Selling Season Research Articles

EXPRESS: Impact of Unreliable Supplier on a Supply Chain with Demand-Generating Marketing

A firm often has multiple sourcing options for its product. Some suppliers may have longer lead times but offer lower prices, whereas reliable suppliers with quick response capabilities may have higher prices. Purchasing from a cheaper, less reliable supplier can help a firm lower its inventory cost but will expose itself to potential risks. This paper analyzes a supply chain in which a manufacturer can source from a cheaper, less reliable supplier with a long lead time and/or a reliable quick-response supplier. The manufacturer also has to make demand-generating marketing investments before the selling season. Our analysis shows that if the unreliable supplier’s reliability is low, the reliable supplier will charge a low wholesale price; otherwise, it will choose a high wholesale price. As the unreliable supplier’s reliability improves, the manufacturer is inclined to increase its marketing investment, but there is a drop at some critical threshold. Interestingly, the existence of an unreliable supplier can lead to a win-win outcome for the reliable supplier and the manufacturer. The unreliable supplier’s reliability has non-monotonic effects on the equilibrium profits of the reliable supplier and the manufacturer.

Can crowdsourcing improve prediction accuracy in fashion retail buying?

Fashion retailers’ buyers must decide how much to buy of merchandise well before a selling season. This order quantity decision is always challenging due to limited historical data and high demand unpredictability arising from the novelty of fashion merchandise. Despite many attempts to solve this longstanding problem, industry reports consistently show that fashion buyers’ predictions of product salability and future sales are frequently inaccurate, leading to loss of profits for retailers. In this research, the authors take an “Empirics-First” approach to explore an alternative solution, crowdsourced forecasts from ordinary customers, and investigate whether crowdsourced forecasts would be more accurate than those of expert fashion buyers and if so, how should a crowd be formed in terms of size and composition? After conducting an online experiment, finding that forecasts by a “crowd” of ordinary customers are significantly more accurate than those of expert fashion buyers, the authors test a contingency framework in a second empirical study examining how crowd size and composition impact forecasting accuracy for products of varying fashionability. The results revealed that heterogeneity in a crowd is a key factor in prediction accuracy. Specifically, crowds with more variation in income and shopping frequency made notably accurate predictions. Another key finding of the study pertains to the required crowd size; increasing the size of a crowd at first sharply decreased the crowd's prediction error. However, after a certain point, there were diminishing returns in prediction accuracy. Given the interesting results, the paper concludes with guidelines for implementing crowdsourced forecasting by fashion retailers and directions for future research.

Competitive Dynamic Pricing Under Capacity Constraints: An Experimental Study

Keeping up with competitors’ prices is one of the top operational challenges in pricing. However, competitive interactions in revenue management have not received much research attention in the past due to their complexity. We conduct a series of laboratory experiments to investigate the dynamic pricing behavior of two capacity-constrained firms under competition. Our experiments initially control for strategic interactions between the sellers and then allow for them. To gain a broader understanding, we also manipulate demand uncertainty and the expected market size. Our results confirm the dependency of dynamic pricing decisions on the competitor's behavior. We find that the theory is much more forgiving—in the sense that it predicts a lower level of competition among the sellers—than what we actually observe in the laboratory. The modeling literature indicates that the seller with the lower capacity has a competitive advantage, but our results reveal the opposite. Further, there is potential for high-capacity sellers to benefit from competition. Sellers tend to underprice (resp., overprice) their units at the beginning (resp., end) of a selling season. Also, competition lasts longer than the theory predicts, and customers benefit from the biases of the competing sellers. The higher-capacity seller following the best-response policy is not harmed due to the biases of the competitor. However, the lower-capacity seller's performance is greatly influenced by the competitor's degree of rationality.

Inventory reallocation in a fashion retail network: A matheuristic approach

We consider a fashion retail network consisting of a central warehouse, owned by a fashion firm, and a fairly large number of retail stores. Some stores are owned by the firm itself, whereas others are owned by franchisees. An initial inventory allocation decision is made at the beginning of the selling season and is periodically revised. Inventory reallocation comprises both direct shipments from the warehouse to stores and lateral shipments among the stores. Besides stock availability and shipping costs, a suitable reallocation policy must take into account the probability of selling each item, some operational constraints, as well as other preference factors that define the utility of shipping an item from a node of the network to another one. Since the problem does not lend itself to the application of typical tools from inventory theory, we propose an optimization model that complements such tools. The model, given the number of nodes and SKUs, may involve about one million binary variables, and just solving the LP relaxation may take hours using state-of-the-art software. Since typical metaheuristics for combinatorial optimization do not seem a viable alternative, we propose a matheuristic approach, in which a sequence of maximum-weight matching problems is solved in order to reduce the problem and restrict the set of potential shipping pairs, with a corresponding drop in the number of decision variables. Computational results obtained on a set of real-life problem instances are discussed, showing the viability of the proposed algorithm.

Green supply chain finance strategies with market competition and financial constraints

In the context of sustainable development, market competition is intensifying, and financial constraints have emerged as a significant hindrance to green project investment. Green Supply Chain Finance (GSCF), characterized by long-term collaboration, has emerged as a crucial financial approach to mitigate corporate financial limitations and channel capital flows into environmentally friendly industries. We propose a two-echelon supply chain with one supplier and two competing retailers over a single period and investigate ordering, sales, and financing decisions simultaneously under competition. Retailers constrained by financial considerations may secure GSCF or traditional bank financing (BF) loans. This study investigates the influence of competition on pricing and sales strategies during the selling season. The results demonstrate that retailers select between clearance and responsive selling strategies based on the level of market competition. During the ordering season, retailers share the product market equally when interest rates are uniform, and the supplier formulates a supply chain contract while considering the financing interest rate. In the presence of differential interest rates, retailers may not always opt for the GSCF, even when they offer an interest rate advantage, due to the comprehensive impacts of operational and financial strategies. Remarkably, competitive retailers do not choose the GSCF when their initial green investment capital surpasses a certain threshold.

Optimal Timing Strategies in the Evolutionary Dynamics of Competitive Supply Chains

This study investigates the dynamics of endogenous order placement timing among competing retailers within a single period, driven by the evolution of demand-forecast information. Despite the critical role of accurate market trends and demand forecasts in determining firm success during selling seasons, the existing literature lacks a comprehensive understanding of how firms strategically adjust their order timing with imperfect and evolving information landscapes. By leveraging resources such as predictive analytics systems operated by big data and social media, firms tend to enhance their market demand precision as the selling season approaches, aligning with market practices. With this background, we aim to address the strategic behaviors of competing retailers in timing their orders, filling the aforementioned research gap. We construct a non-cooperative game-theoretical model to analyze the strategic behaviors of competing retailers in timing their orders. The model incorporates factors such as imperfect and evolving information landscapes, considering how firms leverage resources to enhance their market demand precision as the selling season approaches. Our analysis shows two primary equilibria, each shedding light on distinct strategic choices and their implications. First, the better-informed firm decides to execute early orders, capitalizing on the first mover’s advantage, particularly when initial information imprecision exceeds a specific threshold. Conversely, a second equilibrium emerges when the better-informed firm delays its orders, yielding the first mover’s advantage to the less-informed competitor. These equilibria highlight the correlation between order timing strategies and the trajectory of information evolution within the competitive landscape. Additionally, our study extends beyond equilibrium analysis to investigate these strategic choices on supply-chain performance.

Optimal product and after-sales service decisions considering risk attitudes under price-dependent uncertain demand

In this paper, we investigate a service-oriented manufacturer that provides fashionable products with after-sales services to customers. Before the selling season, the manufacturer invests in service capacity in advance and then decides on the production quantity and selling price based on updated demand information. Interestingly, it is found that the manufacturer’s production and pricing decisions will not be affected by the unit production cost when the market base exceeds a threshold. We investigate and compare the manufacturer’s decisions in two cases: imperfect and perfect demand information updates. The selling price in the imperfect demand updates case is always set lower than that in the perfect demand updates case when the service capacity is sufficient but may not be under insufficient service capacity. The possibility that the prepared service capacity limits the production quantity may decrease with the unit production cost in the imperfect demand updates case. In contrast, this possibility is invariant with the unit production cost in the case of perfect demand updates. The effects of risk aversion are also investigated. It is found that under the conditional value-at-risk (CVaR) criterion, the manufacturer with a higher risk-averse degree establishes a lower service capacity and sets a lower selling price.

Two-period decision strategies in a dual-channel supply chain considering reference price and online reviews

The rapid development of e-commerce and Internet technology impacts the consumer purchasing practices and the decision-making of the supply chain. In this regard, dealing with online reviews and reference price together for a competitive interaction in a two-period dual-channel scenario is one of the main challenges. To address this interaction, in the present research, we consider a dual-channel supply chain with a two-period. The selling price and the reference price impact on the retailer’s demand in both channels. Whereas, the online reviews influence the demands of the direct channels only. The manufacturer, who announces wholesale prices and direct channel selling prices, is Stackelberg game’s leader and the retailer is the follower. Two different decision-making strategies (I, II) are made by both players: (I) The manufacturer and the retailer both make all their decisions at the beginning of the selling season. (II) Here all decisions are made at the beginning of each selling period. In order to compare these strategies, we create a centralized policy as a benchmark scenario. The optimal solutions of the supply chain and each player are determined and analyzed. The numerical and sensitivity analysis suggests that the responsive pricing can bring additional benefits to both the players.

Quick Response Under Strategic Manufacturer

Problem definition: Quick response is a classic operations strategy that allows a retailer to place a rapid replenishment order during the selling season using information learned from early sales. The benefits of quick response are widely studied in the literature under the condition that the manufacturer’s wholesale prices are exogenously given. Motivated by the practice of emerging small and medium-sized enterprise (SME) fashion brands, this paper revisits the value of quick response for a retailer when a manufacturer can strategically set its wholesale prices. Methodology/results: We develop a game-theoretic model consisting of one manufacturer and one retailer. In contrast to the traditional quick response setting, the manufacturer can dynamically adjust wholesale prices for both regular and replenishment orders. First, we investigate whether and when quick response still benefits the retailer. We find that, under low or significantly high demand uncertainties, the firms share a common preferred ordering strategy, and quick response benefits the retailer as well as the supply chain. But, under moderately high demand uncertainty, the retailer’s favored ordering strategy conflicts with the manufacturer’s interest; as a result, the manufacturer would set wholesale prices to counter the retailer’s ordering strategy, which makes quick response detrimental to the retailer. Second, we search for mechanisms that can resolve this conflict and restore the beneficial effect of quick response. We show that letting the manufacturer commit to wholesale prices up front is ineffective in fixing the problem. However, if the retailer can propose a take-it-or-leave-it wholesale price for the replenishment order (possibly with the replenishment quantity) once the regular wholesale price is set, then quick response leads to a win–win outcome for both firms. Managerial implications: The findings caution retailers with weak power (e.g., SMEs) when adopting quick response, especially when facing moderately high demand uncertainties. The retailer, although weak, should be aware of the retailer’s natural ability to propose replenishment terms because, otherwise, the retailer can always forgo quick response; this opens up an opportunity to design more favorable arrangements. Funding: The work of J. Chen was supported by the National Natural Science Foundation of China [Grants 72171202, 72232007]; the work of Q. Hu was supported by the National Natural Science Foundation of China [Grants 72271057, 72091211]; the work of D. Shi was supported by the National Natural Science Foundation of China [Grant 72102205] and Shenzhen Stable Support Program for Higher Education Institutions; the work of F. Zhang was supported by the National Natural Science Foundation of China [Grants 71929201, 72131004]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0561 .

Impacts of gray market selling on the supply chain under product upgrade and pricing flexibility decisions

AbstractGray markets are infamous for unauthorized diversion of authentic products at lower prices to harm the manufacturer's authorized distribution channel. Hence, manufacturers are becoming more proactive in shaping strategies that can counter gray markets. By considering the risk of unauthorized selling through gray markets, we analyze the manufacturer's strategic channel choice and product upgrade decision. We analytically explore the provision of granting flexibility to the legitimate retailer to adjust price during the selling season as a strategy to cope with gray market. We find that unauthorized selling through gray markets has a severe negative impact on the manufacturer's profitability depending on the degree of channel differentiation and product brand equity. We counter‐intuitively reveal that the overall decentralized supply chain can be better‐off, owing to higher product leakage, especially because the legitimate retailer gains an increased sales volume through product diversion to the gray market. However, the manufacturer's loss is more severe in such a decentralized supply chain. We show that the manufacturer can eliminate unauthorized sales through gray market by offering price adjustment flexibility to the retailer. Another compelling finding exhibits that the manufacturer's decision to launch an upgraded product can intensify the diversion of an existing product. However, unfair competition from the unauthorized channel cannibalizes the sales of an upgraded product, thereby making the manufacturer worse‐off. Interestingly, despite the cannibalization of upgraded product sales, the manufacturer can be better‐off in terms of overall profitability via price adjustment flexibility if both the degree of upgrade and brand equity are higher. Overall, in addition to deploying monitoring mechanisms for supervising legitimate retailers, price adjustment flexibility can reduce product diversion if the degree of upgrade is moderate and channel differentiation is higher.

Network revenue management with online inverse batch gradient descent method

We consider a general class of price‐based network revenue management problems that a firm aims to maximize revenue from multiple products produced with multiple types of resources endowed with limited inventory over a finite selling season. A salient feature of our problem is that the firm does not know the underlying demand function that maps prices to demand rate, which must be learned from sales data. It is well known that for almost all classes of demand functions, such as linear, exponential, multinomial logit, and nested logit models, the revenue rate function is not concave in the products' prices but is concave in products' market shares (or price‐controlled demand rates). This creates challenges in adopting any stochastic gradient descent based methods in the price space. We propose a novel nonparametric learning algorithm termed the online inverse batch gradient descent (IGD) algorithm. This algorithm proceeds in batches. In each batch, the firm implements each product's perturbed prices and then uses the sales information to estimate the market shares. Leveraging these estimates, the firm carries out a stochastic gradient descent step in the market share space that takes into account the relative inventory scarcity for the entire horizon and then inversely maps the updated market shares back to the price space to obtain the prices for the next batch. Moreover, we also propose an inventory‐adjusted algorithm (IGD‐I) that the feasible market share set is dynamically adjusted to capture the real‐time relative inventory scarcity for the remaining season. For the large‐scale systems wherein all resources' inventories and the length of the horizon are proportionally scaled by a parameter k, we establish a dimension‐independent regret bound of [Formula: see text]. This result is independent of the number of products and resources and works for a continuum action‐set prices and the demand functions that are only once differentiable. Our theoretical result guarantees the efficacy of both algorithms in the high‐dimensional systems where the number of products or resources is large and the prices are continuous. Our algorithms also numerically outperform the existing algorithms in the literature.

Optimal pricing and donation policy for fresh goods

This paper studies a socially responsible food-retailer’s operational planning problem for a continuously deteriorating inventory over two periods with the consideration of donation and quality-sensitive customers. Each year, millions of tonnes of food are wasted causing economic, environmental, and social misfortunes, while at the same time millions are undernourished. Besides expired items, edible foods are often deliberately disposed of to attract quality-sensitive consumers. We address this issue by presenting an optimization model that incorporates a retailer’s corporate social responsibility act, in the form of charitable donations, and makes use of the internet of things (IoT)-enabled condition tracking technologies to accurately estimate the effective (true) quality of the goods and its impacts on consumer demand. We formulate a quality-dependent newsvendor problem (QDNP) to determine the stocking quantity and the regular price of the goods at the beginning of the selling season, and the second-period price and donation policy at the end of the first period. The optimal donation policy at the end of the first period depends on the quality (time to expiration), on-hand inventory, and donation reward. Moreover, for a given inventory level, expected food waste is always greater in the absence of donations. QDNP outperforms the no-donation model, particularly when the uncertainty is high and/or the length of the second period is short. Interestingly, the two models react to an increase in uncertainty oppositely: QDNP orders more to alleviate future shortages, whereas, no-donation policy orders less to avoid future disposal costs at the end of the selling season.

How to finance the supply chain when you are small? Clearance sale and loan payment timing in financially constrained supply chains

Before inventory can be put into shelves at a retailer, it needs to be produced, which takes time. During this time traditionally the supplier had to cover the money bound in inventory, but new paradigms, such as supply chain finance, change who pays for work-in-process inventory. While this opens new possibilities for smaller, financially constrained suppliers, it puts additional financial pressure on the retailers. So, it may exclude smaller retailers, which are also cash constrained. If demand fluctuates then they may even have to recur to clearance sales to repay the loan. This study provides guidance to these small retailers on which clearance sale strategy to adopt, and when to repay their loans, investigating the interaction between both decisions using Stackelberg game framework in a dyadic supply chain where the supplier is the Stackelberg leader. It considers multiple bank interest rates and contract types chosen by the supplier. Results show that the retailer’s optimal order quantity decreases by adopting a limited clearance sale model for leftover disposal if it pays its loan immediately after the normal selling season. If it decides on loan payment after clearance sale, its optimal order quantity is not influenced by the clearance sale strategy, and we further demonstrate that the retailer’s optimal order quantity, loan payment timing, and expected profit are influenced by the supplier’s chosen contract. We find that buyback (BB) and revenue sharing (RS) contracts coordinate the supply chain only if the retailer resorts to a bank loan for inventory procurement.

A Newsvendor Problem Considering Decision Biases of Strategic Customers with Private Product Value Information

In this paper, we consider a newsvendor system with strategic customers, who are boundedly rational and risk averse in terms of buying during the selling season or waiting for a clearance sale with price discounts. The newsvendor’s decision is to determine the optimal stock quantity. An optimization problem is formulated with the incorporation of competition among strategic customers with private product value information. We embed risk aversion within the quantal response equilibrium to characterize the strategic customer behavior. The influences of the decision biases of strategic customers on the newsvendor’s decision and profit are discussed. We find that the risk aversion considered alone always benefits the newsvendor. However, the bounded rationality considered alone benefits the newsvendor conditionally. Combining the two behavioral factor influences, the decision biases cause the newsvendor to order more and be better off when the critical fractile is high but to order less and be worse off when the critical fractile is low.

Two-period pricing and inventory decisions of perishable products with partial lost sales

Food waste has become one of the most critical issues for individuals and organizations involved in food marketing and policy across the world, which poses serious challenges to sustainable development. To reduce food waste, we develop a two-period dynamic pricing and inventory decision model for perishable products with partial lost sales. Faced with random demand, the firm has two opportunities to make inventory decisions during the selling season. The first inventory decision (for Period 1) is made at the beginning of the selling season and used to detect the market information, and the second decision (for Period 2) is to update the inventory level by reordering or returning based on the fully-realized market demand information. The firm may carry the unsold units left from Period 1 to Period 2, and there may be two types of stocks for the same products, but with different perceived levels of quality (old v.s. fresh) in Period 2. Both price competition and product substitutability exist between the fresh and old units. Using a dynamic programming approach, we derive the optimal solutions for the order quantities and retail prices of products under various quality conditions. We find that the reorder quantity at the middle of the season decreases with the quantity and the quality of old units. The pricing of fresh units does not depend on the quality or quantity of old units, while the pricing of old units increases with their quality and decreases with respect to the quantity.

Optimal sourcing decisions considering quantity constraints under rank-dependent utility theory

To improve profits, retailers can order products from two suppliers: one cheap supplier with a long lead time and one quick-response supplier with a high cost. Business practices and empirical investigations have shown that retailers’ actual ordering decisions often deviate from expected utility due to behavioural factors. This paper investigates a sourcing problem for a retailer from two manufacturers with different lead times and costs under quantity constraints considering rank-dependent utility theory (RDEU). RDEU involves behavioural factors, including loss aversion, reference profit and weighting of probabilities. For this problem, we build a two-stage ordering model and derive the optimal ordering decisions before and during the selling season by using convex optimisation methods. We characterise the optimal ordering decisions of the retailer under RDEU considering different quantity constraints. We compare the results with those under the expected utility theory (EUT). We analyse the effects of behavioural factors on the retailer’s ordering decisions. It is found that when the selling price is larger, a retailer will always order more from the manufacturers before the selling season under EUT but may order less under RDEU when facing demand uncertainty and quantity constraints during the selling season. Additionally, when the selling price is high, the loss-averse retailer should order more than the risk-neutral retailer to avoid inventory shortage. A higher degree of loss averseness will induce the retailer to delay placing orders to gain more demand information and reduce the possibility of stock out or overstock.

Buy now or later? New or remanufactured? How to attract strategic customers

It is common for sellers to markdown product prices late in a selling season which prompts consumers to strategically manage their purchase timing. Facing consumers’ strategic behaviour, the manufacturer should decide the prices for products carefully to ensure its profit. This paper explores the manufacturer’s optimal pricing decisions in a closed-loop supply chain by considering the customers’ type, the limited number of returns, consumers’ acceptability of the remanufactured products, as well as the discounting rate of consumers’ perceived value for a delayed purchase. The results show that the manufacturer’s retailing plan and the optimal prices depend on the unit production cost of the new and remanufactured products. In general, the consumer’s strategic behaviour hurts the manufacturer’s profit. When consumers are strategic, the manufacturer reduces the selling price of the new product and it is less likely to engage in remanufacturing when the unit production cost of the new product is high as compared to the case of myopic customers. The numerical experiments provide additional implications and show that the manufacturer can take some simple counter-measure strategies to react to either the underestimation or overestimation of the expected price by the consumer.

Managing Airfares Under Competition: Insights from a Field Experiment

Airfares evolve dynamically, giving rise to a so-called price path. This price path is controlled via two levers: (i) a fare ladder, which defines a set of airfares before the selling season, and (ii) revenue management algorithms, which control how fares evolve along the ladder during the season. We hypothesize that the current policies to control both levers—which do not account for quality differences between competing airlines—give rise to an inefficient price path and, accordingly, a loss of potential revenue. We substantiate this hypothesis via a field experiment. By partnering with an airline, we introduced quality considerations in the design of fare ladders, across 5,000 itineraries, to show that current ladder-design policies indeed lead to a suboptimal price path. We also show that this inefficiency can be mitigated by incorporating quality differences between competing airlines. This creates a smoother (and more profitable) price path. This paper was accepted by Vishal Gaur, operations management.

Minimizing online retailers’ revenue loss under a time-varying willingness-to-pay distribution

We study revenue management for an online retailer that offers a single product to customers with a time-varying willingness-to-pay (WTP) distribution. When the WTP distribution changes at an unknown time point (i.e., the change point), the original price quoted to customers may not be optimal. The retailer’s objective is to minimize his revenue loss by determining when to quote a new price to incoming customers and what the new price is. We formulate the problem as an optimization model, which is challenging to solve due to the presence of the unknown change point. To overcome this challenge, we first characterize the probability distribution of the estimated change point using maximum likelihood estimation. We then develop two pricing algorithms to solve the model by leveraging the characterized distribution. Lastly, through numerical studies, we illustrate the effectiveness of the proposed algorithms in identifying change points and recommending optimal post-change selling prices. The numerical results suggest that the proposed pricing algorithms significantly reduce the frequency of false change detection in the early stages of a selling season. The proposed pricing algorithms also outperform the benchmark pricing algorithm in reducing the retailer’s revenue loss.

Market supply of livestock and animal products in north-eastern Madagascar – the role of the vanilla boom

The SAVA region, located in north-eastern Madagascar, is a global hotspot of vanilla production. Here, soaring vanilla prices over the past 7 years, the so-called “vanilla boom”, improved the socioeconomic situation of the farming population, which may change dietary patterns towards higher meat consumption and offer an opportunity for regional livestock producers. This paper therefore investigates if the vanilla boom opened new market opportunities to local stakeholders by targeting 81 livestock producers, 23 butchers, and 34 retailers involved in the supply chain of live animals and animal products in the region. Starting from regular food markets of three major cities and three villages, data on animal and meat sales in and outside the vanilla selling season (July-September) was collected. Information on opportunities and challenges faced by different stakeholders was recorded, and the determination of the geographical location of markets, animal and product origins served the compilation of foodsheds. Results revealed that local livestock producers are mostly supplying poultry while butchers are exclusively selling zebu cattle and pigs. Zebus and eggs are completely sourced from neighbouring as well as distant regions to meet the huge local demand, while poultry and pigs are mostly supplied by regional producers. Animal sales volumes increase during the vanilla selling season, due to a temporarily enhanced regional purchasing power based on cash income from vanilla sales. Although the high demand motivates local and more remote producers to intensify their livestock activities, livestock diseases, lack of appropriate animal management, and market information hamper their endeavours. Transparent and timely market information flow, training of livestock producers, as well as investments in transportation and marketing infrastructure could support the development of the local livestock sector and improve the local supply with animal source food in the SAVA region.