Production, Manufacturing, Transportation and LogisticsMulti-sourcing and information sharing under competition and supply uncertainty
Introduction
Increasing globalization, economic integration, and interdependence have complicated the supply chain (SC) network and intensified SC uncertainties both in supply and demand. The uncertainty in supply yield (hereafter abbrev. as supply uncertainty) poses great challenges to SC managers, who often resolve to multi-source to mitigate the adverse effect of uncertain yield. As a result, multi-sourcing has attracted much attention in recent years. To effectively manage demand uncertainty, many upstream and downstream SC members have teamed up to share demand information. However, multi-sourcing and information sharing have traditionally been studied separately. As, in practice, SC managers have to concurrently address both supply uncertainty and demand uncertainty, it is necessary to tackle both issues jointly.
Supply uncertainty may be caused by yield uncertainty, unreliable product quality, natural disaster, or supplier bankruptcy. Facing supply uncertainty, firms often employ multi- or dual-sourcing. For example, in smart phone industry, Apple Inc. has sourced iPhone 7′s key components from multiple suppliers, including Japan Display Inc. (JDI), South Korea's LG Display Co. (LG), Sharp Corp., etc.1 In vaccine supply, healthcare providers sourced influenza vaccine from Sanofi Pasteur, Seqirus, GlaxoSmithKline, etc.2 When suppliers depend on the same material resources and have similar production processes, we deem them to have high supplier correlation. The retailer often can benefit from multi-sourcing by ordering from multiple suppliers to reduce inventory (shortage) risks. Lower supplier correlation reduces the total risk of supply yield to retailers. Such an effect is defined as the risk diversification effect (RDE) (Babich, Burnetas, & Ritchken, 2007). Under multi-sourcing, higher supplier correlation implies more intense competition between suppliers, which brings down the wholesale price. The retailer can thus benefit from lower sourcing costs. Such a benefit is defined as the price competition effect (PCE) (Babich et al., 2007). Both effects depend on supplier correlation. Although high supplier correlation increases the retailer's PCE benefit, it may hurt its RDE. In this research, unlike Babich et al. (2007) and Yang, Aydin, Babich, and Beil (2012) who focus on the competitive supply market, we examine the tradeoff between PCE and RDE when competition occurs in both the supply and demand markets.
As information technology is an integral part of SC, firms can obtain rich market information through sales channels to forecast future demand. For instance, Apple Inc. can acquire the iPhone's market trend through retailers (e.g., Apple store), online channels (e.g., Amazon and BestBuy), and third-party data service providers (e.g., Statista). By disclosing demand forecast information (hereafter abbrev. as forecast signal) to upstream firms, downstream firms may weaken their future price negotiation power. As upstream firms benefit from vertical information sharing (VIS), they may through a side-payment induce downstream firms to disclose their private forecasting information. This could take place if VIS can improve the total SC profit (Li, 2002, Zhang, 2002). Our research differs from the literature in that we address a more complex SC: multiple competing and correlated suppliers with uncertain supply yield, and competing retailers.
Motivated by Apple Inc. sourcing smart phones’ key components from various suppliers, we examine a SC network consisting of multiple competing suppliers and two competing retailers (e.g., Apple Inc. and Huawei Inc.). Such a structure is most relevant for studying the interactions between multi-sourcing and VIS. Retailers compete by selling perfect substitutable products in a common market, i.e., retailers engage in Cournot (quantity) competition. Facing demand uncertainty, each retailer has a private signal (forecast) about the future demand. Suppliers are subject to supply uncertainty and compete by setting a linear wholesale price (engaging in Bertrand competition), which is a function of supplier correlation.
Specifically, we address the following questions under a SC network and competition occurs in both the supply and demand markets:
- (1)
How do retailers decide on VIS strategies? (Addressed in §4.1 and §5)
- (2)
How do retailers choose suppliers in multi-sourcing when weighing tradeoff between RDE and PCE? (Addressed in §4.2)
- (3)
What is the retailers’ optimal strategy when multi-sourcing and VIS are considered jointly? (Addressed in §4.3)
We develop an analytical model to jointly study multi-sourcing and information sharing. A three-stage game is developed to examine the incentive of VIS. The 1st stage involves information sharing, while the 2nd and 3rd stages involve suppliers’ wholesale price and retailers’ order quantity, respectively. In the 1st-stage game, there are 2 × 2 pairs of information sharing strategies for the two retailers, as each retailer can choose to share or not share demand information with all suppliers as a whole. Thus, there exist four subgames. We first solve each subgame by backward induction. Subsequently, we investigate the interactions between multi-sourcing and VIS.
The contributions of this research are fourfold:
- (1)
We jointly investigate multi-sourcing and VIS in a general SC, which comprises multiple competing suppliers with correlated supply uncertainty and two competing retailers with private forecast signals.
- (2)
We derive new insights on VIS within SC. Under side-payment, the retailers’ complete information sharing is Pareto-optimal when retailers have accurate forecasts. Moreover, small forecast error benefits all SC members. As a result, retailers are willing to improve the accuracy of the forecast signal (i.e., reduce forecast error).
- (3)
We examine the tradeoff between RDE and PCE faced by retailers under different VIS strategies. We find that whether the retailer should choose high- or low-correlated suppliers depends on (i) the number of suppliers, (ii) retailers’ VIS strategy, (iii) supply uncertainty, and (iv) forecast error.
- (4)
We examine the interactions between retailers’ multi-sourcing strategy and VIS, and find that under VIS, retailers would choose suppliers with high correlation. We also find that high supplier correlation and large number of suppliers would drive suppliers’ optimal side-payment lower and suppliers would request higher forecast accuracy from retailers. Under high supply uncertainty, the suppliers would pay less side-payment and tolerate higher forecast error.
The rest of the paper is organized as follows. In Section 2, we review the literature. Section 3 formalizes the research problem, develops the research framework, and provides the equilibrium solutions. The interactions between multi-sourcing and VIS are discussed in Section 4. Section 5 presents the numerical study. Managerial insights and conclusions are given in Section 6 and Section 7, respectively.
Section snippets
Literature review
We review two streams of literatures relevant to SC competition: multi-sourcing under supply uncertainty, and incentives for vertical information sharing, but not limit to.
Research framework
To jointly investigate multi-sourcing and VIS under competition and supply uncertainty, we consider a two-echelon SC consisting of multiple competing and correlated suppliers (he) with supply uncertainty, and two competing retailers (she) with private demand forecast information (signal). In Fig. 1, the dotted arrow describes information flow and the solid arrow describes inventory flow. We assume that supply uncertainty and demand uncertainty are independent of each other. All parameters are
Interactions between multi-sourcing and vertical information sharing
In practice, SC managers often need to address supply and demand uncertainty concomitantly. Therefore, when the SC manager jointly employs multi-sourcing and VIS strategies to mitigate supply and demand risks, they need to understand how the two strategies interact. We first study retailers’ VIS decisions in §4.1 and then retailers’ multi-sourcing decisions in §4.2. Finally, we identify retailers’ optimal strategies for simultaneously considering multi-sourcing and VIS decisions in §4.3.
Numerical study
To drive more insights on how the optimal side-payment (m*) and ψ3 evolve with the changes of the three supply characteristic parameters: supplier correlation (ρ), supply uncertainty (δy) and the number of suppliers (n), we first examine how a pair of the parameters jointly impact on m* and ψ3. Then, we investigate how the three parameters together affect m* and ψ3. The impact of a single parameter on m* and ψ3 are discussed in Online Appendix F.
We set . Take a moderate value of ɛ and let
Managerial insights
The findings obtained in this research through numerical and analytical studies provide valuable insights for us to tackle multi-sourcing and VIS issues jointly. From the perspective of VIS, we find retailers have no incentive to disclose demand signal to suppliers for free. Therefore, the suppliers need to offer a side-payment to retailers for purchasing retailers’ demand signals, as suppliers can benefit from retailer's demand forecast. Complete information sharing is a Pareto-dominant
Conclusions
In this research, we examine multi-sourcing and incentives for VIS in an SC network, having multiple suppliers and two retailers. The suppliers are subject to correlated supply uncertainty and compete in the supply market, while the retailers own private demand forecast information and compete in the demand market. We develop a Stackelberg model to address these issues.
We contribute to the VIS literature in SC management by linking the number of suppliers, supplier correlation, supply
Acknowledgments
The authors thank the Editor and anonymous referees for constructive comments. This work is supported by the National Natural Science Foundation of China (Grant no. 71531004); the Humanities and Social Sciences Project of Jiangxi Universities and Colleges (Grant No. GL18226).
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