China
Africa
Explore chapters and articles related to this topic
Model-based systems engineering
Published in Adedeji B. Badiru, Systems Engineering Models, 2019
Six Sigma makes the assertion that quality is achieved through continuous efforts to reduce variation in process outputs. It is based on collecting and analyzing data, rather than depending on hunches or guesses as a basis for making decisions. It uses the steps define, measure, analyze, improve, and control (DMAIC) to improve existing processes. To create new processes, it uses the steps define, measure, analyze, design, and verify (DMADV). Unique to this process improvement methodology, Six Sigma uses a series of karate-like levels (yellow belts, green belts, black belts, and master black belts) to rate practitioners of the concepts in organizations. Many companies who use Six Sigma have been satisfied by the improvements that they have achieved. To the extent that output variability is an issue for quality, it appears that Six Sigma can be a useful path for improving quality.
Process Improvement in Industrial Engineering
Published in Adedeji B. Badiru, The Story of Industrial Engineering, 2018
Six Sigma makes the assertion that quality is achieved through continuous efforts to reduce variation in process outputs. It is based on collecting and analyzing data, rather than depending on hunches or guesses as a basis for making decisions. It uses the steps define, measure, analyze, improve, and control (DMAIC) to improve existing processes. To create new processes, it uses the steps define, measure, analyze, design, and verify (DMADV). Unique to this process improvement methodology, Six Sigma uses a series of karate-like levels (yellow belts, green belts, black belts, and master black belts) to rate practitioners of the concepts in organizations. Many companies that use Six Sigma have been satisfied by the improvements that they have achieved. To the extent that output variability is an issue for quality, it appears that Six Sigma can be a useful path for improving quality.
General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
Six Sigma makes the assertion that quality is achieved through continuous efforts to reduce variation in process outputs. It is based on collecting and analyzing data, rather than depending on hunches or guesses as a basis for making decisions. It uses the steps define, measure, analyze, improve, and control (DMAIC) to improve existing processes. To create new processes, it uses the steps define, measure, analyze, design, and verify (DMADV). Unique to this process improvement method, Six Sigma uses a series of karatelike levels (yellow belts, green belts, black belts, and master black belts) to rate practitioners of the concepts in organizations. Many companies who use Six Sigma have been satisfied by the improvements that they have achieved. To the extent that output variability is an issue for quality, it appears that Six Sigma can be a useful path for improving quality.
A state of the art and comparison of approaches for performance measurement systems definition and design
Published in International Journal of Production Research, 2019
Michel Stella Ravelomanantsoa, Yves Ducq, Bruno Vallespir
It is an improved method of business process focused on cycle-time improvement and the reduction of manufacturing defects by using a set of statistical tools. It includes five steps commonly known as DMAIC: (1) Definition of the process: identify the projects goals and all customers’ deliverables, (2) Measurement of the process: verification of measurement systems and collection of data, (3) Analysis of data: determine the root causes of any defects, (4) Improvement of the process: establish ways to eliminate defects and correct the process and (5) Control of the process: manage future process performance. It is in conformity with the Deming’s PDCA modified later into ‘Plan, Do, Study, Act’ (PDSA) and finally into DMAIC within Six Sigma. (5) Performance Criteria System
Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector
Published in Production Planning & Control, 2018
Mohammad Shamsuzzaman, Mariam Alzeraif, Imad Alsyouf, Michael Boon Chong Khoo
In this study, both qualitative and quantitative data were collected from multiple sources. Qualitative data were obtained from direct observations in the field and unstructured interviews with team leaders, experienced team members and systems experts, while quantitative data were obtained from the company’s historical records. Several tools and techniques, such as a Pareto chart, value stream mapping (VSM), cause-and-effect analysis, process capability analysis, ANOVA, a control chart and Five-Why analysis, were used through the DMAIC methodology. All statistical analyses of data (at a 5% level of significance) and graphical presentations were performed using Minitab statistical software.
Lean Six Sigma meets data science: Integrating two approaches based on three case studies
Published in Quality Engineering, 2018
Inez M. Zwetsloot, Alex Kuiper, Thomas S. Akkerhuis, Henk de Koning
LSS consists of a well-established methodological framework for improving operational efficiency and effectiveness in organizations (George 2003). It is mostly known because of its stepwise approach to improvement, called DMAIC, which is an acronym for Define, Measure, Analyze, Improve, and Control. The approach is very much like the scientific approach to problem solving. As a complete methodology, it also lays out how a culture of effective and lasting continuous improvement can be realized. For example, it provides guidelines concerning project selection, project management, as well as deployment.