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Lean Six Sigma Catapult Challenge 1

Select [Factors] and the dialog box below will show-up; load in the factor names and level settings Click OK: Minitab will create a worksheet containing the DOE array: The first blank column in the worksheet here C7 is reserved for the Response values After running all of the experimental runs enter the results in to the worksheet The second series of steps allow us to analyze the results as well as produce the charts and graphs that help us communicate our results.

Enter the column here C7 that contains the response in the open window called Responses or just double-click on C7 in the left box Then click on [Terms…]: Select the terms you want in the model in our case we want both factors; Fertilizer and Water Then click [OK]: This time select [Graphs…]: Reduction of defects invariably means improve- ment of process quality, and hence, increase in customer satisfaction level.

Six Sigma is one of the most widely used methodologies to improve the quality of an existing process at a company.

How Minitab Helped

The methodology was devel- oped and used by Motorola in , and followed by General Electric and other companies in both manufacturing and service industries. Six Sigma uses a systematic five-phase approach called DMAIC define-measure- analyze-improve-control to improve a process— i define: the problem faced by the process is defined in this phase; ii measure: in this phase, the current performance of the process is measured; iii analyze: this phase analyzes the process to identify the root causes of the problem; iv improve: in this phase, recommendations are made to minimize or eliminate the root causes of the problem and then those recommendations are implemented to improve the process; and v control: this phase ensures that the improved process is con- trolled so that the process does not slide back to the previous problem.

It is user-friendly, has hundreds of sample datasets, and can perform very basic to very advanced statistical analyses of both fractional and integer data. Kishore K. Pochampally, PhD Surendra M. Gupta, PhD Surendra M. He is a registered professional engineer in the state of Massachusetts. He is mostly interested in environmentally conscious manufacturing, reverse and closed-loop supply chains, disassembly modeling, and remanufacturing.

Teaching DoE with Paper Helicopters and Minitab

He has authored or coauthored more than technical papers published in books, journals, and international conference proceedings. His publications have been cited by thousands of researchers all over the world in journals, proceedings, books, and dissertations. He has traveled to all seven continents—Africa, Antarctica, Asia, Australia, Europe, North America, and South America— and presented his work at international conferences on six continents.

Gupta has taught more than courses in such areas as operations research, inventory theory, queueing theory, engineering economy, supply chain management, and production planning and control. Section I Background This chapter illustrates a number of terms used in this methodology and also gives an introduction to the DMAIC define-measure-analyze-improve-control approach used to improve a process.

Section 1. Finally, Section 1. Process: A process is a set of tasks that convert inputs to outputs. For exam- ple, the process that manufactures the Toyota Camry output on an assembly line uses inputs such as capital, workforce, machines, facilities, and so on. If the output is a tangible product e. If the output is a service e.

Full Factorial DOE with Minitab

Process Mean: It is the average of the characteristic values of a population e. For exam- ple, the process mean for the process that bottles 2-liter soda bottles may be 1. The closer the process mean is to the process target, the better the process is. For example, the process standard deviation for the process that bottles 2-liter soda bottles may be 0. The lower the process standard deviation, the better i. For example, the process target for a process that bottles 2-liter soda bot- tles is 2 liters.

Process Tolerance: It is the deviation of the product characteristic value from the process target that the customer is willing to tolerate.


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This means, the lower specification limit LSL is 1. In other words, the customer in this example is satisfied if the volume is between 1. Quality: Quality is the extent to which customers are satisfied. If most of the customers are satisfied with a product, we can say that quality is high for the product as well as for the process that produces that product. It includes process target and process tolerance. It includes process mean and process standard deviation. Assume that the lower spec- ification limit LSL is 5. Calculate the DPMO of the valve manufacturing process. We also assume here that the population is normally distributed.

Using This means that the area on the right side of 6. The higher the sigma level, the lower the DPMO value is, and hence, the better the process is. It is impossible to 6. Reasoning for the numbering of sigma levels 1—6 and proofs for the DPMO values of the respective sigma levels, is beyond the scope of this book. A tolerance of 6 mm above or below 25 mm is acceptable to customers. The mean and standard deviation of the manufacturing process assuming normal distribution are calculated to be 28 mm and 2 mm, respectively.

Calculate the defects per million opportunities DPMO. This means that the area on the right side of 31 is 0.

The target delivery time is 30 minutes or less. Calculate the DPMO for this delivery process. What is the sigma level of this process? Assume normal distribution for the delivery times. This means that the area on the right side of 1, is 0.


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  • Minitab Case Study: Design of Experiments.

Define: The problem is defined in this phase. For example, after taking a sample of deliveries, the average delivery time is calculated as 41 minutes, which is 11 minutes over the pro- cess target of 30 minutes. Analyze: This phase analyzes the process to identify the root causes of the problem. For example, the pizza delivery process is analyzed to identify the potential causes and then the root causes of the long average delivery time. Improve: In this phase, recommendations are made to minimize or elimi- nate the root causes of the problem, and then those recommenda- tions are implemented to improve the process.

Control: This phase ensures that the improved process is controlled so that the process does not slide back to the previous problem. Chapter 3 is a case study about how confidence intervals can be used in a Six Sigma project to assess variation in fat content at a fast- food restaurant.

Chapter 4 is a case study about how hypothesis testing can be used for quality control at a manufacturing company. Chapter 5 is a case study about how chi-square analysis can be used in a Six Sigma project to collect the VOC data and then to check whether a claim made about product quality is true. Chapter 6 is a Six Sigma case study that illustrates how process capabil- ity analysis can be performed at a manufacturing company. Chapter 7 demonstrates how binary logistic regression can be used to predict customer satisfaction at a restaurant.

Chapter 8 is a Six Sigma case study that illustrates how item analy- sis and cluster analysis can be used to gather the VOC data from employees at a service firm. Chapter 9 shows how to use the DMADV approach and mixture design and analysis of experiments to optimize pollution level and temper- ature of fuels. Chapter 10 is a Six Sigma case study that demonstrates how to use mul- tivariate analysis to reduce patient waiting time at a medical center. Chapter 11 shows how to use a Pareto chart and fishbone diagram to minimize recyclable waste disposal in a town. Chapter 12 illustrates how to perform gage repeatability and reproduc- ibility analysis at a medical equipment manufacturer.

Chapter 13 is a Six Sigma case study that demonstrates how to perform Taguchi design and analysis of experiments to improve customer satisfaction at an airline company. Chapter 14 is a Six Sigma case study that illustrates the use of facto- rial design and analysis of experiments to optimize a chemical process. Finally, Chapter 15 is a Six Sigma case study that demonstrates how to perform chi-square analysis to verify source association with parts purchased and products produced at a manufacturing company.

Also, it is important to note that, although these case studies are fictitious, the problems addressed in them are often faced in the real world in a variety of industries. References Gitlow, Howard S. Pyzdek, T. The Six Sigma Handbook. Because this is not a statistics textbook, in-depth cov- erage of each of these tools or techniques is beyond the scope of the book.

However, a useful reference is provided for the interested reader for each tool and technique. The estimate is calculated for a given confidence level and is expressed as an interval. The higher the confidence level is, the less precise the interval estimate. See Montgomery and Runger for an excellent introduction to confidence interval estimation. These two hypotheses are mutually exclusive if one is true, the other one is not and collectively exhaustive no other hypoth- esis is possible.

Minitab Case Study: Design of Experiments | Process Excellence Network

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The Challenge

Expand All. What's covered? Who should participate? What will I learn? Participants achieve the following learning outcomes from the programme; Plan designed experiments to include appropriate factors and responses Analyse factor effects and interaction effects using specialist computer software Interpret the outcome of designed experiments so as to choose factor settings for optimum process performance Demonstrate knowledge of the statistics underlying the design of experiments.