There are many materials for which the quantity needed by a firm is at best indirectly related to the quantity of final product produced by that firm, such as solvents in manufacturing processes or office supplies. For any such "indirect" materials, an inescapable incentive conflict exists: The buyer wishes to minimize consumption of these indirect materials, while the supplier's profits depend on increasing volume. Both buyer and supplier can exert effort to reduce consumption, hence making the overall supply chain more efficient. However, no supplier will voluntarily participate unless contract terms are fundamentally revised. This can be done through a variety of "shared-savings" contracts, where both parties profit from a consumption reduction. This paper analyzes several such contracts currently in use for chemicals purchasing. We show that such contracts can always increase supply-chain profits but need not lead to reduced consumption. We analyze equilibrium effort levels, consumption, and total profits, and show how these change with the contract parameters. We find that the goals of maximizing joint profits and minimizing consumption are generally not aligned. Also, surprisingly, a decrease in a cost parameter can lead to a decrease in profits; it may be necessary (but is always possible) to renegotiate the shared-savings contract to reap the benefits of a cost decrease.Supply-Chain Management, Supply Contracts, Shared Savings, Game Theory, Environmental Management, Indirect Materials

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Abstract:In this paper, we extend the results of Ferguson M. Naval Research Logistics 50, 2003, 917-936. on an end-product manufacturer's choice of when to commit to an order quantity from its parts supplier. During the supplier's lead-time, information arrives about end-product demand. This information reduces some of the forecast uncertainty. While the supplier must choose its production quantity of parts based on the original forecast, the manufacturer can wait to place its order from the supplier after observing the information update. We find that a manufacturer is sometimes better off with a contract requiring an early commitment to its order quantity, before the supplier commits resources. On the other hand, the supplier sometimes prefers a delayed commitment. The preferences depend upon the amount of demand uncertainty resolved by the information as well as which member of the supply chain sets the exchange price. We also show conditions where demand information updating is detrimental to both the manufacturer and the supplier.

We model a modular assembly system in which a final assembler outsources some of the assembly task to first-tier suppliers (subassemblers), who produce modules made up of multiple components. The assembler sets module prices it will pay to the subassemblers, the subassemblers set component prices they will pay to suppliers, and then all players choose how much capacity to install, with the minimum capacity choice determining system capacity. Finally, stochastic end-product demand is observed and all players produce (and are paid for) the same number of units -the minimum of demand and system capacity.We characterize equilibrium price and capacity choices, and then use that characterization to derive results regarding higher-level structural choices by the assembler -such as how to group components into modules and which suppliers to choose as subassemblers. We also compare performance of the system to a traditional assembly system with an assembler and suppliers but without subassemblers.

Problem definition: This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining the service provided by a driver. A portion of that price is paid to the driver who delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multihoming” (or “multiapping,” as it is known in practice), drivers deliver their service through both platforms. Academic/practical relevance: This is one of the first papers to study competition and multihoming in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multihoming). Methodology: We formulate our problem as a pricing game between two platforms and employ the concept of a Nash equilibrium to analyze equilibrium outcomes in various settings. Results: In both the single-homing and multihoming settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multihoming. Managerial implications: We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or multihome. We find that although individual drivers may have an incentive to multihome, all players are worse off when all drivers multihome. We conclude by proposing an incentive mechanism to discourage multihoming.