Coordinating Supply Chains via Advance‐Order Discounts, Minimum Order Quantities, and Delegations

Abstract

To avoid inventory risks, manufacturers often place rush orders with suppliers only after they receive firm orders from their customers (retailers). Rush orders are costly to both parties because the supplier incurs higher production costs. We consider a situation where the supplier's production cost is reduced if the manufacturer can place some of its order in advance. In addition to the rush order contract with a pre‐established price, we examine whether the supplier should offer advance‐order discounts to encourage the manufacturer to place a portion of its order in advance, even though the manufacturer incurs some inventory risk. While the advance‐order discount contract is Pareto‐improving, our analysis shows that the discount contract cannot coordinate the supply chain. However, if the supplier imposes a pre‐specified minimum order quantity requirement as a qualifier for the manufacturer to receive the advance‐order discount, then such a combined contract can coordinate the supply chain. Furthermore, the combined contract enables the supplier to attain the first‐best solution. We also explore a delegation contract that either party could propose. Under this contract, the manufacturer delegates the ordering and salvaging activities to the supplier in return for a discounted price on all units procured. We find the delegation contract coordinates the supply chain and is Pareto‐improving. We extend our analysis to a setting where the suppliers capacity is limited for advance production but unlimited for rush orders. Our structural results obtained for the one‐supplier‐one‐manufacturer case continue to hold when we have two manufacturers.

Stochastic production capacity: A bane or a boon for quick response supply chains?

“…Lin and Parlaktürk (2012), and Wang, Thomas, and Rudi (2014) extended the work from monopoly to duopoly and explored the impacts of QR under different competition structures. Other QR-related studies include Cachon and Swinney (2011), which considered an QR supply chain with strategic consumers; Jain, Moinzadeh, and Zhou (2012), which studied the retailer's optimal ordering policy with continuous ordering opportunities; Amornpetchkul, Duenyas, andŞahin (2015), which investigated the multiperiod procurement problem under information asymmetry and demand forecasting; Calvo and Martínez-de-Albéniz (2016), which explored the multiple sourcing problem with QR; Li and Petruzzi (2017), which analyzed the effects of reducing demand uncertainty in a decentralized supply chain; Chintapalli, Disney, and Tang (2017), which studied the coordination issues for the case where the manufacturer and the supplier have to deal with rush orders; Niu and Zou (2017), which examined the use of demand signal for enhancing remanufacturing channel's operations; and Choi et al (2018), which explored QR with a stochastically risk sensitive retailer.…”

“…In this section we extend the benchmark case by considering that the manufacturer has stochastic production capacity in Stage 1. Under the OO system, the manufacturer has a long time for production so that the retailer's order can be fully fulfilled (Chintapalli et al, 2017). Then the OO system remains the same as that in the benchmark case presented in Appendix S1.…”

Stochastic Production Capacity: A Bane or a Boon for Quick Response Supply Chains?

“…Lin and Parlaktürk (), and Wang, Thomas, and Rudi () extended the work from monopoly to duopoly and explored the impacts of QR under different competition structures. Other QR‐related studies include Cachon and Swinney (), which considered an QR supply chain with strategic consumers; Jain, Moinzadeh, and Zhou (), which studied the retailer's optimal ordering policy with continuous ordering opportunities; Amornpetchkul, Duenyas, and Şahin (), which investigated the multiperiod procurement problem under information asymmetry and demand forecasting; Calvo and Martínez‐de‐Albéniz (), which explored the multiple sourcing problem with QR; Li and Petruzzi (), which analyzed the effects of reducing demand uncertainty in a decentralized supply chain; Chintapalli, Disney, and Tang (), which studied the coordination issues for the case where the manufacturer and the supplier have to deal with rush orders; Niu and Zou (), which examined the use of demand signal for enhancing remanufacturing channel's operations; and Choi et al (), which explored QR with a stochastically risk sensitive retailer.…”

“…In this section we extend the benchmark case by considering that the manufacturer has stochastic production capacity in Stage 1. Under the OO system, the manufacturer has a long time for production so that the retailer's order can be fully fulfilled (Chintapalli et al, ). Then the OO system remains the same as that in the benchmark case presented in Appendix S1.…”

Managing Television Commercial Inventory under Competition: An Equilibrium Analysis

“…Both Weng and Parler () and Tang, Rajaram, Alptekinoglu, and Ou () study the single‐firm ABD problem while McCardle, Rajaram, and Tang () provide a game theoretic model that provides a competitive generalization for the single‐firm models. Chintapalli, Disney, and Tang () explore the issue of coordinating supply chins through ABD contracts. Kleindorfer and Wu () provide a survey of inventory management under such settings.…”