Chain Cost Research Articles

Production, distribution, and capacity planning for an integrated buyer-vendor system incorporating different production and shipping scenarios

Supply chain management generally seeks to reduce costs, improve customer satisfaction, and gain a competitive advantage for all members of the chain. If decisions are made independently, there can be no guarantee that network members will reach the optimal decision as a whole. In integrated buyer-vendor optimization models, decisions are made centrally, and different parts are integrated and coordinated. This paper examines two different scenarios, namely Continuous Production-Instantaneous Shipping and Continuous Production-Gradual Shipping. In each scenario, an integrated inventory model with multiple warehouse capacity levels is proposed under uncertain conditions. We solved both mathematical models using proposed exact algorithm. Based on the calculated results, capacity planning leads to proper inventory management, efficient use of space, and making the most appropriate decision to have the least chain cost. Furthermore, the outputs of the models show that limited access to transport fleet leads to an increase in inventories and the subsequent reduction of stock-out effects requires effective changes in plans and management measures.

On Sidechain-Assisted Transaction Service Management for Internet of Things: A Random Contract Approach

With the evolution of industrial technologies, the Internet of Things and blockchain show enormous potential. Different institutes with heterogeneous devices indicate diversity in their private information, including service demand and risk-bearing capability. Low efficiency and rising transaction prices of the public blockchain have been significant barriers to these potential service purchasers. The sidechain is recognized as one of the scalability solutions to a public chain. However, it is challenging to design the economic incentive mechanism and study the transaction fee pricing for transaction services on a sidechain due to the uncertainty of the network's performance and the miner's effort. Considering the asymmetric information (i.e., anonymous private information) and uncertainty, we propose a random-contract-based scheme to maximize the sidechain service provider's revenue and assign the service buyers the feasible service price under the framework of a sidechain linked to the public blockchain. Furthermore, we show that random contracts outperform the deterministic contracts under the increasing absolute risk-aversion assumption. We investigate service pricing in various scenarios using Geometric Brownian Motion. The simulation findings reveal that random contracts can increase the sidechain's revenue by 24.70%, compared with deterministic contracts. The efficient service payments have been reduced by 44.65%, compared with the main chain's cost.

Methodology of measuring the benefits of participation in business clusters

Objective – Running a business in the complex and global world requires the cooperation of entities with different complementary resources and competencies. A socio-economic organization that wants to be competitive in the modern market must have relevant human, financial, material, and information resources and be able to make use of them in the most effective way. Methodology – Enterprises, and institutions that join together in cluster initiatives bring to them their material potential, to multiply the possibilities of achieving benefits. The scientific basis of the article is a hypothesis that participation in the above-mentioned clusters provides economic, or marketing, benefits to the economic, social, and scientific entities, as well as know-how development and socio-economic revival of a region. Findings – The main scientific issue to delve into, not only for the purposes of this thesis but also for the knowledge that is essential to managers, is the effectiveness level of the adopted project. The main goal of the article is to present the economic benefits of cluster enterprises resulting from the rationalization of their operating costs in a common chain of utilising their potential. Novelty – The research problem is to present the way and procedures for examining the benefits that companies achieve by operating in a joint economic venture. The empirical study was based on the cost-consumption rates of selected groups of costs as well as calculated profitability rates calculated for logistics processes in the year 2014 and 2020 in Thermal Energy Company Morąg. Type of Paper: Empirical JEL Classification: C83, D24, D61 Keywords: Clusters, Innovation, Efficiency, Competitiveness, Logistics. Reference to this paper should be made as follows: Dmuchowski, R; Szmitka, S. (2022). Methodology of measuring the benefits of participation in business clusters, J. Bus. Econ. Review, 7(1), 51–60. https://doi.org/10.35609/jber.2022.7.1(1)

Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

E-commerce and logistics are symbioses with each other, but cross-border e-commerce (CBEC) still cannot break away from cross-border logistics. With the progress of economic internationalization, economic and trade ties around the world have become closer and closer, and the level of international business exchanges has been improved. The rise of multinational e-commerce has also caused unprecedented difficulties to multinational logistics and supply chain management. The application of deep neural networks in various fields provides opportunities for cross-border e-commerce platforms to solve these problems. The existing logistics distribution model cannot keep up with the development of CBEC and has become a constraint and bottleneck for the development of CBEC. Therefore, this article introduces deep learning neural network to cross-border logistics and supply chain based on the analysis of the existing cross-border logistics model and supply chain model and the status quo of e-commerce development. It optimizes the existing cross-border logistics and supply chain network in order to break through the current bottleneck in the development of CBEC. This paper shows through research that introducing deep learning neural networks into CBEC logistics and supply chain can improve the efficiency of logistics and supply chain. Compared with the previous efficiency, the efficiency of network optimization can be increased to about 50%, reducing the cost of cross-border logistics and supply chain. The research in this article has great theoretical and guiding significance for the development of CBEC.

Retail Sales Forecasting Using Deep Learning: Systematic Literature Review

This systematic literature review examines the deep learning (DL) models for retail sales forecast. The accuracy of a retail sales forecast is a prevalent force for uninterrupted business operations. Accuracy for retailers means limiting supply chain and storage costs, ensuring no product is out of stock, and facilitating smooth promotional operations. The study analyses the DL frameworks used in reviewed literature. Tested DL models are listed, as well as other machine learning and linear models used for the evaluation comparison. Additionally, the review presents the metrics used by the authors for the model evaluation. This article concludes by describing the benefits and limitations of DL models for sales forecasting.

Cloud Computing Leads Towards Sustainable Supply Chain Management

In today's environment, businesses are looking into cutting-edge strategies to improve their supply chain's cost and operational effectiveness. Cloud computing appears to be a promising technology that can help with this optimization by providing infrastructure, platform, and software solutions for the entire supply chain network over the Internet. The use of cloud-based services in supply chain management has financial and operational benefits, but all supply chain stakeholders should be aware of potential dangers and restrictions. Cloud computing can improve supply chain collaboration and infrastructure needs by allowing for more flexible outsourcing of software. Rather of sustaining and paying for maximum use, this technology proposes an approach that allows for flexibility in adding on the way, depending on the entire business process and supply chain network model. Aside from the normal tech hype, cloud computing has the potential to be an ideal technology for supporting and managing a constantly changing and dynamic network, and thus for supply chain management (SCM). This paper provides an overview of cloud-based supply chain management. A quick introduction to cloud technologies is given first. The factors of cloud computing which effect supply chain activities is then examined, as well as the benefits and drawbacks of incorporating this technology into current supply chains. Finally, findings are offered, as well as future study directions.

Green Human Resource Management: Proposed Conceptual Model Through Systematic Literature Review

This evaluation serves two goals: to conduct a systematic literature review (SLR) on Green Human Resource Management (GHRM) and to provide a support to facilitate future researchers to examine issues faced during practice of GHRM. Analysis derived from SLR of empirical research papers from 2010 until 2021 published in Scopus-listed journals highlighted strongly favored theories, i.e. the abilities-motivation-opportunities theory and resource-based-view theory. Unveiling research hypothesis that GHRM trainings influences green skills and green behaviors among employees. The literature analysis resulted in conceptual model that shows integration of supply chain management, cost cutting, corporate governance, lean manufacturing, and operations management with GHRM practices resulting in improved business outcomes especially for natural resources and/or labor intensive businesses.

Remanufacturing and low-carbon investment strategies in a closed-loop supply chain under multiple carbon policies

ABSTRACT Closed-loop supply chain is an important path to realize the sustainable development of supply chain, and cap-and-trade and carbon tax policies further promote closed-loop supply chain to invest in low-carbon technologies. This paper combines Economic Production Quantity with Economic Order Quantity models and designs multiple carbon policies. Abatement rate and collection rate are the key variables used to explore the mechanism of the interaction between carbon tax and cap-and-trade, inventory dynamics of closed-loop supply chain and impact of multiple carbon policies on closed-loop supply chain. Construct and analyze benchmark model, investment model under multiple carbon policies, remanufacturing model under multiple carbon policies and mixed strategies model under multiple carbon policies, and the results show that there is a non-linear complementary relationship between carbon price and carbon tax, and both remanufacturing and low-carbon investment can optimize cost and carbon emissions when carbon price is distributed at a certain interval. Furthermore, mixed strategies combining remanufacturing and low-carbon investment can optimize cost and carbon emissions simultaneously. The main contribution of this paper is to build a low-carbon circular system, which can help balance cost and carbon emissions of closed-loop supply chain.

Simulation Study on Complex Systems of Forest Biomass Power Generation Supply Chain in China.

Based on the perspective of complex system management of forest biomass power generation (FBPG) supply chain, this paper explores a new way to reduce the operation cost of forest biomass raw materials supply chain and improve the operation efficiency of forest biomass supply chain in China. Firstly, the supply chain model of FBPG is summarized through the survey data, and the existing problems are found. Then, the behavior strategies among farmers, middlemen, and enterprises are analyzed, and the data that cannot be obtained in the model are estimated by using statistical methods. Finally, the complex supply chain system is simulated by computer. Through setting different forestry policies, this paper compares and analyzes the profit of the supply chain and puts forward policy suggestions to promote the development of the FBPG industry in China. In this paper, the actual situation, principles of statistics, and system simulation are effectively combined to make the behavior rules of various stakeholders fit the reality, and the research conclusions are more practical.

Optimization of Large Passenger Aircraft Supply Chain Resilience Based on Bilateral Grey Quality Function Deployment considering Cooperation Uncertainty

To deal with emergencies in the development of large passenger aircraft, the optimization of supply chain resilience is studied. First, based on the “main manufacturer–supplier” model adopted by the synergistic development supply chain of large passenger aircraft, a resilience analysis framework is established from the perspective of the main manufacturer and supplier, based on which a bilateral grey quality function deployment resilience measurement model is proposed. Second, a single-perspective resilience optimization model of the main manufacturer and supplier under cost constraints is constructed. Then, an overall resilience optimization model of the supply chain considering the synergistic effect and cooperation uncertainty is presented. Finally, the development of a large airliner’s fuselage is taken as an example to illustrate the feasibility and validity of the proposed model. The results show that (1) the synergy between the main manufacturers and suppliers can not only improve resilience of the whole supply chain, but also reduce their own competitiveness and resilience, respectively; (2) a benign cooperative relationship can effectively improve the overall resilience; on the contrary, it can reduce the overall elasticity; (3) the cost of supply chain has a great impact on the resilience of the main manufacturers and suppliers, respectively.

A Data-Driven Approach for Electric Energy Equipment Using Wireless Sensing Technology in the Context of Carbon Neutrality

In this paper, we use wireless sensing technology to conduct an in-depth study and analysis of data-driven power energy equipment in the context of carbon neutrality. For the high-order uncertainty of renewable energy power generation and the nonlinearity of the tidal equation, a set of orthogonal bases under arbitrary probability space can be constructed by itself using the high-order information of renewable energy power generation statistics, and then, polynomials fit the state variables such as voltage in the tidal equation using the orthogonal bases and calculate the fitting parameters using the stochastic Gallatin integration method. Based on the analysis of the online monitoring project of the main power equipment in the substation, a substation power equipment condition monitoring system is designed to realize the real-time monitoring of the temperature status of power equipment, substation smoke and temperature, and humidity environment, and the feasibility and advanced of the system are verified by elaborating the analysis. Using wireless transmission to send real-time temperature information to the monitoring background, the infrared thermal imaging online monitoring system is designed around the front-end data acquisition system, transmission network, background data processing, and display module. Saving-investment equilibrium means that total investment equals total savings; government budget balance can be achieved through government savings or deficits. When both supply chain approaches enter into the same environmental competition, the added value that consumers value the product because of the carbon-neutral approach becomes smaller in the coefficient constraint of green effort investment cost due to the competition between the two supply chains, which in turn increases the green investment cost of the supply chain with the green effort carbon-neutral approach, indicating that in the case of competition, consumers, when faced with two products produced by two supply chains, are interested in the product with the carbon-neutral approach has less value-added, and some of the demand is shifted to the product not produced with the carbon-neutral approach.

An Integrated Optimization Model for Medicine Order Distribution and Delivery Problem of Online Pharmacy Based on the Optimal Supply Chain Strategy

The issue of health and medicine has always been very important. The volume of demand in the field of medicine is very high and is considered as one of the sensitive and vital products, so the accuracy in using the medicine and the accuracy of time of its use and access are among the strict requirements in the field of drug circulation. As we know, in order to achieve the best therapeutic effect and cure the disease, several drugs for a specific disease are usually used simultaneously. Therefore, an order which contains different types of medicines must be delivered on time, together and at the same time. By focusing on the supply chain, companies are experiencing significant reductions in healthcare costs. The purpose of this article is to provide a model and solution for online pharmacies through which chain costs can be optimized. In this regard, it is assumed that after the orders are received through the protocol in each period, a third-party logistics organization will collect the medicines required for each order from the physical pharmacies that have these medicines in their stock and transfer them to pharmacies that have the ability to send the medicines to the customer and a shipping fleet so that, considering logistical costs, orders are delivered to customers on time.

Factors influencing utilization of municipal solid waste compost among urban farmers in western Uganda

BackgroundEffective management of solid waste is one of the most serious environmental problems confronting urban governments in developing countries due to insufficient financial resources and institutional capacity to provide basic solid waste management infrastructure, impoverished urban populations, low rates and coverage of collection, and rising food consumption rates. Composting has been touted as the most economical solution for reducing organic fraction of urban waste volumes while releasing vital nutrients for the soils. However, there is a paucity of information on utilization and associated factors of urban wastes in Uganda. This study aims to assess the level of utilization of Municipal Solid Waste (MSW) compost produced from Clean Development Mechanism (CDM) plants and associated factors among farmers in Fort Portal and Mbarara Municipalities, western Uganda.MethodsData was collected using a cross-sectional survey from 359 and 361 randomly selected farmers in Mbarara and Fort Portal, respectively. A semi-structured questionnaire was employed to collect quantitative data and analyzed statistically using SPSS and STATA statistical programs. Probit model was used to determine factors that influence farmers’ decisions to utilize MSW compost as a soil conditioner.ResultsThe findings revealed a very low level of utilization in both municipalities, 1.7% in Fort Portal and 2.2% in Mbarara. The results indicated that annual farm income, access to other soil conditioners, experience with the use of fertilizers, membership to a farmers’ group and the cost of MSW compost significantly influenced farmers’ decision to use MSW compost.ConclusionsThe findings call for government investment in policies aimed at increasing the level of utilization of the MSW compost by improving accessibility to soil conditioners, creating opportunities for maximizing household incomes, farmer-to-farmer experience sharing, dissemination of information through extension programmes and other innovative communication approaches, operationalization of farmers’ groups and introduction of subsidy schemes on the price of compost through operational tax waivers in urban areas of Uganda. Finally, to guarantee quality and to improve the adoption of compost generated at the CDM plants, there is a need for research to assess the quality of MSW compost, undertake a supply chain analysis and cost–benefit study and set a price commensurate with the quality, and develop guidelines and rates of application of the MSW compost.

ENSAIO NÃO DESTRUTIVO (END) DE ANÁLISE DE VIBRAÇÕES APLICADO À MANUTENÇÃO PREDITIVA

Predictive maintenance consists of monitoring data obtained from monitoring, with the main results of increasing the availability and reliability of equipment. Several non-destructive testing can be performed by this type of maintenance, with vibration analysis being one of the most common. The aim of this study is to evaluate the relevance of vibration analysis in the context of industrial predictive maintenance, as well as its applicability in mechanical and electrical equipment. The scientific production of this article was based on the experiments carried out by several authors in their areas of expertise, based on bibliographic source. It was concluded that the vibration analysis is an important test for the application of predictive maintenance, being able to be implemented in several equipments, besides being a method that contributes directly to the reduction of costs in the supply chain. Finally, it is necessary to take into account that there are points of attention that must be considered for a more adequate implementation of the method.

Mathematical modeling for optimizing the blood supply chain network

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

Production/Inventory Policies for a Two-Echelon System with Credit Period Incentives

Trade credit is a crucial source of capital particularly for small businesses with limited financing opportunities. Inventory models considering trade credit financing have been widely studied. However, while there is extensive research on the single-vendor single-buyer inventory model allowing delays in payments, the systems where the vendor supplies to more than one buyer have received less attention. In this paper, we analyze a two-echelon inventory system where a single vendor supplies an item to two buyers who face a constant deterministic demand. The vendor produces the items at a finite rate and offers the buyers a delay payment period. That is, the buyers can delay the payment for the purchased items until the end of the credit period. Therefore, during such a period, the buyers sell the items and use the sales revenue to earn interest. At the end of the credit period, the buyers should pay the purchasing cost to the vendor for which external funding may be necessary. It is widely accepted that, in general, centralized policies reduce the total cost of the supply chain. Therefore, we first deal with an integrated model assuming that the vendor and the buyers make decisions jointly. However, in some cases, the buyers are not willing to collaborate, and the management of the supply chain has to be carried out in a decentralized manner. Hence, we also address the problem under a non-cooperative setting. Numerical examples are presented to illustrate both models. Additionally, we perform a computational experiment to compare both strategies, and a sensitivity analysis of the parameters is also carried out. From the results, we derived that, in general, it was more profitable to follow the integrated policy excepting when the replenishment costs for the buyers were high. Finally, in order to validate the computational results, a statistical analysis is performed.

Supply chain network design for perishable products under trade credit

ABSTRACT We explore the supply chain network design problem for a retailer selling perishable products when the supplier provides trade credit and the deterioration rate can be reduced by freshness-keeping effort. Using a continuous approximation model, we derive the total cost of the supply chain, including facility set up and operation cost, ordering cost, freshness keeping cost, transportation cost, inventory-holding cost, and interest cost of trade credit. We prove the convexity of the total cost functions and derive the closed-form solutions of the decision variables, including the service regions of distribution centers, the replenishment cycle time, and the fresh food freshness keeping effort. Finally, we conclude with some managerial insights from the numerical analysis.

Multi-Period Cost Optimization of Multi-Mode Carbon Capture and Storage Chains

A generally applicable multi-period and multi-mode CCS value chain cost model is developed to account for all individual costs and evaluate different options for CCS value chain implementation. The approach followed involves the configuration of a mixed integer linear programming optimization model, in which the objective function is to minimize the total cost of the CCS chain, considering constraints of mass balance, injection wells, intermediate storage sizing, flow and routing. The model functionality is illustrated through a case study based on a Norwegian CCS chain which is assessed for a planning horizon of 30 years, assigned in five-year long intervals. The model adopts the methodology developed previously by the authors for pipeline transport and geological storage of CO2 and introduces CO2 ship transport and intermediate storage at two design pressures (7 and 15 bar). It also includes a newly introduced CO2 conditioning step (compression and liquefaction) and a further injection mode, directly from ship. For the CCS chain analyzed, it is found that higher CO2 supply provides economy of scale gains, thus lower levelized cost. Pressure design for ship transportation plays a significant role on the cost of the chain with the trade-off between low conditioning costs and higher transport cost. In the Norwegian CCS case analyzed, individual pipelines from a single hub to each storage site are more economical than a single trunk-line with multiple connections to injection sites. Geological storage costs are largely driven by the availability and the operational costs of the individual sites.

The economic relationships of essential oil production as a basis for supply chain modeling

This article reveals that essential oil production is a complex system with a large number of economic ties arising in the process of growing and processing as well as distribution of products made from essential oils raw materials. Multileveled manufacturing process of essential oils products requires coordination of all the elements to reduce costs in the entire supply chain. It is proposed to use mathematical modeling of the supply chain based on the method of main faces, which allows to take into account the specifics of the problem and makes it possible to use a variety of combinatorial and heuristic algorithms.

A Rapid Cost Modeling Tool for Evaluating and Improving Public Health Supply Chain Designs.

Effective and efficient health supply chains play a vital role in achieving health outcomes by ensuring supplies are available for people to access quality health services. However, supplying health commodities to service delivery points is complex and costly in many low- and middle-income countries. Thus, governments and partner organizations are often interested in understanding how to design their health supply chains more cost efficiently.Several modeling tools exist in the public and private market that can help assess supply chain efficiency and identify supply chain design improvements. These tools are generally capable of providing users with very precise cost estimates, but they often use proprietary software and require detailed data inputs. This can result in a somewhat lengthy and expensive analysis process, which may be prohibitive for many decision makers, especially in the early stages of a supply chain design process. For many use cases, such as advocacy, informing workshop and technical meetings, and narrowing down initial design options, decision makers may often be willing to trade some detail and accuracy in exchange for quicker and lower-cost analysis results. To our knowledge, there are no publicly available tools focused on generating quick, high-level estimates of the cost and efficiency of different supply chain designs.To address this gap, we designed and tested an Excel-based Rapid Supply Chain Modeling (RSCM) Tool. Our assessment indicated that, despite requiring significantly less data, the RSCM Tool can generate cost estimates that are similar to other common analysis and modeling methods. Furthermore, to better understand how the RSCM Tool aligns with real-world processes and decision-making timelines, we used it to inform an ongoing immunization supply chain redesign in Angola. For the use cases described above we believe that the RSCM Tool addresses an important need for quicker and less expensive ways to identify more cost-efficient supply chain designs.