Pricing strategy and product substitution of bullwhip effect in dual parallel supply chain: aggravation or mitigation?

The bullwhip effect (BE) affects not only the revenue of the retailer but also the revenue of the manufacture. Thus, a lot of retailers and manufacturers aim to attenuate the negative impact of the BE. In this research, two parallel supply chains distributing two substitutable products with price-sensitive demands are considered, the order-up-to inventory policy, as well as the MMSE forecasting method, are employed by retailers in these chains. The retailer’s price-setting follows the first-order vector autoregressive process, suggesting that its pricing decision depends on its previous price as well as its rival’s price, owing to the BE. The analytical expression of the BE is calculated by the statistical method. Besides, the effects of pricing strategy and product substitution on the BE are studied through simulation. A conclusion can be drawn that the BE of the two parallel supply chains will be affected by lead time, product substitution rate, and pricing coefficient. Of particular interest is that the BE can be efficiently alleviated by adopting a price strategy with many correlations and a small coefficient of autocorrelation.

Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.

Online Assortment Optimization with Reusable Resources

We consider an online assortment optimization problem where we have n substitutable products with fixed reusable capacities [Formula: see text]. In each period t, a user with some preferences (potentially adversarially chosen) who offers a subset of products, St, from the set of available products arrives at the seller’s platform. The user selects product [Formula: see text] with probability given by the preference model and uses it for a random number of periods, [Formula: see text], that is distributed i.i.d. according to some distribution that depends only on j generating a revenue [Formula: see text] for the seller. The goal of the seller is to find a policy that maximizes the expected cumulative revenue over a finite horizon T. Our main contribution is to show that a simple myopic policy (where we offer the myopically optimal assortment from the available products to each user) provides a good approximation for the problem. In particular, we show that the myopic policy is 1/2-competitive, that is, the expected cumulative revenue of the myopic policy is at least half the expected revenue of the optimal policy with full information about the sequence of user preference models and the distribution of random usage times of all the products. In contrast, the myopic policy does not require any information about future arrivals or the distribution of random usage times. The analysis is based on a coupling argument that allows us to bound the expected revenue of the optimal algorithm in terms of the expected revenue of the myopic policy. We also consider the setting where usage time distributions can depend on the type of each user and show that in this more general case there is no online algorithm with a nontrivial competitive ratio guarantee. Finally, we perform numerical experiments to compare the robustness and performance of myopic policy with other natural policies. This paper was accepted by Gabriel Weintraub, revenue management and analytics.

Sharing Demand Information with Retailer Under Upstream Competition

We analyze demand information sharing collaboration between two manufacturers and a retailer under upstream competition. The manufacturers produce partially substitutable products, which are stocked by the retailer that sells them in the market characterized by random demand. The manufacturers are privately informed about uncertain demand and decide on whether to share this information with the retailer. We show that by not sharing information, a manufacturer ends up distorting its wholesale price upward to signal its private information to the retailer, and under upstream competition, this distortion is propagated to the competing manufacturer. Thus, although a manufacturer’s decision to not share information may benefit or hurt its own profit, this always benefits the competing manufacturer. Under low intensity of competition, signaling-driven distortions exacerbate double marginalization and hurt all parties, whereas under more intense competition, these distortions help manufacturers offset downward pressure on wholesale prices. Thus, in equilibrium similarly informed manufacturers share information in the former case but not in the latter case. Additionally, when manufacturers differ in their information accuracies, only the better-informed manufacturer shares information. The retailer always benefits from both manufacturers sharing information, and its benefits are larger when the better-informed manufacturer shares information. We show existence of a contracting mechanism the retailer can employ to enable information sharing. Finally, we analyze manufacturers’ information acquisition decisions and find that under competition, two manufacturers acquire minimal information so that they are better off not sharing information in the information sharing game. This paper was accepted by Vishal Gaur, operations management.

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.