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The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

In this paper, a new bi-level model is presented in order to set the interaction between different players in electricity market contracts. The proposed model framework is multi-objective so that the aims of retailer and customers are met simultaneously. In this study, new parameters and indexes for interaction of players will be introduced. In the upper level of this model, the retailer’s profit will be maximized and in the lower level, the customer’s cost will be minimized. Based on the retailer’s interaction with customers in the proposed model, some customers will be selected from the customers group to contract with power retailer. In the proposed model, a robust optimization approach (ROA) is used to manage the retailer’s risk in different conditions due to the uncertainty of energy supply by the wholesaler. The analysis of multi-objective approach in this model is also carried out based on Torabi-Hassini (TH) method. Finally, after the required linearization, the proposed Mixed Integer linear programming (MILP) model in the presence of Karush–Kuhn–Tucker conditions will implement in GAMS software and solved via GUROBI solver.

What are the drivers of retailer pricing tactics over time? Based on multivariate time-series analysis of two rich data sets, we quantify the relative importance of competitive retailer prices, pricing history, brand demand, wholesale prices, and retailer category-management considerations as drivers of retail prices. Interestingly, competitive retailer prices account for less than 10% of the over-time variation in retail prices. Instead, pricing history, wholesale price, and brand demand are the main drivers of retail-price variation over time. Moreover, the influence of these price drivers on retailer pricing tactics is linked to retailer category margin. We find that demand-based pricing and category-management considerations are associated with higher retailer margins. In contrast, dependence on pricing history and pricing based on store traffic considerations imply lower retailer margins.

With the explosive growth of online sales, multi-channel retailers are increasingly focused on finding ways of integrating the online channel with traditional retail stores. The need for the development of effective multi-channel strategies is strongly felt by the retailers. The present research normatively addresses this issue and using a game theoretic approach, derives optimal strategies that maximize profits under different competitive market structures. Managerial implications are discussed and probable paths of future research are identified.

This study addresses the problem of the optimal design of price-based Demand Response (DR) programs such as Real-Time Pricing (RTP) utilizing the load profiling tool. The proposed model corresponds to a profit maximization problem of a retailer that serves a group of residential consumers. Through a clustering process the consumers are grouped together in several clusters. For each cluster a dynamic tariff is offered that is specially design to fit to the typical load pattern of the cluster. The sensitivity of the demand over the offered selling price is modeled through a price responsive demand function. Apart from implementing different demand functions, the flexibility of the proposed model offers a selection of different clustering algorithms and different retailer pricing policy.

Many retailers have recently started to offer customers the option to buy online and pick up in store (BOPS). We study the impact of the BOPS initiative on store operations. We build a stylized model where a retailer operates both online and offline channels. Customers strategically make channel choices. The BOPS option affects customer choice in two ways: by providing real-time information about inventory availability and by reducing the hassle cost of shopping. We obtain three findings. First, not all products are well suited for in-store pickup; specifically, it may not be profitable to implement BOPS on products that sell well in stores. Second, BOPS enables retailers to reach new customers, but for existing customers, the shift from online fulfillment to store fulfillment may decrease profit margins when the latter is less cost effective. Finally, in a decentralized retail system where store and online channels are managed separately, BOPS revenue can be shared across channels to alleviate incentive conflicts; it is rarely efficient to allocate all the revenue to a single channel. This paper was accepted by Vishal Gaur, operations management .

This study investigates the optimal pricing and the remanufactured product’s greening decisions in a supply chain consisting of one manufacturer and one retailer. Under manufacturer-led Stackelberg games, three remanufacturing systems, namely, centralized, decentralized manufacturer, and decentralized retailer-remanufacturing, are considered. Consumers in the market are divided into normal and green consumers according to whether they consider environmental issues. We first demonstrate the conditions under which the manufacturer or retailer should engage in remanufacturing. Second, despite cannibalization, a centralized remanufacturing system exhibits higher efficiency linked with higher market coverage and leads to a higher profit compared to manufacturer/retailer decentralized alternatives. Finally, numerical studies and sensitivity analyses are used to examine the sensitivity of optimal pricing and greening decisions.

In recent years, online retailers (also called e-tailers) have started allowing manufacturers direct access to their customers while charging a fee for providing this access, a format commonly referred to as agency selling. In this paper, we use a stylized theoretical model to answer a key question that e-tailers are facing: When should they use an agency selling format instead of using the more conventional reselling format? We find that agency selling is more efficient than reselling and leads to lower retail prices; however, the e-tailers end up giving control over retail prices to the manufacturer. Therefore, the reaction by the manufacturer, who makes electronic channel pricing decisions based on their impact on demand in the traditional channel (brick-and-mortar retailing), is an important factor for e-tailers to consider. We find that when sales in the electronic channel lead to a negative effect on demand in the traditional channel, e-tailers prefer agency selling, whereas when sales in the electronic channel lead to substantial stimulation of demand in the traditional channel, e-tailers prefer reselling. This preference is mediated by competition between e-tailers—as competition between them increases, e-tailers prefer to use agency selling. We also find that when e-tailers benefit from positive externalities from the sales of the focal product (such as additional profits from sales of associated products), retail prices may be lower under reselling than under agency selling, and the e-tailers prefer reselling under some conditions for which they would prefer agency selling without the positive externalities. This paper was accepted by Chris Forman, information systems.

[Display omitted] •We study retailers’ store management actions and subsequent profitability.•Net store closings (NSC) are a sufficient measure of store management actions.•Ten contingent factors help explain the complex relationship between NSC and profit.•Eight factors reflecting retailer competencies and resources are internal moderators.•Two factors reflecting the retail environment are external moderators. Retailers constantly face the decision of whether to close existing stores and/or to open new ones. Closing a store may reduce a retailer’s costs, while opening a new store may increase revenue. Thus, it is far from obvious which action yields the maximum profit. Furthermore, retailers need to align these store-by-store tactical decisions with their overall distribution strategies to achieve a superior performance. Using a sample of 157 public retailers from 1999 to 2015, this study examines how store closings and openings are associated with retailer profitability. To complement extant research, we construct a parsimonious measure that captures a retailer’s store management decision, namely, net store closings (NSC). Drawing on the contingency theory of organizations, we develop a comprehensive framework that studies the moderating roles of retailer competencies, retailer resources, and retail environment. Through panel fixed effects estimation, we identify ten contingent factors that significantly moderate the relationship between NSC and profit. Specifically, e-tail experience, total experience, receivables intensity, retailer innovativeness, industry e-tail prevalence, and industry concentration positively moderate the association between NSC and subsequent profitability, while inventory turnover, sales force intensity, capital intensity, and firm size negatively moderate this association. In addition, we explore how individual measures (i.e., store closings and store openings) are associated with retailer profitability and find consistent results. Anecdotal and statistical evidence indicates that NSC is a sufficient measure of store management actions. The study has important implications for how retailers should manage their channel distribution strategies and resource allocation decisions.

We propose an alternative dynamic price experimentation policy that extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory. Pricing managers at online retailers face a unique challenge. They must decide on real-time prices for a large number of products with incomplete demand information. The manager runs price experiments to learn about each product’s demand curve and the profit-maximizing price. In practice, balanced field price experiments can create high opportunity costs, because a large number of customers are presented with suboptimal prices. In this paper, we propose an alternative dynamic price experimentation policy. The proposed approach extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory. Our automated pricing policy solves this MAB problem using a scalable distribution-free algorithm. We prove analytically that our method is asymptotically optimal for any weakly downward sloping demand curve. In a series of Monte Carlo simulations, we show that the proposed approach performs favorably compared with balanced field experiments and standard methods in dynamic pricing from computer science. In a calibrated simulation based on an existing pricing field experiment, we find that our algorithm can increase profits by 43% during the month of testing and 4% annually. Data files and the online appendix are available at https://doi.org/10.1287/mksc.2018.1129 .