نشریه مدیریت صنعتی
سال پانزدهم شماره 51 (زمستان 1402)

Designing a multi-level supply chain network with efficient product flow management is an important issue in supply chain management. Determining the price of products, which can be affected by different factors such as environmental uncertainty, will have a significant impact on the strategic decision of designing the supply chain network. In recent research on dynamic pricing, the majority focused on two-level supply chains, exploring the impact of advertising on the supply chain. In this study, the levels of the supply chain are upgraded to three levels, and the advertising and inventory variables as well as the pricing issue in the supply chain are investigated. Hence, the overarching objective is to scrutinize the coordination of a supply chain involving three variables: advertising, warehouse inventory, and a comparison between static and dynamic pricing. Given the accelerated expansion of e-commerce, dynamic pricing emerges as a potent strategy for profit augmentation within the supply chain, an aspect that has received comparatively less attention. The supply chain in this research includes three levels: producer, supplier, and retailer. In addition, many sources, including Chen et al, 2018, have indicated that dynamic pricing is being used in many innovative businesses, that 60% of market development managers around the world are familiar with dynamic pricing methods, and that 35% of CEOs and business managers plan to base all their pricing models on dynamic pricing methods in the next three years. This research initially aims to provide a stable model for dynamic pricing and compare it with static pricing in multi-level supply chains using a game theory approach. Secondly, it examines the theoretical foundations, thirdly, the research methodology is presented, and fourthly, the discussion and conclusions are discussed.

The present research falls under the category of applied research. It considers discount-oriented models to examine the planning horizon, the use of dynamic pricing in some actors at one level of the chain from the concept of dynamic pricing, and the use of static pricing by others at the same level and considering the demand in a non-deterministic (stable) manner. Finally, the use of Stackelberg’s game is presented to examine the core of the game and the Nash equilibrium point. The robust optimization model is presented by Malloy et al. Initially, the model introduces symbols: x as the vector of design variables, and y as the vector of control variables. Parameters A, B, and C are coefficient parameters, while b and e are parameter vectors. Values for A and B are predetermined, and B, C, and e involve uncertainty. A particular understanding of the parameter is called scenario uncertainty, which is assigned the symbol s, and its probability is specified by ps. K retailers operate in the supply chain, and each retailer supplies the desired product from only one manufacturer. In general, the outcome for each retailer is the disparity between their income and expenses. In such a situation, the retailer faces ordering, holding, and marketing costs for each product. The profit margin of each retailer is the difference between the price paid to the manufacturer for the bulk purchase and the selling price to the customer. It is worth noting that considering that the final product of each manufacturer is sent to only one retailer, the number of manufacturers and retailers is equal. For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. Initially, the model underwent analysis in static mode, followed by dynamic mode analysis using Games software. Ultimately, the outcomes of the two pricing methods were compared.

This article introduces the development of a stochastic demand function utilizing genetic algorithms and particle optimization. It presents two distinct single-period models designed for a competitive environment. In the first model, retailers have only an intermediary role and do not decide on pricing or optimal order quantity. In the mentioned model, it is assumed that retailers transfer the demand exactly and to the same extent as it is from the customers to the suppliers. Based on the amount of their production, suppliers face shortages and excess supply. The purpose of this model is to determine the optimal and desirable amount of production as well as the price for each supplier. In addition, routing costs are approximated. The results indicate that the presented approximation is very accurate and evaluates the equilibrium point in much less time than the original model. In the second model, retailers are assumed to incur shortage or maintenance costs according to the number of orders they send. Here, the suppliers determine the final price, and the goal of the retailers is to determine the optimal amount of the order. In this model, it is assumed that suppliers’ production is customized, and inventory costs are considered only for retailers. A noteworthy aspect of this model is the emphasis on dynamic pricing—an inherently crucial and intricate element within the domain of supply chain dynamics theory

The results underscore the substantial efficacy of the proposed combination in addressing the multi-level supply chain network design model with both dynamic and static pricing. The sensitivity analysis of the manufacturer’s profit showed that the changes indicating an increase in the market base of a manufacturer will lead to an increase in profit for both manufacturers. The graph of the change in the producer’s profit compared to the change in the costs showed that a decrease in the production cost leads to an increase in the profit for the producer, while the profit of the next producer decreases in these conditions. Like the producer’s profit, the distributor’s profit also increases with the market base. Expanding the market base not only leads to an increase in the distributor's profit through collaboration with producers but also occurs through two distinct mechanisms. Firstly, there's a profit increase due to raising retailer prices. Although wholesale prices increase with the expanded market base and heightened demand, the distributor, to maintain balance, can elevate retail prices at a higher ratio. Consequently, the value of the pi-wi term increases. Secondly, there's a profit increase resulting from boosting sales volume. The distributor's profit is positively influenced by the increase in sales volume, thereby reinforcing the overall financial outcome.

Implementing the "Vendor-Managed Inventory" strategy and sharing inventory information among retailers and manufacturers play crucial roles in reducing holding costs, shortage costs, transportation costs, and excess inventory costs. This leads to enhanced coordination in the decision-making processes of supply chain members. Therefore, this research delves into the investigation of the supply chain coordination mechanism by combining the vendor-managed inventory strategy and revenue-sharing contracts. Additionally, in the developed model, the demand function incorporates a multiplicative factor of elements influencing the profit function, such as retail price, retailer, and manufacturer advertising investments.
 

In this article, we study the coordination in a two-level supply chain including one manufacturer and multiple retailers under a revenue-sharing contract, where the pricing and advertising investment strategy of each member of the supply chain is implemented despite vendor-managed inventory in a centralized supply chain. Subsequently, in the decentralized state, we employ the Stackelberg game with manufacturer leadership to determine the optimal variables for both manufacturers and retailers. Finally, to reach coordination and achieve the maximum benefit of the supply chain, we proposed a revenue-sharing contract so that the retailer compensates a part of the costs incurred by the manufacturer by sharing a proportion of the revenue.
 

A numerical example is presented to show the implementation impact of the revenue-sharing contract on the profit of the chain members. Costs and profit functions are calculated in the Iranian Rial and this system is planned for one year. In this example, it is clear that this contract has been worked successfully and the profit of the supply chain in the coordinated state is equal to the profit under the centralized state. The sensitivity analysis of the model showed that the profit of each member of the chain in a coordinated state is more than their profit in a decentralized state. In addition, advertising investment increases the profits of manufacturers and retailers significantly. As a managerial insight, it is recommended that both levels of the chain place special emphasis on investing in advertising activities.
 

This research was conceived based on a real-world scenario, where a single manufacturer engages with multiple retailers within a market. The findings of this study have broad applicability across various retail industries, including but not limited to fruit and vegetable markets, grocery stores, protein supply chain outlets, health and beauty product suppliers, drugstores, pharmaceutical supply chains, as well as industries such as automotive and petrochemical. The results of this research proved that the vendor-managed inventory strategy increases the interaction between manufacturers and retailers and helps manufacturers access the retailer information to charge products as soon as possible and also avoid excess production.

According to the studies conducted on New Technology-Based Firms (NTBFs), the biggest problem of these companies is their access to financial resources. Studies show that among the indirect intervention policies, credit guarantee schemes have been considered by governments for facilitating the financing of these enterprises due to the need for less budget and a higher leverage effect. The purpose of this article is to present the appropriate credit guarantee model for new technology-based Firms.

In this study, which used a combination of qualitative and quantitative methods, firstly, by reviewing the literature, credit guarantee models, key components, and main criteria for choosing the appropriate credit guarantee model were identified and validated by the opinion of an expert group. Finally, the most appropriate model was selected using the Multiple Criteria Decision-Making Model and Analytical Network Process (ANP). Also, to form the grounds for policy implications for credit guarantee programs in Iran, an in-depth study of credit guarantee programs in the country and a comparison of the constituent components of credit guarantee programs with the classified models for credit guarantee in the world was carried out.

The findings of this study indicate a lack of effective policy formulation and a deficiency in the precise and targeted selection of a local credit guarantee model for emerging technology-based firms in Iran. The central outcomes of this study revolve around the identification of credit guarantee models, the delineation of key components, and the establishment of criteria for choosing the most suitable credit guarantee model. Furthermore, the research entails the ranking of both these criteria and the various models under consideration. According to the insights gathered from experts in this domain, the research findings highlight the primary criteria for selecting a credit guarantee model. These criteria, ranked in order of significance include: facilitating the provision of credit services, assessing the consequences of the credit guarantee system, effective risk management, and governance of the credit guarantee system. This ranking reflects the experts' assessment of the most pivotal factors when evaluating credit guarantee programs. The criterion of facilitating the provision of credit services has the most weight (importance). Also, policy implications such as: "Government support policies regarding the development of guarantee tools", " creating a centralized and reliable credit risk database" and "Governance and functional changes in the country's credit guarantee institutions" were proposed to create a local credit guarantee system for NTBFs.

In this research, utilizing the ANP methodology, the selection of an apt credit guarantee model for the nation aligns with the specific requirements of emerging technology-based firms, the prevailing economic and institutional landscape, and global best practices and successful models. This approach holds substantial promise for achieving significant success, not only in shaping effective policies but also in their successful implementation within the realm of financing for these companies. Reforming the institutional and functional structure of the country's credit guarantee institutions in the short term, as well as the formation of new credit guarantee institutions according to successful credit guarantee models in the world and their development in the long run can play a significant role in the financing of NTBFs.

The demand-driven material requirements planning approach (DDMRP) considers inventory buffers at certain points in the bill of materials to respond more quickly to customer demands and demand fluctuations. In this approach, the presence of a buffer at each point of the bill of material does not necessarily improve the material flow and may lead to an increase in inventory costs. Moreover, this approach includes parameters that are experimentally set by the manager, and inappropriate values for these parameters can result in deficient performance. Therefore, this paper aims to simultaneously determine optimal values for the parameters of this approach at both the strategic level (strategic inventory positioning) and the operational level (planning phase) to minimize inventory cost and achieve a 100% service level within customer tolerance time.

In this research, a simulation-optimization model is introduced to determine the optimal values of three fundamental parameters: strategic inventory position, variability, and lead time factors. To address this, a combination of genetic algorithm and mixed-integer linear programming with a CPLEX solver is utilized. In the genetic algorithm phase, the allowed positions of the buffer are randomly selected in a way that the delivery of the final product is less than the customer tolerance time. In the mixed-integer linear programming phase, the optimal values of variability and lead time factors are determined with the aim of minimizing inventory cost and avoiding stockout issues.

The proposed model is evaluated across 12 randomly generated instances of the bill of materials, each varying in levels and the number of parts. This set includes the specific case studied in the article by Jiang and Rim. The performance of our presented model is then compared with that of Jiang and Rim's model. The results consistently reveal that the proposed model demonstrates superior efficiency across all instances. The comparison of results with the data from the main article proves that the inventory cost of the proposed model has decreased between 82% and 86%, with an average reduction of 83.6%. The improvement percentage of on-time deliveries ranges from 0% to 4%, with an average improvement of 2.2%. Comparison with randomly generated data indicates that the average inventory cost using the proposed model is reduced between 73% and 91%, with an average reduction of 81.8%. Moreover, with 100% confidence, all orders are fulfilled within a time frame shorter than the customer tolerance time.

In the presented model, despite more restrictions regarding the buffer position in the bill of material, the performance results of the proposed model demonstrate a significant reduction in average inventory costs compared to the Jiang and Rim model. Additionally, with full confidence, all orders will be fulfilled. The methodology employed in this research can function as a decision-support tool for managers. It aids in determining the optimal quantity and timing of manufacturing or purchasing orders, minimizing inventory costs while aiming to achieve a 100% service level within the customer tolerance time

In the realm of green supply chain management, the pivotal responsibilities of companies involve supplier selection and order allocation. These tasks were treated as independent challenges in prior studies. This issue holds significant importance within the context of green supply chain management, demanding the simultaneous consideration of both quantitative and qualitative criteria. These criteria, at times conflicting, necessitate an in-depth analysis of trade-offs. There is a pressing need for a comprehensive framework capable of concurrently addressing both aspects. In addition to accommodating green criteria, such a framework should enable the determination of order quantities allocated to suppliers.

The utilized approach in the present study included Fuzzy DNP, Fuzzy TOPSIS, and a Bi-objective optimization model. In the first stage, the criteria weights, encompassing both traditional and green criteria, were determined using the fuzzy DNP. Subsequently, the suppliers' scores were computed using the fuzzy TOPSIS. In the second stage, a non-linear bi-objective model was formulated, aiming to minimize the total cost and maximize the purchased value. The first objective function comprised the purchase price, suppliers' variable costs, ordering and setup costs, as well as buyer's and suppliers' inventory holding costs. Additionally, the second objective function ensured a higher allocation of orders to suppliers who had achieved a superior score in the supplier evaluation process. The model's constraints encompassed the suppliers' capacity, buyer's demand, and constraints associated with the suppliers' discounts. Acknowledging the bi-objective nature of the mathematical model, the Global Criterion method was employed to transform the bi-objective problem into a single-objective one. According to this method, the relative deviations of the objectives from their corresponding single-objective optimal values were minimized. Furthermore, due to the non-linearity of the mathematical model, a harmony search algorithm was developed for the problem, and a repair algorithm was proposed to handle constraints in the problem.

The previous studies and expert opinions were utilized to identify the criteria, encompassing both traditional and green criteria. The results of fuzzy DANP indicated that the supplier's credit holds the utmost importance, followed by quality, green purchasing, green design, green transportation, green production, on-time delivery, and distance. Furthermore, all criteria exhibit an effect on the supplier's credit. Subsequently, six suppliers underwent evaluation based on the criteria using fuzzy TOPSIS, and their final scores were employed as the suppliers' weights in the second objective function of the mathematical model. To validate the proposed approach, a case study with real data in the field of the medical equipment supply chain was examined, and the optimal solution to the problem was presented. Subsequently, by adjusting the weights of the objective functions in the single-objective problem, the Pareto solutions were identified. The proposed approach facilitates the optimal allocation to the suppliers by considering the green criteria.

The results showed that a decision maker can choose the best solution according to the Pareto solutions in such a way that there is a correct trade-off between the supply chain costs and purchased value.

Waste collection poses a significant challenge for contemporary societies. Given the inevitability of ongoing human waste production, the organization of municipal waste collection holds paramount importance. Against the backdrop of escalating environmental pollutants over recent decades and crises induced by global warming, governments have increasingly prioritized addressing sustainability issues. The objective of this study was to formulate an urban waste collection network with a municipal sustainability perspective. To achieve this goal, we proposed a multi-objective mathematical model that incorporates economic, social, and environmental considerations pertaining to the routing of urban waste collection vehicles.

This study introduces an integer multi-objective mathematical model centred on stability components to address the routing problem of urban waste collection vehicles, to design an optimal network for urban waste collection. The model was addressed using real data from waste collection in Iran’s Saveh city. GAMS software was employed for solving the model in small dimensions, while MATLAB software was utilized for solving the model in larger dimensions. The proposed model incorporates a robust approach to handle uncertainty. Multi-objective meta-heuristic algorithms were applied to solve the model in scenarios with larger dimensions. A comparative analysis was subsequently conducted, evaluating solution methods based on both the values of the objective function and the solution time.

The economic objective of this study encompasses the overall costs associated with transporting waste from collection points to processing and recycling centres, along with the expenses related to waste recycling. Its environmental objective focuses on minimizing pollution resulting from the transportation of collected waste. Lastly, its social objective is to maximize citizens' satisfaction with urban waste collection. The results demonstrated that the proposed mathematical model establishes a rational relationship between the incurred costs, the quantity of waste collected, the distance travelled, and the amount of pollution generated during the transportation of waste.

The model presented in this study optimized the urban waste collection system by incorporating dimensions of sustainability. This was achieved by formulating separate objective functions to address various aspects of urban waste collection. The results showed that in the economic dimension, waste collection costs, which account for the largest share of the total cost of waste management, decreased significantly. The collection cost was reduced by optimizing the collection routes and reducing the costs related to recycling collected waste. In addition, in the social dimension, by considering the amount of waste collected compared to the waste produced, the level of satisfaction of citizens was calculated. Finally, the results showed that by reducing the environmental effects related to the recycling and transportation of the collected waste, the proposed model had an acceptable performance.