Joint product assortment,inventory and price optimization to attract loyal and non-loyal customers

In this paper, we analyze the emerging retail practice of carrying a combined product assortment consisting of both regular “standard” products and more fashionable and short-lived “special” products. The purpose of this practice is to increase store traffic by attracting heterogeneous classes of customers, which drives up sales of standard products due to the potential cross-selling effect. Customers who are primarily attracted by special products will also buy some standard products. In this context, we analyze three decisions that are crucial for a retailer׳s commercial success: the product assortment, the inventory levels and the pricing. We propose an optimization model and an iterative heuristic to analyze the trade-offs between the combined product assortment, the inventory level and the price per product when there is limited shelf space. Using numerical experiments, we show that our heuristic can be trusted and that its accuracy improves when the number of products increases. Our findings indicate that to attract more customers for standard products, a retailer may benefit from carrying low priced special products which, if considered in isolation, would be non-profitable. As the cross-selling effect decreases, a retailer should focus more on the standard assortment by increasing its size and decreasing the prices. However, introducing special products and ignoring the cross-selling effect may decrease a retailer׳s profitability. We show that the introduction of special products involves more than just choosing the right specials for non-loyal customers but impacts the global assortment planning, the standard products and the products pricing.     

The Effect of Product Variety and Inventory Levels on Retail Store Sales: A Longitudinal Study

We examine the effects of product variety and inventory levels on store sales. Using 4 years of data from stores of a large retailer, we show that increases in product variety and inventory levels are both associated with higher sales. We also show that increasing product variety and inventory levels has an indirect negative effect on store sales through their impact on phantom products—products that are physically present at the store, but only in storage areas where customers cannot find or purchase them. Our study highlights a consequence of increased product variety and inventory levels that has previously been overlooked in studies of retail product variety and inventory management. It also quantifies the impact of phantom products on store sales. In addition, our study provides empirical evidence to support earlier claims that higher product variety and inventory levels lead to an increase in defect rate. We discuss the implications of our findings for retail inventory and assortment planning and for the design of retail stores.     

A Single‐Period Assortment Optimization Model

This paper studies a single‐period assortment optimization problem with unequal cost parameters. The consumer choice process is characterized by a Multinomial Logit (MNL) model. When the store traffic is a continuous random variable, we explicitly derive the structure of the optimal assortment. Our approach is to use a comprehensive measure–profit rate to evaluate the profitability of each variant and then determine which product should be offered. We demonstrate that the optimal assortment contains the few items that have the highest profit rate. When the store traffic is discrete, the optimal solution is difficult to obtain. We propose a “profit rate” heuristic, which is inspired by the result for the case of continuous store traffic. In a special case with equal cost parameters and normal demand distribution, the profit rate heuristic is indeed optimal. Using randomly generated data, we test the effectiveness of the heuristic and find that the average percentage error is less than 0.1% and that the hit rate is above 90%. Our research provides managerial insights on assortment planning and accentuates the importance of measuring the profitability of each product when the demand is random and cannibalization among different products exists.     

On the Benefit of Inventory‐Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price‐sensitive demand, focusing on the benefit of the inventory‐based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.     

Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management

Retail requires efficient decision support to manage increasing product proliferation and various consumer choice effects with limited shelf space. Our goal is to identify, describe and compare decision support systems for category planning. This research analyzes quantitative models and software applications in assortment and shelf space management and contributes to a more integrated modeling approach. There are difficulties commonly involved in the use of commercial software and the implementation and transfer of scientific models. Scientific decision models either focus on space-dependent demand or substitution effects, whereas software applications use simplistic rules of thumb. We show that retail assortment planning models neglect space-elastic demand and largely also ignore constraints of limited shelf space. Shelf space management streams on the other hand, mostly omit substitution effects between products when products are delisted orout-of-stock, which is the focus of consumer choice models in assortment planning. Also, the problem sizes of the models are often not relevant for realistic category sizes. Addressing these issues, this paper provides a state-of-the-art overview and research framework for integrated assortment and shelf space planning.     

Late season low inventory assortment effects in the newsvendor problem

The assumption of the newsvendor being able to satisfy demand as long as on-hand inventory is positive does not hold for a non-homogenous product. Consumers who do not find a unit of the product which satisfies their secondary features preferences may not purchase the product even though the newsvendor has positive on-hand inventory. This is likely to occur late in the season as inventory level declines. We solve a newsvendor problem in which the probability of purchase by consumers is increasing in on-hand inventory for any inventory level below that which is needed to have a complete assortment. We identify the sufficient optimality condition for the order quantity. We show that, unlike the case of inventory-dependent demand models in the literature, the optimal order quantity may decrease due to the assortment effect. We investigate two types of pre-end of season discounts, immediate all-units and delayed, as ways to mitigate the late season assortment effect and show that in some cases, they can increase the newsvendor׳s profit and free up the shelf space for other products.     

考虑策略性消费者的生鲜农产品定价和库存决策

消费者的策略性行为使零售商的生鲜农产品的定价和库存决策面临更大挑战。本文基于报童模型,综合考虑消费者的策略性行为,对生鲜农产品价值下降进行离散化处理。刻画策略性消费者的决策行为,构建零售商的单阶段和两阶段定价及库存决策模型,分析了产品价值剩余率对消费者行为、零售商最优定价、最优库存水平以及零售商利润的影响机理。研究发现,在单阶段模型中零售商最优价格和最优库存水平均随产品价值剩余率的递增而递增;而在两阶段模型中,第二阶段最优价格随价值剩余率的变化趋势可能存在阈值。     

Improving Valuation Under Consumer Search: Implications for Pricing and Profits

There is a growing trend in the retail industry to improve customer experience. In this article, we study retailer‐initiated strategies to increase consumer valuation for a product under duopoly. In such a setting, it is possible that a consumer's valuation may be increased by one retailer; however, the consumer may decide to buy the product from the competitor. We consider a two‐stage game where retailers first decide whether to invest in improvements in customer valuation and then engage in price competition. We computationally explore the Nash equilibria in terms of both investment and pricing. We find that in the majority of cases retailers price in a manner to discourage their local customers to buy from the competitor. Next, we focus on the pricing game and theoretically characterize the pricing Nash equilibrium. We find that a retailer could overcome competitive effects by improving consumer valuation beyond a certain threshold. We also find that a retailer who does not invest could benefit from competition in situations where his competitor increases consumer valuation beyond a threshold. Finally, we explore through a computational study the Nash equilibria of the two‐stage game using an alternate model to establish the robustness of our findings.     

The Value of Category Captainship in the Presence of Manufacturer Competition

This research investigates the value of category captainship (a management practice in which a retailer relies on a manufacturer for recommendations regarding strategic category management decisions) in retail supply chains. We consider a setting where the scope of category management is limited to assortment decisions and demand enhancing activities. We assume that the retailer selects a category captain among multiple competing manufacturers with privately known capabilities for driving category traffic. First, we consider a benchmark scenario where the retailer is responsible for category management. Then, we consider the category captainship scenario where the retailer selects one of the manufacturers as a captain to manage the category. We find that captainship is more likely to emerge in categories where the cost of managing variety, the retail margins, and the competition for captainship are moderate and the captain is more capable of driving traffic compared to the retailer. In such categories the collaboration between the retailer and the captain ensures sufficient surplus for both parties. Finally, we show that captainship can also benefit the non‐captain manufacturers.     

The Retail Planning Problem Under Demand Uncertainty

We consider the retail planning problem in which the retailer chooses suppliers and determines the production, distribution, and inventory planning for products with uncertain demand to minimize total expected costs. This problem is often faced by large retail chains that carry private‐label products. We formulate this problem as a convex‐mixed integer program and show that it is strongly NP‐hard. We determine a lower bound by applying a Lagrangian relaxation and show that this bound outperforms the standard convex programming relaxation while being computationally efficient. We also establish a worst‐case error bound for the Lagrangian relaxation. We then develop heuristics to generate feasible solutions. Our computational results indicate that our convex programming heuristic yields feasible solutions that are close to optimal with an average suboptimality gap at 3.4%. We also develop managerial insights for practitioners who choose suppliers and make production, distribution, and inventory decisions in the supply chain.     

零售商主导下生鲜农产品供应链的定价策略对比研究

运用RFID技术来监控与管理产品,零售商可以依据记录、传递的产品实时价值信息制定更为灵活的定价和订货策略,提升自身以及供应链的利润。针对生鲜农产品在价值损耗及消费者需求等方面进行特征分析,分别在固定定价、动态定价以及带有降价时点考虑的定价三种不同模式下,构建了单周期零售商的决策模型,得到各自的最优定价与订货策略,通过分析最优策略与关键参数之间的关系,发现:价值衰减系数越大,库存成本参数越大,三种模式下的总体零售价格都越小,价格的下调幅度越大;而两个参数对降价折扣时间点的影响却截然相反。进一步,着力在最优销售价格、订货总量与利润值三个方面对三种定价策略进行对比分析,结果显示基于RFID技术的灵活定价模式有助于零售商获取更大的市场需求量及更大的收益。数值实验进一步验证了理论研究的结果,为生鲜农产品供应链中对RFID技术的应用以及相应定价模式的选择提供了参考依据。     

Competing for Shelf Space

In recent years, the competition for shelf space has intensified, as more products now compete for a retail space that has remained roughly constant. In this paper, we analyze the dynamics of this competition in a multi‐supplier retail point. Assuming that sales are shelf space dependent, we consider a retailer that optimizes its shelf space allocation among different products based on their sales level and profit margins. In this context, product manufacturers set their wholesale prices so as to obtain larger shelf space allocations but at the same time keep margins as high as possible. We analyze the equilibrium situation in the supply chain, and find that generally the retailer's and the suppliers' incentives are misaligned, resulting in suboptimal retail prices and shelf space allocations. We however find that the inefficiencies induced by suboptimal shelf space allocation decisions are small relative to those induced by suboptimal pricing decisions.     

基于“搭便车”行为的双渠道供应链库存竞争和促销策略

本文建立了由一个制造商和一个零售商组成的双渠道供应链模型，研究了随机需求下"搭便车"行为和渠道间缺货替代行为对供应链库存竞争与促销决策的影响。结果表明：分散式决策下，网络渠道替代率越高，零售商的最优订购量越大，同样的零售渠道替代率越高，制造商会为网络渠道提供更多的库存；而"搭便车"行为对零售商与制造商订购量的影响则取决于渠道替代率的变化，但"搭便车"行为会降低零售商促销努力水平。数值分析发现：分散式决策下，零售商最优订购量随"搭便车"行为的增加而减少。然而制造商的最优库存量并非随着"搭便车"行为程度的增加而增加，不同的是制造商网络渠道最优库存量取决于市场需求对传统零售渠道促销努力水平的弹性系数，即制造商网络渠道最优库存量随"搭便车"行为的增加先增加后减少。通过比较发现在不同的"搭便车"行为程度、传统零售渠道替代率、网络渠道替代率下，集中式决策下供应链期望收益大于分散式决策下供应链期望总收益。     

Managing Product Rollovers

We study the product rollover strategy decision, where a firm decides whether to phase out an old generation of a product to be replaced by a new with either a dual or single roll. Our model considers a final build of the old product and preannouncement of the new, and incorporates dynamic pricing and inventory decisions. We find that the optimal price path closely follows changes in reservation price curves for the two products over time. We also identify the drivers of the rollover strategy decision, finding that lower market risk (faster diffusion, higher market responsiveness to preannouncements) and higher performance improvement for the new generation are associated with the single roll strategy.     

Pricing Decisions During Inter‐Generational Product Transition

How should companies price products during an inter‐generational transition? High uncertainty in a new product introduction often leads to extreme cases of demand and supply mismatches. Pricing is an effective tool to either prevent or alleviate these problems. We study the optimal pricing decisions in the context of a product transition in which a new‐generation product replaces an old one. We formulate the dynamic pricing problem and derive the optimal prices for both the old and new products. Our analysis sheds light on the pattern of the optimal prices for the two products during the transition and on how product replacement, along with several other dynamics including substitution, external competition, scarcity, and inventory, affect the optimal prices. We also determine the optimal initial inventory for each product and discuss a heuristic method.     

Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved

Time-of-use tariffs are a pricing strategy for a product or service in which the supplier establishes time-differentiated prices. Dynamic (e.g., day-ahead) time-differentiated electricity prices can contribute to increase the retailer's profit, allow end-users to reduce the consumption costs and enhance grid efficiency.The electricity retailer and the consumer are hierarchically related. The interaction between them can be modeled by a bi-level (BL) optimization model – the retailer is the upper level decision maker and the consumer is the lower level decision maker. The retailer and the consumer have different and conflicting goals: the retailer establishes the pricing scheme to sell electricity to consumers to maximize his profit; the consumer reacts to these prices by determining the operation of the controllable loads in order to minimize the discomfort and the electricity bill.In this work, a BL optimization model incorporating shiftable, interruptible and thermostatic loads is proposed. The upper level problem is tackled by a particle swarm optimization algorithm while the lower level problem is solved by an exact mixed-integer programming solver. The inclusion of the thermostatic load in the lower level problem imposes a much higher computational burden. Therefore, it may not be possible to find the optimal lower level solution, and a sub-optimal lower level solution is infeasible to the BL problem. Considering a computational budget, this work proposes an approach to compute good quality estimates of bounds for the upper level objective function, providing the leader further information and allowing him to make sounder decisions in an adequate time frame.     

An Auction Mechanism for Pricing and Capacity Allocation with Multiple Products

We consider a pricing and short‐term capacity allocation problem in the presence of buyers with orders for bundles of products. The supplier's objective is to maximize her net profit, computed as the difference between the revenue generated through sales of products and the production and inventory holding costs. The objective of each buyer is similarly profit maximization, where a buyer's profit is computed as the difference between the time‐dependent utility of the product bundle he plans to buy, expressed in monetary terms, and the price of the bundle. We assume that bundles' utilities are buyers' private information and address the problem of allocating the facility's output. We directly consider the products that constitute the supplier's output as market goods. We study the case where the supplier follows an anonymous and linear pricing strategy, with extensions that include quantity discounts and time‐dependent product and delivery prices. In this setting, the winner determination problem integrates the capacity allocation and scheduling decisions. We propose an iterative auction mechanism with non‐decreasing prices to solve this complex problem, and present a computational analysis to investigate the efficiency of the proposed method under supplier's different pricing strategies. Our analysis shows that the problem with private information can be effectively solved with the proposed auction mechanism. Furthermore, the results indicate that the auction mechanism achieves more than 80% of the system's profit, and the supplier receives a higher percentage of profit especially when the ratio of demand to available capacity is high.     

Optimal responses to stockouts: Lateral transshipment versus emergency order policies

In the retail industry, stockouts have a significant effect on a firm׳s profitability. When a stockout takes place, retailers often apply one of two strategies to resolve the issue – placing an emergency order with their supplier or arranging a lateral transshipment with a nearby partner store. Choosing the optimal response to a stockout is complicated by customers׳ spontaneous reactions. Customers who find that a product is out of stock may choose to give up on the purchase, to wait for delivery (through emergency order or lateral transshipment), or go to a partner store to search for the product on their own. In this study, under a single-period setting with two retail stores, we investigate the optimal inventory decisions under each strategy, and conduct a comparison between lateral transshipment and emergency order options. We also analyze the effects of the customer requesting rate and switching rate on the optimal inventory decision. Through numerical analysis, the two strategies are compared in terms of inventory levels and profitability. The results suggest that in addition to the cost associated with each of these strategies, the customers׳ behavior in response to a stockout has a significant effect on the optimal decision. The emergency order strategy is a better option when more customers request deliveries or when more customers switch to another store. Extending this analysis, we also examine the combined strategy when an emergency order is placed after a transshipment fails to fulfill unmet demands, and explore the circumstances under which this strategy provides the highest additional profit for the stores. Finally, we also find that a higher requesting rate does not necessarily increase profits, particularly when there is a high customer switching rate, because requesting emergency order or transshipment reduces switching demand.     

The Interplay between Information Acquisition and Quality Disclosure

This study investigates the interactions between a manufacturer's information acquisition and quality disclosure strategies in a supply chain setting in which the manufacturer privately knows his product quality but is uncertain about consumer preferences. We argue that the manufacturer should treat his information acquisition and quality disclosure decisions as an integrated process because these decisions can significantly influence a retailer's rational inferences about product quality and can have conflicting effects on his own profitability. Although information acquisition helps a manufacturer subsequently craft better pricing and quality disclosure strategies, it also leaks certain product information to the retailer, thus helping the retailer better estimate product quality. Therefore, in equilibrium, a manufacturer may choose not to acquire any consumer information, even when such acquisition is costless. Moreover, we find that this adverse effect of acquisition is highly dependent on the cost of disclosure and consumers’ preference differentiation. Increased consumer preference differentiation may have a non‐monotonic relationship with the manufacturer's profit, and information acquisition can become detrimental to the manufacturer once the disclosure cost is sufficiently high.     

Grocery Perishables Management

In this article, we study the joint pricing and inventory control problem for perishables when a retailer does not sell new and old inventory at the same time. At the beginning of a period, the retailer makes replenishment and pricing decisions, and at the end of a period, the retailer decides whether to dispose of ending inventory or carry it forward to the next period. The objective of the retailer is to maximize the long‐run average profit. Assuming zero lead time, we propose an efficient solution approach to the problem, which is also generalized to solve three extensions to the basic model. A feature of the present study is that we consider explicitly the influence of perishability on the demand. Among the insights gathered from the numerical analysis, we find that dynamic pricing aids extending shelf life and when disposal incurs a lower cost, or even a positive salvage value, the retailer is induced to dispose earlier since the benefit of selling new inventory offsets the loss due to disposal. We also observe that the faster the perceived rate of deterioration, the lower the threshold of the ending inventory for disposal. Perhaps a bit counter‐intuitive, maximizing profits does not mean eliminating disposals or expirations.