Six sigma benchmarking of process capability analysis and mapping of process parameters.
Author: Jagadeesh Rajashekharaiah
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract
Process capability analysis (PCA) is a vital step in ascertaining the quality of the output from a production process. Particularly in batch and mass production of components with specified quality characteristics, PCA helps to decide about accepting the process and later to continue with it. In this paper, the application of PCA using process capability indices is demonstrated using data from the field and benchmarked against Six Sigma as a motivation to improve to meet the global standards. Further, how the two important process parameters namely mean and the standard deviation can be monitored is illustrated with the help of what if analysis feature of Excel. Finally, the paper enables to determine the improvement efforts using simulation to act as a quick reference for decision makers. The global benchmarking in the form of Six Sigma capability of the process is expected to give valuable insight towards process improvement.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Six Sigma Implementation to reduce rejection rate of Pump Casings at local Ma...IOSR Journals
Six Sigma is being Implemented all over the World as a successful Quality Improvement
Methodology. This article provides a description of Six Sigma Project implemented at Local manufacturing
Company. The Company Manufactures Pump Casings where it was receiving high nonconformance rate that
resulted in to Rejection of Product. This study deals with Six Sigma DMAIC methodology implementation and
gives a frame work of how the non-conformance rate was first monitored and then brought in to acceptance
limits. A complete Coverage of the statistical analysis performed during the study is given and results are
shown to describe that how Six Sigma helped the Project members to Improve Quality of Pump casings at
manufacturing facility.
ANALYZING THE PROCESS CAPABILITY FOR AN AUTO MANUAL TRANSMISSION BASE PLATE M...ijmvsc
The industry today is working intensively on a goal-oriented way towards introducing regular studies in
manufacturing. The current study is part of a large overall spanning project aiming towards an increase in
productivity, i.e. more products produced per year with availability. In this paper we have analyze what
Process Capability is and how it is implemented on a current process. All the steps are listed out in an easy
to understand manner. In current scenario, specifications for products have been tightened due to
performance competition in market. Statistical tools like control charts, process capability analysis and
cause and effect diagram ensure that processes are fit for company specifications while reduce the process
variation and improve product quality characteristic. Process capability indices (PCIs) are used in the
manufacturing process to provide numerical measures on whether a process is capable of producing items
within the predetermined limits. For the analysis purpose MINITAB 16.0 is used and is found that the
process is placed exactly at the centre of the control limits. Analysis also shows that process is not
adequate. The cause and effect diagram is prepared to found out the root cause of variation in diameter of
work. In this study, a process-capability analysis was also carried out in a medium-sized company that
produces machine and spare parts.
if your process is ready for improvement?
well for starting the journey of process improvement you must know the process area at which capability level then by moving his certain process area into capability level 3 you continue the improvement journey by applying high maturity levels process areas ( level4 &5)and bring them also into Capability level3.
I wish it benefits you in your process improvement journey.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Six Sigma Implementation to reduce rejection rate of Pump Casings at local Ma...IOSR Journals
Six Sigma is being Implemented all over the World as a successful Quality Improvement
Methodology. This article provides a description of Six Sigma Project implemented at Local manufacturing
Company. The Company Manufactures Pump Casings where it was receiving high nonconformance rate that
resulted in to Rejection of Product. This study deals with Six Sigma DMAIC methodology implementation and
gives a frame work of how the non-conformance rate was first monitored and then brought in to acceptance
limits. A complete Coverage of the statistical analysis performed during the study is given and results are
shown to describe that how Six Sigma helped the Project members to Improve Quality of Pump casings at
manufacturing facility.
ANALYZING THE PROCESS CAPABILITY FOR AN AUTO MANUAL TRANSMISSION BASE PLATE M...ijmvsc
The industry today is working intensively on a goal-oriented way towards introducing regular studies in
manufacturing. The current study is part of a large overall spanning project aiming towards an increase in
productivity, i.e. more products produced per year with availability. In this paper we have analyze what
Process Capability is and how it is implemented on a current process. All the steps are listed out in an easy
to understand manner. In current scenario, specifications for products have been tightened due to
performance competition in market. Statistical tools like control charts, process capability analysis and
cause and effect diagram ensure that processes are fit for company specifications while reduce the process
variation and improve product quality characteristic. Process capability indices (PCIs) are used in the
manufacturing process to provide numerical measures on whether a process is capable of producing items
within the predetermined limits. For the analysis purpose MINITAB 16.0 is used and is found that the
process is placed exactly at the centre of the control limits. Analysis also shows that process is not
adequate. The cause and effect diagram is prepared to found out the root cause of variation in diameter of
work. In this study, a process-capability analysis was also carried out in a medium-sized company that
produces machine and spare parts.
if your process is ready for improvement?
well for starting the journey of process improvement you must know the process area at which capability level then by moving his certain process area into capability level 3 you continue the improvement journey by applying high maturity levels process areas ( level4 &5)and bring them also into Capability level3.
I wish it benefits you in your process improvement journey.
Process Quality Control
SPC, SQC Defined
Difference between SQC and SPC
Controlling Process Inputs (independent variables)
Process Capability with MINITAB
Monitoring process outputs (dependent variables)
7QC-Tools with MINITAB
A lean model based outlook on cost & quality optimization in software projectsSonata Software
A large quantum of effort and research is being invested to address the Cost and Quality factors in software projects. Though the solutions, models and methodologies are well established through experimented processes, adoption and optimization of the required parameters for a specific project to obtain predictable and acceptable quality with minimum costs has always remained a challenge.
This paper discusses the Lean process in detail with the help of project data and demonstrates that simple, affordable and adoptable processes are more economical and focused on quality. Also, it is observed that the Lean Model enables a project to be well monitored and controlled by focusing on critical elements, thereby reducing overheads of bulky documentation and irrelevant processes. The above findings are statistically analyzed using the coefficient of variation, which strikes a direct correlation with the predictable quality in a project.
Six sigma aims to reduce defects and conform to customer specifications. To make sure that each project adheres to customer specifications Adev Research assists your organization focus on process improvements and variation reduction.
In recent years, management and, consequently, supply chain performance measurement, has attracted the attention of a large number of managers and researchers in the field of production and operations management. In parallel with the evolution of organizations from a single approach to a network and supply chain approach, performance measurement systems have also changed and moved towards network and supply chain performance measurement. Therefore, in order to face the storm of great change and transformation and not give in to the wave of competitive aggression, organizations have long had one thing in common, and that is to focus approaches and focus efforts towards achieving results. Results that lead to a competitive advantage and are more effective and decisive in the performance indicators of the organization, including earning more. In this study, in order to identify and prioritize the factors affecting the supply chain in manufacturing companies, using indicators such as cost, timely delivery and procurement time to evaluate the supply chain efficiency is considered. And performance evaluation was performed at the manufacturer level. Therefore, in order to evaluate the performance of the supply chain using the AHP integration approach and the DEA method approach in the fuzzy environment, the suppliers and suppliers of the manufacturing company were evaluated and ranked in terms of performance.
Fundamental knowledge on pharmaceutical
product development and translation from laboratory to market.
Quality management systems: Quality management & Certifications.
Si x sigma concepts.
Aim of 6 sigma concepts.
Process of 6 sigma concepts.
To Analyze the Use of Statistical Tool/S for Cost Effectiveness and Quality o...iosrjce
Companies can lose money because they fail to use significant opportunities to improve product
quality as well as product cost. In order to survive in a competitive market, reduce cost of product by improving
the quality and productivity of product is must for any company.
The objective of this study is to provide system/instructions and methodology to reduce cost of product by
implementing statistical tools in local plastic injection molding company. Injection molding Company deployed
some part of the “Seven Basic Tools” to improve the quality of the product used by our society and reduce cost
of the product. Companies may reluctant to their quality status and customer satisfactory status without
implementing any helping tool which may reduce cost by identifying and decreasing number of defects and
consequently improving product quality and productivity of the system/process. Statistical techniques like
“Seven Basic Statistical Tools” (SPC) provides a very valuable and cost effective way to meet these objectives.
The principle aim of this study is to train quality personnel that how to use these SPC tools and exploit these
data in Pareto analysis, control Chart, Cause & Effect Diagram and histogram analysis. The causes of nonconformity
and root causes of quality problems were specified and possible remedies were proposed to
organization to overcome their problems. Some significant Improvement was also observed after SPC
implementation in Process potential capability (Cp), Process Actual Capability (Cpk) and Defective parts per
million (DPM)
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
Intersections: Beyond the Operations FunctionFGV Brazil
The identification of key success factors in sustainable cold chain management: Insights from the Indian food industry
Authors:
Shashi ., Rajwinder Singh, Amir Shabani
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract
Supply chain sustainability has emerged as an indispensable research agenda for the government, industry as well as non-profit orientation bodies. As a developing country, cold supply chain management in India is still in infancy. The demand pattern of food products has been dramatically changing since last few years. Nowadays, the customers are more conscious to use products for better health and highly expecting for food safety, toxic free and eco-friendly delivery of food products. However, sustainable cold supply chain has not yet received good heed throughout the world. Hence, in this paper an attempt has been made to address these important issues. A conceptual model was proposed in the consultation of practitioners and literature support to address the important issues in cold supply chain management for food companies. Therefore, in order to identify the key success factors for sustainable cold chain management, in this study a conceptual model developed. The proposed framework is then validated by an empirical research in the Indian food industry. This research has several alarming findings. Explicitly, in India i) environmental issues and social responsibility are not as important as other economical supplier selection criteria, ii) among 19 food supplier selection criteria, the rank of social responsibility is 18, iii) low carbon emission is less important value addition trait as compare to other sustainable cold chain value addition (which means in India the buyers focus more on their individual and prompt received benefits rather than long lasting advantages), iv) the use of life cycle analysis, renewable energy sources and passive cold chain are the least important implemented sustainable cold chain practices (although this might be because of utilization complexities), v) the joint development of product is implemented at the lowest extent judging against other dynamic capacity factors, vii) government usually backed the firms to adopt and implementing sustainability in their operations, but training courses that will guide how to achieve sustainability are less as their requirement, and viii) business sustainability builds the trust among the government, suppliers, firm and all stakeholders that build strong cold chain relationships.
Process Quality Control
SPC, SQC Defined
Difference between SQC and SPC
Controlling Process Inputs (independent variables)
Process Capability with MINITAB
Monitoring process outputs (dependent variables)
7QC-Tools with MINITAB
A lean model based outlook on cost & quality optimization in software projectsSonata Software
A large quantum of effort and research is being invested to address the Cost and Quality factors in software projects. Though the solutions, models and methodologies are well established through experimented processes, adoption and optimization of the required parameters for a specific project to obtain predictable and acceptable quality with minimum costs has always remained a challenge.
This paper discusses the Lean process in detail with the help of project data and demonstrates that simple, affordable and adoptable processes are more economical and focused on quality. Also, it is observed that the Lean Model enables a project to be well monitored and controlled by focusing on critical elements, thereby reducing overheads of bulky documentation and irrelevant processes. The above findings are statistically analyzed using the coefficient of variation, which strikes a direct correlation with the predictable quality in a project.
Six sigma aims to reduce defects and conform to customer specifications. To make sure that each project adheres to customer specifications Adev Research assists your organization focus on process improvements and variation reduction.
In recent years, management and, consequently, supply chain performance measurement, has attracted the attention of a large number of managers and researchers in the field of production and operations management. In parallel with the evolution of organizations from a single approach to a network and supply chain approach, performance measurement systems have also changed and moved towards network and supply chain performance measurement. Therefore, in order to face the storm of great change and transformation and not give in to the wave of competitive aggression, organizations have long had one thing in common, and that is to focus approaches and focus efforts towards achieving results. Results that lead to a competitive advantage and are more effective and decisive in the performance indicators of the organization, including earning more. In this study, in order to identify and prioritize the factors affecting the supply chain in manufacturing companies, using indicators such as cost, timely delivery and procurement time to evaluate the supply chain efficiency is considered. And performance evaluation was performed at the manufacturer level. Therefore, in order to evaluate the performance of the supply chain using the AHP integration approach and the DEA method approach in the fuzzy environment, the suppliers and suppliers of the manufacturing company were evaluated and ranked in terms of performance.
Fundamental knowledge on pharmaceutical
product development and translation from laboratory to market.
Quality management systems: Quality management & Certifications.
Si x sigma concepts.
Aim of 6 sigma concepts.
Process of 6 sigma concepts.
To Analyze the Use of Statistical Tool/S for Cost Effectiveness and Quality o...iosrjce
Companies can lose money because they fail to use significant opportunities to improve product
quality as well as product cost. In order to survive in a competitive market, reduce cost of product by improving
the quality and productivity of product is must for any company.
The objective of this study is to provide system/instructions and methodology to reduce cost of product by
implementing statistical tools in local plastic injection molding company. Injection molding Company deployed
some part of the “Seven Basic Tools” to improve the quality of the product used by our society and reduce cost
of the product. Companies may reluctant to their quality status and customer satisfactory status without
implementing any helping tool which may reduce cost by identifying and decreasing number of defects and
consequently improving product quality and productivity of the system/process. Statistical techniques like
“Seven Basic Statistical Tools” (SPC) provides a very valuable and cost effective way to meet these objectives.
The principle aim of this study is to train quality personnel that how to use these SPC tools and exploit these
data in Pareto analysis, control Chart, Cause & Effect Diagram and histogram analysis. The causes of nonconformity
and root causes of quality problems were specified and possible remedies were proposed to
organization to overcome their problems. Some significant Improvement was also observed after SPC
implementation in Process potential capability (Cp), Process Actual Capability (Cpk) and Defective parts per
million (DPM)
Logistics and Supply Chain: An integrated production, inventory, warehouse lo...FGV Brazil
An integrated production, inventory, warehouse location and distribution model
Author: Lokendra Kumar Devangan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract:
This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a cement manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.
Intersections: Beyond the Operations FunctionFGV Brazil
The identification of key success factors in sustainable cold chain management: Insights from the Indian food industry
Authors:
Shashi ., Rajwinder Singh, Amir Shabani
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract
Supply chain sustainability has emerged as an indispensable research agenda for the government, industry as well as non-profit orientation bodies. As a developing country, cold supply chain management in India is still in infancy. The demand pattern of food products has been dramatically changing since last few years. Nowadays, the customers are more conscious to use products for better health and highly expecting for food safety, toxic free and eco-friendly delivery of food products. However, sustainable cold supply chain has not yet received good heed throughout the world. Hence, in this paper an attempt has been made to address these important issues. A conceptual model was proposed in the consultation of practitioners and literature support to address the important issues in cold supply chain management for food companies. Therefore, in order to identify the key success factors for sustainable cold chain management, in this study a conceptual model developed. The proposed framework is then validated by an empirical research in the Indian food industry. This research has several alarming findings. Explicitly, in India i) environmental issues and social responsibility are not as important as other economical supplier selection criteria, ii) among 19 food supplier selection criteria, the rank of social responsibility is 18, iii) low carbon emission is less important value addition trait as compare to other sustainable cold chain value addition (which means in India the buyers focus more on their individual and prompt received benefits rather than long lasting advantages), iv) the use of life cycle analysis, renewable energy sources and passive cold chain are the least important implemented sustainable cold chain practices (although this might be because of utilization complexities), v) the joint development of product is implemented at the lowest extent judging against other dynamic capacity factors, vii) government usually backed the firms to adopt and implementing sustainability in their operations, but training courses that will guide how to achieve sustainability are less as their requirement, and viii) business sustainability builds the trust among the government, suppliers, firm and all stakeholders that build strong cold chain relationships.
Logistics and Supply Chain: A simulation of contract farming using agent base...FGV Brazil
A simulation of contract farming using agent based modeling
Authors: Yuanita Handayati, Togar Mangihut Simatupang, Tomy Perdana, Manahan Siallagan
Journal of Operations and Supply Chain Management
Vol 9, No 2 (2016)
FGV's Brazilian School of Public and Business Administration (EBAPE)
Abstract
This study aims to simulate the effects of contract farming and farmer commitment to contract farming on supply chain performance by using agent based modeling as a methodology. Supply chain performance is represented by profits and service levels. The simulation results indicate that farmers should pay attention to customer requirements and plan their agricultural activities in order to fulfill these requirements. Contract farming helps farmers deal with demand and price uncertainties. We also find that farmer commitment is crucial to fulfilling contract requirements. This study contributes to this field from a conceptual as well as a practical point of view. From the conceptual point of view, our simulation results show that different levels of farmer commitment have an impact on farmer performance when implementing contract farming. From a practical point of view, the uncertainty faced by farmers and the market can be managed by implementing cultivation and harvesting scheduling, information sharing, and collective learning as ways of committing to contract farming.
Digital Marketing Strategy is the most crucial to promote business and engage with customers. And here you can’t kick back and relax due to the sheer pace at which it is evolving and impacting the customers worldwide
Process Capability in Manufacturing: Achieving Excellence in QualityMileyJames
In the realm of manufacturing, achieving consistent and high-quality products is paramount. Process capability, a fundamental concept in quality management, plays a pivotal role in ensuring that manufacturing processes meet or exceed customer expectations. This essay explores the significance of process capability in the manufacturing field, its methodology, the benefits it offers, and its role in continuous improvement.
Industrial Examples - Process Capability in Total Quality ManagementDr.Raja R
Industrial Examples - Process Capability
in Total Quality Management, What is Process Capability?, Practical Concerns when Conducting Capability Studies,
This is PMBOK Guide Planning Process Group Part three. It includes five Knowledge Area - Quality, Human Resource, Communications, Procurement and Stakeholder management - with five processes - Plan Quality Management, Plan Human Resource Management, Plan Communications Management, Plan Procurement Management, Plan Stakeholder Management - .
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Regulatory expectation & design approach on continuous process verificationKaran Rajendra Khairnar
This presentation will guide you on regulatory expectation & how to design approach on Continuous process verification (Stage III) of Process Validation
Karan7may@gmail.com
Good practice standardizes parameters and metrics across the entire operation to enable meaningful manufacturing decision support and continuous improvement. Frequently manufacturing and business parameters are combined into Key Performance Indicators (KPI) to simplify monitoring more complex functions. One commonly deployed KPI is Overall Equipment Effectiveness (OEE) which combines measures of availability, throughput and quality.
There exists tremendous value potential for companies coupling OEE with SPC, and making it part of manufacturing-decision support. It sets the company on the path to state-of-the-art manufacturing process management by enabling them to:
Apply SPC to automated OEE solutions – looking at single values of a KPI adds little to one’s process management capability, but using control charts and process capability analysis will enable developing world-class manufacturing;
Rapidly determine where improvement opportunities exist;
Focus on information, not data – data is the raw material; information provides the decision support that will improve performance levels.
What are the chances of your country winning the 2018 World Cup?
FGV's mathematical model predicts that Brazil has the greatest chances of winning.
http://fgv.br/emap/copa-2018
Interval observer for uncertain time-varying SIR-SI model of vector-borne dis...FGV Brazil
The issue of state estimation is considered for an SIR-SI model describing a vector-borne disease such as dengue fever, with seasonal variations and uncertainties in the transmission rates. Assuming continuous measurement of the number of new infectives in the host population per unit time, a class of interval observers with estimate-dependent gain is constructed, and asymptotic error bounds are provided. The synthesis method is based on the search for a common linear Lyapunov function for monotone systems representing the evolution of the estimation errors.
Date: 2017
Authors:
Soledad Aronna, Maria
Bliman, Pierre-Alexandre
Ensuring successful introduction of Wolbachia in natural populations of Aedes...FGV Brazil
The control of the spread of dengue fever by introduction of the intracellular parasitic bacterium Wolbachia in populations of the vector Aedes aegypti, is presently one of the most promising tools for eliminating dengue, in the absence of an efficient vaccine. The success of this operation requires locally careful planning to determine the adequate number of individuals carrying the wolbachia parasite that need to be introduced into the natural population. The introduced mosquitoes are expected to eventually replace the Wolbachia-free population and guarantee permanent protection against the transmission of dengue to human. In this study, we propose and analyze a model describing the fundamental aspects of the competition between mosquitoes carrying Wolbachia and mosquitoes free of the parasite. We then use feedback control techniques to devise an introduction protocol which is proved to guarantee that the population converges to a stable equilibrium where the totality of mosquitoes carry Wolbachia.
Date: 2015-03-19
Authors:
Bliman, Pierre-Alexandre
Soledad Aronna, Maria
Coelho, Flávio Codeço
Silva, Moacyr da
The resource curse reloaded: revisiting the Dutch disease with economic compl...FGV Brazil
This paper shows that the Dutch disease can be more formally characterised as low economic complexity using ECI-type indicators; there is a solid and robust inverse relationship between exports concentrating on natural resources and economic complexity as measured by complexity indicators for a database of 122 countries from 1963 to 2013. In a large majority of cases, oil answers for shares in excess of 50% of exports. In addition to empirical panel analysis, we address case studies concerned with Indonesia and Nigeria and introduce a brief review of the theoretical literature on the topic. Indonesia is considered in the literature as a good example in avoiding the negative effects of the Dutch disease, whereas Nigeria is taken as a bad example in terms of institutions and policies adopted during the seventies and eighties. The empirical results show that complexity analysis and Big Data may offer significant contributions to the still-current debate surrounding the Dutch disease.
Date: 2017-03
Authors:
Camargo, Jhean Steffan Martines de
Gala, Paulo
The Economic Commission for Latin America (ECLA) was right: scale-free comple...FGV Brazil
The main purpose of this paper is to apply big-data and scale-free complex network techniques to the study of world trade, with a specific focus on the investigation of ECLA and structuralist ideas. A secondary objective is to illustrate the potentialities of the use of the new science of complex networks in economics, in what has been recently referred to as an econophysics research agenda. We work with a trade network of 101 countries and 762 products (SITC-4) which generated 1,756,224 trade links in 2013. The empirical results based on network analysis and computational methods reported here point in the direction of what ECLA economists used to argue; countries with higher income per capita concentrate in producing and exporting manufactured and complex goods at the center of the trade network; countries with lower income per capita specialize in producing and exporting non-complex commodities at the network’s periphery.
Date: 2017-03
Authors:
Gala, Paulo
Camargo, Jhean Steffan Martines de
Freitas, Elton
Cost of equity estimation for the Brazilian market: a test of the Goldman Sac...FGV Brazil
As an approach to determining the degree of integration of the Brazilian economy, this paper seeks to test the explanatory power of the Goldman Sachs Model for the expected returns by a foreign investor in the Brazilian market during the past eleven years (2004-2014). Using data for the stocks of 57 of the most actively traded firms at the BM&FBovespa, it begins by testing directly the degree of integration of the Brazilian economy during this period, in an attempt to better understand the context in which the model has been used. In sequence, in an indirect test of the Goldman Sachs model, the risk factor betas (market risk and country risk) of the sample stocks were estimated and a panel regression of expected stock returns on these betas was performed. It was found that country risk is not a statistically significant explanation of expected returns, indicating that it is being added in an ad hoc fashion by market practitioners to their cost of equity calculations. Thus, although there is evidence of a positive and significant relationship between systematic risk and return, the results for country risk demonstrate that the Goldman Sachs Model was not a satisfactory explanation of expected returns in the Brazilian market in the past eleven years, leading us to question the validity of its application in practice. By adding a size premium factor to the model, there is evidence of a negative and significant relationship between companies’ size and return, although country risk remains not satisfactory to explain stock expected returns.
Date: 2017-03
Authors:
Guanais, Luiz Felipe Poli
Sanvicente, Antonio Zoratto
Sheng, Hsia Hua
A dynamic Nelson-Siegel model with forward-looking indicators for the yield c...FGV Brazil
This paper proposes a Factor-Augmented Dynamic Nelson-Siegel (FADNS) model to predict the yield curve in the US that relies on a large data set of weekly financial and macroeconomic variables. The FADNS model significantly improves interest rate forecasts relative to the extant models in the literature. For longer horizons, it beats autoregressive alternatives, with a reduction in mean absolute error of up to 40%. For shorter horizons, it offers a good challenge to autoregressive forecasting models, outperforming them for the 7- and 10-year yields. The out-of-sample analysis shows that the good performance comes mostly from the forward-looking nature of the variables we employ. Including them reduces the mean absolute error in 5 basis points on average with respect to models that reflect only past macroeconomic events.
Date: 2017-03
Authors:
Vieira, Fausto José Araújo
Chague, Fernando Daniel
Fernandes, Marcelo
Improving on daily measures of price discoveryFGV Brazil
We formulate a continuous-time price discovery model in which the price discovery measure varies (stochastically) at daily frequency. We estimate daily measures of price discovery using a kernel-based OLS estimator instead of running separate daily VECM regressions as standard in the literature. We show that our estimator is not only consistent, but also outperforms the standard daily VECM in finite samples. We illustrate our theoretical findings by studying the price discovery process of 10 actively traded stocks in the U.S. from 2007 to 2013.
Date: 2017-03
Authors:
Dias, Gustavo Fruet
Fernandes, Marcelo
Scherrer, Cristina Mabel
Disentangling the effect of private and public cash flows on firm valueFGV Brazil
This paper presents a simple model for dual-class stock shares, in which common shareholders receive both public and private cash flows (i.e. dividends and any private benefit of holding voting rights) and preferred shareholders only receive public cash flows (i.e. dividends). The dual-class premium is driven not only by the firm's ability to generate cash flows, but also by voting rights. We isolate these two effects in order to identify the role of voting rights on equity-holders' wealth. In particular, we employ a cointegrated VAR model to retrieve the impact of the voting rights value on cash flow rights. We finnd a negative relation between the value of the voting right and the preferred shareholders' wealth for Brazilian cross- listed firms. In addition, we examine the connection between the voting right value and market and firm specific risks.
Date: 2017-03
Authors:
Autor
Scherrer, Cristina Mabel
Fernandes, Marcelo
Mandatory IFRS adoption in Brazil and firm valueFGV Brazil
Using diff-in-diff approaches and the propensity-score matching, this study focuses on firm-level Tobin´s q and Market-to-book outcomes for Brazilian firms who in 2008 were required by Law 11.638/07 to adopt the full International Financial Reporting Standards (IFRS) by 2010. Brazil’s tier-system of corporate governance standards for publicly-traded firms, its uniquely wholesale adoption of the IFRS, and the previously considerable gap between its national GAAP and IFRS readily lend the scenario to research, which thus far finds small or inconsistent results when focused on IFRS adoption-related outcomes in Europe and China. However, while these features recommend the transitioned Brazilian equity market to analysis, additional unique features, such as its small population size and its limited historical data -- of varied quality – increase the challenge in selecting a suitable empirical methodology. Using quarterly data from 2006-2011, control firms in the Nivel II and Novo Mercado tiers of Bovespa which already complied with higher quality accounting standards are matched to treatment firms in the Regular and Nivel I tiers with similar averaged values of size and sector. Our results suggest that there is a positive impact on Tobin´s q and Market-to-book for firms who are forced to adopt IFRS in Brazil. We can observe the same results when we consider all variables winsorized at 5% level. We also find a positive relation between the firm value (measured by Tobin´s q and Market-to-book) and net income. Firms with higher net income are more likely to have higher Tobin´s q and Market-tobook. In an opposite way, we find a negative relation among firm value, size, Ebit-to-sales, sales growth and PPE-to-sales. All results are statistically significant at 1% level. '
Date: 2017-03
Authors:
Sampaio, Joelson Oliveira
Gallucci Netto, Humberto
Silva, Vinícius Augusto Brunassi
Dotcom bubble and underpricing: conjectures and evidenceFGV Brazil
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Operations Management: Six sigma benchmarking of process capability analysis and mapping of process parameters
1. Submitted 10.10.2016. Approved 19.12.2016.
Evaluated by double blind review process. Scientific Editor: José Barros Neto
SIX SIGMA BENCHMARKING OF PROCESS
CAPABILITY ANALYSIS AND MAPPING
OF PROCESS PARAMETERS
Jagadeesh Rajashekharaiah
Professor at SDM Institute for Management Development - Mysore, India
jagadeeshraj@sdmimd.ac.in
ABSTRACT: Process capability analysis (PCA) is a vital step in ascertaining the quality of the output
from a production process. Particularly in batch and mass production of components with specified
quality characteristics, PCA helps to decide about accepting the process and later to continue with it.
In this paper, the application of PCA using process capability indices is demonstrated using data from
the field and benchmarked against Six Sigma as a motivation to improve to meet the global standards.
Further, how the two important process parameters namely mean and the standard deviation can be
monitored is illustrated with the help of what if analysis feature of Excel. Finally, the paper enables to
determine the improvement efforts using simulation to act as a quick reference for decision makers.
The global benchmarking in the form of Six Sigma capability of the process is expected to give valuable
insight towards process improvement.
KEYWORDS: Benchmarking, process capability, Six Sigma, mapping, control chart.
Volume 9 • Number 2 • July - December 2016 http:///dx.doi/10.12660/joscmv9n2p60-71
60
2. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7161
1. INTRODUCTION
Quality control and improvement is the most impor-
tant part of organizations engaged in the manufac-
turing of products or delivery of service. Monitoring
the quality using the standard references, or metrics,
is vital to any organization that cares about custom-
ers and ultimately helps the organization to capture
the market.
The advent of new technologies, increased demand
for high quality products, and quality based competi-
tion mandate close scrutiny and careful selection of
processes. The overall cost depends on the judicious
selection of the process and thus process capability
analysis (PCA) is considered as a vital step in ensur-
ing the quality of products. Increasing competition,
availability of low-cost suppliers, global supply
chains and information technology driven manufac-
turing, all have caused new paradigms in process de-
cisions. Manufacturers are moving towards more of
outsourcing and trying to cut down the cost of pro-
duction. In addition, these decisions are more influ-
enced by global standards, and benchmarking with
best practices. Hence it is imperative to ascertain the
quality of output from a production process before
that process is identified for batch or mass produc-
tion. Further it is necessary to find out how the pro-
cess can further be improved to meet the global stan-
dards so as to remain competitive in the market.
2. ORGANIZATION OF THE PAPER
In this paper, first a brief overview of PCA is pro-
vided and the numerical measures in the form of
Process Capability Indices (PCI) are described. The
mathematical formulae to calculate these PCI’s along
with their interpretation are also given. A descrip-
tion of relevant concepts of benchmarking and Six
Sigma are also provided for completeness as well as
continuity. Later, using field data PCA is performed
and benchmarked against Six Sigma standards. The
two key process parameters namely process mean
and the variance are optimized to accomplish the
required Six Sigma standards, using Excel’s “what
if” analysis. Next, using simulated data how much
of improvement efforts are needed to reach global
standards is demonstrated. Finally mapping of pro-
cess parameters with respect to the required level of
Six Sigma (SS) standards is carried out and how the
two process parameters are simultaneously tracked
is illustrated. This enables continuous monitoring
and improvement and helps to set the goals clearly.
All the calculations, scenario building, optimiza-
tion, and simulation, including the development
of charts, have been done using Excel 2013 version,
which has powerful features to support the current
research work.
3. BRIEF LITERATURE REVIEW
Process capability analysis (PCA) forms the initial
step in establishing acceptability of a production pro-
cess to produce output as per the specified tolerance.
The literature on process capability studies, besides
being rich and diversified, has a long historical back-
ground. Different aspects of process capability have
been covered with varying details to satisfy the needs
of practitioners and researchers. It is not the intention
here to provide an exhaustive coverage of PCA, but
a brief overview of the major aspects relevant to this
paper, is presented in the following sections.
Process capability refers to the ability of a process to
produce the output namely a product or a service,
according to the specifications as suggested or pre-
scribed by the designer or the customer. Because of
the variations that occur in a process due to assign-
able as well as the chance causes, a process will not
be always performing as per the expectations and
hence the output quality can deviate from the pre-
set standards. Process capability studies help to ver-
ify whether the processes adopted by the manufac-
turer or the service provider are capable of meeting
the specifications. In addition, process capability as-
sessment studies have several objectives as follows,
(Summers, 2005):
1. To ascertain the extent to which the process will
be able to meet the specifications.
2. To determine whether the process will be able to
meet the future demand placed on it in terms of
the specifications.
3. To help the industries to meet the customers’ de-
mands.
4. To enable improved decision making regard-
ing product or process specifications, selection
of production methods, selection of equipment,
and thus improve the overall quality.
Besides the above, it is reported that process capabil-
ity studies help in vendor certification, performance
monitoring and comparison and also for setting tar-
gets for continuous improvement.
3. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7162
Process capability indices are simple numerical
measures to express the potential and performance
capabilities of a process under different conditions.
These indices essentially link the key parameters like
process mean and variance to design specifications
of a quality characteristic. Thus they act as a bridge
between the fixed or static tolerance values and the
dynamic process values as seen over a period of
time. A lucid paper by Kane (1986) explains the fun-
damentals of process capability indices along with
numerical illustrations. The importance of sampling
for the estimation of process capability indices has
been vividly presented by Barnett (1990). A detailed
explanation about the process capability indices is
available in Porter and Oakland (1991). It is impor-
tant to observe that the process capability measures
are all basically sample based and both the sample
size chosen and the method of sampling need to be
carefully considered. In addition, it is essential that
the process be stable and in the state of statistical
control before taken up for capability assessment.
Assessment of process capability is commonly
done using process capability indices and Table 1
shows the different types of indices used in prac-
tice. The corresponding formulas are also shown
in the Table 1. A Cp of 1.00 indicates that the pro-
cess is judged capable. It is generally necessary to
estimate the process standard deviation so as to
estimate Cp of the process. Due to sampling varia-
tion and machine setting limitations, Cp = 1.00 is
not used as a minimally acceptable value, and a
minimum acceptable value of Cp is 1.33 which
ensures acceptable quantity within the specifica-
tions, as a shift in the process mean from the tar-
get value and a change in the process variance oc-
cur over a period of time. Since the Cp and Cpk
indices do not take into account the departure
from the target/nominal value, Chan, Cheng, and
Spiring (1988) have introduced another measure
of process capability, called the Cpm index. This
index takes into account the proximity to the tar-
get value as well as the process variation when
assessing process performance. The Cpm index
is also referred to as “Taguchi capability index’,
as illustrated by Boyles (1991) and Balamurali and
Kalyanasundaram (2002).
Table 1: Process Capability Indices
Name Index Formula
Process Potential Index Cp
6
USL LSL
Cp
σ
−
=
Scaled Distance Factor K ( )
2
m
K
USL LSL
µ−
=
−
Process Performance Index Cpk Cpk = Cp (1 – K)
Cpu
3
USL
Cpu
µ
σ
−
=
Cpl
3
( )
2
LSL
Cpl
m
K
USL LSL
µ
σ
µ
−
=
−
=
−
4. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7163
Taguchi Capability Index Cpm
2 2 2
6 ( )
1
USL LSL Cp
Cpm or Cpm
T Tσ µ µ
σ
−
= =
+ − −
+
Third Generation Process
Capability Index
Cpmk
Min
pmk
(USL - , - LSL)
=C
3
µ µ
σ
pk
pmk
2
C
=C
-T
1+( )
µ
σ
Symbols and notations used:
USL = Upper specification Limit
LSL = Lower Specification Limit
σ = Standard Deviation
μ = Process Mean
T = Target value
m = Midpoint of USL and LSL
Process capability indices that are commonly used
are all based on the assumption of normally dis-
tributed data, however, the case of non-normality
is also discussed in the literature, for example,
Clements (1989), Somerville and Montgomery
(1996), Rao and Xia, (1999), and Hou and Wang
(2012), to name a few. But typically across the in-
dustries the assumption of normality is followed
and rarely the non-normality is taken into account
because of complexity of calculations, and long
procedures. Further, it is interesting to note that
the process capability indices are categorized as
first, second and third generation indices depend-
ing upon what process conditions are being ex-
plored and indicate also their relative sensitivity
in recognizing process changes. The process capa-
bility index Cp is considered as the first generation
index and Cpk and Cpm are regarded as the “sec-
ond generation” indices. Pearn, Kotz, and Johnson
(1992) while discussing the distributional and in-
ferential properties of process capability indices,
also propose a ‘third generation’ process capabil-
ity index and two new multivariate indices. These
are claimed to be possessing better properties than
the earlier developed indices. However, in most of
the industries it is still the first and second genera-
tion indices that are commonly used for process
assessment and monitoring. The interpretations to
be made based on the values of the indices are as
shown in Table 2.
Table 2: Process Capability Index Cpk and Inter-
pretation
Value of Cpk Interpretation
< 1 Not at all capable
= 1 Not capable
= 1.33 Minimum requirement
= 1.67 Promising
= 2 Total confidence with process
The procedure used in PCA, as well the recommend-
ed values need to be ascertained against statistical
basis and analysis. Montgomery (1986), comments
that some of the industry practices do not satisfy the
statistical tests. Thus serious doubts arise about the
validity of such measures calculated using those in-
dustries prescribed procedures. The recommended
values according to Montgomery (1986) are given in
the Table 3.
Table 3: Recommended Minimum Values of Cp
Two-sided
specifications
One sided specification
1.33 1.25
1.50 1.45
1.50 1.45
1.67 1.60
5. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7164
1.1 Six Sigma and benchmarking of the process capability
Six Sigma (SS) needs no introduction, as it is now re-
garded as an intensive approach to improve the pro-
cess quality and be able to meet the global standards.
The concept of Six Sigma is basically to produce error
free output. Sigma, s, is a letter in the Greek alpha-
bet used by statisticians to measure the variability in
any process and the Greek letter σ, represents stan-
dard deviation. Today a company’s performance is
measured by the sigma level of their business pro-
cesses, (Breyfogle, 1999). Traditionally companies
followed three or four sigma performance levels as
the norm, despite the fact that these processes cre-
ated between 6,200 and 67,000 problems per million
opportunities. However, the Six Sigma standard of
3.4 defects per million opportunities is a response to
the increasing expectations of customers, who want
their products to be free from defects.
SS is defined in many ways as researchers, prac-
titioners, and corporate people have given differ-
ent perspectives about Six Sigma. Consequently,
many definitions have been put forth to indicate
what SS is all about. Some of the definitions of SS
are as follows:
◊ According to Pyzdek (2003), SS is the applica-
tion of the scientific methods to the design and
operation of management systems and business
processes which enable employees to deliver the
greatest value to customers and owners.
◊ Persico (1992) states Six Sigma as a direct ex-
tension of total quality management which, in
turn, is based on the principles and teachings
of W. Edwards Deming, the legendary quality
guru.
◊ Six Sigma is a disciplined, quantitative approach
for improvement-based on defined metrics-in
manufacturing, service, or financial processes,
(Hahn, Hill, Hoerl, & Zinkgraf, 1999).
In many industry and business environments, the
Six Sigma culture is deployed through a systematic
and uniform approach and set of techniques for con-
tinuous quality improvement, (Harry, 1998). A Six
Sigma program leads to better decision making by
developing a system that prompts everyone in the
organization to collect, analyze, and display data in
a consistent way (Maleyeff & Kaminsky, 2002), and
hence appreciated in the industries.
As a good amount of literature is available about the
technique and applications of SS, a detailed descrip-
tion of SS technique is not attempted in this paper,
but useful references are quoted to provide the nec-
essary initial reading. Some of the useful resources
suggested are Hahn, Doganaksoy, and Hoerl (2000),
Hammer and Goding (2001), and Pande and Holpp
(2002). Many authors have discussed about the
goodness of SS by thoroughly reviewing the litera-
ture, and hence the following reviews should be ad-
equate for understanding the growth and expansion
of the literature pertaining to Six Sigma:
1. Six Sigma Literature: A Review and Agenda for
Future Research, (Brady& Allen, 2006),
2. Six Sigma: Literature review and key future re-
search areas, (Nonthaleerak & Hendry, 2006)
3. Six Sigma: A literature review, (Oke, 2007)
4. The origin, history and definition of Six Sigma:
a literature review, (Prabhushankar, Devadasan,
Shalij, & Thirunavukkarasu, 2008)
5. Six sigma: A literature review analysis, (Cagnazzo
& Taticchi, 2009)
6. Six Sigma: A literature review, (Tjahjono et al., 2010)
All these reviews cover the concepts, theory and the
applications of the SS technique in many diverse ar-
eas which has prompted many companies to adopt
the technique.
Table 4: Conversion between DPMO and process sigma level
Shift (sigma)
1.5 1 0.5 0
Sigma
Area between
both Z values
DPMO (1.5
sigma shift))
DPMO (1
sigma shift))
DPMO (0.5
sigma shift))
DPMO (No
sigma shift))
1 0.68268949 691462 500000.0 308537.5 158655.3
1.5 0.86638560 500000 308537.5 158655.3 66807.2
6. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7165
2 0.95449974 308538 158655.3 66807.2 22750.1
2.5 0.98758067 158655 66807.2 22750.1 6209.7
3 0.99730020 66807 22750.1 6209.7 1349.9
3.5 0.99953474 22750 6209.7 1349.9 232.6
4 0.99993666 6210 1349.9 232.6 31.7
4.5 0.99999320 1350 232.6 31.7 3.4
5 0.99999943 233 31.7 3.4 0.3
5.5 0.99999996 32 3.4 0.3 0.0
6 1.00000000 3.4 0.3 0.0 0.0
An important metric in the SS technique is the De-
fects per million opportunities, (DPMO), which
refers to the number of unacceptable fraction ex-
pressed as a ratio of one million opportunities. A
typical conversion between DPMO and the process
sigma level is shown in Table 4, (Six Sigma Daily,
2016). Because quite often the acceptable quantity
expressed as percent of output is expressed, the cor-
responding Sigma levels are shown in Table 5.
Table 5: Percent output acceptable from the process
and the corresponding Sigma level
Percent acceptable DPMO Sigma level
30.9 6,90,000.0 1
62.9 3,08,000.0 2
93.3 66,800.0 3
99.4 6,210.0 4
99.98 320.0 5
99.9997 3.4 6
When the process sigma level is plotted against the
percent acceptable quantity, the relationship takes
the form as shown in Figure 1. From this figure it is
evident that as the quantity acceptable approaches
100%, the process sigma level reaches six and fur-
ther increase is not necessary. Though process sigma
level leads to 3.4 defects per million, it is considered
a zero defects. It is observed that almost like a habit
companies continue to accept three or four sigma
performance levels as the norm, despite the fact that
these processes can have between 6,200 and 67,000
problems per million opportunities, (Pyzdek, 2003).
Considering this comment, in this paper the optimi-
zation attempt is towards reaching four sigma first
and later moving towards Six Sigma. But essentially
the global standards mandate that the processes be
benchmarked against Six Sigma only as Six Sigma
is considered the ultimate stamp of acceptance in a
highly competitive environment.
Figure 1. Process sigma level and acceptable output
1.2 Why benchmarking is required to ascertain quality?
Continuous quality improvement of products and/
or service offered by a company is essential for sur-
vival in the market and meeting the demands of the
customers. Hence the organizations are continuous-
ly searching for new techniques and tools to enable
them to improve quality. Benchmarking is one such
quality improvement technique that helps qual-
ity improvement by comparing the performance or
any other measurable attribute with those who are
doing it better. In essence benchmarking involves
comparison with the superior performer, identify
the gaps, and take proper action to overcome those
gaps, thereby improving the quality. This process is
not a one-time application but has to be used as an
on-going process. Since new benchmarks are regu-
larly created, it is necessary that the spirit of bench-
marking is maintained. Benchmarking has been
historically used as a technique for comparison of
anything, a product, service, performance, output,
or any measurable characteristic, with the superior
performer or the “best in class” so as to find the gaps
that prompt for improvement. After the publication
of the success story of Xerox Corporation of USA,
which adopted the technique to defend against the
7. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7166
stiff competition from the Japanese manufacturers
in the copier market, (Camp, 1989, the application
of benchmarking has increases manifold. Though
benchmarking exercises have been in existence for
a long time, it is in the recent times customary to
probe whether the subject under consideration has
been benchmarked against the best in class, (Elmuti
& Kathawala, 2013). The term “benchmark” was
included in the guidelines of the prestigious US
Quality Award, Malcolm Baldrige National Quality
Award, in 1985, and benchmarking became a quali-
fying criterion to participate in the award process.
The literature related to benchmarking for quality
improvement that covers the concepts, models, and
applications, is abundant and has thus attracted the
attention of several researches who have provided a
comprehensive picture of the growth and spread of
research based on benchmarking studies across the
globe. For a complete list of literature on the process
of benchmarking literature, some of the prominent
review papers on benchmarking can be consulted,
(Zairi & Youssef, 1995), (Kozak & Nield, 2001), (Scott,
2011) and (Dattakumar & Jagadeesh, 2003).
These papers also illustrate the various applications
of benchmarking besides indicating the popularity
of the topic of benchmarking and its applications.
The present paper which is focused on improving
the process performance through benchmarking
the process capability, decided to develop a generic
benchmark which can be statistically established
and capable of expressing using the main process
parameters, namely process mean and process stan-
dard deviation. These two parameters are also the
building block of the process capability assessment.
In this context, the globally accepted ultimate per-
formance level namely Six Sigma was selected as the
“benchmark” to be used to ascertain the quality of
the process. Any process that exhibits the Six Sigma
standard of performance would obviously be con-
sidered as the “best performer” and this paper has
used Six Sigma to essentially mean the ultimate level
of comparison for a given process to be considered
as on par with the global standard.
1.3 The problem on hand
The problem considered here pertains to a discrete
manufacturing process adopted in a company which
used to supply components to major auto manufac-
turers in India. Though many different components
were produced by the company, for the purpose of
illustrating the methodology, only one component,
namely a threaded fastener is considered here. The
component has one critical dimension namely the
core diameter which had a specification of 3.4 ± 0.05
millimeter, and thus considered as “critical to qual-
ity”, (CTQ). The data pertaining to a batch of 125
components is shown in Table 6, which shows the
core diameter values in millimeter pertaining to 125
values under 25 subgroups with each subgroup hav-
ing five components.
2. PRELIMINARY ANALYSIS
Before the process output data was used to analyze
the process capability, the preliminary analysis in-
cluded (1) Plotting the histogram, (2) Testing the data
for normality, and (3) plotting the control charts.
The histogram of the data collected is shown in Fig-
ure 2. Using the Kolmogorov-Smirnov test, the value
of P is found to be 0.016, which barely indicates nor-
mality. Further, the normal probability plot was also
drawn and the data was found to be only approxi-
mately normally distributed. This is well accepted
as perfect normality is not expected in an industrial
process, as is the case on hand.
Figure 2. Histogram of core diameter values
The typical control charts, namely x-bar chart and
R- chart were plotted to ascertain the stability of the
process. These two charts are shown in Figures 3 and
4 respectively. From these charts it is evident that the
process is under statistical control and also stable.
Further, the charts do not exhibit any questionable
patterns and hence further analysis was carried out.
2.1 Process capability analysis
Considering the data available, the typical process
capability assessment was made and the typical pro-
8. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7167
cess capability indices have been calculated. These
are shown in Table 6. It is observed that the process
spread exceeds the specification spread and thus out
of specification values are expected. Further, process
mean is not centered and process variance also needs
to be reduced. However, the process capability indi-
ces clearly reveal that the process is not capable of
meeting the requirements, and currently producing
defects. This obviously demands improvement of
the process by proper process control.
Figure 3. Control chart for averages
Figure 4. Control for ranges
Table 6: Process capability analysis
Process parameters Core Diameter
Lower specification limit, LSL 3.35
Upper specification limit, USL 3.45
Target value, T 3.4
Process Mean, μ 3.4248
Process standard deviation, σ 0.0225
Process capability measures / indices
Process capability, 6 σ 0.1350
Process potential index, Cp 0.7407
Process performance index, Cpk 0.3733
Taguchi capability index, Cpm 0.4977
Scaled distance factor, K 0.496
In the next step, when the process is assessed for
Six Sigma capability, it is observed that the pro-
cess is currently performing at the sigma level of
2.62635812, as shown in Table 7, which is too in-
adequate. Considering a sigma value of at least
4.00, it was decided to find out the values of pro-
cess mean and standard deviation to reach the de-
9. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7168
sired result. Using a process sigma level of 4.00
as the threshold value, Excel’s what if analysis is
performed and the possible values and combina-
tions of process mean and standard deviation are
established. These values are shown in Table 8. Be-
cause the intention is to have a process sigma of
at least 4.00, only those desirable combinations of
process mean and standard deviation are selected
and shown in Table 9.
Table 8: Combinations of process mean and standard deviation for different sigma level
Process St.
deviation
Process mean
3.38 3.39 3.40 3.41 3.42 3.43
0.0150 #NUM! #NUM! #NUM! 4.309557537 3.525651912 2.84161
0.0175 #NUM! #NUM! 4.66050891 3.834468896 3.229182321 2.649383
0.0200 #NUM! #NUM! 4.0856508 3.525651912 3.010505032 2.505594
0.0225 #NUM! 4.309558 3.76409138 3.294455072 2.841609503 2.393923
0.0250 4.5344 3.965158 3.52565191 3.112290281 2.706980784 2.304669
0.0275 4.1432 3.719935 3.33614272 2.964360975 2.597064745 2.231688
Table 9. Desirable combinations of process mean and standard deviation for sigma level greater than 4.00
and corresponding DPMO values
Process sigma Greater than 4 Process mean Std. Dev. DPMO (rounded)
4.53436 3.38000 0.02500 1205
4.14315 3.38000 0.02750 4107
4.30956 3.39000 0.02250 2481
4.66051 3.40000 0.01750 788
4.08565 3.40000 0.02000 4860
4.30956 3.41000 0.01500 2480
Table 7: Si Sigma capability analysis
Performance Analysis
Mean + 3 Sigma 3.4923
Mean - 3 Sigma 3.3573
Area to the left of LSL 0
Area to the right of USL 0.130006983
Total area 0.130006983
DPMO (total rejects X million) 130006.983
Process Sigma Level 2.62635812
From the Table 9, it is understood that if by strict
monitoring and proper centering, process mean can
be controlled within a distance of 0.01 mm from the
target value of 3.4 mm, then the process standard
deviation could be ranging from 0.015 to 0.0225 mm,
to yield a process sigma of more than 4.00. Hence,
the process manager can decide as to which quality
parameter can be easily “fixed”, mean or the vari-
ance. By controlling the process standard deviation
within a range of 0.015 to 0.0225 mm, and ensuring
the process mean is within the range of 3.39 to 3.41
mm, the process manager should be able to reach a
process sigma value greater than 4.00. This kind of a
trade-off enables better process control and leads to
substantially lowering the rejects as observed under
the DPMO values column in Table 9, from a previ-
ous DPMO of 130006 when the process sigma level
was less than 3.00. The lowest value of DPMO is 788
occurring for process mean of 3.40 mm and standard
deviation of 0.01750 mm, with a corresponding sig-
ma level of 4.66051.
2.2 Reinforcing the analysis with simulation
Having find out the desirable combinations of pro-
cess mean and the standard deviation, it is now
possible to go backward to find out the desirable
values of the individual variable which is the core
diameter so that the process sigma level is at least
10. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7169
4.00. This can be easily done by generating a set of
normally distributed values using the optimum
values of process mean and standard deviation as
given in the Table 9. For example, for a combina-
tion of process mean of 3.38 and standard deviation
0.02500, a desired set of values of the core diameter
can be generated using Excel itself, and then ob-
serve how the individual values should be existing.
As these values are under hypothetical conditions,
it is important to note that these are only the ex-
pected values of the core diameter and hence not
to be taken as the actual output from the process.
Further, the actual output is influenced by several
variables and that pattern is not captured by the
simple simulation model described here. For a bet-
ter visualization of the changes in process mean
and standard deviation affecting the process sigma
level, the values from Table 9 are mapped with the
Six Sigma scale, as shown in Figure 5, with process
sigma values along the vertical axis, and process
mean along the horizontal axis. In this Figure, the
different values of process mean are plotted against
various values of process standard deviation lead-
ing to process sigma values ranging from 2 to 5.
As the desired target value of process sigma level
is 4 and above, only those combinations of mean
and standard deviation need to be selected. This is
shown as the shaded area of the figure, at the top of
the chart which can be called as the feasible region.
Figure 5. Mapping of process sigma level (vertical axis) for process mean (horizontal axis) and process
standard deviation along the curves
Using Figure 5, it is now possible to identify the op-
erating levels in the process which enable the desired
sigma level performance. For a given sigma level of
performance, the process mean and the standard de-
viation can be identified and used as process param-
eters. For example, if the process standard deviation
is 0.0275 mm, the process mean when set at 3.38 mm
would yield 4 sigma level of performance and then
will decrease with the increase in the mean value.
This clearly indicates that if both the process mean
and the standard deviation increase, the sigma level
of the process decreases. Thus it is now left to the
process manager to decide as what level of sigma
is to be targeted and accordingly decide the process
parameters as found from Figure 5, and then aim to
maintain the same in the process. Thus Figure 5 can
be suggested as decision making aid to enable the
selection of process parameters for a desired sigma
level of performance by the process.
3. CONCLUSION
Statistical process control in the earlier times typi-
cally involved ensuring that the process is under sta-
tistical control and the process is stable. The control
charts served the purpose of assessment and the ad-
ditionally done process capability analysis complet-
ed the assessment. These two assessments helped
the process managers to control the processes to en-
11. Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process Parameters
ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7170
sure better output and thus enabled smoother pro-
duction. With the advent of process improvement,
and Six Sigma becoming a major development, it
became necessary for the process managers to con-
tinuously improve and also assure minimum global
standards. This requires a thorough understanding
of the Six Sigma metrics, which are globally recog-
nized and used as common measures of process
quality. Both the Six Sigma level of the process, and
the DPMO have to be continuously monitored.
Today it is well known that “error free” output is
expected by the customers and hence the process
managers have to redefine the process performance
as per the new standards set by the customers. In
this context the process managers obviously look for
benchmarks against which they can compare their
processes and thus understand the gaps to proceed
towards improvement. Six Sigma is one such bench-
mark that is easy to understand and convince the cus-
tomers when selecting the benchmarking initiatives.
As Six Sigma is commonly accepted benchmark to
define the quality, it is quite logical and prudent to
choose Six Sigma for the purpose of comparison. In
this paper such an attempt has been made to illus-
trate how global benchmarking of the process needs
to be done using Six Sigma metrics and further, how
using “what if” analysis feature of Excel, it is possi-
ble to get a clear picture of the desirable values of the
process parameters. The inherent assumptions like
normality of the output, and unchanged process be-
havior, are here also made, and the usual limitation
of making the assessments based on sample based
values, also exist. However, the paper serves an im-
portant purpose of helping the process managers to
benchmark against the Six Sigma metrics, and thus
aim towards global standards. The charts and tables
illustrated in this paper provide a convenient meth-
od of selecting the process parameters for a desired
level of performance of the process. This tradeoff
provides a wide opportunity of setting the process
parameters depending on the resources. The idea is
to illustrate how process improvements can happen
by controlling the process parameters and get in to
a predictive model to enable improved results. The
overall objective is developing and demonstrating
a decision making model through the established
techniques.
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