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.

The Application of Big Data Technology in Supply Chain Cost Control: The Case of IKEA

Abstract: Supply chain management is important in all aspects of business processes. With the advent of the information age, big data technology has become important in developing all aspects of our country. Appropriate use of big data technology in supply chain cost management by enterprises can not only improve their ability to analyse and master data but also timely control the cost expenditure issues in different links of the supply chain, improve management systems, and enhance the companys core competitiveness. In order to improve the efficiency and accuracy of supply chain management, this study explores the application of big data technology in enterprise supply chain cost control and data analysis. This study uses big data as the background to analyse the costs of several important links in the supply chain of IKEA. It combines the discovered problems with big data technology to improve them, hoping to be helpful to the development of IKEA and related industries.

Integrating spare part inventory management and predictive maintenance as a digital supply chain solution

Purpose This study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC). Design/methodology/approach A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution. Findings Several major organisational, infrastructure and cultural obstacles were revealed, and an optimum scenario for the integration of spare part inventory management with PdM was recommended. Practical implications The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and addressing shortage of spare parts. Originality/value This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.

Navigating Economic Uncertainties: The Role of Technological Innovations in Enhancing Supply Chain Resilience

This study explores the multifaceted impact of economic indicators, financial market dynamics, and technological innovations on supply chain efficiency and resilience. Through comprehensive quantitative analyses, including panel data regression, multivariate regression, and time-series analysis, we dissect the relationship between GDP growth rates, inflation, unemployment rates, interest rate fluctuations, exchange rate volatility, stock market trends, and supply chain performance metrics. Our findings highlight a significant correlation between economic growth and supply chain efficiencies, the exacerbating effect of inflation and unemployment on supply chain costs and demand, and the nuanced impacts of financial market dynamics on supply chain financing and operational strategies. Moreover, we delve into the transformative potential of digitalization, automation, blockchain technology, and advanced analytics in mitigating risks associated with economic fluctuations and financial uncertainties. The empirical evidence suggests that technological innovations not only enhance supply chain resilience but also offer strategic advantages in navigating the complexities of global economic landscapes. This study underscores the critical need for adaptive strategies that leverage technological advancements to sustain supply chain competitiveness in an era of economic and financial volatility.

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.

Drone Integration in Last-Mile Delivery Operations

Purpose: This research examines the integration of drone technology into last-mile delivery operations, focusing on its impact on supply chain efficiency, cost reduction, and sustainability. The study explores operational challenges, such as air traffic management, payload limitations, regulatory compliance, and public perception, providing a comprehensive understanding of how drones can reshape last-mile logistics. Methodology: The study employs a multi-faceted approach, combining historical analysis, in-depth case studies, and scenario planning to assess the impact of drone technology on last-mile delivery efficiency, cost optimization, and accessibility. Data is gathered from industry reports, interviews with logistics professionals, and regulatory documents. Thematic analysis is conducted to identify critical patterns, while scenario-based modeling offers insight into the potential outcomes of various integration strategies. A comparative evaluation of existing last-mile delivery methods against drone-based delivery models is also provided. Findings: The research reveals that drone technology offers significant potential for enhancing last-mile delivery efficiency, particularly in urban areas and remote locations, aligning with findings from studies like those by Murray & Chu (2015). However, the study identifies considerable obstacles, including regulatory challenges, limited payload capacities, and public acceptance issues. Solutions such as airspace regulation frameworks, collaborative logistics networks, and advanced battery technologies are highlighted as critical enablers for successful drone integration. Contribution to Theory, Policy and Practice: This study contributes to the growing body of knowledge on drone technology's role in logistics by providing a comprehensive framework for businesses and policymakers to navigate the complexities of drone integration. It recommends actionable steps for integrating drones into last-mile delivery, bridging the gap between theoretical innovation and practical application. It provides policy recommendations for regulatory bodies to develop airspace management systems that support drone operations. For practitioners, the research offers a strategic guide to overcoming operational challenges, facilitating the adoption of drones as a sustainable solution for last-mile delivery.

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.

Supply chain vertical common ownership and cost of loans

This study explores how vertical common ownership reduces supply chain risk and influences creditors' decisions, focusing on the cost of loans. Findings reveal that creditors view such firms as less likely to default, evidenced by lower loan spreads. This effect is stronger for suppliers with more relationship-specific investments, weaker bargaining power, higher information asymmetry, and long-term common institutional ownership. Results remain robust through quasi-natural experiments from financial institutions' M&As, supplier-customer pair analyses, etc. We also compare vertical common ownership with the horizontal one and demonstrate its incremental contributions. Moreover, the fraction of customer sales and the institutional investors' share ownership likely influence the likelihood of vertical common ownership, with our results consistently holding under Heckman analysis. Overall, the results suggest that vertical common ownership enhances coordination and monitoring, reducing risks and creditor risk premiums. The findings may offer valuable insights into managing supply chain risks and understanding their financial implications.

Impact of resource reconfiguration on the dairy supply chain resilience

Investment in food supply chain resilience, as a critical infrastructure, has become necessary for all governments. Disruption of food supply chains can lead to significant economic challenges. Building a resilient supply chain requires resources; however, it is difficult for firms to allocate resources to various resilience strategies. This study allocates the budget to resilient capacity, that is, absorptive, adaptive, and restorative capacity, to minimize supply chain costs and maximize service levels. We developed a novel multi-objective mixed-integer nonlinear programming method for problem formulation. The developed model was converted into an equivalent linear model. We used the Monte Carlo approach to generate the scenarios and the average sample approximation to determine the required scenarios. Finally, the Lexicographic max-min approach solves the model using actual data from a dairy supply chain. The analysis revealed that allocating 50% of the budget to restorative capacity and the remaining to adaptive and absorptive capacity optimizes supply chain performance. This study provides insights for managers to make better decisions with a knowledge-based background, allocate resources to various resilient strategies, and build a more resilient and efficient supply chain.

Life cycle-based multi-objective model for optimal gaseous fuel generation and portfolio allocation in gas grids: A strategic decarbonization

Biomethane and hydrogen are acknowledged as transformative opportunities for decarbonizing the conventional gas grid. Essential to this transformation is the modeling of the gaseous fuel supply chain, particularly with hydrogen and biomethane, offering crucial insights for decision-makers. This study introduces a life cycle thinking-based multi-objective optimization model for the integrated design of biomethane and hydrogen gaseous fuel supply chain networks. The model determines optimal resource allocation for the production of the two fuels, integrating them into the conventional gas network. Moreover, it allocates conventional natural gas, biomethane, and hydrogen optimally across building, industry, and transport sectors, considering the life cycle environmental and economic performance of fuel integration paths. Objective functions include minimization of life cycle emissions and levelized cost of energy while maximizing revenue from fuel sales. Integrating life cycle assessment and cost analysis tools, the optimization model quantifies emissions and life cycle costs for biomethane and hydrogen paths. Results identify Pareto-optimal fuel production paths and portfolios, revealing that integrating the alternative fuels into the current gas grid can significantly reduce emissions (up to 250 tonCO2eq/year) and generate substantial carbon tax savings (up to $16,250/year). This model is useful for gaseous fuel industry stakeholders, offering a comprehensive view of supply chain costs and detailed insights into emission benefits when integrating alternative fuels into existing gas networks.

Review on Location of Fresh Cold Chain Logistics Distribution Center

This paper reviews the research status, existing problems and challenges, and future research directions of the location of fresh cold chain logistics distribution centers. The research background is based on factors such as the development of the fresh product market, the technological progress of cold chain logistics, the requirements of environmental protection and sustainable development, and the intensification of market competition. The significance of site selection includes improving transportation efficiency, reducing costs, improving customer satisfaction, optimizing supply chain networks, and achieving environmental sustainability.The former focuses on low-carbon, demand uncertainty, and competitive distribution center location, while the latter focuses on the new retail environment and cold chain distribution. The review of relevant literature points out that some progress has been made in the research, but there are challenges such as market demand uncertainty, incomplete data and limited model applicability.Future research directions include the location model of cold chain logistics distribution center based on big data, the location model considering sustainable development factors, and the location method of cold chain logistics distribution center integrating multi-index evaluation.In the conclusion, it is pointed out that in the past, the time window and the overall logistics network were rarely used as research evaluation indicators in the site selection problems, and the cost of reverse logistics and closed-loop supply chain was rarely considered. In the prospect part, it is proposed that in the future, we should pay attention to the problems of reverse logistics and closed-loop supply chain in the distribution of fresh agricultural products, consider the multi-objective optimization of low-carbon cold chain logistics network in different policies and fields, and combine full simulation technology and optimization technology to realize the system optimization research.

Supply Chain Management Optimization and Cost Reduction Based on Data Mining Algorithm

Supply Chain Management (SCM) is an evolving industry that constantly evolves, but one constant is the significance of effective inventory control in optimizing profits and customer satisfaction. The primary objective of the research is to identify the most effective method for a business to supply several products while maintaining customer service and keeping inventory costs low. The main aim of this paper's Stochastic Gradient Descent (SGD)-enhanced Support Vector Machine (SVM) approach is to predict ideal order and lack quantities, considering into account historical demand, level of inventory, vendor performance, and cost data. The key goal is to reduce the total cost of inventory (which includes holding, placing orders, and supply-chain costs) while maintaining excellent customer service. In order to accomplish this, the proposed system combines SGD's efficiency to enhance predictions and SVM's predictability. A consumer products business chain's real-world dataset was employed to train and test the algorithm. The dataset provides three years' worth of revenue, stock levels, vendors, and costs. Parameters like Mean Absolute Error (MAE), Inventory Turnover Ratio (ITR), and Service Level Achievement (SLA) have been employed to test the methodology, and parameters were optimized using a 5-fold cross-validation. Based on the study findings, the simulation model obtains an average MAE of 1.81 following 100 training epochs, substantially boosting ITR and SLA. As it effectively and accurately balances inventory levels with customer needs, the findings demonstrate that the SVM model with SGD is an excellent match for IMO.

The Effects of Industry 4.0 Technologies on Supply Chain Management Performance: A Case Study of DHL Zambia

Technological advancements related to the Fourth Industrial Revolution are causing disruptive changes that are widely felt at national, industry, and company levels. Hence this mixed methods study aimed to assess the effects of Industry 4.0 technologies on the supply chain management performance at DHL Zambia. The 50 employees selected out of 56 employees at DHL Zambia through a simple random sampling technique resulted in 50 valid responses translating into a 100% response rate. Empirical data was derived from both the Likert scale questionnaire and the interview guide distributed through Google Forms. Quantitative data was analyzed using descriptive statistics, and inferential statistics such as regression analysis, and correlation analysis while qualitative data was analyzed using a thematic approach. The study revealed that the Internet of Things, Blockchain, Big Data Analytics, Augmented Reality and Virtual Reality as Industry 4.0 technologies used at DHL Zambia. The study also found that on-time delivery, order cycle time, inventory turnover, perfect order rate, supply chain cost, lead time, and forecast accuracy, are the supply chain management metrics adopted at DHL Zambia. The multiple regression test results found a significant effect of Industry 4.0 technologies on supply chain management metrics (P>0.05). The study recommended that managers at DHL Zambia prioritize training skill development initiatives aimed at equipping the workforce with the technical competencies required for leveraging Industry 4.0 technologies effectively. This will ensure that the workforce is prepared to embrace digital transformation and adapt to the changing demands of the industry.

(Invited) Towards Rechargeable Zn/CuO Batteries for Grid-Scale Storage

Alkaline Zn batteries are a strong candidate for electrical grid storage applications due to Zn’s high capacity (820 mAh/g), established materials supply chain and low cost. To realize the highest energy dense batteries, Zn needs to be coupled with a similarly low cost, abundant and high-capacity cathode. CuO (674 mAh/g) is an intriguing high-capacity cathode when paired with a Zn anode in alkaline electrolyte, a battery that until recently has been relegated to the history books as a primary system. In 2021 Schorr et al. reported a rechargeable Zn/CuO battery that utilized 10% of a Bi additive to help facilitate the electrochemical reversibility of the Cu conversion electrode. Bi2O3, a species with comparable redox potentials to Cu2O, promoted reversibility and minimized passivation in the historically non-reversible system. The battery cycled without any observable Cu and Bi mixed oxide phases, cycling between metallic Cu and Bi and Cu2O/Cu(OH)2 and Bi2O3, respectively. Although the Bi additive did not eliminate capacity fade completely, limiting the cells to a 30% depth of discharge (relative to CuO) enabled 250 cycles at > 124 Wh/L. Optimization studies performed for Bismuth content indicate that changes in Bi2O3 concentration can impact the charge-discharge behavior of the CuO cathodes resulting in the suppression of metal hydroxide-related redox peaks on charge. The absence of these peaks correlates with improved cyclability, suggesting that methods and materials approaches to further suppress these recharge pathways may lead to CuO high-capacity cathodes with improved cycle-life in the future.Seeking to improve performance and minimize the spatial segregation of Cu- and Bi-phases observed upon cycling in the prior system, D. Arnot et al. prepared nanoscale carbon coated (Cu/Bi) particles, where the coating partially minimized dissolution and diffusion of soluble cuprate and bismuthate complexes, where 200 cycles at 300 mAh/g was demonstrated (@ ~ 100 Wh/L). CuBi2O4 and CuO phases were formed upon oxidation, indicating carbon coatings can also affect the battery cycling mechanism and may have future roles of increasing performance.Data collected from a variety of experimental techniques, including cyclic voltammetry, rotating ring-disk electrode voltammetry, electrochemical impedance spectroscopy, electron microscopy, transmission electron microscopy, Raman spectroscopy, operando energy-dispersive X-ray diffraction measurements, battery cycling along with recent DFT modeling will be presented to help elucidate the role of additives, carbon coatings, polymer gel electrolytes and ion selective polymers in enabling reversible Cu-based cathodes. The general challenges of achieving a highly reversible energy dense battery based on two conversion electrodes operating in highly alkaline environment will be covered as will development towards a > 200 Wh/L industrially relevant Zn/CuO battery.This work was supported by the U.S. Department of Energy Office of Electricity. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. The views expressed in this article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This article has been co-authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title and interest in their contribution to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Information Design and Sharing in Supply Chains

We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an independent and identically distributed Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs [Formula: see text] in two extreme information-sharing cases: (a) full information sharing and (b) no information sharing. To find the retailer’s inventory policy that minimizes [Formula: see text], we formulate an infinite-dimensional optimization problem whose decision variables are the MA([Formula: see text]) coefficients that characterize a stationary ordering policy. Under full information sharing, the optimization problem admits a simple solution and the optimal policy is given by an MA(1) process. On the other hand, to solve the optimization problem under no information sharing, we reformulate the optimization from its time domain formulation to an equivalent z-transform formulation in which the decision variables correspond to elements of the Hardy space H2. This alternative representation allows us to use a number of results from H2 theory to compute the optimal value of [Formula: see text] and characterize a sequence of ϵ-optimal inventory policies under some mild technical conditions. By comparing the optimal solution under full information sharing and no information sharing, we derive a number of important practical takeaways. For instance, we show that there is value in information sharing if and only if the retailer’s optimal policy under full information sharing is not invertible with respect to the sequence of demand shocks. Furthermore, we derive a fundamental mathematical identity that reveals the value of information sharing by exploiting the canonical Smirnov–Beurling inner–outer factorization of the retailer’s orders when viewed as an element of H2. We also show that the value of information sharing can grow unboundedly when the cumulative supply chain costs are dominated by the supplier’s inventory costs. Funding: R. Caldentey acknowledges the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.008 .

A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm

To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.

Development of an Information System for Wood Harvesting Planning

The article presents the results of developing a software system for production planning in the field of wood harvesting. The objects of research are the planning of harvesting operations, forest transport, construction, repair and maintenance of forest roads, as well as solving various related problems. The purpose of the work is to develop a native specialized software system for production planning in the field of wood harvesting, which is integrated with existing accounting and monitoring systems and is based on solving complex optimization problems of large dimensions using original specially developed methods and algorithms that take into account all the features of wood harvesting production in the Russian Federation. As a result of this work, an approach to creating such а system was developed, which includes a set of requirements for the software product, as well as formulations of the main tasks to be solved, listing the necessary initial data and information obtained as an output. The proposed approach was implemented by Russian IT-company Opti-Soft in the form of software product Opti-Wood — a multipurpose Decision Support System (DSS) designed for the use at forestry enterprises as a tool for optimizing production planning, management and control. The Opti-Wood system was developed in close contact with a large wood harvesting enterprise in the Republic of Karelia. The system is based on original algorithms for solving several optimization problems of increased complexity. Using the system allows one to increase the efficiency of logging processes by reducing costs in supply chains, the volume of warehouse stocks, the number of required vehicles; by mutual agreement of plans for harvesting operations, forest transport, road construction, etc., as well as by significantly reducing the time for taking management decisions. Increased profitability occurs due to the identification of additional options for the sale of products.

Selecting Resilient Strategies for Cost Optimization in Prefabricated Building Supply Chains Based on the Non-Dominated Sorting Genetic Algorithm-Ⅱ: Facing Diverse Disruption Scenarios

As a new sustainable building production mode, prefabricated building supply chains can realize energy saving, environmental protection and full cycle value maximization of building products. Prefabricated building supply chains often experience disruptions due to supply instability, transportation delay and force majeure, resulting in project delays and cost escalations and posing challenges to the sustainable development objectives of enterprises. Therefore, it is important and essential to study the strategy of enhancing the resiliency of prefabricated building supply chains, which has not been comprehensively explored in previous papers. This paper constructs decision-making models for supply chain cost resilience strategies under varying scenarios of supply disruptions, incorporating both redundant inventory and back-up supplier strategy. It considers the total cost and resilience of the supply chain as dual objective functions. Parameter-tuned non-dominated sorting genetic algorithm-П (NSGA-П) algorithms were used innovatively to solve the project case, and the impacts of the redundant inventory coefficient and back-up supplier supply price coefficient on the model result were analyzed. The results indicate that the supply chain with resilience construction has a superior capability to cope with disruption. The results show that when there is a mild supply disruption, the general contractor uses the capacity within the supply chain and chooses a redundant inventory strategy to restore resilience. In the event of moderate disruption, both the easy inventory strategy and back-up supplier strategy are selected to maintain supply chain stability. In the event of a severe disruption, only the back-up supplier strategy is selected to cover the losses and maintain the project schedule. In addition, the choice of resilience strategy is impacted by the inventory levels and component prices of back-up suppliers. It further verifies the effectiveness of the model and the impacts of uncertain parameters in the model on the results. This study contributes to enhancing the resilience management of the prefabricated building supply chain by the general contractor, thereby elevating the overall efficiency and competitiveness of the supply chain and furthering the sustainable development of prefabricated buildings.

Research on the application of financial sharing service in internal supply chain cost management -- taking Haier as an example

Research on the application of financial sharing service in internal supply chain cost management -- taking Haier as an example

Strategic alignment between supply chain partners and cost stickiness of suppliers

AbstractWe investigate the relationship between strategic alignment between suppliers and their major customers and the asymmetric behavior of costs, specifically cost of goods sold (COGS) and selling, general, and administrative (SG&A) costs. Utilizing mandated disclosures regarding major customers from 1978 to 2018, we construct supplier–customer dyads to examine this connection. Our results indicate a positive association between strategic alignment in supply chain partnerships and the stickiness of COGS and SG&A costs for suppliers. This positive association is more pronounced for suppliers in the early stages of their relationships than those with long-term partnerships. Additionally, strategic alignment increases the stickiness of suppliers’ research and development (R&D) costs. Furthermore, we observe that strategic alignment correlates with an extended duration of supplier–customer relationships and improved supplier performance. Our findings hold across alternative constructs of strategic alignment, various supplier strategy types, and when accounting for characteristics of the supply-chain relationship.