Title: A Mechanism Design Approach to Vendor Managed Inventory
This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage in- ventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help insti- tute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically de- termine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit im- provements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer. more »« less
A New Approach to Contract Design with Private Inventory Information
In a typical decentralized supply chain, a downstream retailer privately observes its inventory level and has an informational advantage over the upstream supplier. In “A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information” by Bensoussan, Sethi, and Wang, the authors study how to optimally design a stationary, truth-telling, long-term contract in such a setting. In contrast to the classic first order approach in literature, they formulate the contract design as an optimization over a functional space and develop a solution approach based on the calculus of variations. They further apply their necessary optimality condition to the class of batch-order contracts, which replenish a prespecified inventory quantity for a fixed payment in each period only when the retailer has zero inventory on hand.
When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically do not know precisely how customers will react to price changes in the short term, during a disruption. In this article, we investigate the customer's order variability and the firm's profit under several representative heuristic pricing strategies, including no change at all (fixed pricing strategy), changing the price only (naive pricing strategy), and adjusting the belief and price simultaneously (one‐period correction [1PC] and regression pricing strategies). We show that the fixed pricing strategy creates the most stable customer order process, but it brings lower profit than the naive pricing strategy in most cases. The 1PC pricing strategy produces a more volatile customer order process and smaller profit than the naive one does. Although the regression pricing strategy is a more advanced approach, it leads to lower profit and greater customer order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that (i) completely eliminating the customer order variability by employing a fixed pricing strategy is not advisable and adjusting the price to match supply with demand is necessary to improve the profit; (ii) frequently adjusting the belief about customer behaviors under imperfect information may increase the customer's order variability and reduce the firm's profit. The conclusions are robust to the inventory assumption (i.e., without or with inventory carryover) and the firm's objective (i.e., market clearance or profit maximization).
This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.
In this work, we proposed a two‐stage stochastic programming model for a four‐echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two‐stage stochastic model with a multi‐period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.
Supply chain complexity is perceived to exacerbate the supply disruptions or shocks experienced by a city. Here, we calculate two network measures of supply chain complexity based on the relative number—horizontal complexity—and relative strength—vertical complexity—of a city’s suppliers. Using a large dataset of more than 1 million annual supply flows to 69 major cities in the United States for 2012–2015, we show that a trade-off pattern between horizontal and vertical complexity tends to characterize the architecture of urban supply networks. This architecture shapes the resistance of cities to supply chain shocks. We find that a city experiences less intense shocks, on average, as supplier relative diversity (horizontal complexity) increases for more technologically sophisticated products, which may serve as a mechanism for buffering cities against supply chain shocks. These results could help cities anticipate and manage their supply chain risks.
@article{osti_10128751,
place = {Country unknown/Code not available},
title = {A Mechanism Design Approach to Vendor Managed Inventory},
url = {https://par.nsf.gov/biblio/10128751},
DOI = {10.1287/mnsc.2019.3297},
abstractNote = {This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage in- ventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help insti- tute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically de- termine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit im- provements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer.},
journal = {Management Science},
author = {Kadiyala, Bharadwaj and Özer, Özalp and Bensoussan, Alain},
}
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