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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.

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.

This study aims to present a model and analysis of automotive body outer cover panels (OCPs) assembly systems to predict assembly variation. In the automotive industry, the OCPs assembly process directly influences the quality of the automobile body appearance. However, suitable models to describe variation propagation of OCPs assembly systems remain unknown.

An adaptive state space model for OCPs assembly systems is introduced to accurately express variation propagation, including variation accumulation and transition, where two compliant deviations make impacts on key product characteristics (KPCs) of OCP, and the impacts are accumulated from welding process to threaded connection process. Another new source of variation from threaded connection is included in this model. To quantify the influence of variation from threaded connection on variation propagation, the threaded connection sensitivity matrix is introduced to build up a linear relationship between deviation from threaded connection and output deviation in KPCs. This matrix is solved by homogeneous coordinate transformation. The final deviation of KPCs will be transferred to ensure gaps and flushes between two OCPs, and the transition matrix is considered as a unit matrix to build up the transition relationship between different states.

A practical case on the left side body structure is described, where simulation result of variation propagation reveals the basic rule of variation propagation and the significant effect of variation from threaded connection on variation propagation of OCPs assembly system.

The model can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase. The calculated results of assembly variation guide designers or technicians on tolerance allocation, fixture layout design and process planning.

A process improvement sampling methodology, known as process variation diagnostic tool (PROVADT), was proposed by Cox et al. (2013). The method was designed to support the objectivity of Six Sigma projects performing the measure-analyse phases of the define-measure-analyse-improve-control cycle. An issue in PROVADT is that it is unable to distinguish between measurement and product variation in the presence of a poor Gage repeatability and reproducibility (R&R) result. The purpose of this paper is to improve and address PROVADT’s sampling structure by enabling a true Gage R&R as part of its design.

This paper derives an enhanced PROVADT method by examining the theoretical sampling constraints required to perform a Gage R&R study. The original PROVADT method is then extended to fulfil these requirements. To test this enhanced approach, it was applied first to a simulated manufacturing process and then in two industry case studies.

The results in this paper demonstrates that enhanced PROVADT was able to achieve a full Gage R&R result. This required 20 additional measurements when compared to the original method, but saved up to ten additional products and 20 additional measurements being taken in future experiments if the original method failed to obtain a valid Gage R&R. These benefits were highlighted in simulation and industry case studies.

The work into the PROVADT method aims to improve the objectivity of early Six Sigma analyses of quality issues, which has documented issues.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

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.

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.

Complicated workpiece, such as an engine block, has special rough locating datum features (i.e. six independent datum features) due to its complex structure. This locating datum error cannot be handled by current variation propagation model based on differential motion vectors. To extend variation prediction fields, this paper aims to solve the unaddressed variation sources to modify current model for multistage machining processes.

To overcome the limitation of current variation propagation model based on differential motion vectors caused by the unaddressed variation sources, this paper will extend the current model by handling the unaddressed datum-induced variation and its corresponding fixture variation.

The measurement results of the rear face with respect to the rough datum W and the pan face with respect to the hole Q by coordinate measuring machine (CMM) are −0.006 mm and 0.031 mm. The variation results for rear face and pan face predicted by the modified model are −0.009 mm and 0.025 mm, respectively. The discrepancy of model prediction and measurement is very small.

This paper modifies the variation propagation model based on differential motion vectors by solving the unaddressed variation sources, which can extend the variation prediction fields for some complicated workpiece and is useful in the future work for many fields, such as process monitoring, fault diagnosis, quality-assured setup planning and process-oriented tolerancing.

The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness.

This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart).

The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness.

This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process.

The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

The paper considers model‐based management and, based on it, proposes a heuristic‐based management. This paper aims to assert that heuristic‐based management, for complex systems, a process of free variation, of pairs of models and actions – called organisational strategies, maximizes the chances of improving the system's performance in open environments.

A conception of complex systems are introduced and characterized as open and self‐organising systems. Then, the proposal to heuristically use pairs of models and actions, called organisational strategies, to manage social systems based on evolutionary thought is supported. Subsequently, a computational experiment is proposed to show that, even in a simple framework, variation processes are required.

The paper shows that two processes may be required to preserve self‐organising systems. This finding indicates that variation and selection processes, related to evolutionary thought, are necessary for managers to deal with complex systems interacting with complex environments. Finally, it is shown that, even in simple computational environments, variation may be required.

The paper is the first part of an ongoing research agenda on the subject of heuristic‐based management and only refers to variation processes.

The paper links complex systems theories to evolutionary thought. It also relates principles of cybernetics to those of game theory. The proposal has been formalized based on these relations, and has been called heuristic‐based management. Principles first developed in information theory, organisational cybernetics, and evolutionary thought are used so that a complex system can be effective when interacting with a complex environment.