Reducing schedule instability by identifying and omitting complexity-adding information flows at the supplier–customer interface

Within the supply chain context, schedule instability is caused by revisions to forecast demand from customers, problems with scheduled deliveries from suppliers, and disruptions to internal production. Supply chain partners attempt to address schedule instability by regular exchanges of information flows on current demand and delivery forecasts. However, if these updating information flows are unreliable and likely to be over-ridden by subsequent updated schedules, then the problem of schedule instability at the supplier–customer interface is not being solved. The research hypothesis investigated in this paper is whether supply chain partners may reduce schedule instability at the supplier–customer interface by identifying and omitting complexity-adding information flows. To this aim, previous work by the authors on an information-theoretic methodology for measuring complexity is extended and applied in this paper for identifying complexity-adding information flows. The application consists of comparing the complexity index of actual exchanged information flows with the complexity index of scenarios that omit one or more of these information flows. Using empirical results, it is shown that supply chain partners may reduce schedule instability at the supplier–customer interface by identifying and omitting complexity-adding information flows. The applied methodology is independent of the information systems used by the supplier and customer, and it provides an objective, integrative measure of schedule instability at the supplier–customer interface. Two case studies are presented, one in the commodity production environment of fast-moving consumer goods, and another in the customised production environment of electronic products sector. By applying the measurement and analysis methodology, relevant schedule instability-related insights about the specific case-studies are obtained. In light of the findings from these case studies, areas for further research and validation of the conditions in which the proposed research hypothesis holds are also proposed.