Label-checking strategies to adapt behaviour to design

Abstract

Despite robust quality control procedures, labelling errors on fresh produce are estimated to cost the UK supermarket industry approximately £50million pounds per year in product recalls and wastage. Changing the format of the labels themselves is not a viable option. Instead, the challenge is to change or guide human operatives' behaviour so that label printing errors do not go undetected during quality control procedures. To this end, a simulated label checking task was presented to naïve participants to compare more systematic and strategic methods of label checking. Two conditions in which behaviour was computer-led were compared with a control condition in which checkers adopted their own idiosyncratic checking method. The data indicate that the two computer-led approaches resulted in improved levels of accuracy. Pushing label checkers towards a more systematic approach would appear to be effective in reducing undetected label errors, and could lead potentially to significant financial savings and reduced environmental wastage in the fresh produce industry.

Optimal approaches to the quality control checking of product labels

“…Preliminary, successful attempts to do the latter through the computer-controlled, serial release of information relating to the search have been reported by Smith-Spark et al (2015, 2016.…”

Factors affecting accuracy in the quality control checking of fresh produce labels: A situational and laboratory‐based exploration

“…Finally, the number of fields of information requiring checking has been purposefully restricted to seven for two reasons. First, previous work has shown that a larger amount of information to inspect is likely to result in poorer performance (Gallwey & Drury, ) so intertrial variation in the number of fields to be checked has been avoided in the label‐checking studies to date (Katz et al, ; Smith‐Spark et al, , , ). Second, poorer visual inspection performance has been found when errors are present in multiple fields rather than in single fields (see Gallwey & Drury, , for a review).…”

“…On the basis of the current data, two different approaches to improving performance suggest themselves: selecting label‐checking personnel based on their cognitive profile or changing label‐checking behavior in such a way that the chances of individual differences having an impact on performance are reduced significantly. Methods of achieving the latter have been considered in related work (Smith‐Spark et al, , ), in which label‐checking can be computer‐guided to constrain the approach taken to the task such that it follows a systematic, one field at a time pattern. In this way, the cognitive load associated with label‐checking is reduced and all fields of the label are checked.…”

“…These include aircraft inspection (Drury, ); airport X‐ray security inspection (Ghylin, Drury, & Schwaninger, ); telecommunications (Jameeson, ); inspection of small steel cylinders (Kleiner & Drury, ); the use of video magnifiers for inspecting electronic and mechanical components of consumer products (Lee, Man, & Chan, ); pharmaceutical and biological drug products (Melchore, ); contact lens inspection (Rao, Bowling, Khasawneh, Gramopadhye, & Melloy, ); circuit patterns, sheet steel or glass inspection, circuit board comparison (Wang & Drury, ); and simulated solder joint arrays (Wang, Lin, & Drury, ). However, despite the costly nature of human error in this domain, only a small amount of direct research has been carried out on product label‐checking (Smith‐Spark, Katz, Marchant, & Wilcockson, , , ). The research reported in this paper focused on one fresh produce packaging company which served a range of UK supermarkets.…”