Total Supply Chain Cost Research Articles

The impact of transshipment and staggered deliveries on pharmaceutical supply chain performance

ABSTRACT This paper studies the impact of proactive transshipment and staggered deliveries on the pharmaceutical supply chain performance. Therefore, a mathematical model considering transshipment and staggered deliveries is developed to minimise the total inventory management costs of a pharmaceutical supply chain under uncertain demand. To deal with this problem, a two-stage stochastic model is presented and solved using the sample average approximation. The proposed solution shows an important gain of 5% on average in the total inventory management costs when incorporating transshipment of drugs between hospitals and a gain of 32% on average when considering both transshipment and staggered deliveries.

Optimizing Supply Chain Design under Demand Uncertainty with Quantity Discount Policy

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits.

Strategic supply and transportation planning of a supply chain for agricultural biomass to hydrogen and syngas

Strategic supply and transportation planning of agricultural biomass to hydrogen and syngas supply chain with inter-fuel and inter-biomass substitution was studied in this paper. The sourcing and transportation decision was modeled, according to bi-level optimization, where suppliers competed on supply capacity by maximizing their profits at minimum transportation and holding cost. Considering supply chain superstructure, dual biomass suppliers were modeled to supply a biofuel producer who produced two different types of biofuels. The supply planning was combined with transportation planning to minimize total supply chain costs. The results showed that the model was capable of finding optimum purchase and selling prices and production capacity, by maximizing inter-fuel and inter-biomass substitutability. Another key finding was that the proposed model was capable of finding optimum order allocation to both biomass suppliers in such a way that there was an equal profit distribution between suppliers and biofuel producers.

Capacity management under uncertain demand and distribution policies: the case of Indian vaccine supply chain

The present study investigates the cause-and-effect relationships between production capacity decisions and the choice of distribution policy aspects of managing an extensive vaccination programme. The Indian COVID-19 vaccine supply chain is taken as a case study, and Indian demographic data is utilised. A Monte Carlo Markov Chain (MCMC) model is developed to estimate time period-wise demand corresponding to each distribution policy. Furthermore, eight different capacity management models involving different capacity addition and transfer strategies for four different distribution policies are formulated. The trade-off between effectiveness and efficiency objectives, namely Vaccine Supply Chain total cost and service level, is investigated by comparing the Pareto frontiers of the proposed models. Furthermore, the models are compared based on capacity transfer cost, capacity addition cost, and variation in region-wise service level. The results reveal that organisations can simultaneously save significant costs and achieve higher service levels if they cautiously align their capacity management approach and the distribution policy on the demand front. Capacity management interventions at the national level result in more service levels and lower variation and costs than at the regional level.

Decision-Making Approach to Design a Sustainable Photovoltaic Closed-Loop Supply Chain Considering Market Share for Electric Vehicle Energy

This study aims to develop a model for the closed-loop supply chain of photovoltaic (PV) systems. The primary objective addresses strategic and tactical decision-making using a two-stage approach. To pinpoint suitable locations for solar power plants, the PROMETHEE II method is utilized, which is a component of multi-attribute decision making (MADM) approaches. Next, a multi-objective modeling of the closed-loop PV supply chain is conducted. This model aims to minimize total supply chain costs, reduce environmental impacts, mitigate adverse social effects, maximize the on-time delivery (OTD) of manufactured products, and maximize market share. Additionally, a robust fuzzy mathematical model is introduced to examine the model’s sustainability under various uncertainties. An evaluation of the effectiveness and utility of this model is conducted in Tehran city. Furthermore, a comprehensive analysis of various supply chain costs indicates that production centers have the highest costs, while separation centers have the lowest costs.

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Production Planning Optimization in a Two-Echelon Multi-Product Supply Chain with Discrete Delivery and Storage at Manufacturer’s Warehouse

In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs of their services in order to satisfy customers and survive the competition in their respective industries. This research paper investigates a multi-product problem in a two-echelon supply chain consisting of a single manufacturer and several retailers. The main objective of this research is to develop and present a multi-product optimization model in which retailers receive their orders through discrete delivery and surplus manufactured goods are stored in the manufacturer’s warehouse. The objective function of the mathematical model in the economic dimension includes the minimization of the total supply chain costs and the maximization of profit. The retailers in this model place new orders when their inventory level drops to zero, and the manufacturer responds to the retailers’ orders at the same time as it begins processing each product. After delivering the last set of orders, the manufacturer stores surplus items in its warehouse in case the retailers place new orders. This optimization problem is modeled using mixed integer nonlinear programming, while numerical scenarios are coded using the MATLAB software which helps estimate the total cost within a short time. Finally, a sensitivity analysis is performed to determine the effects of a number of factors on the total cost, including problem parameters, demand and production rates, the production quantity, and the number of times the manufacturing machines are operated at each production cycle.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Designing a Multi-Objective Optimization Framework for Global Supply Chains Using Genetic Algorithm and NSGA II

Supply chain optimization is crucial for firms to boost efficiency and competitiveness amidst increasing complexity and risks in global markets. This study provides light into the optimization of a global supply chain network using genetic algorithms, concentrated on avoiding disruptions and decreasing total supply chain costs. The main objectives comprise decreasing total supply chain costs, managing disruptions in product quantity and quality, and optimizing operating parameters and features. The study employs genetic algorithms for optimization, explaining the approach and providing final interpretations to boost supply chain efficiency and resilience against disturbances.

ANALYSIS OF SUPPLY CHAIN PERFORMANCE AND ADDED VALUE OF TEMPEH IN MATARAM CITY

Supply chain performance measurement presents a valuable opportunity to enhance and advance supply chain management practices across various industries, including the tempeh agroindustry. The research was aimed at analyzing the structure and performance of the supply chain, as well as the added value of the tempeh agroindustry in Mataram city. The study utilized a descriptive method that involved interviewing several respondents. Collected data was analyzed using the Supply Chain Operation Reference (SCOR) method version 9, and Hayami Model Added Value Analysis. The results indicated that the stakeholders involved in the agroindustry supply chain in Mataram city were soybean importers in Java, local soybean supply agents, tempeh agroindustry entrepreneurs, and tempeh retailers. The study found that the tempeh agroindustry supply chain performance attained superior performance in the Reliability, Flexibility, Responsiveness, and Asset attributes. On the other hand, the Total Supply Chain Cost attribute recorded poor performance. Processing one kilogram of soybeans into tempeh yielded an added value of IDR. 9,280.82, with an added value ratio of 43.56%, and is categorized as moderate.

Analysis Effect of Parameters of Genetic Algorithm on a Model for Optimization Design of Sustainable Supply Chain Network Under Disruption Risks

Abstract Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

A novel adjusted real-time decision-making for dynamic distribution in the grocery supply chain

PurposeThe current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.Design/methodology/approachThe constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.FindingsThe proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.Originality/valueIntroducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

Identifying a selection mechanism of distribution channel for the supply chain: The barriers to the application of web 3.0

The application of Web 3.0 will bring unprecedented changes to the supply chain, where many industries are seeing significant cost reductions. However, due to the requirement of stability for supply chain, sometimes the total cost of supply chain cannot decrease effectively under Web 2.0, which may hinder the application of Web 3.0. Following the application from Web 2.0 to Web 3.0, this paper discusses the selection of distribution channel whose cost is under user stickiness and the degree of digitalization. The system optimum model, which is commonly used in dynamic traffic allocation, is applied to solve the distribution channel selection problem. The results implies that when the degree of digitalization is low, the distribution channel cost first decreases and then increases as user stickiness increases. This trend of channel cost illustrates the price induction on user stickiness initially, and gradually adjust the price to be higher when user stickiness makes channel transformation costly. Customers with unclear user stickiness perceptions and weak channel control can easily be trapped in the transition from Web 2.0 to Web 3.0. Distributors will also be forced to accept high product costs due to the price induction from product distribution channels.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

Resilience of sustainability for a smart production system to produce biodiesel from waste animal fat

The smart production system is implemented for more effective labor, less waste, and a reduction of energy consumption. Biofuels are significant substitutes for conventional fuels and decreases air pollution and carbon monoxide, can be made through a smart production system with automated machines. Biodiesel can be produced using an assortment of sources that can be both edible and inedible, but animal fat is a sensible alternative among inedible sources because of its affordable price and ease of availability. The model studies the smart biodiesel production system with energy utilization and less carbon emissions to produce pure biodiesel. The multi-objective mathematical model minimizes the total biodiesel supply chain cost followed by minimizing the total biodiesel supply chain environmental impact and enhancing its social impact. The smart production system with a variable production rate produces the biodiesel with less impurities, but still impurities in the biodiesel, that can be detected by a two-stage autonomated inspection system. The impure biodiesel is remanufactured in the biorefineries at the end of every natural production cycle. For the best utilization of energy, various energy costs, such as lighting and air handling costs are calculated. The improved augmented ϵ-constraint approach with lexicographic optimization is utilized to solve the model. The four mathematical examples are studied, and the repercussions of mathematical experiments reveal that the animal fat purchasing cost is 44.32% of the total cost, and the percentage of impurities in biodiesel can be decreased through minimum energy consumption. The managerial insights of the model, graphical representation, and sensitivity analysis of key parameters are provided in this research. The conclusions can be useful to investors in a sustainable smart production system and biofuel policymakers.

Assessing the Impact of Preprocessing and Conversion Technologies on the Sustainable Aviation Fuel Supply from Forest Residues in the Southeast U.S.A.

Forest residues have been suggested as potential feedstock for sustainable aviation fuel (SAF) production through different pathways. However, the bulky density and scatter distribution require efficient size-reduction processes to enhance the feedstock supply. Thus, this study analyzes the impact of alternative preprocessing and conversion technologies on the SAF supply chains from logging residues (the predominant share of forest residues) using a two-stage mixed-integer linear programming model. The model determines the optimal location of biomass preprocessing depots, conversion facilities, and airports receiving SAF by minimizing the net present value of the total supply chain cost over 10 years. Using high-resolution spatial data in the Southeast U.S.A. as a case study, the results show that the technology and scale of the facilities heavily influenced the SAF’s breakeven cost and maximum supply quantity. The most economically efficient system that adopts a rotary shear milling system and pyrolysis conversion process could generate up to 650 million gallons of SAF from logging residues in the region, with nearly 62% of the SAF being produced from hardwood residues. Also, the most efficient system has an estimated breakeven cost of US$5.51 per gallon of SAF.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

Supply chain network design

Goal: This study aims to minimize the total cost of a supply chain network and determine the optimal product flow under demand uncertainty. Design / Methodology / Approach: A mathematical model is presented to minimize the total supply chain cost by identifying the optimal facility locations and product flows. The applicability of the proposed model is evaluated through a real-life case study of a multinational sporting goods retailer with sensitivity analysis. Moreover, Monte Carlo simulation is used to capture the demand uncertainty and test the robustness of the model. Results: The minimized cost is achieved with optimal facility locations and product flows. The optimal result shows a 3% reduction in the total cost, making it the most robust solution under demand uncertainty. Limitations of the investigation: The proposed model is only applicable to a single-commodity supply chain network. In addition, the cost components of the network are limited to facility costs and transportation costs, disregarding the other cost components. Practical implications: This research demonstrates a methodology that can be used as a decision support system by managers to make strategic and tactical decisions in a supply chain network when demand is uncertain. Originality / Value: The MILP and simulation techniques used together to construct a three-tiered supply chain under uncertainty receive little attention in the literature. In addition to developing a novel three-echelon MILP model, this research makes use of a real-world case study to illustrate the methodology's performance in the context of demand uncertainty through simulation.

Collaborative Planning in a Two-Stage Supply Chain: Transparent Coordination, Distributed Response, Integrated Optimization

In the competitive global market, large manufacturing enterprises are striving to achieve a competitive edge through constructing a transparent collaborative supply chain. One of the important and effective ways is to cooperate with distributed response from suppliers in the operation of the supply chain, such as production planning. This article proposes a new collaborative planning mode in a two-stage supply chain considering the supplier part production and product assembly simultaneously. In this context, a mathematical model is formulated and the integrated optimization method is proposed. To reduce the complexity of the problem, the part production decisions of suppliers are first addressed by some lemmas. Then, the transformation process of the mathematical model is described as the decision variables gradually decrease. On this basis, we further propose a LIMA-GVNDS (less is more approach-general variable neighborhood decomposition search) algorithm to solve the integrated supply chain planning problem for the assembly manufacturing enterprise. The objective is to minimize the total supply chain costs. A new neighborhood structure is designed according to the problem characteristics and a series of variable neighborhood descent (VND) variants are proposed. Some computational experiments are carried out, and the results show that the proposed LIMA-GVNDS algorithm has obtained an average 3.69%, 13.07%, 2.47%, and 2.99% improvement of objective value against the other four meta-heuristic algorithms. The model transformation and the integrated optimization method proposed in this article can provide a reference for large manufacturing enterprises to formulate collaborative planning in a transparent supply chain network.

Designing profitable supply chains for lithium-ion battery recycling in the United States

<abstract> <p>Recycling spent lithium-ion batteries (LIBs) has attracted lots of attention recently, due to the increasing demand for critical materials contained in LIBs, putting high pressure on their geological reserves. We evaluated the potential of bioleaching technology as a sustainable solution for recycling spent LIBs to help inform decision-making processes for stakeholders involved in LIB recycling supply chains. A supply chain model was developed to include required upstream processes with the objective of maximizing economic feasibility of LIB recycling through the technology. The model has been applied to the U.S. and an optimal supply chain configuration was identified, considering the major factors affecting the economic viability of the technology. The net present value of the supply chain was estimated to be ＄18.4 billion for operating over 10 years, achieving the maximum processing capacity of 900,000 tons of black mass per year. The economic viability of the technology was identified to be highly sensitive to the cost associated with purchasing black mass, which accounted for more than 60% of the total supply chain cost. The breakeven price of black mass was identified as ＄8.7/kg over which the supply chain was not economically sustainable. Additionally, we examined the non-cooperative scenarios where each tier tries to maximize its own profit to demonstrate how the overall profitability of the supply chain changes with different pricing strategies of sortation facilities and acid producers. We estimated that the maximum prices of non-recyclable paper and acid that the supply chain could tolerate were ＄0.89/kg and ＄8.5/kg, respectively, beyond which the supply chain was no longer sustainable.</p> </abstract>