Analysing the impact of production control policies on the dynamics of a two-product supply chain with capacity constraints

• Published in: International journal of production research Vol. 61; no. 6; pp. 1913 - 1937
• Main Authors: Corsini, Roberto Rosario, Costa, Antonio, Cannella, Salvatore, Framinan, Jose M.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 19.03.2023; Taylor & Francis LLC
• Subjects: changeover; Design of experiments; Factorial design; Fill rate; Hedging; Impact analysis; Material requirements planning; multi-product; Policies; production control; Production controls; Response surface methodology; simulation; Supply chains
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2022.2053224

In this paper, we investigate the impact in terms of Fill Rate of production control policies in a two-product, two-echelon supply chain dynamic problem with production capacity. The factory node is subject to two different disruptive occurrences (i.e. failure events and changeovers) that can cause unforeseen problems in the supply chain. To control these adverse events, the factory can adopt different production control policies. We compare the well-known Hedging Corridor Policy with two variants, namely Modified Hedging Corridor Policy and Improved Modified Hedging Corridor Policy, and Demand-Driven Material Requirements Planning policy. Firstly, we use the Response Surface Methodology to calibrate the endogenous factors for each strategy. Then, through an extended full-factorial Design Of Experiments, we evaluate the effectiveness of the production control policies for several operational and market scenarios defined by varying exogenous factors. Interestingly, our study reveals that the Hedging Corridor Policy represents the best rule to increase the Fill Rate. The policies have been compared also in terms of indicators related to the factory inventory level. The results seem to point out that efficient production control policies (measured in terms of operational efficiency) do not necessarily yield the best results when measured in terms of supply chain efficiency.