Today's supply chains are more complex than ever due to globalization and its effects especially on the logistics activities. Therefore, understanding and managing complexity in supply chains are very popular topics nowadays. Measures for complexity in supply chains contribute to their manageability and controllability. This paper describes an approach to the measurement of complexity in supply chains based on the Shannon's information entropy. The new proposed approach gives a formal approach that is able to measure and analyze the supply chain complexity. The main contribution of this study is to extend two formulas (structural and operational complexity) building on the Shannon's entropy measure to evaluate the complexity of a supply chain. The aim is to measure complexity associated with information and material flows in the chain. A numerical example is presented to demonstrate the approach.
Lack of knowledge about demand responses or about behavioural aspects of decision-making within procurement processes is a significant cost driver in modern supply chains. Very often, this lack of knowledge leads to a substantial increase in inventories and may endanger negotiated service levels. For instance, various studies reveal that decision-makers tend to anchor orders close to the average past demand although the target order size is significantly higher or lower. In order to improve this situation, feedback has to be systematically provided to the decision-makers. In combination with modern big data analytics and reporting instruments that enable exhaustive monitoring, effective indicators have to be applied in order to directly detect processes with significant potential for improvement. Hence, this paper proposes a new approach for measuring the intricacy in purchase order sizing that addresses self-awareness skills of decision-makers. By simultaneously analysing the amount and structure of occurring costs, processes with a significant and simple structured error pattern are identified. In order to identify these processes more reliably, a new approach that supplements former information-theoretic entropy measures by an additional cost value is proposed. By analysing costs and the structure of deviations from target values in a two-dimensional measure, a more comprehensive understanding of the considered order sizing process is pursued. In order to illustrate the application of the new approach and show limitations of one-dimensional measures, different scenarios that exemplify the new approach are presented.
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