Case study in six sigma methadology : manufacturing quality improvement and guidence for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities

Principles in Patterns (PiP) : Institutional Approaches to Curriculum Design Institutional Story

The principal outputs of the PiP Project surround the Course and Class Approval (C-CAP) system. This web-based system built on Microsoft SharePoint addresses and resolves many of the issues identified by the project. Generally well received by both academic and support staff, the system provides personalised views, adaptive forms and contextualised support for all phases of the approval process. Although the system deliberately encapsulates and facilitates existing approval processes thus achieving buy-in, it is already achieving significant improvements over the previous processes, not only in reducing the administrative overheads but also in supporting curriculum design and academic quality. The system is now embedded across three faculties and is now considered by the University of Strathclyde to be a "core institutional service". Alongside the C-CAP system the PiP Project also cultivated a suite of approaches: an incremental systems development methodology; a structured and replicable evaluation approach, and; Strathclyde's Lean Approach to Efficiencies in Education Kit (SLEEK) business process improvement methodology Each is based on recognised formal techniques, providing the basis for a rigorous approach. This is contextualised within and adapted to the HE institutional context thus building the foundation not only for the project but ultimately for institution wide process improvement. This "institutional story" report summarises the principal outcomes of the Project

Design of experiments for non-manufacturing processes : benefits, challenges and some examples

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

A snap on quality management in Zimbabwe: a perspectives review

Purpose: The main objective of this article was to provide evidence concerning the level of Quality Management (QM) in Zimbabwe. Submitted evidence regarding QM in Zimbabwe will help organizations that want to implement QM systems. The results can guide government agents in making informed decisions towards QM systems implementation since very few organizations are officially quality certified. Research Methodology: The survey followed online search query on quality management in zimbabwe from journal articles, proceedings and institutional repository. 51 publications were selected and excel file was used to capture data and analyse. Results: The results expose that there was high interest in QM in 2014, 2015 and 2016. The results showed industries lack of capacity and resources, lack of skills and expertise, poor communication with stakeholders, poor raw materials, changing customer preferences, lack of top management commitment and costs of QM systems as key barriers to QM implementation. Limitations: The study limitation was survey of few studies retrieved through Bindura University online library and open access journal articles, proceedings papers and dissertations/thesis available on institutional repository. Keywords: Zimbabwe, Quality Management (QM), Drivers, Barriers, Benefit

Diversity in Software Development Routines are Attractive: A Preliminary Analysis of GitHub Repositories

Free, libre, open-source software projects (FLOSS) are known for their chaotic development style and unique collaboration model. How does such chaotic development produce high quality software and attract users and developers? To provide insight into this conundrum, this study explores the roles of diversity and change in design routines. It investigates the relationship between routine diversity and change on project attraction to users and developers. Various sequence-mining techniques such as motif analysis and hidden Markov models (HMM) are applied to examine design routines of 88 FLOSS projects on GitHub.com. Regression analysis reveals that development processes with high routine-diversity and relatively low change-magnitude attract more users and developers

The impact of supply chain analytics on operational performance: a resource-based view

This study seeks to better understand the role of supply chain analytics (SCA) on supply chain planning satisfaction and operational performance. We define the architecture of SCA as the integration of three sets of resources, data management resources (DMR), IT-enabled planning resources and performance management resources (PMR), from the perspective of a resource-based view. Based on the data collected from 537 manufacturing plants, we test hypotheses exploring the relationships among these resources, supply chain planning satisfaction, and operational performance. Our analysis supports that DMR should be considered a key building block of manufacturers’ business analytics initiatives for supply chains. The value of data is transmitted to outcome values through increasing supply chain planning and performance capabilities. Additionally, the deployment of advanced IT-enabled planning resources occurs after acquisition of DMR. Manufacturers with sophisticated planning technologies are likely to take advantage of data-driven processes and quality control practices. DMR are found to be a stronger predictor of PMR than IT planning resources. All three sets of resources are related to supply chain planning satisfaction and operational performance. The paper concludes by reviewing research limitations and suggesting further SCA research issues

Analysis reuse exploiting taxonomical information and belief assignment in industrial problem solving

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ‘‘Rex Manager’’ with principles of sustainable management for continuous improvement of industrial processes in companies

SITUATIONAL ROADMAP DEVELOPMENT FOR BUSINESS PROCESS IMPROVEMENT VIA A MODELING TOOL

The integration of new technologies as well as the need to increase customer satisfaction and reduce costs require companies to continuously analyze and improve their business processes. Hence, Business Process Improvement (BPI) ranks high on the agenda of many companies. However, existing methods like Six Sigma are often perceived as overly complex for projects with a limited scope. Therefore, more and more companies focus on the application of a few selected BPI techniques only, which are logically arranged in the form of “roadmaps” to tackle process weaknesses. Against this backdrop, the concept of “tool-supported situational roadmap development for BPI” along with a corresponding prototype are introduced. The approach builds on conceptual modeling and is technically realized by means of a metamodeling platform. Accordingly, the research offers practitioners a solution to systematically create project-specific roadmaps for BPI to improve process performance

Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that “Analyse” is by far the best-supported DMAICs phase through techniques such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context