Combined demand and capacity sharing with best matching decisions in enterprise collaboration

Demand and capacity sharing (DCS) among entities within a supply network are common practice, and have become attractive strategies for competing and non-competing supply enterprises (SEs). Examples include airlines, test and assembly factories, and outsourced maintenance and logistics providers. The purpose: maximize profit and resource utilization, enable timely delivery to customers in spite of uncertain market demands and unexpected capacity shortages, and maximize the overall stability. DCS protocols are defined for the SEs with capacity shortage, known as demand sharing SEs, to utilize excess capacities of other SEs, known as capacity sharing SEs, thus fulfilling their current customers' demand more effectively, while eliminating excess inventory of capacity sharing SEs. These DCS roles vary over time. High frequency of DCS decisions could impose additional costs to the Collaborative Network of SEs (CNSE) in terms of transactions, negotiations, and lateral transshipment of stocks between SEs. Attention must be paid to the inevitable costs of collaboration for DCS. Best Matching (BM) protocol is proposed to minimize the DCS costs through dynamic matching of SEs and customers with respect to the customers' demand and SEs' available capacity to share. BM protocol is also applied for finding the best matches between DCS proposals during collaboration negotiations among SEs. A novel Mixed-Integer Programming (MIP) formulation is developed for modeling and analyzing the combined DCS–BM decisions. The DCS–BM model is then validated using the Queuing Theory. It is shown mathematically and through numerical experiments that the DCS–BM model: (1) outperforms the previous non-collaborative models in terms of resource utilization and stability, and (2) provides a dominating strategy, compared with both collaborative and non-collaborative models, for optimizing the total CNSE profit and service level. •Lateral collaboration is performed through Demand and Capacity Sharing Protocol.•Collaboration decisions are improved through Best Matching Protocol.•Profit, resource utilization, demand fulfillment rate, and stability are optimized.•A novel mathematical formulation is developed and validated by Queuing Theory.