A Study about Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty
This paper investigates the impact of Supply Chain Management on logistical performance indicators in food supply chains. From a review of quantitative and more qualitative managerial literature, we believe that Supply Chain Management should be concerned with the reduction or even elimination of uncertainties to improve the performance of the chain. The following clusters of sources of uncertainty are identified: order forecast horizon, input data, administrative and decision processes and inherent uncertainties. For each source of uncertainty, several improvement principles are identified. A case study was conducted in a food chain in which a simulation model helped quantify the eects of alternative configurations and operational management concepts. By comparing this simulation study with a pilot study, the model is validated against real data, and organisational consequences are identified.
Introduction: Recent literature on Supply Chain Management has been stressing the need for collaboration among successive actors, from primary producer to end-consumers, to better satisfy consumer demand at lower costs (see, for example, Scott and Westbrook, 1991; Ellram, 1991; Towill, 1996). Jones and Riley (1985) define Supply Chain Management (SCM) as an integrative approach to dealing with the planning and control of the materials how from suppliers to end-users. According to Fearne (1996), SCM seeks to break down the barriers which exist between each of the links in the supply chain, in order to achieve higher levels of service and to substantially reduce costs. “It seeks to achieve a relationship of mutual benefit by defining the organisational structures and contractual relationships between buyer and seller, which up until now have been classified as adversarial” (Fearne, 1996). Iyer and Bergen (1997) emphasise Pareto improvement, referring to the situation in which all parties are at least as well o, and oneparty is better o than before. Keep reading…
