Both traditional retailers and e-tailers have been implementing omnichannel strategies such as buy online, pick up at store (BOPS). We build a stylized model to investigate the impact of the BOPS initiative on store operations from an inventory perspective. We consider two segments of customers, namely store-only customers who only make purchases offline and omni-customers who strategically choose between offline and online channels. We show that BOPS may either benefit or hurt the retailer depending on two fundamental system primitives: the store visiting cost and the online waiting cost. If the online waiting cost is relatively low and the store visiting cost is even lower, BOPS can induce omni-customers to migrate from online buying to BOPS, leading to demand pooling at the brick-and-mortar (B&M) store. Such demand pooling provides two benefits for the retailer: it reduces the overstocking cost, and after inventory reoptimization, it results in a higher fill rate at the B&M store, which benefits existing customers and potentially attracts more customers to the store. In contrast, if both store visiting and online waiting costs are relatively high with the latter even higher, introducing BOPS can result in demand depooling as a result of the migration of the omni-customers from offline purchasing to BOPS. This leads to a lower fill rate after inventory reoptimization, likely the result of a lower profit margin under BOPS, which turns away store-only customers and hurts the retailer. This paper was accepted by Charles Corbett, operations management.

In this paper, we consider a production system which is capable to produce two types of products. The first type of products is make-to-order, while the second type is make-to-stock whose demand is satisfied by the on-hand inventory. The demand arrival rates of both types of products are price-sensitive. The excess demand that cannot be satisfied immediately is either backlogged or lost. The system costs include the holding costs of product inventories and shortage costs of unsatisfied demand. The objective is to maximise the total discounted profit over an infinite planning horizon by coordinating the production process and pricing decisions. By analysing the properties of objective functions, we characterise the optimal control policy by two switch curves and the optimal price is also given for different ordering and inventory levels. We also explore the monotonicity of both switch curves which will reduce the computation effort. Numerical experiments are conducted to demonstrate the use of the switch curves in managing the production system and illustrate that compared with the static pricing policy, the optimal integrated price and inventory control policy can result in a significant profit improvement in the make-to-order/make-to-stock system that is much higher than in a single-product system.

Inventory Management in Stochastic Distribution Systems

Inspired by emerging financing techniques that offer additional financing options for entrepreneurs, we establish a stylized model to compare two prevalent crowdfunding strategies: Reward-based crowdfunding and Initial Coin Offerings (ICOs). First, we highlight that the fundamental difference between reward-based crowdfunding and ICO lies in the distinct types of price distortion, resulting in different agency costs. Specifically, compared to the first-best benchmark, reward-based crowdfunding sets a lower funding price to attract more consumers to pre-order, while ICO sets a higher market price due to speculators’ revenue-sharing in the market stage. Second, we show that the financing limit (i.e., the maximum funds raised) of reward-based crowdfunding is driven by the platform traffic, whereas that of ICO is driven by the marginal production cost. Therefore, even with numerous speculators, ICO may result in a lower financing limit than reward-based crowdfunding when the platform traffic or the marginal production cost is high. Third, we explore how products’ cost structures shape entrepreneurs’ financing preferences. ICO suits intangible products with high fixed cost and low marginal production cost, while reward-based crowdfunding aligns better with products exhibiting the opposite cost structure. Finally, we extend our model to show the robustness of our findings and offer some additional insights. For example, ICO may amplify the risk of market uncertainty compared to reward-based crowdfunding; Security Token Offerings (STO) benefits from the absence of agency costs between the entrepreneur and speculators compared to ICO while it may suffer from equity loss, particularly in risky and regulated markets, etc.

This paper explores how a strategic B&M retailer monetizes its showrooming service and how this affects a brand's incentive to use the service, consumer surplus, and social welfare. We build a stylized model in which the retailer decides the showrooming fee, and the brand decides whether to pay the fee to showcase its product in the retailer's offline channel in addition to its own online channel and separately sets the optimal prices for both channels. Consumers who face the uncertainty of the product value are assumed to have different levels of hassle costs of buying from the online channel and choosing which channel to visit. Our equilibrium analyses show that the brand has an incentive to use the retailer's offline channel to achieve an intended market segmentation based on hassle cost levels. Interestingly, the retailer's optimal profit and the brand's optimal prices both increase in search cost. Showrooming decreases consumer surplus when the value of a good match is relatively low or high, and a “Win–Win–Win–Win” situation in which all stakeholders (the retailer, the brand, consumers, and the social planner) benefit from monetizing the showrooming service may exist. Our paper also explores the role of a price-matching policy and service costs incurred by the retailer. Relevant managerial implications are discussed.