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Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

This research is elucidating quality control theories to reduce variation in chocolate manufacturing process in the UK food company that will help maintain the processes stable and predictable. The purpose of this paper is to reduce defects of the output; to identify the root causes of variation; to establish and implement solutions to this variation problem; and to establish a control system to monitor and report any variation in the process.

The authors use experimental case study of a chocolate company to achieve the objective. In this paper, the authors predominantly use established theory define–measure–analyse–improve–control, customised to the case of the chocolate factory to reduce variations in production processes.

The results confirm that customised-traditional theoretical quality models will support manufacturing companies to maintain customer satisfaction while enhancing quality and reliability.

Implementation of customised approach reduced the rate of defect from 8 to 3.7 per cent. The implications of reduced variation are improved product quality; reprocessing elimination; and a more stable process that support sustainability and reliability in producing chocolates to meet customer needs.

The authors used an experimental-based case study approach to test with one company. Testing in multiple case companies may help to generalise results.

The research study experimentally tested quality approach with a real case company and hence the findings of this study can be applied to other cases working in similar settings.

Highlights that one service industry in the USA ‐ health care ‐ has accepted high inherent rates of variation into its process designs. Notes that, increasingly, health care industry leaders recognize that elimination of unnecessary variation is a necessary, but not a sufficient, condition for producing quality professional services at reasonable costs. Using the innovation model of Boynton et al. (1993), identifies continuous improvement, rather than mass production, as the key step in the rationalization of what has been a craft industry and the ultimate objective of delivering health care in a mass customization mode. Claims, however, that it is not sufficient, because high levels of inherent variation will continue to exist and must be managed, even in the best of all possible worlds. Reviews the health care experience (in the context of that model) to suggest how service operations managers and researchers should conceptualize variation, and then discusses what that conceptualization of variation implies about how operations management should treat variation in its modelling and decision making.

The goal of this work is to clarify seven useful DMAIC Analyze phase options for developing process improvement opportunities required for successful projects.

Using a scientific method problem solving structure, IO possibilities are shown to be predicted by rejecting a conceptual testable hypothesis.

Seven analysis paths are identified that enable learners to develop multiple IO discovery strategies and to narrow tool selection options. Four benefit areas for identifying analysis paths are given: improved training, continuous improvement foundation, leadership support and framework clarification.

Any starting list of analysis paths for developing IOs would be incomplete. The diversity of application experiences and tools will add to the current list.

Learners participating in LSS activities are aware of management's expectation that they will develop IOs to justify the LSS investment. Tool-focused training may leave some learners unclear about the multiple possible sources for IOs. Identifying useful analysis paths with associated tools for IO discovery will address any learner's Analyze phase uncertainty and facilitate expanded opportunities.

Any successful LSS project must discover IOs to develop improvement actions. Clarifying IO discovery alternatives will encourage team brainstorming on Analyze phase investigative options. This framework identifying LSS improvement paths will assist practitioners in training and communicating with leadership and learners the range of approaches for developing improvement actions.

The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM) and six‐sigma approaches.

The research methodology was experimental prototyping conducted in collaboration with industry partners. A prototype IT system was developed and tested to implement the approach. A quality cost‐based system was used to assess variation at each operation stage, for every product characteristic.

A comprehensive approach to the management of manufacturing variation is introduced, based on a new process risk matrix which can be used to specify an individual variation risk for every manufactured characteristic, throughout a manufacturing process chain. The approach has been implemented in a prototype software system and is aimed at the complex products such as those manufactured by the aerospace industry.

The IT approach described was developed during the research and is not commercially available.

Manufacturing industry should be able to use this approach, in particular the process risk matrix concept, to develop more effective management of product variation and resultant cost, in complex process chains.

The paper describes a novel approach to combine VRM and six‐sigma concepts, and introduces the process risk matrix as a structure to understand process variation.

The management and control of random process variations is often neglected in Europe. The Japanese emphasise the control and reduction of these variations and some multinationals have realised that this is an area of great potential for quality improvements. There are essentially two approaches. The first is to design the effects out of the product and therefore make it robust against both the random and the non‐random process variations. The second approach is to improve the process capability. It is stressed that if industry is to stay competitive then it must tackle product quality at all levels of design and manufacture. Some useful insights into process variation reduction are given.

This study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.

This is a case study within an Irish meat processor where the structured Define, Measure, Analyse, Improve and Control (DMAIC) methodology was utilised along with statistical analysis to highlight areas of the meat boning process to improve.

The project led to using Six Sigma to identify and measure areas of process variation. This resulted in eliminating over-trimming of meat cuts, improving process capabilities, increasing revenue and reducing meat wastage. In addition, key performance indicators and control charts, meat-cutting templates and smart cutting lasers were implemented.

The study is one of Irish meat processors' first Six Sigma applications. The wider food and meat processing industries can leverage the learnings to understand, measure and minimise variation to enhance revenue.

Organisations can use this study to understand the benefits of adopting Six Sigma, particularly in the food industry and how measuring process variation can affect quality.

This is the first practical case study on Six sigma deployment in an Irish meat processor, and the study can be used to benchmark how Six Sigma tools can aid in understanding variation, thus benefiting key performance metrics.

The purpose of this paper is to extend the understanding of Six Sigma (SS) and the underlying dimensions of its critical success factors (CSF) via an analysis of the effects of top management support (TMS), implementation strategy (IS), and collaborative team (CT) on project performance (PP) in Mexican manufacturing companies.

Based on a SS literature review, a survey was conducted to capture practitioners’ viewpoints about CSFs for SS implementation and their impact on performance in manufacturing companies. A factor analysis and structural equation modeling were conducted in order to identify and analyze causal relationships.

The results suggest that CSFs grouped in the constructs TMS, IS, and CT have a positive impact on PP as measured by cost reduction, variation reduction, and quality improvement.

Although the empirical data collected supported the proposed model, results might differ among organizations in different countries. In addition, the study did not analyze a unique performance metric; instead, general PP dimensions were used.

Boosting the TMS, IS, and CT enhances positive PP of SS in manufacturing companies.

IS as a construct has not been studied exhaustively; this work contributes to a better understanding of it and its impact on PP. Additionally, studies of SS in Latin America are limited, so this study gives a complementary vision to practitioners and researchers about it.

The purpose of this paper is to extend the role of the theory of constraints (TOC) to complement, reinforce, and help integrate conventional operations management (OM) concepts by using an Excel‐based version of the dice game discussed in The Goal by Goldratt.

The paper discusses the motivation for and the development and evaluation of an Excel‐based dice game model of a production system for novice managers to experiment with. A set of experiments related to OM concepts (e.g. inventory, capacity, and variability) is designed and counterintuitive results are discussed. The paper concludes by demonstrating how TOC provides an integrative OM framework.

The novel The Goal by Goldratt serves as a comprehensive case study in OM. The computerized dice game provides a mechanism for understanding relationships among various OM concepts. The proposed set of experiments strengthens the linkages between OM and TOC concepts. Managers can conduct additional experiments and predict/interpret the results without spending time in the logistics of setting up the manual dice game repeatedly.

The proposed dice game simulates a fairly simple serial production system so the generalization of results obtained might not be intuitively convincing for more complex production systems. More advanced OM concepts such as push (MRP) and pull (JIT) systems can easily be investigated using the underling logic of the dice game proposed here.

The model provides an innovative way to integrate TOC concepts with mainstream OM concepts and thereby, renews interest in OM.

Several versions of dice games, both manual and spreadsheet based, have appeared in the literature, however, none attempt to address as wide a variety of operations issues as the game proposed here.

Today’s global business environment essential requirement to an industries reduction of wastes, reduction of variations, reduction of lead time and innovative feature quality product with minimum cost. That strives to improve customer satisfaction, break through strategies to solve problems with fastest process speed. Lean Six Sigma (LSS) strategy and framework can help during recession or upcoming recession to improve business excellencies and companies strategies against recession. The paper aims to discuss these issues.

The paper followed views of authors, industrial experts from oil industries regarding LSS strategy, framework, comparative key factors of Lean and Six Sigma to overcome continuously decline oil prices globally. LSS strategy will proactively work as preventive tool for insipid economical growth of oil-exporting countries.

To follow the LSS guidelines, oil-exporting countries can improve their business performance during ongoing oil price fall that may be influenced on gross domestic product of countries.

The case study may provide some help to survive ongoing crisis of continuously fallen oil price that is highly problematic for oil-exporting countries.