A conceptual data model to enhance SKU management in food SMEs

Abstract

This paper reports on an action research project (two-cycle approach -problem solving cycle and research cycle) that investigated the role of data management in supporting product variety/Stock Keeping Unit (SKU) management in a food SME (Small and Medium Enterprise). We find that the SME did not view data as a key resource and hence data redundancy and data integrity issues were fundamental problems. Application of a conceptual data model instilled discipline with regard to data management and this resulted in: (1) a change in data management behaviour within the food SME, and (2) the development and deployment of SKU management tools for the food SME. IntroductionConsumer food manufacturing is characterised by increasing demand for product variety. Recent research has investigated the role of hierarchical planning approaches and Stock Keeping Unit (SKU) classification in product variety management and order fulfilment. Appropriate data management is a key enabler of such planning and associated classifications; however, this has received less attention, especially with regard to the needs of small and medium enterprises (SMEs). Within SME environments, there is an overreliance on spreadsheet products (e.g. MS Excel) to manage data. Such 'literal' data representations are devoid of adequate 'abstraction' which is necessary to maintain an appropriate parent-child relationship between a master SKU recipe and the associated SKU varieties. Hence, this research focuses on SKU data management and adopts an action research (AR) approach in the food SME environment. This paper is structured as follows: a brief review of relevant literature establishes key concepts and trends associated with product variety with particular reference to the food industry; this is followed by a discussion of data management and data ontologies; the methodology is then outlined and findings are presented; the conclusion reflects on the contribution of the AR approach adopted and reports on practical outcomes.

The role of hierarchical production planning in food manufacturing SMEs

“…This required specific problem solving cycles (e.g. SKU labelling conventions, data cleaning and building a relational database), indeed this learning prompted a subsequent study and this is reported in Sammon and O'Reilly (2013). Hence both practitioners and scholars benefited from iterative cycles which in turn provided the impetus for further initiative.…”