Developing statistical thinking for performance improvement in the food industry

Abstract

PurposeThe 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).Design/methodology/approachA 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe paper shows that the filters model is of generic applicability and interest to current and future managers, and to other researchers in this area.Originality/valueThe 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.

Impact analysis of critical success factors on the benefits from statistical process control implementation

“…The most cited CSFs in the literature (80% of the 173 citations about CSF) are shown in Table 1. (1) Gordon et al (1994), (2) Does et al (1997), (3) Rungtusanatham et al (1999), (4) Xie & Goh (1999), (5) Mason & Antony (2000), (6) Antony et al (2000), (7) Rungasamy et al (2002), (8) Antony & Taner (2003), (9) Grigg & Walls (2007a), (10) Grigg & Walls (2007b), (11) Chen et al (2008), (12) Elg et al (2008), (13) Putri & Yusof (2008), (14) Phyanthamilkumaran & Fernando (2008), (15) Rohani et al (2009), (16) Rohani et al (2010), (17) Mahanti & Evans (2012), (18) Rantamaki et al (2013), (19) Sharma & Kharub (2014), (20) Lim et al (2014).…”

Quality Management: Important Aspects for the Food Industry

“…According Grigg, the initiatives of training of new graduates entering the industry in the principles of quality assurance and statistical methods and training the existing workforce and management in applying statistical control procedures to processes will make this methods more use of it than they are [9,10].…”

Adoption and Benefits of Statistical Methods in Enterprises: Differences between Croatian Regions

“…The main reason for the scarce use of statistical methods is thought to be the insufficient familiarity of employees with statistical methods' abilities and benefits (Tanco et al, 2008). Some efforts in additional statistical methods training for employees have been done (Vere-Jones 1995, Dransfield, Fisher and Vogel 1999, Wild and Pfannkuch, 1999, Grigg and Walls, 2007. Unfortunately, the aversion of employees is too high to change their attitudes towards statistical methods and to achieve better results in the short run (Hahn and Hoerl, 1998, Deleryd, Garvare and Klefsjö, 1999, Rungasamy, Antony and Ghosh, 2002, Makrymichalos et al, 2005.…”