is a level that measures the process improvement and thus can be used to measure the defect rate. Six-sigma define–measure–analysis–improve–control (DMAIC) methodology is a highly disciplined approach that helps industrial world to focus on developing perfect products, process, and services. Six sigma identifies and eliminates defects or failures in product features concerned to the customers that affect processes or performance of system.

The literature reveals that most of the waste in developing countries comes from the automobiles (Rathore et al. 2011 ; Govindan et al. 2016 ), and out of the total automobile waste, most of the waste comes in the form of tires. There are several barriers faced during the remanufacturing these wastes (Govindan et al. 2016 ). Around the world, only few studies have been carried out for the tire industries and these studies are focused on analyzing the profitability of car and truck tire remanufacturing (Lebreton and Tuma 2006 ), system design for tire reworking (Sasikumar et al. 2010 ), value analysis for scrap tires in cement industries (de Souza and Márcio de Almeida 2013 ), and analyzing the factors for end-of-life management (Kannan et al. 2014 ). In addition, some researchers proposed methodologies for improving the process in tire-manufacturing companies out of which few industries implemented lean and six-sigma methodologies (Gupta et al. 2012 , 2013 ; Visakh and Aravind 2014 ; Wojtaszak and Biały 2015 ). Other studies also found implementing just in time (Beard and Butler 2000 ) and Kanban (Mukhopadhyay and Shanker 2005 ).

However, numerous studies are available for process improvement in the automobile industries using various methods (Dangayach and Deshmukh 2001a , b ; Chen et al. 2005 ; Dangayach and Deshmukh 2004a , b , 2005 ; Laosirihongthong and Dangayach 2005a , b ; Sharma et al. 2005 ; Radha Krishna and Dangayach 2007 ; Krishna et al. 2008 ; Cakmakci 2009 ; Prabhushankar et al. 2009 ; Mathur et al. 2011 ; Dhinakaran et al. 2012 ; Dangayach and Bhatt 2013 ; Muruganantham et al. 2013 ; Sharma and Rao 2013 ; Kumar and Kumar 2014 ; Venkatesh et al. 2014 ; Surange 2015 ; Bhat et al. 2016 ; Dangayach et al. 2016 ; Jain et al. 2016 ; Gidwani and Dangayach 2017 ; Meena et al. 2017 ).

A review of the literature shows that the DMAIC method is the superb practice for improving the process capability in automobile industries. Hence, the current research concentrates on the use of DMAIC method aimed for process capability enhancement of the bead splice appearing in a tire-manufacturing industry.

Methodology

In this study, the six-sigma DMAIC phases were applied to enhance the process capability (long term) for bead splice. In every phase of DMAIC method, a compound of both techniques qualitative as well as quantitative was utilized. The DMAIC steps followed in the current research are as follows:

In the first phase, the goals were defined to improve the current process. The most critical goals were acquired using the voice of customer (VOC) method. These goals would be helpful for the betterment of the company. In addition, the goals will direct to bring down the defect level and increase output for a specific process.

Without measuring the performance attributes, the process cannot be improved. Therefore, the ultimate target of measure phase was to establish a good measurement system to measure the process performance. Process capability index C pk was selected to measure the process performance. To compute the process capability index, observations of bead splice variation were taken and MINITAB (version 16.0) was used for analysis.

In the analyze phase, the process was analyzed to identify possible ways of bridging the gaps between the present quality performance of the process and the goal defined. In addition, it was started by determining the existing performance statistics obtained with the help of six-sigma quality tools (process capability index). The further analysis of these data was done for finding root cause of the problem using Ishikawa diagram.

In improvement phase, the alternative ways were searched creatively to do things better and faster at low cost. Different approaches (i.e., project management, other planning and management tools, etc.) were used to establish the new approach and statistical methods were proposed for continuous improvement.

The improvement gained through the previous steps needs to be maintained for continuous success of the organization. Control phase was used to maintain these improvements in process. The new process/improved process was proposed for sustaining the quality control in the organization.

Company profile

Company A was the leading Indian tire manufacturing who started exclusive branded outlets of truck tires. Company started its first manufacturing plant at Perambra, Kerala state of India in the year 1977. Furthermore, the company started its second manufacturing plant in Limda, Gujarat. Company expanded its business and established third plant at Kalamassery, Kerala in year 1995, where premier-type tires are produced. Then, company established a special tubes plant in the year 1996 at Ranjangoan, Maharashtra. Company increased its capacity to produce exclusive radial tires at Limda, Gujarat plant in the year 2000. In year 2004, company initiated production of high-speed rated tubeless radial tires for passenger cars.

Implementation of DMAIC methodology

Problem definition.

In the current research, the problem was identified on the basis of VOC data. The customer complaints on wastage of material due to variation in the bead splice of a particular product were recorded. Table  1 shows the specification of the product (tire).

This wastage increases financial loss to the organization. Therefore, the problem is variations in the bead splice which has to be reduced to minimize the wastages.

Establishment of measures

Initially, the normality test for the collected data was performed and Fig.  1 shows the normal distribution curve for the bead splice data. After passing the normality test, process capability index C pk was calculated to measure the present process performance using the observations of bead splice variation, which is presented in Table  2 .

Normality test of bead splice

These data were used to create an overall baseline for the system to assess its performance based on the necessary improvement areas established in the define phase. Figure  2 shows that the value of process capability index C pk is 0.94 which is less than 1; hence, the process is not capable.

Process capability diagram of bead splice: before improvement

Data analysis

In this phase, the data were analyzed and control charts were constructed. Figure  3 shows the X-bar and R-chart for the existing data. From the figure, it is clear that the few points are outside the lower control limit; however, the process is in statistical control.

X- and R-bar chart of present data

Identification of root cause

The Ishikawa diagram was used for finding the root cause of the problem, which is shown in Fig.  4 . The identified causes of the problem are as follows:

Ishikawa diagram

First cause of the problem was bead splice setting on higher side caused by slippage of bead tape from gripper. The slippage of bead tape from gripper was generated due to worn out of the griper key.

Second cause was variation in the advancer setting caused due to change in skill of worker. This man-to-man variation was caused due to lack of the standard setup guidelines available.

The third cause was related to the frequency of sensor setting. Setting of sensor is required frequently as the former diameter changes. However, due to non-availability of guideline, sensor setting could not change frequently.

The last cause was identified that the workers were not using the measuring tape.

After finding the root causes, the corrective actions were taken, which are presented in Table  3 . After implementing these corrective actions, again observations were taken to measure the process performance.

The collected data are shown in Table  4 and run chart for bead splice variation was drawn for the observations taken before and after corrective actions (Fig.  5 ). From Fig.  5 , it is clear that variability in the process reduced drastically.

Run chart for bead splice

The process capability index was also computed after implementing corrective actions. Figure  6 shows that after improvement in process, the capability index C pk value is improved to 2.66 which shows that process is capable.

Process capability diagram of bead splice: after improvement

To maintain the achieved process performance of the six-sigma quality level, the above four steps of DMAIC methodology must be applied periodically.

Conclusion and Discussion

In this research, DMAIC approach was implemented for process improvement in tire industry. First, process capability index C pk of the current process was computed which was found less than 1. Therefore, to improve the value of process performance, the root causes of problem were determined with the help of cause and effect diagram. In addition, substantial analysis of existing system was done for finding the solution of root cause identified. Finally, in the improve phase, statistical analysis was done for identifying the process capability index value which was improved after taking corrective actions. From outcomes of the study, it can be concluded that process performance of a tire-manufacturing plant can be improved significantly by implementing six-sigma DMAIC methodology.

Cause and effect diagram was also used in an Indian study by Gupta et al. ( 2012 ), although no manufacturing aspects were discussed. One more exploratory research was implemented for finding the enablers for successful implementation of lean tools in radial tire-manufacturing company in India (Gupta et al. 2013 ); however, no manufacturing aspects were discussed in this study also. In the current study, six-sigma DMAIC method is used for improving the process performance.

The main aim of this study was to improve the process capability index of the bead splice, which is achieved by increasing the value of process capability index up to 2.66. This study is based on six-sigma DMAIC quality methodology which provides information about the decision-making power for particular type of problem and the most significant tool for improvement of that type of problem in which data used must come from a stable process (under statistical control: Chen et al. 2017 ).

Six sigma is a standard of measurement of the product or process quality, also having a caliber for improvement in efficiency and excellence of process. The main aim of implementing six-sigma approach is delivering world-class quality standards of product and service while removing all internal as well as external defects at the lowest possible cost. For proper and successful implementation of a six-sigma project, organization must have the required resources, the guidance to the employees by top management, and leadership of top management. The case company follows several quality standards, which have research and development cell, and good coordination system for managing the issue faced on shop floor. Hence, the corrective actions were implemented successfully.

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Gupta, V., Jain, R., Meena, M.L. et al. Six-sigma application in tire-manufacturing company: a case study. J Ind Eng Int 14 , 511–520 (2018). https://doi.org/10.1007/s40092-017-0234-6

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Six Sigma: A Case Study in Motorola

A Six Sigma Overview

Nowadays, organizations are constantly striving to understand and meet the customer’s expectations by focusing on the quality of the products offered. Luckily, there are many tools and techniques available which enable management to improve the quality of their products and services. Six Sigma has proven to be one of the most successful tools in this regard. 

Six Sigma is a methodology which uses specific principles and mechanisms that ensure excellence within the organization. The ultimate goal of this methodology is to create products or services with less than 3.4 defects per million products or services produced. Witnessing its benefits, many of world’s most famous and successful organizations have decided to implement and integrate Six Sigma principles in their business processes.

The Beginning of Six Sigma

A look back in history indicates that the implementation of Six Sigma principles was pioneered by Motorola Company in 1980s. Motorola has always been a high tech company, offering highly reliable products. However, by 1970, every business in which Motorola was engaged in, was already targeted by Japanese. 

During that time, Motorola, like many other American companies, was struggling to keep up with Japanese competition. Motorola’s customers were unhappy with the product defects and customer support. On the other hand, Japanese had already built an amazing quality standard that many American companies simply could not keep up with. As a result, dealing with severe financial pressure, Motorola had to take action. 

The top management summoned the Motorola engineers and sought to reduce the amount of errors in their products before they were even shipped out of their factories. They combined all the quality management practices known till that time and created a methodology that would be the baseline of Motorola’s quality improvement program. Bill Smith, an engineer and scientist at Motorola, developed a methodology that would reduce the amount of product defects. He created the original statistics and formulas initiated the implementation of Six Sigma methodology. Convinced in the huge success that this methodology would have, he presented the ideas to CEO Bob Galvin. Bob came to recognize this approach as the solution to their quality concerns. They followed the four phase Six Sigma methodology (measure, analyze, improve and control) and started their journey of documenting their key processes, aligning those processes to customer requirements, and installing measurement systems to continually monitor and improve these processes. 

As a result, Motorola’s performance improved instantly. However, even though they were doing well, the analysis revealed that Japanese were still way ahead of them.

Thus, to remain competitive, top management vowed to make improvements in their quality by tenfold over a five-year period. Initially, this seemed to be impossible, but by the end of 1985, everyone in Motorola had started working toward that goal. 

By the end of the five year period, every business in Motorola had reached their targeted scale of improvement. Motorola managers decided to fly to Japan to better evaluate how their competition was doing, and what they found out was mind-blowing. They saw that the Japanese companies were doing 2000 times better than them. This was due to the fact that Japanese had been using similar technologies for a longer period of time. 

The information unveiled in Japan changed the objectives of Motorola again. The executives became even more ambitious, and decided to set a tenfold target one more time, but deadline was set for a two year period now. Motorola goal for 1992 was to have 3.4 defects per million opportunities. 

After implementing Sig Sigma, Motorola realized how important the methodology had been in improving their processes. In fact, they have documented more than $16 billion in saving as a result of Six Sigma adoption. Therefore, they decided to make the methodology public for every company that wanted to adopt it in their processes. Since then, tens of thousands of companies around the world have been considering Six Sigma as a way of doing business. 

Bearing in mind the previous points, it can be concluded that Motorola implementation of Six Sigma has been a stepping stone in the modern times of quality improvement. We may wonder where will the Six Sigma journey lead us to. This path, however, will certainly be challenging while we seek perfection. But the highly satisfied customers, motivated employees, increased benefits, among many other reasons, lead to believe that the employment of Six Sigma as the best business support will never cease to exist.

Author:   Hana Tahiri is the Portfolio Marketing Manager for Quality Management System and Transportation, Telecom and Energy at PECB. She is responsible for continually conducting research and writing articles and marketing materials related to QMS and TTE. If you have any questions, please do not hesitate to contact her: [email protected] .

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Six Sigma for improving cash flow deficit: a case study in the food can manufacturing industry

International Journal of Lean Six Sigma

ISSN : 2040-4166

Article publication date: 14 May 2020

Issue publication date: 1 December 2020

Cash flow deficit situations and working capital control are major challenges for many companies, especially those whose suppliers and clients have strong bargaining power. This study aims to describe the application of the Six Sigma methodology for solving these problems in a large German food can manufacturing company.

Design/methodology/approach

This paper follows the qualitative methodology of case study research. During different define, measure, analyse, improve and control process phases, the problem and critical aspects are identified to improve the quality of the payment process and improvements are suggested and implemented.

The results provide evidence of how Six Sigma can be useful in administrative–financial processes that are carried out within a company. This result is particularly interesting because it is about processes that have not applied Six Sigma methodology. For the company studied, this methodology has balanced its cash flow and this meant large amounts of savings, especially in bank interest to avoid having to ask for bank credits.

Originality/value

This case can be extrapolated to other companies, regardless of the company size, that present similar symptoms of cash deficit, especially if their bargaining power with suppliers and customers is low.

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• Accounts payable
• Can industry
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Sánchez-Rebull, M.-V. , Ferrer-Rullan, R. , Hernández-Lara, A.-B. and Niñerola, A. (2020), "Six Sigma for improving cash flow deficit: a case study in the food can manufacturing industry", International Journal of Lean Six Sigma , Vol. 11 No. 6, pp. 1105-1126. https://doi.org/10.1108/IJLSS-12-2018-0137

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Copyright © 2020, Maria-Victòria Sánchez-Rebull, Ramon Ferrer-Rullan, Ana-Beatriz Hernández-Lara and Angels Niñerola.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Six Sigma is a philosophy that pursues excellence, offering reliable products or services. There is no standard definition about it. However, it is clear that it has two well-defined perspectives ( Prabhushankar et al. , 2008 ). From a business point of view, Six Sigma is a powerful methodology that enhances the efficiency of business processes and significantly reduces product defects ( Antony, 2006 ; Kwak and Anbari, 2006 ). This allows to achieve customer satisfaction ( Karout and Awasthi, 2017 ; Raisinghani et al. , 2005 ). On the other hand, from a statistical point of view, its goal, as its name suggests, is the reduction of variability in business processes ( Kwak and Anbari, 2006 ; Linderman et al. , 2003 ; Snee, 2004 ). Six Sigma means that the company offers only 3.4 defects per million opportunities (DPMO), which means a high quality of 99.99966%. Its success in the industry began in the late 1980s when Motorola got the Malcolm Baldrige National Quality Award for its improved competitiveness through this quality strategy ( Raisinghani et al. , 2005 ). Since then, it has been a business process improvement strategy that has reached all kinds of companies, industrial and services companies, including small and medium-sized enterprises ( Vendrame Takao et al. , 2017 ) and, consequently, to all the processes.

Six Sigma has also been defined as a powerful problem-solving strategy ( Prabhushankar et al. , 2008 ). There are multiple cases in literature applying Six Sigma and achieving substantial improvements in its performance ( Snee, 2004 ) on employee satisfaction ( Sunder, 2013 ) or increasing customer satisfaction ( Karout and Awasthi, 2017 ) or solving specific problems in transactional projects ( Antony et al. , 2012b ).

The objective of this paper is to highlight the potential of Six Sigma methodology, detailing the problems of cash carried out in a large food can enterprise. Six Sigma has been previously implemented in other transactional environments ( Antony et al. , 2012b ). The case is interesting because in food can industry, suppliers and clients have great negotiation power and they influence and mark the rules, often very inflexible, in their conditions and payment terms. Porter pointed out the can manufacturing industry, to which the studied company belongs, is one of the industries whose collective resistance is “intense” ( Porter, 1979 , p. 137). The bargaining power of clients in an industry affects its competitive environment and also its ability to generate profitability ( Porter, 2008 ). Strong clients, with high bargaining power can pressure the company to lower prices, improve product quality, set longer payment terms, etc. and all this represents costs for the company. Also, strong suppliers can also take advantage of their power, especially in terms of payment terms and supply time. Therefore, companies from this industry usually present liquidity problems.

Next, the structure of the article is detailed. Following this introduction, a theoretical part focused on the food can industry and its cash flow problems is presented in Section 2. Section 3 is dedicated to methodology and it briefly explains the define, measure, analyse, improve and control (DMAIC) steps. In Section 4, the case study is presented and the problem is contextualised. Section 5 describes how the objective is achieved through the phases of this methodology. In Sections 6 and 7, respectively, the results are discussed, and the managerial implications and the lessons learned are presented. Finally, the main conclusions of the research are highlighted.

2. Food can manufacturing industry and Six Sigma

Six Sigma has been carried out in multiple sectors. We find case studies conducted in automotive industry ( Sambhe and Dalu, 2011 ; Surange, 2015 ; Valles et al. , 2009 ), electronics ( Choi et al. , 2012 ; Patterson et al. , 2005 ), construction ( Negi et al. , 2017 ; Siddiqui et al. , 2016 ; Stewart and Spencer, 2006 ), health care ( Antony et al. , 2018 ; Benedetto, 2003 ), banking ( Sunder, 2016 ; Sunder and Antony, 2015 ), tourism ( Pearlman and Chacko, 2012 ), airlines ( Gibbons et al. , 2012 ), among others. Thus, it was observed that Six Sigma has been widely implemented with different objectives in industrial and services companies ( Raja Sreedharan and Raju, 2016 ; Sunder et al. , 2018 ).

Even in the food can manufacturing sector, which is the one that belongs to the company studied in this article, it has also been applied. For example, Rexam, one of the largest producers in the world (North American sector of this company has 12 plants, South American sector has 10 plants and Europe and Asia have 21 plants), has applied it to improve the quality maintenance processes. Its target has been to gain more production time avoiding unplanned stops or breakdowns and to improve the communication about the maintenance actions, so it is essential to keep equipment in excellent condition ( Nieminen, 2016 ). Moreover, to understand and improve recycling rates, Six Sigma methodology was used in Fayette County, Kentucky, a manufacturing enterprise of beverage cans ( Das and Hughes, 2006 ).

2.1 Food can manufacturing industry peculiarities and the problem of cash

Food can manufacturing industry needs to produce high-quality cans approved for food use. The main raw material is electrolytic tinplate of several thicknesses and it represents between 17% and 41% of the weight of the cans. Tinplate is a flat rolled product, formed by steel (iron and carbon alloy) and covered by a layer of tin. It is an ideal material for manufacturing of metal containers because it combines the mechanical strength and conformability of steel with corrosion resistance of tin. With this material, complementary products are manufactured, e.g. a whole range of plugs, studs, handles, slings, lids and metal cans for food, produced synthetics, oils and derivatives.

Iron and ferrous metals market is the second largest commodity market after crude oil in terms of volumes. There are three key producers of iron who own between 70% and 75% of the market ( DeGroot et al. , 2012 ). This means, an oligopolistic market where large companies have a great negotiation power. The price of iron, necessary for the production of steel products, has suffered different oscillations over the time. Moreover, on certain occasions, this increase in price comes with a decrease of domestic production which makes Europe more dependent on iron importations. In addition, China began to consume more of this material, going from 209 million tons in 2004 to more than 1,000 million tons in 2016. The demand growth and the integration of several global steelmakers resulted in a strong increase of steel price in all its varieties imposed by the large suppliers. Iron price increase has an unavoidable impact in the value chain of companies of the food can manufacturing industry, especially in the supply of materials. At the same time, the most important final clients of these companies are usually large supermarket chains which also have a great power of negotiation. This situation makes companies of this industry vulnerable and they barely negotiate with suppliers and clients the terms of accounts payable and receivables days. So, the financial departments of these companies have a great challenge in cash flow management.

2.2 Six Sigma for solving financial issues

The need of efficient cash flow management has achieved consensus among researchers and practitioners. Under the pecking order model, developed by Myers (1984) and Myers and Majluf (1984) , the availability of internal funds, through cash flow or current profitability, implies that firms have less need to make recourse to external debt, implying a lower debt ratio. Moreover, for a given level of cash flow, the amount of debt will be increasing in the investment being undertaken by the firm ( Benito, 2003 ). This highlights the importance of cash flow balance. Cash flow volatility not only increases the likelihood that a firm will need to access capital markets, it also increases the costs of doing so ( Minton and Schrand, 1999 ; O’Connor Keefe and Yaghoubi, 2016 ). O’Connor Keefe and Yaghoubi (2016) find that cash flow volatility is an important determinant of firm’s debt and debt’s cost. Thus, there is a positive relationship between cash flow volatility and the cost of debt ( Black and Scholes, 1973 ).

A company could survive for a while without achieving profits or even with losses, but it may collapse because of lack of cash even if it has a very positive balance ( Peer and Rosental, 1982 ). Some models have been developed to manage cash flow: mathematical models and cost and time integration models ( Navon, 1996 ). Nevertheless, Six Sigma methodology has been barely applied to the financial company department, especially to solve a deficit cash flow situation, although it can help to improve cash flow, earnings or productivity in using assets ( Foster, 2017 ).

Six Sigma may be used for monitoring accounting processes ( Krehbiel et al. , 2007 ). We found, for example.

A big Portuguese car dealer group successfully used all the stages of a Six-Sigma DMAIC to improve the warranty billing process (paid by Car Brands). It shows that the project allowed car dealership managers to understand that the use of financial metrics did not control compliance standards for Car Brands, in warranty services, or assure a good cash-flow for the car dealers. Necessary changes and new metrics (% time compliance to do the service and bill it, % time compliance reception, % time to find a defective part in an audit) generated time benefits and consequently a more controlled cash flow ( Cunha and Dominguez, 2015 p. 885).

Also related to financial processes, a project was carried out to streamline financial processes (included payroll, purchasing and payable accounts, accounts receivable, monthly reconciliation and budget), reduce cycle time and improve quality and accuracy in a city government ( Furterer, 2016 ). In a logistics project, the methodology Six Sigma approach was used to improve the freight payment process. The results included a reduction in the number of payment processes from 38 to a single one-company process, 30% reduction in labour, 15% reduction in third-party logistics fees and a 25% savings from mode shifts as well as other soft benefits ( Ogg, 2003 ). For the logistics services, in a large consumer electronics company, a project with Six Sigma was conducted to improve payment process, to have a more transparent process with zero failures using Lean Six Sigma methodology ( Blackman et al. , 2013 ; Gutierrez-Gutierrez et al. , 2016 ). They describe the implementation of Six Sigma in the areas of international bank payments, foreign exchange and operating savings gained from simplification and centralisation of the treasury management function. Also, there was increased focus on quality processes within the treasury function and the operating companies, particularly on measuring, improving and controlling the accuracy and completeness of bank data for suppliers. This issue was that rejected payments had to be re-inputted to the netting system in time to complete the process. Motorola originally developed this technique in manufacturing. However, the use of this technique in a finance function was a new venture ( Blackman et al. , 2013 , p. 137). Therefore, Six Sigma can be applied to numerous and different processes that are carried out in a company regardless of the sector in which it operates, and one of those processes could be financial, as the payment process.

In this context, we detailed the great benefits that this business management improvement strategy produced in the financial area of a food can manufacturing company.

3. Research methodology

We use a case study-based methodology to gather the information and explain the implementation of the Six Sigma project that took place in the company. The case study methodology is widely used in Six Sigma research ( Brady and Allen, 2006 ; Thomas et al. , 2016 ). This is a useful and valuable method of research, with distinctive characteristics applicable to different types of research ( Tellis, 1997 ), which facilitates a closer access to the data of a company to research studies. It can also be used in combination with other methods. The food can industry provides a case study context to show the benefits of Six Sigma methodology in a finance department, an area where its use has been very scarce.

In this paper, the case of study is a descriptive, holistic and single case study ( Yin, 1984 ) based on company data for demonstrating the applicability of Six Sigma. There exist different types of case studies. In particular, a descriptive case describes an event or a situation in its real-life context ( Yin, 2003 ) and require that the investigator begins with a descriptive theory or face the possibility that problems will occur during the project ( Tellis, 1997 ). It is also a holistic design of case studies as it is based on a single unit of analysis.

In addition, case studies can involve single or multiple-case designs. Single cases are used to confirm or challenge a theory, or to represent a unique, extreme or revelatory case ( Yin, 1994 ). When a researcher has access to an especial and significant situation previously inaccessible, single case studies become relevant. Each single case study represents a complete study where data have to be gathered usually from different sources and the conclusions are obtained from the analysis of these data ( Tellis, 1997 ).

Even if it is difficult to generalise the results obtained from a single case study, publications on case studies through the use of Six Sigma methodology have been growing. These case studies have contributed to professionals and researchers who have acquired a greater practical knowledge which helps this generalisation to become more consistent ( Antony et al. , 2012a ).

For this reason, it is important to guarantee, as far as possible, the reliability of case studies. Reliability can be reached in different ways in a case study. One of the most important methods is the development of the case study protocol ( Tellis, 1997 ), as Yin (1984) recommended with these four sections: an overview of the project (project objectives, case study issues and topics being investigated); field procedures (credentials and access to sites and sources of information); questions to keep during the project; and guide for the report (outline and format for the narrative) ( Yin, 1994 ).

In this sense, the team studied the problem in a company, gathering some important data and applying a brainstorming process, and drafted a project charter that includes the goal and project statement as well as other project’s features.

The data gathered for the project were analysed using measurement system analysis through the gauge R&R tool, regression analysis, simulation, etc. Also, some graphical analyses such as histogram, Pareto diagram, process map, work flow diagram and flow chart were used for summarizing the data. Finally, from these data, understandable conclusions for the management of the company were reached. All these steps and tools are explained in the following sections.

4. Case study

The case focuses on a large food can manufacturing company in Germany (“CM” name anonymised) with an annual turnover of €800m, about 1,300 employees and a working capital of €22m. It is a true case and, the project was executed by management. The parent company, “US” company (name anonymised), was launching a Six Sigma deployment across all the functions and all the manufacturing sites (with sponsors, black belts, master black belts and with people from the different departments involved in each project). The case explained in this paper, include one of the projects. Concretely, the one to solve the problems of cash.

The main raw material used for the food can manufacturer was electrolytic tinplate of several thicknesses, as explained previously. The price of iron, necessary for the production of steel products, increased sharply in 2003 because of the concentration of operating companies mining. This increase was reflected throughout the value chain resulting in an increase of more than 25% in the price of the electrolytic tinplate used by CM. Moreover, the main suppliers of these raw materials were very large companies. At the same time, the most important final clients were Lidl, Carrefour, Oldenburger, Campbell, Tesco, DIA, Aldi, among others.

Therefore, as mentioned in the introduction, suppliers and clients of CM had a great power of negotiation. The model of the five competitive forces of Porter was one of the management tools of compulsory use in the “US” company, parent company of CM, so the management was very aware of the threat of these forces, in others words, the bargaining power of clients and suppliers that they represent.

The company studied had completed the implementation of the SAP software only two years before, which should ensure better management and control of its payments and collections system. Specifically, the accounting module at that time allowed setting alarms to warn of payment deadlines. However, there was no alarm when payments were made before the scheduled date. This fact, the advance payment of invoices, as it has been proved later, generated economic problems for CM. At the same time, because of the corporate culture, at the close period, normally monthly, the maximum number of payments were made, even if their expiry date were some days later. This fact, along with other reasons specific to the market where the company operated, meant that very often it was necessary to request money from financial institutions to cover the cash deficit. This represented a real problem of liquidity that worried the management, so they decided to intervene.

The implementation of Six Sigma in CM aimed to balance its cash flow. CM suffered from chronic problems of cash flow deficit. The deficit range was from €−7.5m to €−19m. One of the origins of the problem was the need to produce an inventory or stock ( make to inventory ) imposed by the client. Therefore, CM absorbed the effects of the seasonality of its retail sales instead of its clients. To produce inventory, it was necessary for CM to purchase large quantities of electrolyte tinplate and other raw materials in advance to ensure that it had the necessary stock. The payment terms established by tinplate manufacturers did not reflect the seasonality of CM clients. Moreover, the increase of the price of the electrolytic tinplate used by CM by over 25% aggravated the situation. This was the main cause of the negative cash flow periods.

From the corporate point of view, this situation was unacceptable as it directly affected the interests of the shareholders. At the proposal of the group’s European vice president, who had previously worked at the General Electric and had already led a Six Sigma implementation, the board of directors made the decision to implement this strategy in one of their plants to fix the problem. Once the problem was solved effectively, the solution adopted could be applied to other plants of the corporation that suffered similar problems. One of the strengths of Six Sigma is precisely that it analyses problems in depth to solve them in the long term and, its actions could become “transferable” to other similar cases.

Six Sigma is instrumented through the five DMAIC steps methodology and most of its papers apply it in different areas and industries ( Srinivasan et al. , 2016 ). General Electric played a very important role in the development of Six Sigma as a methodology because they add the “define” step at the beginning of the measure, analysis, improve and control process to clarify the problem addressed ( Antony et al. , 2017 ). DMAIC is especially useful when the cause of the problem is not clear ( Snee and Hoerl, 2003 ) because its five steps are a systematic approach in the search for the best solution.

Define : Define the requirements and expectations of the customer, the project boundaries and the process by mapping the business flow.

Measure : Measure the process to satisfy customer’s needs, develop a data collection plan and compare data to determine issues and shortfalls.

Analyse : Analyse the causes of defects, sources of variation, determine the variations in the process and prioritise opportunities for future improvement.

Improve : Improve the process to eliminate variations, develop creative alternatives and implement enhanced plans.

Control : Control process variations to meet customers’ requirements, develop a strategy to monitor and control process and implement the improvements of systems and structures.

In each step, it is important to get useful and reliable information for decision-making ( Karout and Awasthi, 2017 ). In this sense, there are multiple tools and techniques that can be used in each step and represent a vital role in the success of the implementation process. Uluskan (2016) conducted a literature review where he identified the factors most used by the authors according to each step and objective. Some of them are: brainstorming, voice of costumer, process capability, critical to quality (CTQ) tree, flowcharts, value stream maps, box plots, failure mode effects analysis, control charts, cause and effect analysis, Pareto charts, hypothesis testing, ANOVA, etc. In the case studied, DMAIC was applied using different tools and techniques in each step. Table 1 summarises the main tools that were used to achieve the objectives of each phase or step in the case of CM.

5. Implementation of Six Sigma define, measure, analyse, improve and control methodology

The implementation process carried out in the five steps of Six Sigma DMAIC methodology is explained in the following sections.

In the “define” phase, the main objective of the implementation must be defined, as well as the critical project to be developed. The company should eliminate “defect” through the application of Six Sigma and the expected economic impact ( Table 2 ).

CM had suffered significant cash deficit in the previous 18 months. The reason was that the correlation between the company production and the demand level required by customers was needed to be achieved partially through its stock. For CM, this meant having to maintain a stock of products to ensure that customers were guaranteed the number of products they needed. Given that, in the sector, the majority of customers were large stores or commercial chains, their bargaining power was very large and the company worked almost exclusively for them. CM felt obliged to work in that way. This form of fabrication required the purchase of materials in advance, especially if the supplier did not guarantee its supply when CM needed it. This in turn, meant that the payment terms did not reflect the CM seasonality, but rather theirs. As a result, this situation affected the company’s working capital reducing its capacity to return value to CM shareholders.

Therefore, on the one hand, CM usually paid as many invoices as possible, even before the deadline, and, on the other hand, the operation department needed to buy raw materials regardless of whether there was enough cash for payments. In summary, there was not a good coordination between departments.

So, the main objective of implementing Six Sigma in CM was to ensure the optimisation of its accounts payable, especially in critical cash months. This was intended to balance and control the level of cash flow, on the one hand reducing or eliminating the advance payments of invoices (considered as defects in this project); and on the other, negotiating with supplier’s new payment terms more in line with the average of the sector and with the payment terms that CM had agreed with its customers. In this way, by optimising the accounts payable, the company must be able to maintain a stable cash flow that facilitates an improvement of its relations with financial institutions. With this improvement, it was planned to achieve savings of more than €100,000 per year in interest payments.

Y is the main variable that must be monitored, that is, on which it is wanted to act. In our case, it is the working capital, which at that time was €21.6m.

y is the unit to improve to get the Y to improve. We identify the creditors payment days as critical moments, we consider any invoice paid in a less time than expected as a defect.

x are all factors that affect the current payment system and that influence the objective pursued. Therefore, x must be improved to avoid the defect defined.

Necessary data was obtained from the accounting module of the SAP software that the company had installed.

A Six Sigma project requires the identification of the CTQs ( Gijo and Rao, 2005 ). CTQs of the project were identified in the first step of DMAIC ( Figure 1 ). A CTQ is a variable or attribute that directly influences the quality of a process that in this case is of a financial nature and whose ultimate goal is the maximisation of the company’s profit and the value of the shareholder. In the case, the main CTQ, or higher level, of the project was the working capital ( Figure 1 ), hence it is considered as the “Y” of the project and the lowest level CTQ ( y ) was the number of payment days to creditors at critical moments of cash. As shown in the figure, Six Sigma acted in payments made according to the contract, not on the pending payments or invoices not paid.

Finally, process map affected by this project is shown in Figure 2 . It represents in an orderly manner the stages that comprise the supplier–client cycle at process level and at financial level.

5.2 Measure

The definition of the defect was “invoices paid before the expected deadline in critical months of cash”.

The unit to be taken into account for the subsequent analysis was: “supplier invoice”.

Opportunity = 1. This means that there was 1 chance that each invoice that was to be analysed was right or wrong, that is, within or outside of the specifications established for payment in the corresponding contract.

The definition of the standard performance served to facilitate a later repeatability and reproducibility. Ensuring repeatability implies confirming that if the research was repeated, there would be very little variability in the calculations, while reproducibility refers to the variability that could occur because of the change of operator. Measurement system analysis was performed using gauge R&R tool ( Raisinghani et al. , 2005 ; Sunder, 2016 ). For the validation, three CM operators were chosen. They had to analyse 30 invoices that were randomly chosen from SAP data. The three operators reviewed invoices separately, and on two occasions, the payment days of each of the 30 invoices as a measurement for the analysis. The information derived from these checks is shown in Table 3 . If the invoice had been paid within the specified time or later, the invoice passed the analysis (PASS). If, on the contrary, it had been paid before the scheduled date, then it was a defect (FAIL). These two options, PASS and FAIL, appear as attributes in the table. The result gave a gauge of 100% in repeatability and in reproducibility given that there had been no discrepancy between the different measurements made by them. It is observed in the sample analysed that there were more cases of FAIL than PASS (17 out of 30). Therefore, it was found that more invoices were paid in advance, i.e. errors.

The repeatability, reproducibility and accuracy of the measurement system was checked and found a 100% of gauge. Hence, the current measurement system was considered adequate to collect data and did not require further improvement.

5.3 Analyse

In view of the fact that the measurement system was correct, the third phase of DMAIC, the analysis phase, was proceeded. It was intended to answer some questions such as: What are the critical months in terms of cash in CM? Which supplier could be chosen in first place for study? With this supplier, what was the maximum payment term in those months? What was the company capacity to be able to assume these defects or fails? And, what were the sources of variability in the payment system?

To answer the aforementioned questions, in the first place, analysis of the evolution of collections (cash in) and payments (cash out) of prior periods was carried out. It is observed that the critical months were those corresponding to the second quarter (April, May and June) ( Figures 3 and 4 ). Therefore, it meant that it was not possible to maintain payment deadlines to suppliers, especially during those three critical months.

Secondly, to decide which provider to study first, a Pareto diagram was made with raw material suppliers ( Figure 5 ). The reason was that tinplate represented 62.4% of the total payments made to suppliers. The high volume of payments done demonstrated the complexity of the process that they were analysing. It can be observed that the six largest suppliers had represented only 1.4% of all suppliers, but they amounted 73% of the total volume of purchases and CM payments.

Suppliers were ordered according to purchase volume ( Table 4 ). The six most important suppliers had payment terms equal to or greater than 60 days, which was the period that had been estimated as industry average. The four most important suppliers had a payment term of 75 days, while the others shown in Table 4 were small suppliers who charged less than 60 days (the amount of that 16% was about €7.63m). Among them, the seventh one was chosen as target supplier for further analysis. This supplier was the first who had a lower payment term compared with industry average and, it represented a purchase spend of 2.9%.

Thirdly, the frequency in terms of number of invoices and their payment days was also analysed for the target supplier ( Figure 6 ) to detect any abnormality in them. If we analyse them, the target supplier had paid 877 invoices with an average of 37 days and a median of 37 days. Likewise, the evolution of these payment terms for such invoices was analysed noting that the disparity detected did not respond to a specific pattern.

As payments to this supplier only represented part of the problem, the solution required considering more providers and more invoices. It was determined the company capacity to assume these payments in the critical months. SAP data revealed that those critical months before the implementation of Six Sigma, 198 invoices out of 216 were paid in less than 60 days, which represents the number of defects of the project. In terms of the definition of defect of the Six Sigma methodology, this data suppose that the company works with a value of DPMO of 916,667, which means that the company will end up paying 916,667 invoices of each million in 60 days. The short-term capacity, or short-term Six Sigma level ( Zst ), was less than 1. This value of 1 Sigma in the short term is very low as the scale of Six Sigma goes from 0 to 6.

In addition, 8 invoices were found paid in less than 30 days, which represents a DPMO of 37,037 and a Zst of approximately 3.3. We were, therefore, facing a common problem in many companies, but complex.

Finally, in the analysis phase, CM should identify the sources of the payment system variability ( X ’s), that is, the sub processes of the company that affected the objective of the project.

The relationships between variable “current payment term days” and “production” and “cash balance” was investigated to establish a common method for executing payments, and to make decisions that involve all the organisation in the same line. Through a statistical regression, it was found that there was no relationship between these variables. It took 18 months (18 observations) of the 2 years prior to the implementation of the Six Sigma project ( Table 5 ). This result was consistent with the transactional nature of the project. That is, it was demonstrated that statistics was not the best tool.

Considering that statistics did not help to solve the problem, the next action was to carry out multiple simulations, in Excel, of the cash flow with different days of payment to see the effect that these produced on cash balance, especially in the critical months. These simulations were carried out with 8 previously detected suppliers whose payment terms were less than 60 days ( Table 4 ). All simulations were shared to provide transparency to the process. These were the perfect visual tools for all the personnel involved to understand that it was necessary to establish a clear policy and protocol regarding the payment terms to suppliers.

In the company, there were several views of this according to the department to which we refer. For example, from the point of view of different departments, for the purchasing department, a short payment period implied incentives and discounts from suppliers. For financial control, the payment days had a marked influence on cash balance. However, the operations department considered that this data apparently did not affect them. The fragmented vision of the company is a frequent mistake in management. It should be considered that ultimately the damage is global.

On the other hand, employees who were involved throughout CM’s payment system were also sources of variability. Therefore, it can be said that the sources of variability came from both, process that was established to make payments to suppliers and people who made decisions and executed the process. Therefore, it was a problem related to people and internal process of cash control.

5.4 Improvement

In this phase, the company had to set the improvement objectives and establish a new method of operation and tolerances in relation to the suppliers’ payment system. Payment work flow improvement is shown in Figure 7 .

Reduce defects or DPMO value by 80%. This would mean that the number of invoices paid in less than 60 days would be 20, and those paid in less than 30 days would be only 2. The new values of DPMO at 60 days would be 91,667 and at 30 days would be 3,704.

Improve short-term performance capacity (30 days) to 4.18 Sigma, and less than 60 days of 2.85 Sigma. These values would be in line with other competitors in the sector.

Maintain, as a reference, 60 days as payment days after doing benchmarking in industry.

It was decided to change all payment terms to 60 days for the critical months (it was called flexible payment), except for those suppliers who were already paying to longer term. This implied establishing a new “method of operation and tolerances”. To this end, a letter was drafted that was sent to all suppliers whose payment term was less than 60 days, informing them that from then on, it would be 60 days. A personalised communication was issued to each supplier justifying and reasoning the change. Data and conclusions collected from monitoring the Six Sigma method created a clear and transparent understanding for all the parties involved. Data is aseptic and this allows effective and realistic decision-making. Opinions, on the other hand, are completely discarded in the Six Sigma methodology. It is a positive point of the Six Sigma methodology, which proves numerically the decisions and proposals.

The letter sent to suppliers was written impeccably and with data generated by SAP. Most suppliers understood the situation, some even felt identified. Certain suppliers who refused to accept the new conditions, such as our target supplier, were informed of the end of the contractual relationships and CM looked for an alternative. The large suppliers stayed within the established 75-day deadlines. Before this decision, even some of these suppliers lent themselves to collaborate improving the price of raw materials that, shortly before the implementation of the project, had increased significantly.

It was at this point that the Six Sigma team and the management of the company began to understand that they were in front of an integrated supplier–client strategy that reverted to the cash balance company level.

To ensure that payments were made at least to 60 days, the rules in SAP were set according to the instructions received from the purchasing and financial control departments. Any modern enterprise resource planning (ERP) system allows different rules to be accommodated for the same supplier or client. This function was not incorporated when SAP was implemented in CM, so it was necessary to introduce an alarm for certain suppliers that was activated during the critical months to avoid the mistake of paying them in advance. It was necessary, therefore, human intervention in the computer application was required.

5.5 Control

In the last step of DMAIC (control phase), the validation methodology for measuring results was followed. It was necessary to ensure that CM could commit to pay suppliers on time with the new process. The SAP system was reconfigured to operate with the new standards, so changes implemented were made official. Thus, it was possible to confirm the capacity of the process to guarantee the different payments in the agreed terms, that is, without defects (DPMO = 0). This meant that the capacity of the process had actually been improved by increasing the Sigma level to 4.2.

The company acquired the routine of periodically reviewing and analysing its cash balance, especially in the months that had previously proved critical.

As a final result and closure of the project, after several months, the actions taken showed that CM would not need to resort to the bank credit line of €11m needed to absorb the previous cash imbalances caused by inventory rules and seasonality. The financial interest savings were €49,000 in the year of implementation of Six Sigma and €120,000 in the following year, which allowed to achieve the expected economic objective.

6. Discussion

This case study conducted in a large company dedicated to the food can manufacture illustrates how Six Sigma may be implemented with higher or less intensity regardless of the type of process or company. In particular, financial processes carried out in the company can also be the objective of a quality improving project. However, quality improvements in this area have not been widely studied in the previous literature, especially as regards the application of the Six Sigma methodology ( Blackman et al. , 2013 ; Cunha and Dominguez, 2015 ; Furterer, 2016 ; Gutierrez-Gutierrez et al. , 2016 ; Krehbiel et al. , 2007 ; Ogg, 2003 ).

In this work, it can be seen that the usefulness of the Six Sigma methodology in administrative–financial issues can be common in several companies, as it is in this specific case, the cash flow management through payables and receivables days, in line with Black and Scholes (1973) and O’Connor Keefe and Yaghoubi (2016) . In addition, it should be highlighted that the context of the case as occurs in an industry where suppliers and customers have great bargaining power, i.e. the competition is very high. This power can choke companies, even large, especially for the financial costs involved in going to bank financing when necessary.

Well-designed payment and collection processes can guarantee financial stability and balance cash flows in a company to counter suppliers bargaining power. Otherwise, company may lead to continued request for external financing and high interest payments may become a significant problem ( Blackman et al. , 2013 ). Previous studies have shown that companies cash flow has a significant relationship with working capital management ( Appuhami, 2008 ; Chiou et al. , 2006 ; Nazir and Afza, 2008 ; Taleb et al. , 2010 ). This working capital was the variable on which it was intended to act, i.e. “Y”. In turn, according to the industry, there were significant differences in terms of working capital, which, moreover, changes over time. Precisely, competitors may influence these changes ( Filbeck and Kruege, 2005 ). That will imply a necessary control to ensure the permanent balance achieved. SAP software, implemented by CM, should facilitate this control by providing reliable data. In this sense, as benefits were obtained from the implementation of ERP systems in Motorola, SAP in CM also led directly to an improvement in the consistency of the data originating from the manufacturing systems ( Blackman et al. , 2013 ).

Company situation after implementing the Six Sigma reflected economic benefits measured in improvements in the payment terms conditions more in line with competitors, renewal of suppliers that did not accept the new requirements, alarms settings in SAP to avoid mistakes, among others. A special mention is needed regarding the great interest savings achieved in CM with Six Sigma, as it was expected according to Minton and Schrand (1999) and O’Connor Keefe and Yaghoubi (2016) . These results are also consistent with debt being issued in response to the shortfall between cash flow and investment under the pecking order model ( Myers, 1984 ; Myers and Majluf, 1984 ).

7. Managerial implications and lessons learned

In this article, we have described the problem of cash deficit during certain periods in a large German food can manufacturing company. It was solved through the implementation of Six Sigma methodology. The study shows how the project team developed the DMAIC phases and tools in an orderly manner to arrive to the solution. The different phases followed allowed to improve the “working capital”, the CTQ variable.

The success achieved was motivated by the extension of the payment days to suppliers. This led to considerable savings in terms of financial interests. The financial interest savings were €49,000 in the year of implementation of Six Sigma (half year) and €120,000 in the following year. The finance department confirmed that savings obtained were real and, therefore, the value of the Six Sigma methodology is well demonstrated. However, supervision and control of the implemented project is necessary to ensure that the level of defects or invoices paid in advance continues to be zero, that cash flow remain stable and that the “power” of suppliers and clients remains balanced with the company, speaking in terms of the five competitive forces model of Porter.

The lessons learned from the case need to be transferred to the different business units across the organisation, as CM was a subsidiary of US, the parent company.

Transactional business, such as financial services or many of the operations in traditional manufacturing businesses, cannot be met with traditional Six Sigma methodology, as data in most times are qualitative and discrete. In transactional Six Sigma projects, statistics do not really help but the rigour of Six Sigma does. This case is an example of the use of Six Sigma in a transactional process achieving a great reduction of costs.

Nowadays the world is more transactional and to be able to apply Six Sigma in non-productive areas opens a range of possibilities. Moreover, the use of Six Sigma in transactional or commercial situations offers a new dimension in terms of rigour of problem-solving and performance improvement in service sector quality ( Goh, 2002 ).

8. Conclusions

Six Sigma has been widely applied to different industries, especially to eliminate defects, reduce processes variability, improve production quality and increase the satisfaction of the companies’ stakeholders. The main contribution of this article focuses on the application of Six Sigma in the financial area of a company and not in production processes in which it has been widely applied and disseminated in the previous literature. Its objective was to balance company cash flow to improve its working capital. With this study, we intend to provide a solution by shedding light on a crucial problem for companies, i.e. cash flow management by using Six Sigma. With this case, we also demonstrated the applicability of Six Sigma in an area where its use has been scarcely attendant. In addition, it should be noted that the project addressed was transferable to other units of the same company and, therefore, it could be applied in other companies that present the same economic situation regardless of its size, industry etc. Moreover, this problem can be common in other companies, regardless of the sector in which they operate, so study results could be easily extrapolated.

On the other hand, the context was also interesting because the food can industry presents some peculiarities. In this industry, the bargaining power of clients and suppliers is very high and therefore, the situation was more difficult to manage.

This work confirms that the Six Sigma is expanding to other fields. Therefore, a greater use of this methodology in other aspects regarding the financial area of the company could be explored in the future.

Project CTQs

Process map

Cash in (in thousands of euros)

Cash out (in thousands of euros)

Distribution of CM raw material suppliers in purchase volume (in %)

Payment frequency of target supplier invoices

Work flow improvement diagram

Summary of DMAIC in CM

Dependent variable: current payment terms days; adjusted R 2 = 0.1135363; F = 0.96058333 (no sig.)

Source: Own elaboration

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Multi-Vari Chart: Powerful Tools for Process Analysis and Improvement

May 6th, 2024

One of the most flexible and revealing tools in the Six Sigma toolbox is the multi-vari chart. This graphical method lets you see and study the different causes of variation in a process.

The multi-vari chart is a handy diagnostic that helps pinpoint and fix the root sources of process inconsistencies.

This article will give you the know-how to correctly build, read, and use multi-vari charts to your advantage. You’ll gain the skills needed to analyze processes and fuel continual upgrades.

Let’s jump in and explore how multi-vari charts work their magic. I’ll show you how to construct them, decipher what they’re revealing, and leverage their insights to transform your business.

Key Highlights

• Understand the fundamentals of multi-vari charts, including their definition, benefits, and applications across various industries.
• Learn the step-by-step process of creating multi-vari charts, from collecting data and determining factors and levels to manual construction and software-based approaches.
• Gain insights into interpreting multi-vari charts, analyzing within-piece, piece-to-piece, and time-to-time variations.
• Explore case studies that illustrate the practical application of multi-vari charts in manufacturing process optimization, quality control, and root cause analysis.
• Discover best practices for using multi-vari charts effectively.
• Understand the role of multi-vari charts in Six Sigma and Lean Six Sigma methodologies.
• Address common questions, compare multi-vari charts with other graphical techniques, and learn how to avoid common pitfalls.

What is a Multi-Vari Chart?

In the process improvement and Six Sigma methodologies, the multi-vari chart stands as a powerful diagnostic tool that enables us to visualize and analyze the sources of variation in a process. 

Definition of Multi-Vari Chart

A multi-vari chart is a graphical representation that illustrates the relationship between factors (input variables) and a response (output variable) in a process. 

It is a two-dimensional chart where time is plotted on the horizontal axis, and the process output measurement is plotted on the vertical axis. 

The chart is designed to display three distinct sources of variation: within-piece variation (positional), piece-to-piece variation (cyclical), and time-to-time variation (temporal).

Benefits of Using Multi-Vari Charts

1. Visualize Multiple Sources of Variation

One of the primary advantages of multi-vari charts is their ability to simultaneously visualize and analyze three distinct types of variation within a single graphical representation. 

This comprehensive view allows for a more holistic understanding of the process and the factors contributing to its variability.

2. Identify Root Causes Quickly

By examining the magnitude of variation across different sources (positional, cyclical, and temporal), multi-vari charts can provide valuable clues about the potential root causes of process variation . 

This visual analysis can significantly accelerate the root cause identification process, leading to more targeted and effective improvement efforts.

3. Simplicity and Accessibility

Multi-vari charts can be constructed manually, making them accessible to operators and frontline personnel. This hands-on approach fosters engagement and ownership among those directly involved in the process, enhancing their understanding and commitment to the improvement efforts.

Applications of Multi-Vari Charts

Multi-vari charts have widely applied across various industries and processes, including manufacturing, healthcare, finance, and service operations. 

They are treasured in situations where multiple factors influence a critical process output, and identifying the sources of variation is crucial for process optimization and quality improvement. Some specific applications of multi-vari charts include:

1. Manufacturing Process Optimization

In manufacturing environments, multi-vari charts can be used to analyze variations in critical product characteristics, such as dimensions, strength, or performance metrics. 

By identifying the sources of variation, manufacturers can implement targeted process improvements to enhance product quality and consistency.

2. Quality Control and Assurance

Multi-vari charts are valuable tools in quality control and assurance initiatives. 

They can help identify sources of variation that impact product or service quality, enabling organizations to take corrective actions and maintain high standards of quality.

3. Root Cause Analysis

In problem-solving and root cause analysis efforts, multi-vari charts serve as powerful diagnostic tools. 

They can provide insights into the underlying causes of process variations, guiding teams towards effective solutions and preventing the recurrence of issues.

How to Create a Multi-Vari Chart

Creating a multi-vari chart is a straightforward process that can be accomplished both manually and with the aid of statistical software. 

However, before diving into the construction methods, it is essential to understand the key components and data requirements for effective multi-vari chart creation.

Factors and Levels in Multi-Vari Charts

Multi-vari charts are designed to analyze the relationship between factors (input variables) and a response (output variable). 

Factors can be any process parameter or condition that may influence the output, such as machine settings, operator techniques, environmental conditions, or material characteristics.

Each factor can have multiple levels, which represent the different values or settings that the factor can take. 

For example, if we are analyzing the impact of three machines on a particular output, the “machine” factor would have three levels (Machine 1, Machine 2, and Machine 3).

It is generally recommended to limit the number of factors to a maximum of six to maintain the interpretability and clarity of the multi-vari chart. 

Additionally, each factor should have at least two levels to enable the analysis of variation.

Collecting Data for Multi-Vari Chart

Effective multi-vari chart creation relies on the collection of high-quality data that accurately represents the process being studied. Here are some key considerations for data collection:

Sample Size

Determine an appropriate sample size that captures the inherent variation in the process. Typically, a minimum of three consecutive units or samples is recommended for each factor combination, but more may be needed depending on the process’s complexity and variability.

Measurement Procedure

Establish a consistent and standardized measurement procedure to ensure data integrity and reliability. 

This may involve the use of calibrated measurement instruments, proper sampling techniques, and adherence to standard operating procedures.

Data Organization

Organize the collected data in a structured manner, typically in a tabular format. This will facilitate the construction of the multi-vari chart and ensure that all relevant factors and levels are properly represented.

Manual Construction of Multi-Vari Chart

While statistical software can streamline the creation of multi-vari charts, it is valuable to understand the manual construction process, as it can provide deeper insights into the underlying principles and foster a better understanding of the tool.

Plot the Measurements

On a two-dimensional graph, plot the individual measurements for each unit or sample, using a consistent symbol or color to represent each measurement within the unit. Connect these measurements with a solid line to illustrate the within-piece variation.

Calculate and Plot Unit Averages

Calculate the average value for each unit or sample and plot these averages using a different symbol or color. Connect the unit averages with a long-dashed line to represent the piece-to-piece variation.

Determine and Plot Overall Averages

Calculate the overall average for each set of consecutive units or samples and plot these averages using a distinct symbol or color. Connect the overall averages with a short-dashed line to represent the time-to-time variation.

Indicate Time Breaks

Use vertical lines or other visual cues to indicate breaks in time or shifts between sets of consecutive units or samples.

Creating Multi-Vari Charts with Software

While manual construction can be valuable for understanding the principles of multi-vari charts, statistical software can significantly streamline the process and provide additional analytical capabilities. Two commonly used software tools for creating multi-vari charts are:

Microsoft Excel, with its powerful charting and data analysis capabilities, can be used to create multi-vari charts. Third-party add-ins or macros may be required to automate the chart creation process and incorporate advanced features.

Minitab is a comprehensive statistical software package that includes dedicated tools for creating multi-vari charts. Its user-friendly interface and specialized features make it a popular choice among Six Sigma practitioners and quality professionals.

Regardless of the software used, the key steps involved in creating a multi-vari chart remain similar: importing or entering the data, selecting the appropriate chart type, specifying the factors and response variable, and adjusting the chart settings and formatting as needed.

Interpreting a Multi-Vari Chart

Once a multi-vari chart has been constructed, the next crucial step is to interpret the graphical representation effectively. 

By analyzing the patterns and variations displayed in the chart, valuable insights can be gained regarding the sources of process variation and potential areas for improvement.

Analyzing Within-Piece Variation

Within-piece variation, also known as positional variation, refers to the variability observed within a single unit or sample. This type of variation can be visually assessed by examining the spread or range of the individual measurements plotted for each unit.

A larger spread or range of measurements within a unit indicates a higher degree of within-piece variation, which may be attributed to factors such as inconsistent measurement techniques, non-uniform material properties, or localized process variations.

By analyzing the within-piece variation, you can identify potential issues related to measurement processes, operator techniques, or inherent material or product characteristics that may require attention.

Analyzing Piece-to-Piece Variation

Piece-to-piece variation, also known as cyclical variation, represents the variability observed between consecutive units or samples. This type of variation can be evaluated by examining the pattern and magnitude of the long-dashed line connecting the unit averages.

A more erratic or fluctuating pattern of the long-dashed line indicates higher piece-to-piece variation, which may be attributed to factors such as machine settings, tooling conditions, or batch-to-batch variations in raw materials or processes.

By identifying and addressing the sources of piece-to-piece variation, you can improve the consistency and reproducibility of your process, leading to higher product or service quality and reduced waste or rework.

Analyzing Time-to-Time Variation

Time-to-time variation, also known as temporal variation, refers to the variability observed over longer periods of time or across different production runs or shifts. This type of variation can be assessed by examining the pattern and magnitude of the short-dashed line connecting the overall averages.

A more pronounced or fluctuating pattern of the short-dashed line indicates higher time-to-time variation, which may be attributed to factors such as environmental conditions (temperature, humidity), equipment wear and tear, or changes in personnel or operating procedures.

By addressing the sources of time-to-time variation, you can enhance the long-term stability and robustness of your process, ensuring consistent performance and quality over extended periods.

Identifying Interactions and Root Causes

While analyzing the individual sources of variation is crucial, it is also important to consider potential interactions between factors. Interactions occur when the effect of one factor on the process output depends on the level of another factor.

Multi-vari charts can provide visual cues about potential interactions by revealing patterns or trends that deviate from the expected behavior. 

For example, if the within-piece variation increases or decreases systematically over time, it may indicate an interaction between positional and temporal factors.

By identifying these interactions, you can gain deeper insights into the root causes of process variation and develop more comprehensive and effective solutions. 

It is essential to complement the visual analysis of multi-vari charts with statistical techniques, such as Analysis of Variance (ANOVA), to quantify the significance of main effects and interactions.

Case Studies of Multi-Vari Chart

To illustrate the practical application and power of multi-vari charts, let’s explore some real-world examples and case studies from various industries. 

These scenarios will demonstrate how multi-vari charts can be used to identify sources of variation, pinpoint root causes , and drive process improvements.

Manufacturing Process Optimization

In a manufacturing facility producing automotive components, engineers were tasked with reducing the variability in the dimensional tolerances of a critical part.

By constructing a multi-vari chart, they were able to visualize the sources of variation in the production process.

The multi-vari chart revealed that the most significant source of variation was piece-to-piece, with the long-dashed line connecting unit averages displaying a highly erratic pattern. Further investigation revealed that the root cause was related to inconsistent machine settings and tooling wear.

Armed with this information, the engineers implemented preventive maintenance schedules, standardized machine setup procedures, and introduced statistical process control (SPC) monitoring. 

As a result, the piece-to-piece variation was significantly reduced, leading to improved product quality and reduced scrap rates.

Quality Control in Baking

In a commercial bakery, maintaining consistent product quality was a top priority. However, the bakery was experiencing significant variation in the width of cookies, leading to customer complaints and potential waste.

To identify the sources of variation, a multi-vari study was conducted, and a multi-vari chart was constructed. The chart revealed that the most significant source of variation was within-piece, with a large spread of measurements observed for individual cookie sheets.

Further investigation traced the root cause to temperature inconsistencies within the ovens, as well as variations in the placement of cookies on the baking sheets. 

Frequently Asked Questions about Multi-Vari Charts

As with any powerful tool, multi-vari charts can raise questions and concerns, particularly for those new to the technique or encountering specific challenges in their application.

Advantages Visual Representation : Multi-vari charts provide a clear and intuitive visual representation of process variation, making it easier to identify patterns and potential sources of variation. Comprehensive Analysis : By simultaneously displaying within-piece, piece-to-piece, and time-to-time variations, multi-vari charts offer a holistic view of the process, enabling a more thorough analysis. Accessibility : Multi-vari charts can be constructed manually, making them accessible to operators and frontline personnel, fostering engagement and ownership in improvement efforts. Diagnostic Capabilities : Multi-vari charts serve as powerful diagnostic tools, providing valuable clues about potential root causes and guiding further analysis and investigation. Disadvantages Interpretation Challenges : While the visual representation is intuitive, interpreting multi-vari charts and identifying patterns can be challenging, especially for complex processes or when interactions are present. Limited Statistical Rigor : Multi-vari charts are graphical representations and do not provide statistical measures of significance. They should be complemented with statistical techniques like ANOVA for a more rigorous analysis. Data Requirements : Constructing accurate multi-vari charts requires sufficient data collection, which can be time-consuming and resource-intensive, especially for processes with high variability or numerous factors. Potential Limitations : Multi-vari charts may not be suitable for certain types of data or situations where the assumptions underlying their construction are violated (e.g., non-normal distributions, non-continuous data).

While multi-vari charts are powerful tools, they are not the only graphical techniques available for process analysis and improvement. It is important to understand how multi-vari charts compare to other commonly used graphical methods, such as scatter plots and histograms. Scatter Plots Scatter plots are useful for visualizing the relationship between two continuous variables, such as a process input and output. They can help identify potential correlations or patterns but do not provide insights into the sources of variation within the process. In contrast, multi-vari charts are designed to analyze the relationship between multiple factors and a single response variable, while simultaneously visualizing different sources of variation (within-piece, piece-to-piece, and time-to-time). Histograms Histograms are graphical representations of the distribution of a single continuous variable, such as a process output. They can provide insights into the central tendency, spread, and shape of the distribution but do not directly reveal the sources of variation or the impact of multiple factors. Multi-vari charts, on the other hand, are specifically designed to analyze the impact of multiple factors on a single response variable and identify the sources of variation within the process. While scatter plots and histograms can provide valuable information, multi-vari charts offer a more comprehensive and diagnostic approach to process analysis, particularly when multiple factors are involved and understanding the sources of variation is critical for process improvement.

Despite their effectiveness, multi-vari chart analysis is not without potential pitfalls and common mistakes that can lead to erroneous conclusions or ineffective improvement efforts.  Ignoring Measurement System Variation Failing to validate the measurement system and account for potential measurement variation can lead to inaccurate multi-vari chart interpretations and misguided improvement efforts. Insufficient Data Collection Inadequate sample sizes or insufficient time intervals can result in incomplete or inaccurate representations of the process variation, leading to incorrect conclusions and suboptimal solutions. Overlooking Interactions While multi-vari charts can provide visual cues about potential interactions between factors, overlooking or failing to investigate these interactions can result in an incomplete understanding of the root causes and ineffective improvement strategies. Overreliance on Visual Interpretation While the visual representation of multi-vari charts is intuitive, overreliance on visual interpretation without complementary statistical analysis can lead to subjective conclusions and potentially miss significant effects or interactions. Lack of Process Knowledge Effective multi-vari chart analysis requires a deep understanding of the process being studied. Lack of process knowledge can lead to incorrect assumptions, misinterpretations, and ineffective improvement efforts. To mitigate these pitfalls, it is crucial to follow best practices, such as conducting thorough measurement system analyses, collecting sufficient data, and incorporating statistical techniques.

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