Profit Of Retailer Research Articles

Fashion retail competition on product greenness with overconfidence

In this paper, we study the impacts of overconfidence in a competitive retailer setting of green fashion. We model a green fashion supply chain comprising one unbiased manufacturer and two biased retailers, to explore how overconfidence affects greenness level of fashion products and expected profit of retailers. An overconfident retailer has a cognitive bias in which it believes consumers are more sensitive to greenness of fashion products than it really is. Our findings show that the competition between two retailers discourages greenness level of fashion products, while overconfidence can provide a counterbalance to the negative impact caused by competition. We also find, a retailer’s overconfidence is not only conducive to the greenness level of its own fashion products, but also can benefit to its rival. Moreover, it shows a low level of overconfidence can be a comparative advantage of the retailer’s profit. Even though one of the retailers is unbiased and has an advantage of information, it can still earn less than its overconfident rival.

Cooperative advertising in social networks with positive externalities

AbstractWe study the decisions of a supplier and retailer in a cooperative advertising program to sell divisible products to consumers in a social network with positive externalities. The supplier decides its participation rate, the retailer with a limited budget decides discriminative advertising expenditure for consumers, and consumers decide their purchase levels. In this study, we solve the subgame perfect equilibrium of the game between the supplier, retailer, and consumers. The equilibrium leads to a new demand function of advertising expenditure, which is different from those demand functions typically assumed in existing studies. In the equilibrium, the higher a consumer's network influence quantified by Bonacich centrality, the higher is the retailer's advertising expenditure on the consumer. We characterize the supplier's participation rate by two network‐dependent thresholds. If the retailer's budget level is lower than one threshold or higher than the other, the participation rate will not depend on the network structure. Although both parties' profits and the retailer's advertising expenditure increase with the network density, the supplier's participation rate decreases with it. Finally, the retailer's profits can be improved significantly by exploiting the network information and advertising cooperatively.

Dynamical complexity of pricing and green level for a dyadic supply chain with capital constraint

In this paper, based on bounded rationality, the dynamic game models are established to study the performance of a dyadic supply chain with one supplier and two competing retailers. According to supply chain practices, capital constraints and greenness issues are taken into consideration. Two financing models are involved respectively, corresponding to two models and scenarios: full trade credit (FTC) and hybrid trade credit (HTC). The equilibrium points of the two models were obtained, and the influence of key parameters such as different decision-making adjustment speeds and trade credit ratios on the local stability of the system was analyzed. Besides, the expected profits of the supplier and two capital-constrained retailers are discussed. It is found that, compared with the adjustable speeds of the two retailers, the supplier’s adjustable speed has a more obvious impact on system stability and expected profits. For the two downstream competitive retailers, the high trade credit ratio leads to the increase in dynamic green levels, wholesale prices, supplier profits, and dynamic pricing of other retailers provided by full trade credit. However, when the market potential is the same, hybrid trade credit may hurt the profits of both retailers.

Profitable Retail Customer Identification Based on a Combined Prediction Strategy of Customer Lifetime Value

As a fundamental concept of customer relationship management, customer lifetime value (CLV) serves as a crucial metric to identify profitable retail customers. Various methods are available to predict CLV in different contexts. With the development of consumer big data, modern statistics and machine learning algorithms have been gradually adopted in CLV modeling. We introduce two machine learning algorithms—the gradient boosting decision tree (GBDT) and the random forest (RF)—in retail customer CLV modeling and compare their predictive performance with two classical models—the Pareto/NBD (HB) and the Pareto/GGG. To ensure CLV prediction and customer identification robustness, we combined the predictions of the four models to determine which customers are the most—or least—profitable. Using 43 weeks of customer transaction data from a large retailer in China, we predicted customer value in the future 20 weeks. The results show that the predictive performance of GBDT and RF is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Because the predictions are not entirely consistent, we combine them to identify profitable and unprofitable customers.

Bi-level energy management and pricing for community energy retailer incorporating smart buildings based on chance-constrained programming

To explore the interaction between community energy retailers and smart building (SB) consumers in the energy market, this paper proposes a bi-level energy management and pricing method for the community energy retailer incorporating SBs based on chance-constrained programming. Aiming at maximizing the profits of both retailers and consumers, a Stackelberg game model is established. At the upper level, the community energy retailer maximizes its profit by optimizing offering prices while ensuring the secure operation of the community distribution system. At the lower level, based on the detailed thermal dynamic model of buildings, consumers flexibly adjust the air conditioning’s energy consumption according to the offering price provided by the retailer to minimize the operational cost of SBs. Then, the chance-constrained programming is exploited to consider uncertainties of market prices, and the proposed bi-level optimization model is transformed into a mixed-integer linear programming problem using Karush-Kuhn-Tucker conditions, strong duality theorem, linearization, and deterministic transformations of chance constraints for solving. Numerical results show that the proposed approach can combine the flexibility of both supply and demand to benefit both retailers and customers. In addition, the economic impact of different confidence levels on the retailer and consumers is analyzed.

Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System

In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering products at a discount, retailers may attract additional demand. In addition, this system enables retailers to reduce the uncertainty of market demand and establish a strategy for inventory allocation based on the results of presales. A Bayesian approach was employed to analyze and update demand information, and inventory allocation was formulated as a newsvendor problem to determine the optimal policy that maximizes retailer profit. A numerical analysis was conducted to validate the effectiveness of the proposed strategy. Results suggest that the proposed strategies can support retailers by more accurately predicting demand and achieving higher profits with less inventory. Furthermore, retailers can experience greater benefits from risk-averse customers than from risk-neutral customers.

Interplays between manufacturer advertising and retailer store brand introduction: Agency vs. wholesale contracts

In retail supply chains, manufacturers' advertising for national brands and retailers' store brand introduction may relate to each other, and two types of contracts, i.e., agency contract and wholesale contract, are widely used. This paper uses game-theoretic models to investigate the strategic interaction between a manufacturer's advertising strategy and a retailer's store brand introduction strategy. We derive the equilibrium outcomes, including wholesale price, retail price, market demand, retailer's and manufacturer's profits under different contract forms. We find that when the product cost is small relative to the perceived value of the store brand, the introduction of a store brand will benefit the retailer. The retailer is more likely to introduce store brands under the wholesale contract than under the agency contract. In addition, compared with the wholesale contract, the agency contract may increase both the manufacturer's and the retailer's profits and lead to Pareto improvement for them.

Price, online coupon, and store service effort decisions under different omnichannel retailing models

This paper explores three omnichannel retailing models—that is, a Buy-Online, Pick-Up In-Store (BOPS) model, a Showroom model, and an Interactive model (a model that combines the characteristics of BOPS and Showroom)—under which the retailer offers coupons online and invests in service efforts offline. For each omnichannel retailing model, three coupon distribution scenarios (i.e. no coupon, coupon with a common value, and coupon with a different value) are discussed to investigate coupon promotion policies and omnichannel operation strategies. Profits in the omnichannel system and under each model are compared by deriving three decision values: price, coupon value, and service effort. The key findings show that the retailer achieves profit improvement when the incremental purchasing reaches a large value. In addition, a great service effort coefficient leads to a high price, service effort, and retailer profit. The service effort and coupon value in the Showroom model are the largest among the three omnichannel models, while its profit is the lowest. Conversely, although the retailer invests less in service effort, and the price and coupon value are not too large in the BOPS model, the retailer still gains the highest profit. Additionally, in the BOPS model, it is profitable for the retailer to distinguish the coupon value for the online-only and BOPS channels when consumers are less sensitive to the online-only channel’s coupon. However, in the Interactive model, the retailer derives increasing profit by providing coupons with a common value for the online-only and BOPS channels.

Baby-cut carrots as convenience incentives – quality-based price discrimination on retail fresh-cut produce

ABSTRACT Empirical evidence of quality-based price discrimination (second degree) is limited. I find consumers have positive and heterogenous valuations for the baby-cut attribute of carrots, and I model and establish evidence that retailers utilize this quality attribute to price discriminate. From a welfare perspective, I show the development and introduction of high-quality substitutes, pre-cut and pre-processed versions of fresh produce, increase retail profits with minimal impact on consumer surplus under quality-based price discrimination.

Less is More? The Strategic Role of Retailer's Capacity

Retailers are often short on capacity, so a logical assumption would be that retailers could improve their profits by acquiring more. In this study, we show that this is not necessarily true, because retailer's capacity has a strategic role in channel distribution. Specifically, we consider a setting with multiple suppliers and a common retailer. Our analysis reveals that, first, when the retailer's capacity is limited, its suppliers will compete head‐to‐head for the retailer's capacity, thereby driving down the equilibrium wholesale prices. Second, when the number of suppliers is large, the retailer finds it optimal to limit its own capacity to induce fierce competition among the suppliers. The result also holds when the suppliers and the retailer are contracted through two‐part tariffs. Third, when capacity is scarce, the retailer prefers two‐part tariffs to wholesale prices, while the suppliers prefer wholesale prices to two‐part tariffs. This is because two‐part tariffs enhance the retailer's capacity allocation power, which is translated into retailer profit. Nonetheless, when suppliers can freely choose between two‐part tariffs and wholesale prices, they always choose two‐part tariffs, leading to a form of prisoner's dilemma. We also demonstrate the robustness of our findings by considering substitutable and complementary products, exclusive contracts, and positive capacity cost. Our results underscore the importance of considering the retailer's capacity in channel management.

BDB: Branding and Developing Business Algorithm Via Corporate Social Responsibility (CSR)

Abstract: In today’s era there are speed economic developments, the corporate Social responsibility has become the main focus. In corporate world marketing through advertisement and Corporate Social Responsibility has become a major factor. Generally, the corporate gains profit via marketing, advertisements, QoS and corporate Social responsibility (CSR). There are more possibilities of raise in corporate share market when they collaborate with government social responsibility. The paper investigates a novel algorithm named as “Branding and developing business (BDB) algorithm via CSR. The proposed system handles both the economy of Government and Corporate mutually. The corporate profit chances tax Benefits and share rate increase are checked continuously. Moreover, when the government subsidizes the corporate and the retailer simultaneously, only the retailer's profit increases in the corporate CSR effort. Further in this paper, the profit perspectives of corporate and Government through CSR are also proposed. Keywords: corporate social responsibility (CSR), BDB, Government, Share Market.

Energy storage configuration and day-ahead pricing strategy for electricity retailers considering demand response profit

Real-time price(RTP) is one of the common methods for electricity retailers to adjust load demands and gain revenue from demand response(DR) programs. However, effectiveness and accuracy of the RTP directly affect the DR quantity, which reflects as revenue returns in DR programs. A method is proposed to maximize profit of the electricity retailer by configurating energy storage system(ESS) and coordinate the operation of ESS with RTP to participate in DR programs, which enriches the profitability measures of retailers. Firstly, an improved CNN-LSTM algorithm is utilized to establish the DR dynamic characteristic model with expected consumption as input and RTP as output. Thus, the demand curve during each period can be generated, from which optimal RTP with maximum profit of the retailer can be estimated. Secondly, a bi-level optimization model is established to maximize the profit from the DR program in a whole day. The upper level real-time pricing model takes maximum profit of the retailer as the objective by optimizing the RTP with fixed ESS configuration. The lower level ESS configuration model takes maximum profit of ESS as the objective with RTP fixed by the upper level model, from which the rated power, capacity and daily operation strategy of ESS can be optimized and sent to the upper level model. Finally, the optimal profit gained by the retailer can obtained by iterative calculation between the upper and lower level models. Based on the historical data from PJM market, the case demonstrates that the algorithm proposed can describe the DR dynamic characteristic effectively and accurately. Moreover, by configurating ESS, the retailer’s profit extra increases by 7.19%.

Optimal credit period and ordering policy with credit-dependent demand under two-level trade credit

This paper investigates the retailer's trade credit strategy and ordering policy in a two-echelon supply chain consisting of one supplier and one retailer. The retailer accepts upstream trade credit from the supplier while offers downstream trade credit to consumers, and the relationship between the upstream and downstream credit period is uncertain. The market demand depends on both the downstream trade credit and the random initial demand rate, and granting downstream trade credit increases not only sales but also default risk. This paper tries to find the retailer's optimal decisions on credit period, replenishment cycle, and order quantity so as to maximize the retailer's profit. It is shown that there exists a unique decision for the retailer. Some numerical examples are given to verify our results and provide more managerial insights. It is found that the retailer is more willing to provide trade credit under higher marginal profit, and that providing downstream trade credit is beneficial to both the retailer and supplier.

RETAILER VULNERABILITY TO LOTTERY FRAUD

ABSTRACT A fraud at an Australian national retail chain over a two-year period exposed deficiencies in first, second, and third line of defence controls (Institute of Internal Auditors). Internal Audit’s role was to data-mine millions of transactions to assure the retailer of the full extent of the fraud and whether other stores had suffered similar losses in the preceding 3 years. In addition to the quantitative findings, the resulting qualitative improvements to internal control helped to offset the AUD$100,000 loss. So the fraud was turned into a beneficial process improvement assignment at less than the cost of engaging management consultants to achieve a similar result. In this way an undesirable fraud shock became an opportunity to terminate under-performing staff, improve internal controls, and increase the retailer's profitability.

A Technical and Economic Criteria Comparison on Demand Side Management with Multi-Level Optimization Model

One-level optimization methods have been proposed to optimize the load profile of a single user or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one considering technical requirements and other considering economic criteria. In the upper level, the supplier optimizes it objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on genetic algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulations results illustrate that both cases improve the demand profile and increase the retailer profit. However, when the supplier tries to maximize the profit, some users receive benefits in detriment of others, concluding that the technical approach is preferable than the economical one.

Dynamic pricing and coupon promotion strategies in a dual-channel supply chain based on differential game

PurposeThis paper investigates a dual-channel supply chain in which a manufacturer offers coupons in the online channel and the retailer in the offline channel. The optimal pricing and coupon promotion policies are explored, and the brand image under different promotion scenarios is studied.Design/methodology/approachThree differential game models, namely no coupon is offered, coupons offered by the manufacturer and coupons offered by the retailer, are constructed.FindingsThe results show that the manufacturer and retailer intend to conduct coupon promotions under a large coupon redemption rate. Coupon promotion derives a higher price and profit for the issuers, and the manufacturer can free-ride on the retailer's coupon promotion. The retailer's profit in the retailer-promotion scenario may be lower than that in the manufacturer-promotion scenario in some special conditions. Besides, price, coupon face value, brand image and profit increase over time. After multiple cycles game, the operational strategy evolves to an optimal equilibrium status.Originality/valueThis paper provides guidance and advice for dual-channel supply enterprises to implement joint pricing and coupon promotion strategies under multiple sales seasons.

Differential game model and coordination model for green supply chain based on green technology research and development

The purpose of this paper is to establish a green supply chain differential game model for green technology research and development based on a secondary green supply chain composed of a single manufacturer and a single retailer. It compares the differential game equilibrium solutions under centralized and decentralized decision-making. The green supply chain members are coordinated through the dynamic wholesale price mechanism, and numerical simulation is used as a methodology, to verify and explain the results. The study found that compared to decentralized decision-making, the level of green technology and the total profit of green channels are higher under centralized decision-making. When the coordination parameters are within a certain range, the dynamic wholesale price mechanism can coordinate the behavior of manufacturers and retailers. The result also discovers that under the dynamic wholesale price mechanism, with the increase of investment cost coefficient, or the increase of price sensitivity or the decrease of consumer's environmental awareness, the green technology level, product green degree, price, retailer's profit, and the total profit of green channel is decreased. In contrast, the wholesale price and manufacturer's profits are increased.

Lead-time quotation and hedging coordination in make-to-order supply chain

In the Make-To-Order (MTO) supply chain, for enticing the retailer to choose a shorter delivery lead-time, which improves the manufacturer's wholesale prices, the manufacturer adopts the Production Time Hedging (PTH) strategy as an incentive. Since the profit of the retailer depends not only on the Quoted Delivery Lead-Time (QDLT) but also on the Realized Delivery Lead-Time (RDLT), effectively hedging production time uncertainty reduces tardy delivery. We build an analytical model to investigate how PTH affects the retailer's QDLT decision and the supply chain performance. We consider models for four scenarios, namely (I) the centralized model, (II) the Nash model, (III) the manufacturer-led Stackelberg model, and (IV) the retailer-led Stackelberg model. We derive close-form results on the optimal hedging and QDLT decisions. In models II and III, PTH reduces the QDLT under all the conditions. However, in model IV, PTH can only coordinate the retailer when the hedging cost is low, or the holding cost/tardiness penalty is high. Besides, we find that model III is the dominant scenario for both players. Extending model IV to consider the case where the retailer sources from multiple manufacturers, we show that multiple sourcing is the dominant strategy when a manufacturer's hedging cost is high, or its holding cost/lead-time sensitivity factor is small. From the numerical study, we are interesting to find that the retailer's profit increases with the manufacturer's holding cost/lead-time sensitivity factor.

Who carries strategic inventory? Manufacturer or retailer

AbstractIt is widely accepted that the retailer's use of strategic inventory can mitigate double marginalization and improve supply chain coordination. However, the recent literature of channel coordination only considers the effects of the retailer's strategic inventory, leaving the manufacturer's incentive to hold strategic inventory mostly unexplored. In reality, there are situations where the manufacturer's inventory can be observed or inferred by the retailer and hence affects the vertical interaction. In our two‐period dynamic model, we show that the manufacturer in equilibrium may carry a positive amount of inventory, which yields a significant impact on the equilibrium decisions and the supply chain players' profits. Specifically, we find that the manufacturer's use of strategic inventory commits a lower propensity to resume production in the second period, thereby encouraging the retailer to carry more strategic inventory at a higher wholesale price in the first period. The manufacturer's use of strategic inventory always hurts the retailer's profit but may enhance channel profit, consumer surplus, and social welfare. Moreover, we show that the manufacturer can earn an even higher profit by directly committing to no replenishment in the second period, observed in industries with longer production lead time and higher production setup costs.

Pricing with bargain hunting consumers

A single-product retailer faces bargain hunting consumers whose willingness to pay incorporates sensations of gain and loss driven by differences between the observed price and prices they rationally expect in the spirit of Koszegi and Rabin (2006). We examine the Bayesian Nash equilibrium (non-commitment) pricing solution in which (i) the retailer maximizes profit given consumers' beliefs and (ii) consumers' beliefs are consistent with the retailer's choice. We show two novel results: First, a pure-strategy, uniform-price, equilibrium does not exist when consumers are bargain hunters who value gains more than losses. Second, in this case there exists a mixed strategy equilibrium and all mixed strategy equilibria involve the same retailer profit. The equilibrium retailer profit is (weakly) lower than in the absence of reference effects.