Developing statistical thinking for performance in the food industry

Grigg, N.P. and Walls, L.A. (2007) Developing statistical thinking for performance in the food industry. International Journal of Quality and Reliability Management, 24 (4). pp. 347-369. ISSN 0265-671X

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The paper aims to describe a recently completed research project on the use of statistical quality control (SQC) methods in the context of food and drinks manufacturing. It discusses issues surrounding the successful uptake of such methods, including organisational motivation, possible application, costs and benefits, critical success factors and the central importance of prerequisite statistical thinking (ST). A three stage, mixed methods approach was adopted, incorporating surveys augmented by case studies and key informant interviews with industry managers and providers of relevant industry training. All data were combined to produce the final model. The paper finds that SQC methods are of relevance in the industry, providing the process is appropriate and management have a basic awareness of the fundamentals of ST. Certain organisational and external factors were found to progressively reduce the effectiveness with which such methods are introduced and sustained. The paper ends with discussion of an original model, developed from the research, which illustrates the "filters" that tend to reduce the effectiveness with which methods are used in the industry, with a discussion of how each can be overcome. The research in this paper is focused on the European food manufacturing and legislative context, and predominantly UK. Low survey response rates numbers necessitated a nonparametric approach to survey analysis.The paper shows that the filters model is of generic applicability and interest to current and future managers, and to other researchers in this area. The paper addresses the "how to" of SQC, and examines an industry where there is not yet widespread literature on the benefits of SQC methods. It presents an original model of the barriers to effective use of statistical methods within a process knowledge and improvement cycle.


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