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

The effects of rescheduling on manufacturing systems complexity

Rescheduling is a means of managing internal and external disturbances in order to satisfy customer demand subject to resource constraints. The extent and nature of rescheduling have some effects on the overall complexity of the manufacturing facility. Complexity is associated with the variety and uncertainty within manufacturing systems. Complexity can be classified into static and dynamic. Static complexity is related to the schedule (variety) whereas dynamic complexity is related to the deviations from the schedule (uncertainty). Informationtheoretic measures can be used to quantify complexity. The aim of this paper is to investigate the relationship between rescheduling and complexity. This will be illustrated using a case study that involves two companies working in a supplier-customer relationship.

Complexity Transfer in supplier-customer systems

This paper uses a multiple case-study methodology to investigate complexity transfer (CT) in manufacturing supplier-customer systems, leading to a new model of complexity transfer. An entropic-related complexity measure is applied to three supplier-customer systems, internally within each organisation and at their supplier-customer interface. The results are compared and integrated to provide cross-case analyses and insights. Although CT has been acknowledged in the literature to occur towards upstream supply chain (SC) partners, e.g. in the context of the bullwhip effect, this paper provides evidence that CT may also occur towards downstream SC partners. This study also highlights that complexity can be managed through significant and sustained operational interventions. Our new empirically-tested model of CT identifies four organisational types: Sink, Source, Equilibrium, and Boom or Bust, according to their transfer of internally-generated and externally-accepted complexity. This new model enables an in-depth representation of the transfer of complexity and of its impact on SC partnerships. Managers may use this CT model to develop complexity management insights and to identify structural and operational changes – at organisational level, and systemic SC changes that may reduce the costs associated with complexity