DOCSLIB.ORG

Statistical Quality Control in Cable Industry Case Study: Copper Consumption Reduction in Nexans IKO Sweden

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Quality Control in Cable Industry Case Study: Copper Consumption Reduction in Nexans IKO Sweden Statistical Quality Control in Cable Industry Case Study: Copper Consumption Reduction in Nexans IKO Sweden Payam Sabet Azad & Reza Mokhlesi Master of Science in Industrial Engineering with a major in Quality & Environmental Management Nr 3/2009 Reza Mokhlesi: [email protected] Payam Sabet Azad: [email protected] Master thesis Subject Category: Industrial Engineering University College of Borås School of Engineering SE-501 90 Borås Telephone +46 033 435 4640 Supervisors: Fredrik Torstensson, Site Manager Nexans IKO Sweden Roy Andersson, University College of Borås “New opinion are always suspected, and usually opposed, without any other reason but because they are not already common” John Locke 1632-1704 (An Essay concerning Human Undersatanding, 1690) Acknowledgments Grateful acknowledgement to my parents, who never give up in giving their support to me in all aspects of life.( Payam) Dedicated to my wonderful parents, for providing me with the opportunity to be where I am, and my girlfriend Nana, for all her love and support during this work. (Reza) This essay was improved by conversations with a large number of people who helped debug it. Particular thanks to Fredrik Torstensson our supervisor in Nexans IKO Sweden for his enthusiasm, support and his patience. He always tried to find some time to discuss our issues and guide us through our research. Abstract This thesis was carried out at the Special Cables group in the Nexans IKO Sweden AB in Grimsås, The company is a worldwide leader in the cable industry, offers an extensive range of cables and cabling systems. The aim with this thesis is to increase mean average of the electrical resistance in cables and accordingly saving the copper as the raw material. In order to achieve such significance the project offers an approach to find and eliminate causes of variation in a manufacturing process. In the beginning, some germane literature for the area was studied to get a deeper understanding of the problem. Some of the literature is summarized in the theory chapter of the thesis. The process was then defined and mapped out. Sources of variation were also chosen, in this case facts inside different machines. Some statistical analysis was accomplished later in the process by applying the theoretical background in the light of the reality of company. Gage R&R was also performed to examine the measurement system in Nexans IKO Sweden. The study concludes with recommendations for Nexans on five areas for implementation; Tracking and recording data, Optimization of the bunching process, Reach to the state of statistical control, More focus on suppliers, Prevention of over adjustment and main activities within these areas are defined. Finally management commitment is introduced as the most important factor for future success. Table of Contents Acknowledgments Abstract 1 Introduction ............................................................................................................... 1 1.1 Background ..................................................................................................................... 1 1.2 Problem Discussion ......................................................................................................... 2 1.3 Purpose & Objectives ..................................................................................................... 3 1.4 Research Area (Company Description) .......................................................................... 3 1.5 Delimitation ..................................................................................................................... 3 2 Theoretical Frame of Reference ............................................................................... 5 2.1 Lean Management ........................................................................................................... 5 2.1.1 Lean production definition ........................................................................................ 5 2.1.2 The Seven Wastes of Lean ......................................................................................... 5 2.1.3 The Principles of Lean .............................................................................................. 6 2.2 Robust Design ................................................................................................................. 7 2.2.1 Robustness Strategy ................................................................................................... 7 2.3 Kano Analysis.................................................................................................................. 8 2.4 Process Map .................................................................................................................... 9 2.5 Affinity Diagram ............................................................................................................. 9 2.6 Statistical Process Control ............................................................................................. 10 2.6.1 Distributions ........................................................................................................... 10 2.6.2 Central Limit Theorem ........................................................................................... 12 2.6.3 Prevention versus Detection.................................................................................... 13 2.6.4 The Magnificent Seven ........................................................................................... 14 2.6.5 Control Chart ......................................................................................................... 15 2.7 Gauge R&R ................................................................................................................... 19 2.7.1 Measurement System Facets ................................................................................... 19 2.7.2 Analysis of variance ................................................................................................ 20 2.7.3 Repeatability/Reproducibility .................................................................................. 21 2.8 The Process Improvement Cycle ................................................................................... 23 2.9 Design of Experiments .................................................................................................. 24 3 Methodology ............................................................................................................. 27 3.1 Research Approach ...................................................................................................... 27 3.1.1 Case Study .............................................................................................................. 27 3.1.2 Data Analysis .......................................................................................................... 28 3.1.3 Literature Studies .................................................................................................. 28 3.1.4 Making Experiments .............................................................................................. 28 3.2 Research Design ........................................................................................................... 29 3.2.1 Inductive ................................................................................................................. 29 3.2.2 Deductive ................................................................................................................ 29 3.3 Validity, reliability and generalization in the thesis ...................................................... 29 4 Empirical Work ....................................................................................................... 31 4.1 Understanding cable process ........................................................................................ 31 4.1.1 The barrel pay-off Machine .................................................................................... 32 4.1.2 Drawing Machine ................................................................................................... 33 4.1.3 Annealing Machine ................................................................................................ 33 4.1.4 Dancer .................................................................................................................... 34 4.1.5 Spool take up machine ............................................................................................ 35 4.1.6 Bunching machine ................................................................................................. 35 4.2 Finding sources of variation......................................................................................... 36 4.3 Focusing on the sources of variation ........................................................................... 37 4.3.1 Bunching Machine ................................................................................................. 38 4.3.1.1 Experiments on Bunching Machine .............................................................. 46 4.3.2 Drawing Machine ................................................................................................... 51 4.4 Gage R&R .................................................................................................................... 52 5 General Discussion and Conclusions ....................................................................... 57 5.1 Analysis of the bunching machines .............................................................................
Load more

Recommended publications
  • The 1999 ASA Quality and Productivity Research Conference Presentation Abstracts Wednesday Keynote Address
    The 1999 ASA Quality and Productivity Research Conference Presentation Abstracts Wednesday Keynote Address Six Sigma: From Strategic Deployment to Bottom-line Results A. Blanton Godfrey, Joseph A. DeFeo and Richard Chua Juran Institute, Inc. There are many challenges in creating a strong, competitive position today. They start with creating the right strategic direction for the organization and developing an aggressive, yet realistic strategic plan. But the plan is only a starting point. Without deployment, the strategic planning process is just another non- valued-added activity. In the last few years a small number of companies have used the emerging ideas, concepts and tools of Six Sigma to develop deployment plans leading to stunning results. We shall review some of the successful approaches - and some of the failed ones - and share lessons learned from a number of case studies. 1999 ASA QPRC Presentation Abstracts 1 Six Sigma Implementation I Applying Six Sigma in an Innovative R&D Organization Jane C. Li, James Rockwell and Anne Raich UOP LLC UOP has a proud history of innovation as a world leader in development and commercialization of new technologies for petroleum refining and petrochemical process industries. Two years after successful introduction of Six Sigma to manufacturing in 1995, over 250 UOP engineers, scientists and managers began participation in an accelerated in-house customized Six Sigma program. Long-term success through new product and process design is emphasized with a broad training approach. A culture change has begun toward aligning complex technical and business issues, providing a fresh view of catalysis, material, and process research and development, and improving testing efficiency.
    [Show full text]
  • X-Bar Charts
    NCSS Statistical Software NCSS.com Chapter 244 X-bar Charts Introduction This procedure generates X-bar control charts for variables. The format of the control charts is fully customizable. The data for the subgroups can be in a single column or in multiple columns. This procedure permits the defining of stages. The center line can be entered directly or estimated from the data, or a sub-set of the data. Sigma may be estimated from the data or a standard sigma value may be entered. A list of out-of-control points can be produced in the output, if desired, and means may be stored to the spreadsheet. X-bar Control Charts X-bar charts are used to monitor the mean of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.). The measurements of the samples at a given time constitute a subgroup. Typically, an initial series of subgroups is used to estimate the mean and standard deviation of a process. The mean and standard deviation are then used to produce control limits for the mean of each subgroup. During this initial phase, the process should be in control. If points are out-of-control during the initial (estimation) phase, the assignable cause should be determined and the subgroup should be removed from estimation. Determining the process capability (see R & R Study and Capability Analysis procedures) may also be useful at this phase. Once the control limits have been established of the X-bar charts, these limits may be used to monitor the mean of the process going forward.
    [Show full text]
  • A Brief History of QUALITY’S Seminal Moments
    A Brief History of QUALITY’s Seminal Moments 1 A SAMPLING OF THE SEMINAL MOMENTS IN THE HISTORY OF QUALITY, AND THE ASSOCIATED PEOPLE, THAT HAVE MADE A DIFFERENCE Stephen N. Luko Statistician, engineering Fellow United Technologies Aerospace Systems November 11, 2018 Forward 2 This presentation is about some of the important moments and people contributing key concepts and knowledge to the theory and application of Quality. It is therefore a sample of these and not the whole story. The people, concepts and techniques reviewed are a testimony to a field that has blossomed in our lifetime to become the “Quality Sciences”. Today, Quality is a multidisciplinary field and applied in many disciplines, comprising such diverse fields as management, psychology, mathematics, statistics, planning, and leadership among others. One thought before we begin 3 It cannot be helped that most of the people in this story were scientists, engineers, mathematicians or statisticians by training, so much of the following presentation has a technical bedrock. Beginning’s 4 Quality goes back a long time 13th century to the early 19th century - The “Guilds” of medieval Europe and elsewhere throughout the world was the banner of quality goods for centuries. Guilds fostered craftsmanship, apprenticeship, and individual piece to piece inspection and quality. This system continued until the modern period and the dawn of the industrial revolution. Artifacts and antiquities have left us materials thousands of years old. Beginning’s, Shewhart’s long view 5 From: Shewhart, “Statistical Method from the Viewpoint of Quality Control”, 1939 Beginning’s - Shewhart’s long view 6 From: Shewhart, “Statistical Method from the Viewpoint of Quality Control”, 1939.
    [Show full text]
  • A Guide to Creating and Interpreting Run and Control Charts Turning Data Into Information for Improvement Using This Guide
    Institute for Innovation and Improvement A guide to creating and interpreting run and control charts Turning Data into Information for Improvement Using this guide The NHS Institute has developed this guide as a reminder to commissioners how to create and analyse time-series data as part of their improvement work. It should help you ask the right questions and to better assess whether a change has led to an improvement. Contents The importance of time based measurements .........................................4 Run Charts ...............................................6 Control Charts .......................................12 Process Changes ....................................26 Recommended Reading .........................29 The Improving immunisation rates importance Before and after the implementation of a new recall system This example shows yearly figures for immunisation rates of time-based before and after a new system was introduced. The aggregated measurements data seems to indicate the change was a success. 90 Wow! A “significant improvement” from 86% 79% to 86% -up % take 79% 75 Time 1 Time 2 Conclusion - The change was a success! 4 Improving immunisation rates Before and after the implementation of a new recall system However, viewing how the rates have changed within the two periods tells a 100 very different story. Here New system implemented here we see that immunisation rates were actually improving until the new 86% – system was introduced. X They then became worse. 79% – Seeing this more detailed X 75 time based picture prompts a different response. % take-up Now what do you conclude about the impact of the new system? 50 24 Months 5 Run Charts Elements of a run chart A run chart shows a measurement on the y-axis plotted over time (on the x-axis).
    [Show full text]
  • Medians and the Individuals Control Chart the Individuals Control Chart
    Medians and the Individuals Control Chart The individuals control chart is used quite often to monitor processes. It can be used in almost all cases, so it makes the art of deciding which control chart to use extremely easy at times. Not sure? Use the individuals control chart. The individuals control chart is composed of two charts: the X chart where the individual values are plotted and the moving range (mR) chart where the range between consecutive values are plotted. The averages and control limits are also part of the charts. The average moving range (R̅) is used to calculate the control limits on the X chart. If the mR chart has a lot of out of control points, the average moving range will be inflated leading to wider control limits on the X chart. This can lead to missed signals (out of control points) on the X chart. Is there anything you can do when the mR chart has many out of control points to help miss fewer signals on the X chart? Yes, there is. One method is to use the median moving range. The median moving range is impacted much less by large moving range values than the average. Another option is to use the median of the X values in place of the overall average on the X chart. This publication explores using median values for the centerlines on the X and mR charts. We will start with a review of the individuals control chart. Then, the individuals control charts using median values is introduced. The charts look the same – the only difference is the location of the centerlines and control limits.
    [Show full text]
  • Phase I and Phase II - Control Charts for the Variance and Generalized Variance
    Phase I and Phase II - Control Charts for the Variance and Generalized Variance R. van Zyl1, A.J. van der Merwe2 1Quintiles International, [email protected] 2University of the Free State 1 Abstract By extending the results of Human, Chakraborti, and Smit(2010), Phase I control charts are derived for the generalized variance when the mean vector and covariance matrix of multivariate normally distributed data are unknown and estimated from m independent samples, each of size n. In Phase II predictive distributions based on a Bayesian approach are used to construct Shewart-type control limits for the variance and generalized variance. The posterior distribution is obtained by combining the likelihood (the observed data in Phase I) and the uncertainty of the unknown parameters via the prior distribution. By using the posterior distribution the unconditional predictive density functions are derived. Keywords: Shewart-type Control Charts, Variance, Generalized Variance, Phase I, Phase II, Predictive Density 1 Introduction Quality control is a process which is used to maintain the standards of products produced or services delivered. It is nowadays commonly accepted by most statisticians that statistical processes should be implemented in two phases: 1. Phase I where the primary interest is to assess process stability; and 2. Phase II where online monitoring of the process is done. Bayarri and Garcia-Donato(2005) gave the following reasons for recommending Bayesian analysis for the determining of control chart limits: • Control charts are based on future observations and Bayesian methods are very natural for prediction. • Uncertainty in the estimation of the unknown parameters are adequately handled.
    [Show full text]
  • Simplificaton of Six Sigma Implementation Using Shainin Tools for Process Improvement Justin T
    International Journal of Scientific & Engineering Research, Volume 8, Issue 4, April-2017 ISSN 2229-5518 390 Simplificaton of Six Sigma Implementation Using Shainin Tools for Process Improvement Justin T. Aichail, Arju D. Jadhav, Aniket P. Baviskar, Chandan S. Gajare, R. S. Patil Abstract— The purpose of this paper is to provide a brief overview of Six Sigma and Shainin methodology and to propose the modification of Six Sigma methodology in order to achieve improved efficiency of DMAIC in the process improvement journey by using some of the Shainin tools. The success of the Six Sigma program in an organization depends to a large extent on the success of the Six Sigma project, which in turn depends on how combination of tools is being applied to address the root cause. Shainin Design of Experiments (DOE) offers powerful and effective experimental design approach for solving the chronic quality problems that plague manufacturers worldwide. The objective of this paper is to examine as to how to simplify the implementation of Six Sigma methodology by the applicability of a simpler but not very frequently used methodology known as Shainin methodology. The comparative study of various approaches of Six Sigma implementation has been done. Index Terms— DOE, DMAIC, Process Improvement, Quality Tools, Shainin, Simplification, Six Sigma Implementation. —————————— —————————— 1 INTRODUCTION HE demands placed on an organization in today‘s global long been used to identify the key input variables responsible T business environment are driven by customer satisfaction for variations in the output. as well as the fulfilment of the expectation of stakeholders A technical feature that distinguishes Six Sigma from other regarding cost reduction, improving business performance quality approaches is its ability to use statistical methods with- and maintaining a competitive advantage.
    [Show full text]
  • Introduction to Using Control Charts Brought to You by NICHQ
    Introduction to Using Control Charts Brought to you by NICHQ Control Chart Purpose A control chart is a statistical tool that can help users identify variation and use that knowledge to inform the development of changes for improvement. Control charts provide a method to distinguish between the two types of causes of variation in a measure: Common Causes - those causes that are inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system. Using your morning commute as an example, it may take between 35-45 minutes to commute to work each morning. It does not take exactly 40 minutes each morning because there is variation in common causes, such as the number of red lights or traffic volume. Special Causes - those causes that are not part of the system all the time or do not affect everyone, but arise because of specific circumstances. For example, it may take you 2 hours to get to work one morning because of a special cause, such as a major accident. Variation in data is expected and the type of variation that affects your system will inform your course of action for making improvements. A stable system, or one that is in a state of statistical control, is a system that has only common causes affecting the outcomes (common cause variation only). A stable system implies that the variation is predictable within statistically established limits, but does not imply that the system is performing well. An unstable system is a system with both common and special causes affecting the outcomes.
    [Show full text]
  • Understanding Statistical Process Control (SPC) Charts Introduction
    Understanding Statistical Process Control (SPC) Charts Introduction This guide is intended to provide an introduction to Statistic Process Control (SPC) charts. It can be used with the ‘AQuA SPC tool’ to produce, understand and interpret your own data. For guidance on using the tool see the ‘How to use the AQuA SPC Tool’ document. This introduction to SPC will cover: • Why SPC charts are useful • Understanding variation • The different types of SPC charts and when to use them • How to interpret SPC charts and what action should be subsequently taken 2 Why SPC charts are useful When used to visualise data, SPC techniques can be used to understand variation in a process and highlight areas that would benefit from further investigation. SPC techniques indicate areas of the process that could merit further investigation. However, it does not indicate that the process is right or wrong. SPC can help: • Recognise variation • Evaluate and improve the underlying process • Prove/disprove assumptions and (mis)conceptions • Help drive improvement • Use data to make predictions and help planning • Reduce data overload 3 Understanding variation In any process or system you will see variation (for example differences in output, outcome or quality) Variation in a process can occur from many difference sources, such as: • People - every person is different • Materials - each piece of material/item/tool is unique • Methods – doing things differently • Measurement - samples from certain areas etc can bias results • Environment - the effect of seasonality on admissions There are two types of variation that we are interested in when interpreting SPC charts - ‘common cause’ and ‘special cause’ variation.
    [Show full text]
  • Statistical Control Process: a Historical Perspective
    International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-2 Issue-2, January 2014 Statistical Control Process: A Historical Perspective Hossein Niavand, MojtabaTajeri Abstract:- One of the most powerful tools in thestatistical In this paper, he led the association to devote a separate quality process is the statisticalmethods.First developed in the department to Industrial Research and Statistics. The 1920’s byWalter Shewhart, the control chart found widespread association’s magazine also published a supplement on use during World War II and has been employed, with various statistics. This movement was the beginning of quality modifications, ever since. There are many processes in which control in the UK. This movement grew faster in UK than thesimultaneous monitoring or control of two ormore quality characteristics is necessary. U.S. Reviewing statistical process control tools and providing a The British Standards Association published a book description about necessity of using these tools for improving the titled as "Application of statistical methods in production process and providing some applications of statistical standardization of industries and quality control". By process control.This study covers both the motivation for publishing this book, they showed their interest in new ways statistical quality process and a discussion of some of the to control quality. This issue led to the fact that UK techniques currently available. The emphasis focuses primarily industries used new statistical quality control methods in on the developments occurring since the mid-980’s producing most of their products by 1937. In 1932 when Shewhart made a trip to London, Egan S.
    [Show full text]
  • Seven Basic Tools of Quality Control: the Appropriate Techniques for Solving Quality Problems in the Organizations
    UC Santa Barbara UC Santa Barbara Previously Published Works Title Seven Basic Tools of Quality Control: The Appropriate Techniques for Solving Quality Problems in the Organizations Permalink https://escholarship.org/uc/item/2kt3x0th Author Neyestani, Behnam Publication Date 2017-01-03 eScholarship.org Powered by the California Digital Library University of California 1 Seven Basic Tools of Quality Control: The Appropriate Techniques for Solving Quality Problems in the Organizations Behnam Neyestani [email protected] Abstract: Dr. Kaoru Ishikawa was first total quality management guru, who has been associated with the development and advocacy of using the seven quality control (QC) tools in the organizations for problem solving and process improvements. Seven old quality control tools are a set of the QC tools that can be used for improving the performance of the production processes, from the first step of producing a product or service to the last stage of production. So, the general purpose of this paper was to introduce these 7 QC tools. This study found that these tools have the significant roles to monitor, obtain, analyze data for detecting and solving the problems of production processes, in order to facilitate the achievement of performance excellence in the organizations. Keywords: Seven QC Tools; Check Sheet; Histogram; Pareto Analysis; Fishbone Diagram; Scatter Diagram; Flowcharts, and Control Charts. INTRODUCTION There are seven basic quality tools, which can assist an organization for problem solving and process improvements. The first guru who proposed seven basic tools was Dr. Kaoru Ishikawa in 1968, by publishing a book entitled “Gemba no QC Shuho” that was concerned managing quality through techniques and practices for Japanese firms.
    [Show full text]
  • The Incredible Story of Statistics by Jean-Marie Gogue
    The incredible story of statistics By Jean-Marie Gogue In the 20th century, seven mathematicians unknown to the general public led to profound transformations : they invented modern statistics. Not the one we usually think of, that of economic studies and opinion polls. No. They invented methods of statistical analysis, algorithms, as they say today, which accelerated scientific research in all fields. Francis Galton (1822-1911) Francis Galton was born in England, in the Midlands. His father was a banker ; he was a cousin of Charles Darwin. He began studying medicine in Birmingham and then in London, but he was not interested in medicine. In 1840 he entered Trinity College in Cambridge to attend mathematics lessons, but he left the university because teaching did not suit him. He went back to medical school in London. At the death of his father in 1844, he inherited a great fortune that will allow him to satisfy his passion : travelling. After a first trip to Egypt, along the Nile, he led in 1852 a great expedition in southern Africa. In 1859, the publication of Darwin's book on natural selection gave him a new subject of interest. He gave up his ideas of travelling and devoted most of his activities to studying the variations of the characters in the human populations, the color of the eyes for example. This requires finding methods to measure, collect and analyze data. This is how he invented regression and correlation. His work was pursued by other statisticians. Karl Pearson (1857-1936) Karl Pearson was born in Yorkshire. His parents enrolled him at University College London, but he left it at sixteen because of poor health.
    [Show full text]