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Purpose: The purpose is to have appropriate planning for reducing costs during the procurement process and increasing profits during the sales period in a supply cycle at a retail store. To make the proposed model practical, efforts have been made to implement the existing constraints in a business environment and legal factors. Design/methodology/approach: To achieve the objectives of this research, a mixed-methods approach was employed. Initially, sales data was collected, and industry experts were consulted to identify key products. A mathematical model and a particle swarm optimization algorithm were utilized for simulation and optimization. Furthermore, time series forecasting techniques were employed to estimate demand accurately. Findings: The findings of this research emphasize the importance of considering all factors affecting costs simultaneously when utilizing tools to reduce costs during the planning period. It was observed that certain tools, which individually have a favorable effect on costs, may collectively result in an increase in costs over the examined period. This highlights the need for a comprehensive and integrated approach to planning. Research limitations/implications: The business environment and legal factors in each country are among the factors that affect the performance of retailers, which has received less attention in previous research. This research aimed to address this gap by considering various factors. However, it is important to note that the proposed model has been investigated in research in hypermarkets and is applicable, but it cannot be generalized to other retailers. Originality/value: Applicability and consideration of new limitations in simulation along with consideration of the complete process from buying to selling goods. [ABSTRACT FROM AUTHOR]

This paper focuses on supply chain coordination under a revenue sharing contract. Demand plays a crucial role in modeling inventory systems, particularly in the retail industry where products need to be brought in and taken out of retail stores within specified timeframes. The question addressed is how to allocate shelf space effectively. Optimal shelf space allocation in retail significantly impacts product sales and profitability. The research demonstrates that by considering factors such as price, allocated shelf space, product brand image, and advertising, a new demand function can be developed. The study explores decentralized, centralized, and coordinated structures using a Stackelberg game model. The findings show that a revenue sharing contract leads to a win-win outcome in the supply chain. Additionally, a numerical example is provided to illustrate the model, and sensitivity analysis is performed on key parameters. The results highlight the significant impact of price elasticity on demand, emphasizing the need to pay close attention to this parameter in real-world applications.IntroductionCoordinating manufacturers and retailers has always been a challenge in decentralized supply chains, as each member seeks individual profit. One practical mechanism for coordination is the revenue-sharing contract, where manufacturers set the wholesale price and retailers pay it. Additionally, retailers share a portion of their profits with manufacturers to incentivize participation in coordinated structures. However, the percentage must be chosen carefully to maximize the profits of both chain members compared to a decentralized structure. In today's competitive world, securing shelf space and determining optimal pricing, particularly in chain stores, has become increasingly intense. Companies can increase their profits by considering various factors that influence customer behavior. This study investigates how manufacturers and retailers in a supply chain leverage advertising and brand image to drive demand while setting prices and allocating shelf space for their products. This new model effectively captures the concept of competition in the market. On the other hand, in today's highly competitive business world, the competition to secure shelf space and determine pricing in department stores has intensified. Pricing and shelf space decisions are influenced by the demand within the supply chain. By considering various factors that impact customer behavior and incorporating them into the model, even though it introduces complexity and challenges in solving the model, the results become more realistic and enhance the model's effectiveness. Customers place great importance on accessing products at the right time and place, making optimal allocation of shelf space in stores and effective shelf space planning crucial steps towards improving and increasing sales in retail stores.Materials and MethodsA two-echelon supply chain, comprising a manufacturer as the leader and a retailer as the follower, is the focus of this study, and Stackelberg game theory is applied to model and analyze this system. The investigated model considers decentralized, centralized, and coordinated structures. Through this research, various factors such as price, allocated shelf space, and the impact of product brand image and advertising are taken into account, leading to the development of a new demand function. The application of the Stackelberg game in the developed model demonstrates how a revenue sharing contract can result in a win-win outcome within the supply chain. Real-world performance analysis of the proposed models is conducted using a dataset from Golrang Holding Supply Chain Network and Ofogh Kourosh Chain Stores Company in Iran.Discussion and ResultsThe proposed problem is modeled under three structures: (1) a decentralized structure where each member of the supply chain (SC) makes independent decisions to optimize its own profit, (2) a centralized structure where a central administrator maximizes the overall SC profit, and (3) a coordinated structure achieved through the design of a revenue-sharing contract. The revenue-sharing contract encourages SC members to transition from the decentralized structure to the centralized one. The results demonstrate that the use of the revenue-sharing contract leads to the sum of retailer and manufacturer profits being equal to the total profit in the centralized structure, with each member achieving higher profit compared to the decentralized structure. Consequently, the revenue-sharing contract facilitates the coordination of the desired supply chain.ConclusionsThis research is based on a real-life case study in the retail industry. The findings of this study are applicable to various retail sectors, including dairy, protein, grocery, cosmetics, fresh fruits, vegetables, and more. Traditionally, price has been viewed as a revenue generation tool. However, it is now recognized that price plays a crucial role not only in generating revenue but also in ensuring customer satisfaction. Therefore, it is important to coordinate and align pricing decisions with factors related to customer management, such as brand image and advertising. The main objective of this research is to design a new model with a multiplicative demand function that considers factors such as price, shelf space, brand image, and advertising. Additionally, the implementation of a revenue-sharing contract has improved system performance. The developed models were solved using Mathematica software, and numerical examples were provided to demonstrate their real-world application and the solution method. The numerical examples revealed that the centralized and coordinated structures experienced price declines compared to the decentralized model, leading to increased profitability in these structures. Furthermore, sensitivity analysis was conducted on key model parameters, highlighting the significance of the price elasticity parameter on demand. This parameter should be given greater consideration in real-world applications. While the study focused on a supply chain structure involving one manufacturer and one retailer, future research can explore supply chains with multiple retailers. It is also possible to combine various supply chain coordination contracts or compare different coordination contracts with each other.

Objective: Increasing sales profits and inventory control leads to productivity in the retailing industry. To minimize inventory costs and reach an optimal order quantity, it is essential to develop a mathematical model for inventory control that is suited to industries. It is a common practice in the retailing industry to consider the shelf space for storing perishable products without taking the warehouse space into account. This study intends to address the issue and touch upon selling high-profit products and signing total minimum commitment contracts with suppliers in order to optimize total costs in the order time. Methods: Reviewing the literature comprehensively, the authors could identify the effective parameters of the model through desk research. On the basis of the defects found, a model was proposed for inventory control considering the new limitations. The particle swarm optimization algorithm was used in MATLAB in order to reach an optimal response in the problem-solving process and a time series analysis was performed by Minitab for demand forecasting. Results: The proposed model can have applied implications for the industry. The results from the numerical analyses of the model revealed that an effective change in product face and a total minimum commitment contract with suppliers reduce the costs during the planning period. However, using both of them doesn’t always deliver favorable results. When a total minimum commitment contract is signed with the supplier and a change is made in the product face, it is necessary to reconsider the extent of commitment. Conclusion: This study addressed the utility obtained from the effects of product face changes on the number of demands and number of orders together with total minimum commitment. The quantity of orders was considered according to the shelf space limitations in order to lower the costs. Results showed that the methods adopted to boost sales and lower costs can be profitable for retailers.

This study evaluates the partial exclusion effects of store promotion. We find that a manufacturer with a better brand name has a higher willingness-to-pay for promotion services offered by retail stores or online platforms. The promotion results in higher sales-weighted average prices (wholesale and retail) and a larger inter-brand price gap. The stores or platforms extract more profits from manufacturers and consumers through the promotion services. The effects on consumer surplus and social welfare depend on whether the promotion alters consumer preferences. If it does, more consumers would be choosing their less-preferred brands because of the larger inter-brand price gap, which would be socially inefficient. If it does not, the promotion may help to correct the price distortion, but the social welfare effect is positive only when the promotion effect is small enough. In both cases, the promotion services reduce the total consumer surplus by softening inter-brand competition. [ABSTRACT FROM AUTHOR]

Objective: Increasing sales profits and inventory control leads to productivity in the retailing industry. To minimize inventory costs and reach an optimal order quantity, it is essential to develop a mathematical model for inventory control that is suited to industries. It is a common practice in the retailing industry to consider the shelf space for storing perishable products without taking the warehouse space into account. This study intends to address the issue and touch upon selling highprofit products and signing total minimum commitment contracts with suppliers in order to optimize total costs in the order time. Methods: Reviewing the literature comprehensively, the authors could identify the effective parameters of the model through desk research. On the basis of the defects found, a model was proposed for inventory control considering the new limitations. The particle swarm optimization algorithm was used in MATLAB in order to reach an optimal response in the problem-solving process and a time series analysis was performed by Minitab for demand forecasting. Results: The proposed model can have applied implications for the industry. The results from the numerical analyses of the model revealed that an effective change in product face and a total minimum commitment contract with suppliers reduce the costs during the planning period. However, using both of them doesn't always deliver favorable results. When a total minimum commitment contract is signed with the supplier and a change is made in the product face, it is necessary to reconsider the extent of commitment. Conclusion: This study addressed the utility obtained from the effects of product face changes on the number of demands and number of orders together with total minimum commitment. The quantity of orders was considered according to the shelf space limitations in order to lower the costs. Results showed that the methods adopted to boost sales and lower costs can be profitable for retailers. [ABSTRACT FROM AUTHOR]

Pricing and shelf space allocation are two main operational decisions in the retailing industry. This study simultaneously optimizes these two decisions in a supply chain with two manufacturers and one retailer under uncertainty of demand and price sensitivity parameters. Two manufacturers have different conditions in terms of parameters affecting demand and production. A robust optimization model and an exact solution approach are developed to find the optimal solution. The results show that price sensitivity, market potential, and production costs can have a synergistic effect on optimal values. Moreover, the market potential can rely on managerial decisions as it has a significantly positive impact on profitability. This parameter is found to be the most important tool for securing the profitability of supply chain members. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Management Journal (IMJ) is the property of University of Tehran, Faculty of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Administraçãao da UNIMEP is the property of Revista de Administracao da UNIMEP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Shelf-space optimization models support retailers in making optimal shelf-space decisions. They determine the number of facings for each item included in an assortment. One common characteristic of these models is that they do not account for in-store replenishment processes. However, the two areas of shelf-space planning and in-store replenishment are strongly interrelated. Keeping more shelf stock of an item increases the demand for it due to higher visibility, permits decreased replenishment frequencies and increases inventory holding costs. However, because space is limited, it also requires the reduction of shelf space for other items, which then deplete faster and must be reordered and replenished more often. Furthermore, the possibility of keeping stock of certain items in the backroom instead of the showroom allows for more showroom shelf space for other items, but also generates additional replenishment costs for the items kept in the backroom. The joint optimization of both shelf-space decisions and replenishment processes has not been sufficiently addressed in the existing literature. To quantify the cost associated with the relevant in-store replenishment processes, we conducted a time and motion study for a German grocery retailer. Based on these insights, we propose an optimization model that addresses the mutual dependence of shelf-space decisions and replenishment processes. The model optimizes retail profits by determining the optimum number of facings, the optimum display orientation of items, and the optimum order frequencies, while accounting for space-elasticity effects as well as limited shelf and backroom space. Applying our model to the grocery retailer’s canned foods category, we found a profit potential of about 29%. We further apply our model to randomly generated data and show that it can be solved to optimality within very short run times, even for large-scale problem instances. Finally, we use the model to show the impact of backroom space availability and replenishment cost on retail profits and solution structures. Based on the insights gained from the application of our model, the grocery retailer has decided to change its current approach to shelf-space decisions and in-store replenishment planning.