Dual versus Triple Sourcing: Decision-making in the Presence of Supply Chain Disruption

For as long as there have been supply chains, there have been disruptions, and no supply chain, logistics system, or infrastructure network is immune to them. Nevertheless, supply-chain disruptions have only recently begun to receive significant attention from practitioners and researchers. Hence, different mitigation and contingency strategies have been addressed. But there is still an almost untouched problem when the disruption is rare but long.
This paper studies a single product setting in which a firm can source from multiple suppliers. One supplier has unreliable capacity while other suppliers are reliable but have lower product quality. The addressed context (where a case study has been made) is disruptions due to sanctions which cause failure in the supply from the unreliable source. The important question which emerges here is how a buyer firm should use different strategies for single/dual/multiple sourcing to handle those potential disruptions. For this sort of event, there are normally some signals before the disruption occurs. Consequently, the perceived probability of disruption has a sharp increase in a period of time which helps managers to advise an appropriate plan to mitigate its effects. There are also some similar disruptions with previous signals, e.g. supplier bankruptcy.
In this paper two possible strategies are addressed and compared as the combinations of mitigating and contingent plans, which are dual and triple sourcing; the former with lower setup cost but high probability of future problems due to risk of supplier monopoly and the latter with higher setup cost but price competition also after disruption.
In this paper the firm operates an infinite horizon with complete lost order for unmet demand where there is not any volume constraint for suppliers. The main focus of current work is on defining the share to give each supplier, and define which sourcing policy (dual or triple) to apply for different probabilities of disruption to minimize the long-run average cost.