From manual to automated purchasing

Abstract

PurposeBusiness conditions of electronics manufacturers are demanding due to ever shortening product life‐cycles, higher variety and increased outsourcing activity. Even though companies could manage the increasing amount of purchased items with modularity, software‐based customization and well designed product platforms; the case is often so that item count in purchasing is increasing with high rates. On the top of this, time of the purchasing is being largely spent in new component search and management of end of product life cycle components/products. Therefore, organizations are faced to automate all of the possible manual ordering phases in order to increase the productivity of this function. Our purpose in this paper is to reveal how electronics manufacturers could achieve this challenging objective, and what are the possible causes for implementation success/failure.Design/methodology/approachThe paper uses case study findings and obtained data from four year period, when middle‐sized electronics manufacturer started to use automated purchasing with some of its high volume purchasing components. Case study also contains brief introduction for the supply chain and business environment of electronics, and highlights the important role of component distributors.FindingsThe research results reveal following outcomes: in a majority of automated ordering components inventory turns were higher as compared to manual mode results, the demand fluctuation in different components showed lower deviation and in limited number of cases internal and external factors caused inventory turn decrease.Originality/valueResearch results provide needed practical evidence for the middle‐sized electronics manufacturers that automated purchasing implementations are valuable for them, and highlight the importance of different services provided by the component distributors. So, the implementation success is a combination of ERP system integration as well as supply chain solutions. Mostly for academics this paper provides empirical evidence from the applied ordering algorithms, and identified caveats of purchasing automation.