Supplier's Profit Research Articles

Data-Driven Order Consolidation with Vehicle Routing Optimization

This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics.

The impact of contract types on carbon-capped food supply chains: options versus wholesale

Given the rising awareness of environmental issues and the growing demand for eco-friendly food products, stakeholders within the food supply chain are investing more in green technologies to reduce carbon emissions. However, the efficacy of carbon caps in achieving emission reductions is challenged by unpredictable consumer demand, complicating both emission control and supply chain efficiency. This study investigates a scenario involving a food supply chain with an environmentally responsible supplier and a retailer facing demand fluctuations. We explore the incentives for suppliers to adopt option contracts and evaluates their impact on carbon emissions through a Stackelberg game model with strict carbon emission caps. Our analysis reveals that while higher green investment generally enhances supplier profitability, it may not always benefit retailers. Although option contracts can improve supply chain efficiency, they might also lead to increased carbon emissions and food waste. Notably, when green investments are decision-based, option contracts tend to encourage greater investments than wholesale contracts, helping to mitigate the financial burdens of high costs. This trend persists even in competitive markets. Our research suggests that option contracts can effectively balance the dual objectives of maintaining supply chain performance and complying with carbon emission limits.

Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented.

Cross-border supply chain coordination of low-carbon agricultural products under the risk of supply uncertainty.

The purpose of this paper is to discuss how cross-border e-commerce enterprises can promote the sustainable development of the supply chain by optimizing the risk of supply disruption and product quality control mechanism of the cross-border supply chain of low-carbon agricultural products in the face of the problem of uneven quality and inventory shortage that prevails in the supply chain of low-carbon agricultural products under the framework of low-carbon economy. Methods: A two-level supply chain model consisting of a risk-averse cross-border e-commerce enterprise and two risk-neutral overseas suppliers is constructed to compare the optimal strategies and their coordination effects under the centralized and decentralized decision-making modes, and to deeply analyze the supply chain's operation mechanism. Further, the quality cost factor is introduced and an option contract model is designed to quantitatively analyze the impact of different decision-making scenarios and parameter changes on the overall supply chain performance. The results of the study show that under the coordination of option contract, the profit of cross-border e-commerce companies tends to decrease as the risk of supply disruption increases, while the profit of suppliers rises accordingly. Meanwhile, improving the quality of agricultural products can significantly improve the overall profitability of the supply chain. The cost-sharing mechanism is positively related to the profit of cross-border e-commerce companies, but negatively affects the profit of suppliers. In addition, the adjustment of the option price is directly associated with the increase of the specific supplier's profit and the decrease of the cross-border e-commerce enterprise's profit. Conclusion: By constructing and analyzing the option contract model considering the risk of supply disruption, this study effectively reveals the key influencing factors and their interactions in the cross-border supply chain of low-carbon agricultural products, and provides cross-border e-commerce enterprises with specific strategies to achieve coordination of the supply chain contract, improve product quality, and mitigate the risk of supply disruption, and then promote the sustainable development of the supply chain of low-carbon agricultural products.

Pricing and bundling decisions for complementary products in an agency selling platform with risk‐averse suppliers and stochastic demand

AbstractThe exponential rise of e‐commerce has prompted many companies to utilize online platforms (e‐tailers) to sell their products. Agency selling is a prevalent policy among e‐tailers where suppliers determine retail prices of their products, and the e‐tailer charges an agency rate. This paper investigates the selling format and pricing problem of two risk‐averse suppliers who sell complementary products on the e‐tailer's platform. The e‐tailer can offer products separately and as a bundle. A game‐theoretic analysis reveals that the equilibrium prices of individual products and the bundle uniquely exist. The study explores that the e‐tailer does not always benefit from a higher agency rate. Furthermore, the agency rate can influence the superiority of mixed bundling over separate selling. Moreover, a mixed‐bundling strategy may hurt the supplier's profitability which is sensitive to marginal costs and the degree of risk aversion. Finally, when demand correlation increases, the supplier sets a lower price to mitigate the risk cost.

The transformative potential of blockchain technology in developing green supply chain: An evolutionary perspective on complex networks

Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a “win-win” scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.

Effect of Governmental Subsidies on Green Energy Supply Chains: A Perspective of Meteorological Disasters

The energy sector, serving as a pivotal propellant within the macroeconomic framework, finds its production, distribution, and consumption aspects considerably influenced by climatic variations. In this study, a two-tier Stackelberg model for the green energy supply chain is developed, which is employed to analyze the profit disparities between suppliers and retailers both in the absence and presence of meteorological disasters. Furthermore, the research delves into the optimal supply chain strategies under three distinct scenarios in the context of meteorological disasters. A comparative analysis is conducted to examine the decision-making variances and the shifts in the interests of each supply chain participant under these scenarios. The findings reveal two critical insights: First, meteorological disasters detrimentally affect the environmental friendliness of energy products, as well as the profits of both retailers and suppliers. Second, the selection of the optimal strategy varies depending on the evaluation criterion used. When product greenness is the metric, subsidies aimed at promoting sales costs emerge as the superior strategy. Conversely, for retailers and suppliers, subsidies that target the environmental friendliness of energy products prove to be the most effective. Based on the conclusions, the paper also makes recommendations for governments and companies.

Mitigating yield uncertainty from the perspectives of contract manufacturing and technology licensing

In the low-carbon environment, green manufacturing by manufacturers often requires upstream to provide precision components. However, the lack of production experience of high-tech upstream leads to yield uncertainty. To explore the impact of yield uncertainty on green supply chain operations and analyze its solution, we propose a contract manufacturing mode with technology licensing and further consider pricing licensing or production licensing. The Stackelberg game is employed to construct these three models and a benchmark model without contract manufacturing. Moreover, we discuss the supplier’s mode preference by numerical analysis. Our findings reveal that the contract manufacturing mode with technology licensing mitigates the detrimental influence of yield uncertainty, boosting supplier profitability by 49.00% and manufacturer profitability by 61.76% when the expected yield rate is small or the expected yield rate is large with a small yield fluctuation. Furthermore, when both expected yield rate and profit-sharing ratio are low, increased yield fluctuation predominantly affects the downstream, whereas a win–win–win situation can be achieved through increased profit-sharing ratio. Additionally, compared with contract manufacturing mode with technology licensing, additional pricing licensing or production licensing augments supplier profitability by more than 10.56% in certain cases. Interestingly, improving production efficiency may not enhance the contract manufacturer profitability in both modes due to potential trade-offs with competition and the high cost. This paper contributes to the development of contract manufacturing policies, guiding suppliers and contract manufacturers towards achieving synergetic economic and environmental development. Future research could examine the applicability of the proposed contract manufacturing mode in various industries or identify additional factors affecting supplier profitability.

How does the power structure affect the acquisition price and inventory decisions in the reverse supply chain

With growing concerns about environmental sustainability, reverse supply chains play a crucial role in recycling, remanufacturing, and reducing waste. Investigating the power structure’s impact on decisions related to acquisition prices and inventory management can provide insights into the integration of sustainable practices. This study analyzes a reverse supply chain with a supplier and a remanufacturer, in which the supplier provides the remanufacturer with sorted used cores. We investigate the acquisition pricing and inventory decisions under different power structures (supplierled Stackelberg model, remanufacturer-led Stackelberg model) through game theory analysis. We first establish the centralized model to explore optimal acquisition decisions and profit of the total system. Then we evaluate the acquisition quantity, wholesale prices, the profit of supplier and remanufacturer in the supplier-led model and in the remanufacturer-led model. Finally, the impact of power structure on the performance of supplier and remanufacturer are explored by numerical analysis, as well as the supply chain system. Our significant findings show that (i) the total profit of the supplier-led and remanufacturer-led reverse supply chain are less than the centralized model due to the double marginalization; (ii) the remanufacturer-led reverse supply chain is superior to the supplier-led model reverse supply chain; (iii) the selected grades in the supplier-led model is proved the same as that in the remanufacturer-led model, which could not been affected by the wholesale price; (iv) the greater the inventory risk (the uncertainty in demand), the greater the profit share for the remanufacturer (supplier) in the supplier-led (remanufacturer-led) model.

Operation strategy for community integrated energy system considering source–load characteristics based on Stackelberg game

Instability in supply and demand, coupled with conflicting market interests, significantly impacts the operational efficiency, reliability, cost-effectiveness, and environmental sustainability of integrated energy systems. To address this issue, an operation strategy for a community integrated energy system based on Stackelberg game theory is proposed, taking into account the characteristics of supply and load. Initially, a master–slave game model for an integrated energy system is established, with energy marketer acting as leader and energy supplier and load aggregator as followers. To manage the uncertainties of wind and photovoltaic power generation, robust optimization theory is employed to construct models for both the short-term and long-term output errors of wind and photovoltaic power generation. This approach allows for a more detailed analysis of the uncertainty associated with wind and photovoltaic power generation. Subsequently, the uniqueness of the equilibrium in the established Stackelberg game model is proven. Utilizing an improved adaptive differential evolutionary algorithm in conjunction with the CPLEX solver, not only is the optimal solution solved for, but also the efficiency of the solution scheme is significantly enhanced. The simulation results demonstrate that the strategy not only boosts the reliability of the integrated energy system but also achieves a cost saving of 1.11%, an increase in energy supplier profit of 5.16%, and a load aggregator consumer surplus of 19.68%. In scenarios considering errors in wind and photovoltaic predictions, the cost savings reach 16.95%, with energy seller and supplier profits improving by 9.34% and 32.47% respectively, and consumer surplus increasing by 18.81%.

Behavioral operations and coordination analysis in e‐commerce supply chain considering negative focus preferences under supplier dominance

AbstractOur study aims to examine variations in behavioral decision‐making by employing the focus theory of choice. We discuss the impact of different personality traits of the retailer on the final decisions and the trends in performance across the supply chain. Our finding indicates that an increase in loss acceptance and pessimism positively impacts the overall profitability of the supply chain. If the retailer's level of pessimism is deterministic, the lower the level of loss acceptance, the lower the supplier's optimal profit. This study can help decision makers understand how to consider the personality traits of collaborators when collaborating.

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.

Low carbon strategy and coordination for a two-echelon automotive supply chain considering dual credit policy

The dual-credit policy currently plays a crucial role in the automobile industry and significantly contributes to low-carbon operations in the automotive supply chain. This paper employs the game-theoretic approach to build a supply chain comprising a supplier (leader) and an automaker (follower), considers the impact of dual-credit policy, and incorporates vertical spillovers into the supply chain abatement. Both parties lower carbon emissions through vertical spillovers while maximizing profits, thereby studying the low-carbon strategy of the automobile supply chain. Our findings reveal that (1) increased vertical spillover rate reduces the prices of fuel vehicles (FVs) and new energy vehicles (NEVs), raises the low-carbon levels and profits of automakers and suppliers. (2) An increase in the NEV credit ratio under the centralized system raises both price and demand for NEVs, while the low-carbon levels of automotive enterprises are reduced. (3) The carbon emissions of each vehicle under the centralized system will be lower than those of the decentralized system, and the order of total carbon emission levels under both systems is closely related to the credit trading price and credits per NEV. Moreover, we explore the automotive supply chain coordination and find that the Nash bargaining model fully coordinates the supply chain, while the cost-sharing and revenue-sharing contracts do not. Also, numerical analysis shows that the optimal system profit under revenue-sharing is greater than cost-sharing.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

Strategies for Investing in Big Data Information Services in Community Group Buying Supply Chains and Their Comparative Study

This study investigates the investment strategy of big data information service in community group-buying supply chain. Combined with the background of big data information service, it constructs three investment decision models, compares the optimal profits of all parties of the game under different modes, and analyzes the problem of choosing the optimal investment strategy for suppliers, community group-buying platforms and group leaders. It is found that: 1) The community group-buying supply chain invests in big data information service, which has a promotion effect on the supply chain profit, and when the supplier's marginal profit and the head's marginal profit satisfy certain conditions, the investment model is the O-mode, which can make the supply chain realize a long-term win-win situation. The profit of the supply chain is related to the consumer conversion rate, discount rate, cost coefficient of supplier's quality management efforts, cost coefficient of platform's big data marketing, and cost coefficient of group leader's marketing.

Customer bargaining power and supplier profitability: the moderating role of product market overlap

PurposeResearch on the relationship between customer bargaining power and supplier performance in supplier–customer relationships has flourished in recent decades. This study aims to empirically investigate whether product market overlap (PMO) in a supply chain moderates the effect of customer bargaining power on supplier profitability.Design/methodology/approachThis study uses large-scale secondary data from multiple databases. Econometric panel data techniques are used to test the hypotheses.FindingsThe results show that PMO in a supplier–customer relationship and PMO in supplier–supplier relationships both exacerbate the negative effect of the bargaining power of customers on supplier profitability.Originality/valueThis study contributes to the field of supply chain management. This study brings new insights into the ongoing debate surrounding the relationship between customer bargaining power and supplier profitability. The study also contributes to the literature on supply chain networks by showing the impact of indirect supply chain relationships.

Contract Theory-Based Collection, Updating, and Packaging Combination Strategies in Data Trading

In the data transaction process, where a single supplier collects and sells data to heterogeneous data buyers, its transaction strategy relies on the data’s value. In this paper, we measure the value of data in terms of volume and currency and analyze the whole process from data collection and update management to sales. The data supplier determines the maximum amount of data to be collected based on the willingness to pay and the cost of collection and decides the optimal update frequency based on the update cost paid to improve the data utility. Because data sets can be replicated and split nearly costlessly, data suppliers maximize revenue by combining data sets of different sizes. In the information asymmetry scenario, contract theory is applied to obtain the optimal data collection quantity, update frequency, and packaging combination scheme to maximize the profit of data suppliers under data buyer incentive compatibility and individual rationality and compare them with the information symmetry scenario. The results show that data suppliers will only provide personalized data sets to some data buyers, and the needs of some buyers will not be met. Information asymmetry causes data suppliers to reduce the number of data collections and the actual purchases by data buyers, and data suppliers’ profits tend to decrease. The surprising conclusion is that data suppliers should set up more frequent update strategies in the information asymmetry case than in the information symmetry case to sustain the decrease in data utility caused by insufficient quantity. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72071042 and 72201113]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0018 .

Renegotiations after procurement auctions: Coping with supply disruptions due to underestimating costs

AbstractIt is common for suppliers to underestimate costs in procurement auctions, thereby decreasing the bid price and profit on completion of the supply, and potentially resulting in supply disruptions. We study the bidding decisions of suppliers that underestimate costs when the buyer uses first‐price or second‐price sealed‐bid procurement auctions. We study the effects of underestimating costs on the bid prices and performance decisions under different auction modes and propose renegotiations after procurement auctions to cope with supply disruptions. The results show that: (1) using first‐price or second‐price auctions, underestimating costs does not guarantee a positive profit on completion of the supply. When the winning supplier's degree of underestimation is high enough, underestimating costs may cause supply disruptions. (2) When the buyer faces supply disruptions, if the expected payment for re‐selecting another supplier is not less than a certain threshold value, the buyer can prevent supply disruptions through renegotiations after procurement auctions. (3) Compared to the situation where all suppliers' budgeted costs are accurate, underestimating costs always decreases the supplier's profit. For the buyer, when the supplier completes the supply under the original or renegotiated contract, underestimating costs can decrease the procurement costs. Finally, we show the main conclusions with numerical examples.

Research on the decision-making method of coal order price and coal purchase quantity based on prediction

The imbalance of coal supply and uncertainty of demand changes have brought great challenges to the stable supply of coal. In the actual trading process, there are problems such as variable demand and incomplete information on both, and the trading process lacks scientific and effective guidance. Therefore, game theory is introduced into the coal trading process, and a decision-making method based on predicted coal supply and demand order price and coal purchase quantity is proposed. Firstly, the prediction method is introduced, and the Long Short Term Memory (LSTM) and Autoregressive Integrated Moving Average Model (ARIMA) model are used to predict the coal purchase quantity and coal price of the supply and demand, which provides a basis for the formulation of the initial scheme of the bilateral game between supply and demand. Secondly, a bilateral game model of coal supply and demand based on prediction is established, and a bilateral order game-solving strategy of coal supply and demand based on a genetic algorithm is proposed. Finally, the proposed method was verified by setting up three different example scenarios for thermal coal trading, and the optimal coal price and coal purchase quantity under equilibrium are solved, and the results showed that the profit of supply and demand is increased by 4.65% and 2.97%, respectively, and the calculation time is reduced by 21.12%, which improves the speed of solving the game model, maximizes the benefits of both the coal supply and demand sides and provides a theoretical basis for the scientific and efficient order formation of coal mining enterprises.

Bonus or Penalty? Designing Service-Level Agreements for a Decentralized Supply Chain: The Implication of Return on Investment

We study the coordination of two-echelon supply chains via service level-dependent bonus and penalty contracts and assume that the manufacturer maximizes its profit while enabling the supplier to achieve its performance target. Profit and return on investment (ROI) are considered as the supplier’s performance measures. We compute optimal contract parameters and perform a numerical study. In addition, we perform a simulation-based study to analyze the profit risk of the considered parties. For supplier profit targets, bonus and penalty contracts lead to the same profit for both parties, but bonus contracts lead to lower costs, hence, a higher ROI. For supplier ROI targets, bonus contracts always lead to higher manufacturer profit, and no optimal penalty contracts exist. For every contract service level, optimal bonus contracts exist that maximize profit and ROI of the manufacturer simultaneously. Thus, we analyze the profit risk for the manufacturer and the supplier and show that it can be significantly reduced by setting an appropriate contract service level. The manufacturer usually should prefer bonus contracts. They allow to optimize profit and ROI simultaneously and even offer an additional degree of freedom to reduce the profit risk.