Strategic Supply Chain Structure Design for a Proprietary Component Manufacturer

This paper examines the choice of supply chain structure for a proprietary component manufacturer (PCM). The PCM, who is the sole supply source of a critical component used to assemble an end product, can either provide its component to an original equipment manufacturer (OEM) in the end‐product market (component supplier structure), develop the end product exclusively under its own brand (monopoly structure), or provide the component to the OEM as well as develop the end product under its own brand (dual distributor structure). Typically, the end products of the PCM and the OEM will be differentiated, and the OEM tends to have a capability advantage (compared with the PCM) in producing the end product. Our paper studies the impact of this degree of differentiation and capability advantage on the optimal choice of distribution structure. We then investigate how investing in component branding, enhancing the value of the end product, using alternative supply contracts, and product valuation uncertainty influence the PCM's optimal choice of distribution structure.     

More efficient logistic analysis using moving extreme ranked set sampling

Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSS_min) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from 2011 Youth Risk Behavior Surveillance System (YRBSS) data are used to illustrate the procedures developed in this paper.     

Entry Deterrence of Capacitated Competition Using Price and Non‐Price Strategies

We analyze the role of pricing and branding in an incumbent firm's decision when facing competition from an entrant firm with limited capacity. We do so by studying two price competition models (Stackelberg and Nash), where we consider the incumbent's entry‐deterrence pricing strategy based on a potential entrant's capacity size. In an extension, we also study a branding model, where the incumbent firm, in addition to pricing, can also invest in influencing market preference for its product. With these models, we study conditions under which the incumbent firm may block the entrant (i.e., prevent entry without any market actions), deter the entrant (i.e., stop entry with suitable market actions) or accommodate the entrant (i.e., allow entry and compete), and how the entrant will allocate its limited capacity across its own and the new market, if entry occurs. We also study the timing difference between the two different dynamics of the price competition models and find that the incumbent's first‐mover advantage benefits both the incumbent and the entrant. Interestingly, the entrant firm's profits are not monotonically increasing in its capacity even when it is costless to build capacity. In the branding model, we show that in some cases, the incumbent may even increase its price and successfully deter entry by investing in consumer's preference for its product. Finally, we incorporate demand uncertainty into our model and show that the incumbent benefits from demand uncertainty while the entrant may be worse off depending on the magnitude of demand uncertainty and its capacity.     

A Stochastic Inventory Model With Fast‐Ship Commitments

We present a multiperiod model of a retail supply chain, consisting of a single supplier and a single retailer, in which regular replenishment occurs periodically but players have the option to support fast delivery when customers experience a stockout during a replenishment period. Because expedited shipments increase the supplier's transportation cost, and possibly production/inventory costs, the supplier typically charges a markup over and above the prevailing wholesale price for fast‐shipped items. When fast shipping is not supported, items are backordered if customers are willing to wait until the start of the next replenishment period. We characterize the retailers and the supplier's optimal stocking and production policies and then utilize our analytical framework to study how the two players respond to changes in supply chain parameters. We identify a sufficient condition such that the centralized supply chain is better off with the fast‐ship option. We find a range of markups for fast‐ship orders such that the fast‐ship option is preferred by both the supplier and the retailer in a decentralized chain. However, a markup that is a win–win for both players may not exist even when offering fast‐ship option is better for the centralized chain. Our analysis also shows that depending on how the markup is determined, greater customer participation in fast‐ship orders does not necessarily imply more profits for the two players. For some predetermined markups, the retailer's profit with the fast‐ship option is higher when more customers are willing to wait. However, the retailer may not be able to benefit from the fast‐ship option because the supplier may choose not to support the fast‐ship option when fast‐ship participation increases due to the fact that the fast‐ship participation rate adversely affects the initial order size.     

Managing Risk of Supply Disruptions: Incentives for Capacity Restoration

A supplier facing the prospect of disruption has to decide whether or not to invest in restoration capability. With restoration capability, if disruption occurs, additional costly effort can be exerted to rebuild capacity, although its outcome is uncertain. We study how a firm (buyer) can use incentive mechanisms to motivate a supplier's investment in capacity restoration, and compare this approach with the traditional approach of diversifying part of the order to an expensive but reliable supplier. Under a Restoration Enhancement (RE) strategy, the buyer uses price and/or order quantity incentives to encourage the supplier's restoration investment decision. Two different cases are considered—when the incentive is committed to ex ante (prior to disruption) and when it is committed to ex post (after disruption). In contrast, under a Supplier Diversification (SD) strategy, the buyer splits orders between a reliable supplier and an unreliable supplier to hedge against the disruption risk. Here, the buyer does not provide any separate incentive to the unreliable supplier. Our analysis indicates that under the RE strategy, where the buyer offers incentives, both the buyer and the supplier (weakly) prefer the ex ante commitment over the ex post one. Furthermore, the RE strategy is preferred over the SD strategy when the unreliable supplier's restoration outcome is more predictable or when a high restoration outcome is more likely. However, the buyer's preference for the SD strategy increases as market demand increases.     

A rational approach to pricing of catastrophe insurance

A methodology for rational pricing of catastrophe insurance is described. The methodology has two components: a solvency- and stability-based pricing framework, and an engine to quantify the loss variability that drives solvency and stability. Generalization to account for contagious effects of catastrophes and multiple occurrence of peril is presented in detail.     

Balancing Inventory and Stockout Risk in Retail Supply Chains Using Fast‐Ship

In this study, we consider a two‐retailer, one‐supplier supply chain in which retailers satisfy excess demand by offering to directly ship out‐of‐stock items on an expedited basis at no extra cost to customers. This practice is referred to as the fast‐ship option. We consider two scenarios along with the fast‐ship option. In the first scenario, retailers transship when out of stock, whereas in the second scenario, they do not. If they do not transship, some customers may perform the search on their own. In each scenario, the wholesale prices are either exogenous, or chosen endogenously by the supplier. For both cases, we determine the supplier's and the retailers’ optimal decisions. The key research question we ask and answer is the following: which of the two scenarios is preferred by either player when all decisions are made optimally? We show that when fewer customers are willing to search on their own and wholesale prices are exogenous, both the supplier and the retailers prefer to transship. In addition, the decision maker in a centralized chain will have the exact same preference as that of players in a decentralized setting when the retailers’ and the supplier's preferences coincide and wholesale prices are exogenous. This preference concordance does not hold if wholesale prices are endogenous.