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Six Sigma Case Study: Ford Motors

May 19th, 2017

The Ford Motor Company is one of America’s, and the world’s, largest and most successful automakers. Named after its founder Henry Ford, the company is known for its innovative and dynamic approach to manufacturing. Henry Ford pioneered and employed such manufacturing concepts as standardization, assembly lines, which came to be known as Fordism. He also paid his workers a living wage, allowing them to purchase the very products they made. Products like his famous Model T.

Ford was a visionary man. He saw the necessity of breaking down complex tasks into simpler procedures, using specialized tools, and interchangeable parts. While Ford’s assembly line was a revolutionary achievement, his work grew from solidified ideas, with an eye for continuous improvement. Ford looked at established modes and broke them down into their core components, before building them back up again. He strove to take existing processes and always make them more functional, efficient, and effective. There were many advantages to Ford’s ideas. Namely, the significant decrease in costs of production, radically simplifying the labor process and reducing required the workforce.

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But how are Six Sigma and all its related approaches, like Lean and Kaizen , related to Ford? As you may know, Ford is a company known for its high quality. The company has pledged to utilize innovative products and use Total Quality Management to accomplish its goal of Quality Is Job 1 . JD Power and Associates ranked Ford as one of the leading high-quality automakers, but Ford has come a long way in the last few decades. Today we examine just how the Ford Motor Company used Six Sigma to transform its processes and achieve its success.

Why Was Six Sigma Necessary for Ford?

There are four core factors behind Ford’s Six Sigma initiative. These are:

• Cost reduction. Ford’s old production process was surprisingly costly. By introducing Six Sigma, they were no longer using resources that were not necessary.
• Improving quality. Ford has always been known for their quality products, but event heir standards slip from time to time. While, for most companies, a mere 99% quality level is considered acceptable, this lets through a surprising amount of defect. As much as 20,000 instances of defect. Six Sigma espouses that only 99.99966% (and up) is ideal. This percentage limits the number of defects per million to just seven As such, Ford made some great astonishing strides in quality improvement using Six Sigma.  
• Poor customer satisfaction rates. Satisfying customer demand is as critical to success as leveraging it. Many of these issues link to one another, as multiple instances of defect are likely to add up to a defective product. This will inevitably dissatisfy the customer which is why Ford chose to implement Six Sigma, to streamline their processes, and improve production issues. All of which adds up to a more productive company and happier customers.
• Lowering environmental impact by reducing solvent consumption. Six Sigma is an extremely green philosophy, and Ford uses it to make some great changes in their environmental awareness. Ford’s consumption of vital resources proved very costly in the long-term. But by committing to a green work culture with Six Sigma, they reduced costs, increased quality, and improved customer satisfaction.

Ford’s Approach to Six Sigma

The Ford Motor Company began using Six Sigma strategy in the late nineties. Their goal was to become a fully-fledged consumer products company and not just another automobile manufacturer. Additionally, they wished to enhance the quality of their products and to improve their customer satisfaction rates. Their approach towards achieving these goals they referred to as Consumer-driven Six Sigma. Furthermore, Ford was the world’s very first automaker company to implement Six Sigma methodology into their business operations on a large scale.

One of the most pressing problems facing Ford at the time was the 20,000 plus opportunities for defects that came with manufacturing cars. Despite the company’s prior history of quality control and innovation, some defects inevitably slipped through their fingers. Following this revelation, they achieved substantial improvements using Six Sigma. Their aim was to reduce their defect rate to only a single defect per every 14.8 vehicles, and they succeeded. Furthermore, this also satisfied their goal of enhancing customer satisfaction. In Six Sigma, even the smallest change can have a ripple effect, helping to change other processes and move towards continuous improvement.

Obstacles for Ford’s Six Sigma Initiative

Despite its success, there were several obstacles in the way of Ford’s Six Sigma implementation. These are:

• Employee commitment. As is often the case, many employees at Ford, including top-level and senior management, initially viewed Six Sigma with skepticism. This meant a lack of commitment was present from the beginning, proving a major cause of concern for Ford’s Six Sigma implementation. The time constraints, on top of this, made it difficult to put its 350 top leaders through weeks of training.
• Time, Money, Productivity. Furthermore, along with a lack of commitment, key resources like time and money meant employee training was often difficult. The lack of commitment also led to a lack of productivity.
• Data needs. Finally, Ford was new to Six Sigma and poorly equipped to follow through with its Six Sigma initiative. Six Sigma, of course, relies on vast amounts of data to This meant that Ford needed to create and implement new measurement systems to tackle the needs of Six Sigma. Only then was it able to provide any great benefit for the company.

Ford’s Six Sigma Successes

Ford’s use of Six Sigma methodology, while it did provide some road bumps, enabled them to eliminate more than $2.19 billion in waste over the last decade and a half. They solved this problem by applying Lean Six Sigma techniques , such as a data-driven problem-solving process, to devise solutions to waste issues. Moreover, the company’s methodologies for quality improvement and waste elimination saw a staggering impact on the company’s operations. Ford’s Consumer-driven Six Sigma has saved them over a billion dollars worldwide, helping complete almost 10,000 improvement projects since the early 2000s. Regarding customer satisfaction, Ford managed to increase their percentage by five points. We may go as far as to say that Six Sigma saved Ford from its deep-rooted problems. These issues include inadequate productivity, poor use of resources, low customer satisfaction, and environmental unfriendliness.

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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 )

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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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The corresponding author grateful to the all authors for their suggestions at every stage of this study. The authors would like to thank the anonymous referees for their valuable comments, which has been improved the contents and format of this paper.

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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 Case Study v.2

Mar 28, 2019

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Six Sigma Case Study v.2. Dr. Ron Tibben-Lembke SCM 494. Six Sigma Case Study - POI. Paper Organizers International Filing, organizing, and paper shuffling services Uses MSD (metallic securing devices)

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• purchasing deparment
• fromvendor msds
• eliminate employee
• detailedprocess map x1
• six sigma case studyv
• annual clipping expenses

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Six Sigma Case Studyv.2 Dr. Ron Tibben-Lembke SCM 494

Six Sigma Case Study - POI • Paper Organizers International • Filing, organizing, and paper shuffling services • Uses MSD (metallic securing devices) • Increasing complaints from the Paper Shuffling Department (PSD) about MSDs breaking and failing to keep papers together • Customers’ papers can get mixed together • Purchasing wants to eliminate MSD complaints

Mission Statement • “Put the right information in the right place.” • Management created a list of objectives and projects that will support those objectives

President Director of Paper Shuffling Dept Business Objectives Increase # of orders Business Indicators # orders per Month (c chart) Area Objectives Increase # orders in PSD Area Indicators No. orders in PSD / mo. (c chart) Potential 6 Sigma projects New customer promotions project 1. avg. # services used per customer, per quarter 2. St dev. of # serv. used (x-bar and s) Increase # Services used by each customer in PSD 1. avg. # services used per PSD cust, per Q 2. St dev. Of # serv. used (x-bar and s) Existing customer promotions project Increase # of POI services used by each customer Minimize production costs Minimize production costs in PSD Production Costs in PSD/mo (I-MR chart) Prod costs per month (I-MR chart) MSD quality project # PSD employee Complaints/mo (c chart) Eliminate employee complaints # employee complaints per month (c chart) Eliminate PSD employee complaints Employee Morale project

Current Costs • Management considers costs production costs in PSD to be too high • Avg. Production costs of $1.1m per month • Standard deviation is $116k. • R-bar / d2 = $116,672 • Average is “too high” but process is under control

Production costs Normally distributed

Prioritizing Six Sigma Projects Potential Six Sigma Projects Existing Customer Promotions New Customer Promotions MSD Quality Employee Morale Business objective Weight 0.35 3 3 0 0 0.10 1 3 0 0 0.40 0 0 9 3 0.15 0 0 9 9 1.15 1.35 4.95 2.55 Increase # orders Increase # POI services used by each customer Minimize production costs Eliminate employee complaints Weighted average of potential

Starting MSD Project Champion and process owner make initial charter. • What is the name of the process? MSD purchasing • What is the aim? Purchase MSDs that improve productivity and morale of PSD • What is economic rationale? • Why do it at all? • Un-durable clips (<4 bends): lost papers, frustrated employees lead to higher processing costs, inefficient labor costs (60% cannot withstand test) • Functionality (broken in box): sorting costs, frustrated employees (60% of boxes have >5 broken MSDs)

Additional charter questions • Why do it now? High production costs, complaints • What business objectives are supported by project? Min. costs, reduce complaints • Consequences of not doing: lower profits, more employee complaints • What projects have higher priority? None. • What is the problem statement? • Low-quality MSDs create additional production costs and employee frustration • What is goal or desired state? • 100-fold increase in durability 0.6% from 60% • 10-fold every 2 years, so 100 over 4 year project • 100-fold would take from 600,000 DPMO to 6,000 DPMO, set goal as 4 sigma (p. 739)

More charter questions • What is scope? • Boundaries? When purchasing receives purchase orders, ends when MSD put in inventory • What is out of bounds? How employees use MSDs • What resources? $30,000, including salaries • Who can approve expenditures? Process owner • Can they go over $30,000? No. • What are obstacles? Budget, 21 weeks • What time commitment expected? Friday 8-9am meetings, progress reports • What about regular duties? OT may be required, not in budget

Gantt Chart for Project

MSD Project Benefits • Benefits: • Soft benefits: eliminating complaints from PSD and increasing employee morale • Hard benefits (financial): minimizing labor costs

Labor savings – Clipping expenses • 100 employees, 40 hrs/wk, spend 10% of time clipping = 400 hrs / wk clipping • $25 / hr * 400 hrs * 50 wks = $500,000 annual clipping expenses • 60% clips defective = $300,000? Currently? • 0.62% defective = $3,100? Improved system? • Annually, 20,000 hrs clipping = 10 employees • 60% = 12,000 wasted clipping hours currently • 0.62% = 124 wasted hours under improved system • Need 6 fewer employees • This does not including time lost from clips failing later, on work in process

Material Cost Savings • 300,000 projects per year, 10 clips each =3,000,000 clips needed each year. • 0.60 defect means 1/(1-0.6) = 2.5 clips used for each one needed = 7,500,000 used • 0.0062 means 1/(1-0.0062) = 1.00625 =3,018,000 clips used • Savings of 4,482,000 clips = $44,820 per year

Team Members • Champion • Process Owner • Team Leader – Black Belt • Team member 1 • Team member 2 • Finance representative • IT representative

Start SIPOC Purchasing receives order fromPaper Shuffling Department • -Suppliers • Inputs • Process • Outputs • Customers Purchasing agentcalls vendor No Does vendorhave MSDin stock? Yes Place order withvendor Receive order fromvendor Store productreceived intoinventory (newboxes go on bottomback of shelf) PSD removesproducts from inventory PSD uses Product Stop

Voice of the Customer • What emotions come to mind when you think about MSDs? • What needs and wants come to mind when you think about MSDs? • What complaints or problems would you like to mention about MSDs? • 3 themes: • Variation in durability • Variation in color • Variation in functionality (# broken MSDs in each box) • CTQ-Critical to Quality factors Tech Specs • Ability to withstand bending >= 4 bends w/o breaking • The number of different MSD colors = 1 color of MSDs • The number of broken MSDs in a box. <= 5 broken in box

Project Objectives • 1. Decrease (direction) the percentage that cannot withstand four or more bends without breaking (measure) bought by the purchasing department (process) to 0.62 percent (goal) by Jan. 1, 2005 (deadline). Go for 4 sigma! • 2. Decrease (direction) the percentage of boxes of MSDs with more than five broken clips (measure) bought by the purchasing deparment (process) to 0.62 percent (goal) by Jan. 1, 2005 (deadline) Go for 4 sigma! • 3. What happened to colors?

Measure phase-I Operationally Define CTQs • Operational definition for CTQ1: Durability • Take top-front box • Close eyes, randomly pull one out • Count number of bends until breaking • Do not count bend being made when it breaks • If >= bends, then MSD conforms, else defective

Operationally Define CTQ2 - Functionality • Take top-front box • Count the number of broken clips • If number of broken is <= 5, box is conforming • If number is > 5, box is defective • Use same boxes for both operational definitions

Measure Phase-II Gage repeatability and reproducibility • 10 top-front boxes tested by 2 inspectors, each box twice • Gage (or gauge) run chart shows no difference between the measurements from the two different inspectors

CTQ Baselines • Hourly inspections for both CTQs • Durability is # bends for one MSD before breaking • Functionality is # of broken clips • Yield is percentage of batches passing the standard • 6/16 passed each • Very similar to claim of 60% unacceptable

I-MR charts show durability not stable over time. • Different vendors, but deal with that soon

Durability “dot plot” – shows how many boxes had a particular durability level • Graph doesn’t look like Normal distribution • Maybe Poisson distribution

C-chart not in control, shift 2 tester bent more slowly, caused it to last longer

C-chart for Functionality under control

Dot-plot for Functionality • Dot-plot for Functionality looks Normally distributed

X’s also could be defined in measure phase Start Purchasing receives order fromPaper Shuffling Department Purchasing agentcalls vendor No Does vendorhave MSDin stock? Yes Place order withvendor Receive order fromvendor MSDs placed into inventory (new boxes go on the bottom back of shelf) PSD removesbox from inventory PSD uses MSDs Stop DetailedProcess Map X1 – Vendor (Ibix or Office Optimum) X2 – Size (Small or Large) X3 – Ridges (With or Without) X4 = Cycle time from order to receipt for MSDs X5 = Discrepancy in count from order placed and order received X6 = Cycle time to place product in inventory X7 = Inventory shelf time (in days) X8 = Type of usage (Large stack of paper or Small stack of paper)

Operational Definitions for each X • X1 – Vendor Ibix Office Optimum • X2 – Size Small Large • X3 – Ridges With Without • X8 – Usage Large stack Small stack • X4 – Cycle time, ordering to receipt (days) • X5 – Discrepancy: # ordered vs. received • X6 – Cycle time to place in inventory (days) • X7 – inventory shelf life (in days) Perform gage study on each, to make sure we can measure consistently (repeatability and reproducibility)

Baseline Data • Every hour for 2 weeks – 80 samples • Collect info about: • X1 vendor • X2 size • X3 ridges • Y1 Durability • Y2 Functionality • Other factors studied separately

Sample Day Hour X1 X2 X3 X7 Durability Function 1 Mon 1 1 0 0 7 2 5 2 Mon 2 0 1 0 7 2 9 3 Mon 3 0 0 1 7 10 7 4 Mon 4 0 1 0 7 1 4 5 Mon 5 0 0 0 7 7 3 6 Mon 6 0 1 1 7 2 5 7 Mon 7 0 1 1 7 1 9 8 Mon 8 0 0 0 7 7 5 9 Tue 1 0 1 0 8 2 8 10 Tue 2 0 1 0 8 1 7 11 Tue 3 0 1 0 8 1 13 12 Tue 4 1 1 1 8 9 5 13 Tue 5 1 1 0 8 9 9 14 Tue 6 1 1 1 8 10 11 15 Tue 7 1 1 1 8 10 11 16 Tue 8 0 0 1 8 8 9 17 Wed 1 1 1 1 9 8 11 18 Wed 2 1 0 0 9 1 11 19 Wed 3 1 1 1 9 10 11 20 Wed 4 0 0 0 9 7 11 21 Wed 5 1 1 1 9 9 9 22 Wed 6 0 0 1 9 9 5 23 Wed 7 1 0 1 9 2 11 24 Wed 8 1 0 0 9 1 10 25 Thu 1 1 0 1 10 1 14 26 Thu 2 0 1 1 10 1 10 27 Thu 3 1 1 1 10 8 13 28 Thu 4 0 0 1 10 10 12 29 Thu 5 0 0 0 10 7 14 30 Thu 6 0 1 1 10 3 13

Sample Day Hour X1 X2 X3 X7 Durability Function 31 Thu 7 0 0 0 10 9 13 32 Thu 8 1 1 1 10 8 11 33 Fri 1 0 1 0 1 2 0 34 Fri 2 0 1 0 1 2 1 35 Fri 3 0 1 0 1 1 6 36 Fri 4 0 1 0 1 3 3 37 Fri 5 0 1 0 1 2 2 38 Fri 6 1 1 0 1 10 6 39 Fri 7 0 0 1 1 10 0 40 Fri 8 0 1 0 1 2 0 41 Mon 1 0 1 1 4 3 4 42 Mon 2 0 1 0 4 3 7 43 Mon 3 0 1 1 4 3 3 44 Mon 4 0 0 0 4 10 2 45 Mon 5 1 1 0 4 8 5 46 Mon 6 0 1 1 4 3 4 47 Mon 7 1 0 0 4 1 4 48 Mon 8 0 0 1 4 10 5 49 Tue 1 1 1 1 5 11 6 50 Tue 2 1 0 1 5 3 4 51 Tue 3 1 1 0 5 10 6 52 Tue 4 1 0 1 5 3 5 53 Tue 5 1 0 0 5 2 4 54 Tue 6 0 0 0 5 9 5 55 Tue 7 0 0 1 5 9 5 56 Tue 8 0 1 0 5 3 7 57 Wed 1 0 0 1 6 9 5 58 Wed 2 1 1 0 6 9 7 59 Wed 3 0 0 0 6 9 5 60 Wed 4 1 0 0 6 2 6

Sample Day Hour X1 X2 X3 X7 Durability Function 61 Wed 5 1 0 1 6 2 5 62 Wed 6 1 1 1 6 10 5 63 Wed 7 0 1 0 6 1 7 64 Wed 8 0 1 0 6 2 5 65 Thu 1 0 0 1 7 10 7 66 Thu 2 1 1 0 7 9 5 67 Thu 3 1 0 0 7 1 7 68 Thu 4 0 1 0 7 2 5 69 Thu 5 1 0 1 7 1 6 70 Thu 6 0 1 0 7 1 5 71 Thu 7 1 0 0 7 1 8 72 Thu 8 1 1 1 7 10 5 73 Fri 1 0 1 1 8 3 7 74 Fri 2 1 1 1 8 9 7 75 Fri 3 1 0 0 8 1 13 76 Fri 4 0 1 1 8 2 8 77 Fri 5 0 1 1 8 3 9 78 Fri 6 1 1 1 8 8 10 79 Fri 7 1 0 1 8 3 11 80 Fri 8 0 0 1 8 10 11 Legend: X1 = Vendor (0 = Office Optimum and 1 = Ibix) X2 = Size (0 = Small and 1 = Large) X3 = Ridges (0 = Without and 1 = With) X7 = Inventory shelf time, in days

Baseline results • Durability 0.4625 • Functionality 0.425 • X1: Office Optimum 56.25% • X2: Small 42.50% • X3: Without ridges 50% • X7: Shelf life average 6.5 days • X7: Shelf life st. dev 2.5 days

Vendor (X1) and Durability Maybe Ibix is more durable? Ibix Off Opt.

Size (X2) and Durability Maybe small is more durable? Large Small

Ridges (X3) and Durability Maybe ridges are more durable? Ridges No ridges

Shelf Life (X7) and Durability

Vendor (X1) and Functionality Maybe Ibix is more functional? Ibix Off Opt.

Size (X2) and Functionality Maybe large is more functional? Large Small

Ridges (X3) and Functionality Maybe ridges are more functional? Ridges No ridges

Shelf Life (X7) and Functionality

Conclusions • Durability – no large effects from any X’s. • Vendor (X1=1 = Ibix) improves functionality • Size (X2=1= large) improves functionality • Ridges (X3=1) seem to improve functionality • Shelf Life (X7) – lower values have better functionality • Best plan is to buy Ibix large MSDs with ridges

Office Ibix small Lg No with Shelf Opt. Ridges Ridges Life

Conculsions – 2 • Best to buy • Ibix • Small • With Ridges • Shelf life doesn’t matter?

X1=Off Opt X1=Ibix Small Large No ridges Ridges

Conclusion? • Ridges don’t seem to affect durability • Buy small Office Optimum, or Large Ibix!

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