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 inventory 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 institute 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 determine 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 improvements 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. This paper was accepted by Gad Allon, operations management.

T his paper empirically investigates using the e-mail channel to target customers with a delayed incentive promotionspecifically, gift card promotion-and derives data-driven e-mail targeting policies. Gift card promotions are popular across retailers because they incentivize customers to spend more than a fixed expenditure level on regularly priced products by rewarding customers with a gift card to be redeemed against a future purchase. The e-mail channel provides retailers with new sources of customer-level data, which enables better prediction of customers' responsiveness to e-mails (e.g., clicking) and the sales promotion that comes with it (e.g., participation in the promotion). We formulate the retailer's promotion e-mail targeting problem by maximizing two objectives-the promotion's profitability (i.e., profit-based targeting) and e-mail click-through rate (i.e., CTR-based targeting). We also take into account the retailer's promotion budget and exclusivity concerns in targeting e-mails. We use a comprehensive dataset from a Fortune 500 luxury fashion retailer's online channel and utilize both parametric and non-parametric methods to predict customers' response to promotion e-mails. Our data-driven targeting policies improve the promotion's profitability by 5.57% and e-mail CTR by 472.57%, on average, compared to our partner retailer's current e-mail policy. We also find that the CTR-based targeting policy lowers the promotion profitability by, on average, 9.09% compared to the profit-based one. However, the CTR-based policy recuperates the short-term losses in the long-term and increases the long-term profitability by 3.94%, on average, compared to the profit-based targeting policy.

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