Profit Of Retailer Research Articles

Return strategies and channel pricing in omni-channel retail considering heterogeneous customer perceptions

As Internet technology advances, the retail industry is increasingly adopting omni-channel operational strategies. This research delves into the interplay between return losses and customer perceptions of online products, and how these factors influence profit-maximizing omnichannel retailers’ optimal return strategy and channel pricing. Previous studies have primarily focused on cross-channel aspects and have not thoroughly explored the impact of return loss on profitability in an omni-channel context. The study assumes that profit-maximizing retailers allow consumers to choose their purchase channels based on perceived utility of different channels, while adhering to the retailer’s stipulated return strategy. To this end, we introduce three distinct return strategies (returning products via the original purchase channel, offline return, and online return) for omni-channel retailers. We aim to identify the most profitable return strategy by considering diverse return losses associated with each channel and consumers’ perceptions of online products. Our analysis identifies scenarios in which a particular return strategy either boosts or reduces retailer profit, thereby facilitating optimal channel pricing decisions. Our findings reveal that customers’ purchasing channel selections are affected by their perception, retailer’s return strategy, and net return loss. We also find that no matter what kind of operation scenarios the retailers adopted, the retailers should let all customers return to the online channel to generate maximum profit when the difference between the retailer’s online and offline net return loss is less than the difference between the customer’s online and offline return hassle cost. Conversely, an offline return strategy proves more profitable. Our findings are supported by numerical simulations, guiding omni-channel retailers in making informed decisions regarding return strategies and channel pricing.

Implications of risk-averse manufacturer on coordination of green supply chain with mixed carbon policies

PurposeThis paper intends to address the decision-making and coordination of green supply chain (GSC) considering risk-averse manufacturers under mixed carbon policy.Design/methodology/approachThis paper focuses on a GSC consisting of a manufacturer and a retailer, in which the manufacturer is risk-averse (R-A). This paper employs Stackelberg game theory and mean variance analysis to assess the pricing decision-making process under various scenarios. Furthermore, cost-sharing contracts are introduced to coordinate the GSC.FindingsThe research results suggest that the green level of the product and the profit of the GSC under a centralized scenario are higher than those under a decentralized scenario, while the retail price is lower. Under the decentralized scenario, the green level of product, wholesale price and manufacturer’s profit in the R-A scenario are lower than the values in the risk-neutrality scenario, while retailer's profit is higher. In addition, when a cost-sharing contract is utilized for coordination in the GSC, it can lead to Pareto improvement, regardless of whether the manufacturer makes risk-neutrality or R-A decisions.Originality/valueThis research provides a deeper understanding of GSC decision-making and coordination strategy under mixed carbon policy with consideration of R-A from a theoretical perspective and provides decision support for enterprises to choose strategies in practice.

USING ECONOMETRIC MODELS FOR MANAGING DISTRIBUTION PROCESSES

Introduction. The constant increase in competition and rapid changes in market conditions compel various business entities to seek more efficient ways to manage the distribution of goods. In this context, econometric models become an indispensable tool for analysis and forecasting, enabling informed decision-making, optimization of logistics processes, and cost minimization. Objective. The aim of this article is to substantiate effective econometric models for managing the distribution of goods to optimize supply processes, sales, and maximize profits for manufacturers, retailers, and other participants in distribution channels. Methods. The theoretical and methodological basis of the research consists of works by domestic and foreign scientists on the implementation of econometric models for managing business processes, including distribution. The informational basis includes scientific, economic, and reference literature, works of leading domestic and foreign scientists, methodological materials, information portals, and periodicals of Ukraine. A set of methods and approaches were used: system analysis – for studying the role and significance of econometric models for goods distribution and identifying their features; methods of analysis and synthesis – for analyzing and systematizing econometric models and determining the advantages and limitations of their application for goods distribution. Results. The study investigates the role and significance of econometric models for goods distribution. It examines how these models can become a key tool in solving optimization tasks, adapting to changing market conditions, and ensuring competitiveness in a world where distribution strategies turn into strategic advantages. The advantages and limitations of applying such models are identified: simple linear or nonlinear demand models; time series autoregressive model ARIMA; exponentially smoothed model ETS; transportation problem model; Vehicle Routing Problem (VRP); Traveling Salesman Problem (TSP); orienteering problem; conjoint analysis. Conclusions. Based on a comparative analysis of the key aspects of using econometric models for goods distribution, their specific aspects depending on the implementation of particular tasks and needs of the business entity are detailed. The main advantages of applying econometric models in distribution are highlighted, primarily providing the ability to effectively consider a large number of factors affecting distribution processes, along with certain limitations of their use. Among the main benefits are more accurate demand forecasting, which allows for optimizing inventories and supply chain management costs, as well as strategies for warehouse placement, route selection for delivery, etc.

Optimal wind power capacity decision consider commitment contracts under uncertain power supply and electricity demand in China

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain.

A deposit-refund system for managing the economical circulation of returnable transport items

While the use of Returnable Transport Items (RTI) is a common practice in supply chains committed to sustainability. An efficient RTI circulation mechanism must be implemented to ensure the profitability of the approach. Within this context, this paper presents a deposit-refund system (DRS) designed for RTI circulation to maximize supply chain profitability. The profit optimization analysis considered three scenarios: the vendor's profit, there tailer's profit, and the overall system profit. Tailored solution strategies are developed for each case, considering retail price components, and leading to the identification of the optimal deposit amount required to maximize RTI circulation. We further examinehow the retail price affects the profits of supply chain actors relative to the RTI return rate and studystudy how changes in return rates influence the profit patterns of supply chain actors. The results are scrutinized via several sensitivity analyses, leading to important implications for the design of deposit-refund systems for RTI. We for example find that the vendor's wholesale price has a significant effect in establishing the decision-maker's optimal policy in designing a DRS for RTI circulation. In other words, the wholesale price is a key component in assessing the economic viability and determining the deposit amount for RTI. Moreover, through a couple of hypothetical, butrealistic cases of RTI applications considering specific parameter settings, we shown that the vendor's position can be either significant or insignificant dependong on the difference between the value of the RTI and that of the goods it holds.

Recycling channel strategy in the presence of free-riding in the carbon neutrality era

Recycling has become a critical response to the goals of reaching a carbon peak and achieving carbon neutrality. This study explores the effects of consumer free-riding behavior, the quality of recycling services, and the costs of channel transfers on the profitability of manufacturers and retailers in a dual-channel closed-loop supply chain (CLSC), focusing on the importance of recycling practices for carbon neutrality. Using consumer utility theory and a Stackelberg game model, we analyze the dynamics among these factors. Our results show that: (i) Consumer free-riding behavior slightly increases market demand and recycling volumes, enhancing profitability for both manufacturers and retailers in the dual-channel CLSC. (ii) The quality of recycling services and the transfer costs associated with retailer free-riding behavior jointly influence the profits of manufacturers and retailers. (iii) The effect of free-riding behavior on recycling services affects both forward sales and reverse recycling channels equally. This study provides valuable insights for decision-making in sustainable development practices in the recycling sector, significantly contributing to the goal of achieving carbon neutrality.

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.

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

Limited‐time pricing strategy considering consumer regret under channel competition

AbstractThis study constructs a pricing strategy model that considers consumer busyness and low price duration to examine the effectiveness of limited‐time pricing in influencing consumer behavior. The results show that local store can develop a limited‐time promotion strategy to improve profit, and there is an optimal low price duration. As consumer busyness increases, online retailer's profit may decrease, as local store is better able to leverage consumer busyness to enhance competitiveness. Interestingly, when local store lower price, online retailer may actually need to increase price to obtain greater profit from consumers who missed out on store discounts.

The cost bearing mechanism for advertising in a capital-constrained supply chain

With rising market competition, increasing numbers of firms are launching adverting to attract customers and promote product sales. The increase in operating costs caused by advertising places greater pressure on small retail businesses that are prone to capital shortages, leading them to seek financing from upstream firms. However, in the financing process, upstream and downstream firms may not be able to acquire all of one another's real information, which inevitably has a significant impact on their operating strategies. In this paper, by constructing a Stackelberg game, we study the effects of information asymmetry on the retailer's initial capital and the manufacturer's financing rate on their advertising strategies. We find that in a capital-constrained supply chain with symmetric information, when the advertising cost coefficient is low, manufacturer advertising is the superior strategy and increases the profits of the retailer and the manufacturer, consumer surplus and social welfare; when it is moderate, retailer advertising is the superior policy; but when it is high, retailer advertising is more beneficial for the manufacturer's profit, consumer surplus and social welfare but is more unfavorable for the retailer's profit. In addition, information asymmetry on the manufacturer's financing rate affects the advertising strategies of the manufacturer and the retailer, but information asymmetry on the retailer's initial capital fails. Additionally, we further extend the model to the Nash game scenario and cooperative advertising scenario and draw some different conclusions. The findings provide new theoretical implications for the formulation of firm advertising strategies.

The two‐stage presale pricing for online products considering anchoring effect and payment pain reduction

AbstractOnline product pricing is an important research field in the platform economy, and a two‐stage mixed presale is a mainstream mode of online sales. In the process of online consumption, the payment method and presale mode have an important impact on consumer behaviour and product pricing. Based on the two‐stage mixed presale mode, this study considers the impact of anchoring and the payment pain reduction effect on consumer behaviour, establishes the corresponding product pricing model and obtains the optimal presale price, sales and profit. It sheds light on the influence mechanism of anchoring and the payment pain reduction effect on consumer behaviour and online product pricing. Theoretical and numerical analyses reveal that the payment pain reduction effect can help to raise the product price, stimulate market vitality and bring higher profits for online retailers, and the anchoring effect and consumers' loss aversion psychology can enhance the profits and sales of online retailers, but they will discourage setting high prices for products. The research results will further enrich the relevant research on customer behaviour and product pricing in the platform economy and will provide decision bases for platform enterprises and online retailers to participate in presale activities and formulate product pricing strategies.

Research on pricing strategy of dual-channel supply chain based on blockchain technology under carbon trading

In the context of the dual-carbon goal, it is of great significance to study the application of blockchain technology in the dual-channel supply chain under the carbon trading mechanism for the carbon emission reduction of enterprises and the overall profit maximization of the supply chain.In this paper, two decision models of the supply chain under secondary carbon trading are constructed to analyze the two cases of whether to adopt blockchain technology. Stackelberg model is used to study the impact of blockchain technology on the pricing decision of the dual-channel supply chain under carbon trading, and the two models are compared and analyzed.According to the research results, after the application of blockchain technology, wholesale prices, direct sales prices, retail prices, and profits of retailers and manufacturers have increased compared with the non-use of blockchain technology, which is conducive to promoting the development of carbon emission reduction for manufacturers.

Closed-loop Supply Chain Model Analysis of Home Appliances based on Promotional Effort and Recycling Effort

Nowadays, a large number of home appliances have entered the node of renewal, if they can be properly disposed of and utilised, used home appliances can also be converted into renewable resources to achieve the economic cycle, and at the same time, green living and circular economy are increasingly accepted by the public. Recycling of used home appliances has both economic and social benefits. At the same time, promotional efforts invested in the market for home appliances have an obvious effect on increasing their market demand. Therefore, it is necessary to study the closed-loop supply chain of home appliances based on promotion and recycling efforts. In this paper, we use literature analysis, mathematical analysis and Stackelberg game analysis to construct a closed-loop supply chain model for home appliances based on promotional and recycling efforts after determining who is the promoter among manufacturers and retailers, and then compare and analyse the decisions and benefits of the supply chain members, taking into account their centralised and decentralised decision-making. The results of this paper show that: (1) when home appliance manufacturers, retailers and third-party recyclers of used home appliances make decentralised decisions, there is a double marginal effect, i.e., the supply chain members only consider their own benefits while ignoring the benefits of the supply chain as a whole, which results in the failure of this closed-loop supply chain system of home appliances to be coordinated. (2) When the retailer's promotional effort is greater, both the retail price and the retailer's profit are lower. (3) The greater the recycling effort of the third-party recycler of used home appliance products, the higher its recycling price, i.e., the consequent increase in recycling costs, leading to a decrease in its profit.

Research on Supply Chain Contract of Omni-channel Retail with BOPS Mode

T With the rapid development of e-commerce, consumers' consumption patterns have changed, more and more consumers are used to shopping online. Therefore, in order to better connect with the consumer market and save total cost of the supply chain, more and more manufacturers have expanded their online channels, which undoubtedly has an impact on the operation of brick-and-mortar shops. Therefore, under the background of new-retail development, BOPS mode is introduced to integrate online and offline. It protects offline physical resources, and brings profit gains to different subjects of omni-channel supply chain. This paper focuses on the omni-channel supply chain members who jointly manage BOPS mode, but as different profit subjects, what kind of supply chain contracts should manufacturers and retailers adopt to guarantee their respective interests, improve the enthusiasm of each subject, and make omni-channel supply chain operate continuously. In this paper, the Stackelberg game model is used to study the influence of traditional wholesale price contract, quantity discount contract, sales rebate contract and revenue sharing contract on the profit and return of manufacturers and retailers in omni-channel supply chain, so as to effectively encourage the main body of omni-channel supply chain to actively provide BOPS, protect offline physical store resources and bring gains to omni-channel supply chain by taking advantage of online and offline advantages. It is found that sales rebate contract and revenue sharing contract can achieve Pareto optimization on the basis of traditional wholesale price contract, and the choice mainly depends on BOPS demand.

Impact of uncertain demand and channel power on dual channel supply chain decisions.

The paper aims to conduct an analysis of pricing strategies in a dual channel supply chain under external uncertainty, utilizing Interval numbers theory and Game theory as the theoretical basis. The focus is on maximizing the expected profits of manufacturers and retailers. Four models are considered: centralized decision-making, manufacturer's Stackelberg, retailer's Stackelberg strategy, and vertical Nash model, with the decision variable being the product price. By solving the game model, the paper compares the optimal decisions under the four models and conducts sensitivity analysis to reflect the influence of key parameters and analyze their relationships. The ultimate goal is to optimize profits under various circumstances by adjusting market potential and price parameters to determine the best price level. The findings suggest that decision-maker's risk indicators have a greater impact on decision results when market demand is less sensitive to price, and that the size of the market has a negative correlation with the impact of decision-maker's risk indicators on decision results.

Integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer with ship-from-store strategy

Retailing has changed dramatically from single-channel brick-and-mortar stores to multi-channel and omnichannel retailers over the last few decades. Omnichannel retailers employ different strategies to integrate online and offline sales channels as well as order fulfillment processes. Among these strategies, the ship-from-store is the most popular and widely accepted among retailers. It enables retailers to use inventory from store locations to fulfill online demand. An omnichannel retailer with a distribution center and a retail store has to make important, interlinked decisions — (1) how much inventory to keep at the retail store, and (2) where to fulfill the online demand from and how much. In this work, we model the integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer employing the ship-from-store strategy. We analyze this problem for both single-period and multi-period settings. We extend the analytical framework of the single-period problem by providing a finite-horizon Markov decision process (MDP) formulation for the multi-period problem. Our findings suggest that for a single-period setting, decentralized inventory replenishment and demand allocation system maximizes the profit of the omnichannel retailer for low values of the incentive for fulfilling the online demand through store inventory, while for sufficiently high values of the incentive, a pooled system provides the optimal profit. An increment in the discount factor has the same effect on the optimal decisions in a multi-period setting as that of salvage value in a single-period setting for a given value of the incentive for the ship-from-store strategy. We also provide several extensions (such as cross selling, endogenous and correlated demand streams) of our analytical framework for the multi-period problem.

Traditional or drop-shipping? channel choice and product quality

Compared to the traditional channel, where a retailer bears the inventory risk, the drop-shipping channel operates with a distinct approach: the retailer avoids stocking products while the manufacturer directly handles stocking and shipping to consumers. Due to the different inventory risk allocations, the two channels have different product qualities and profits for the manufacturer and retailer. In this study, we investigate product quality decision of the manufacturer and delve into the impact of product quality on the manufacturer's and retailer's profitability. First, we find that product quality can be weakly higher in the drop-shipping channel than that in the traditional channel. This is because the drop-shipping channel allows the manufacturer who undertakes demand uncertainty to charge a higher wholesale price, resulting in a higher product quality. Second, our study reveals that the traditional channel can concurrently yield higher profits for the manufacturer and retailer when product quality is considered, which is in contrast to the previous works. Third, in the traditional channel, as demand uncertainty rises, the retailer's production quantity increases, while the manufacturer's product quality decreases. Finally, our results indicate that the product quality is more sensitive to the shipping cost when demand uncertainty is low.

Impact of Logistics Service Quality on E-shopper Satisfaction and Loyalty in Yangon: A Quantitative Study

Myanmar's e-commerce has been growing rapidly despite facing many challenges related to this newly established industry. Despite the nation's enormous growth in smartphone and internet usage, the infrastructure for e-commerce logistics is still in its infancy. Since there is a limited number of studies that have been conducted on e-commerce in Myanmar, this study stands out. The purpose of this study was to determine how e-commerce logistics impact Yangon's e-shoppers' satisfaction and loyalty. A quantitative approach was used to collect data from 400 e-shoppers in Yangon. A multiple regression analysis was conducted to investigate the relationship between six dimensions of logistics service quality (information quality, product quality, delivery service, customer service, order condition, and reverse logistics) and customer satisfaction. The findings revealed a positive correlation between e-shopper satisfaction and loyalty as well as a statistically significant relationship between each of the six logistical service quality factors and customer satisfaction. Customer satisfaction increased the most due to reverse logistics, followed by order condition, delivery service, customer service, product quality, and information quality. For greater e-shoppers' attraction and loyalty, retailers in Yangon should concentrate on improving all six factors of logistics service quality dimensions. They will improve satisfaction and loyalty among e-shoppers, boosting sales and profits for retailers while enhancing the shopping experience for e-shoppers.

WeStore or AppStore: How Customers Shop Differently in Mobile Apps vs. Social Commerce

In the dynamic e-commerce environment, social commerce has emerged as a revolutionary force, transforming how consumers interact and transact online. This paper investigates the differences in customers’ search and purchase patterns between a prominent online retailer’s burgeoning social commerce channel, the WeChat mini-program, and its native mobile app. We analyze the customers’ entire journey through a sequential search model that encapsulates decisions from channel selection to product search, search termination, and the final purchase. This study contributes to the search model literature by being the first to estimate both fixed and marginal search costs in a sequential search model in an omnichannel retail environment. We calculate fixed search costs, marginal search costs, and preferences for each channel, revealing differences in customers’ behaviors across channels. Our analysis shows that customers’ fixed search costs are higher, but marginal costs are lower on WeChat channel compared to the App channel. Also, customer characteristics like historical spending levels and search timing influence their search costs. From these insights, we suggest strategies tailored to each channel capitalizing on the differences in customers’ search costs. The first strategy encourages search initiation by lowering fixed search costs through peer-to-peer link sharing in the WeChat channel. The second strategy aims to minimize marginal search costs using search-triggering coupons in the App channel. Implementing these strategies significantly boosts conversion rates and profits for the online retailer. This research is one of the first to explore the differences between traditional retail channels and emerging social commerce channels.

Decision support system in determining the location of new supermarket branches using the copras method

Supermarkets are one of the ideal and profitable retail business sectors to try because they are located in various urban and rural areas. This causes many people to be interested in setting up a supermarket. However, determining a strategic location is not easy and requires many strategic location considerations. The research objective is to develop a Decision Support System (DSS) to determine the location of new supermarket branches using the Complex Proportional Assessment (COPRAS) method, which is expected to be helpful for management and supermarket partners as a business strategy. The COPRAS method excels in calculating alternative utilities and selecting the best alternative. There are nine criteria (land rental price, distance to competitors, security, distance to education, warehouse distance, cleanliness, land area, building price, crowd) and five alternative locations (Juanda, Hos Cokroaminoto, Bayangkara, Batoro Katong, Sumoroto) are considered. This research created a web-based DSS that selects the best location for supermarket, with Juanda (A1) ranked first and scored 100, followed by Somoroto (location A5) with a score of 99.861, Bayangkara (A3) with a score of 97.099, Batoro Katong (A4) with a score of 91.293, and HOS Cokroaminoto (A2) with a score of 88.877. From the results of the COPRAS calculation, it can be concluded that Juanda is the best location to build a new supermarket branch location. This result provides a valuable tool for management and supermarket partners seeking to make informed decisions about branch expansion strategies.