Supply Chain Problem Research Articles

Hidden Exposure: Measuring US Supply Chain Reliance

ABSTRACT: Supply chain problems, previously relegated to specialized journals, now appear in G7 Leaders' Communiqués. Our paper looks at three core elements of the problems: measurement of the links that expose supply chains to disruptions, the nature of the shocks that cause the disruptions, and the criteria for policy to mitigate the impact of disruptions. Utilizing global input-output data, we show that the US exposure to foreign suppliers, and particularly to China, is "hidden" in the sense that it is much larger than what conventional trade data suggest. However, at the macro level, exposure remains relatively modest, given that over 80 percent of US industrial inputs are sourced domestically. We argue that many recent shocks to supply chains have been systemic rather than idiosyncratic. Moreover, systemic shocks are likely to arise from climate change, geoeconomic tensions, and digital disruptions. Our principal conclusion is that the concerns regarding supply chain disruptions, and policies to address them, should focus on individual products rather than the whole manufacturing sector.

A Fuzzy Programming Based Approach to a Multi-Objective Multi-Echelon Green Closed-Loop Supply Chain Problem

Abstract It has become a social and legal obligation to take into account environmental factors as well as economic factors when designing the supply chain network. Reducing the emission of harmful gases in supply chain operations, recycling and efficient use of resources must be taken into consideration for future generations. The supply chain created in this study, in addition to the abovementioned features, includes supplier selection, warehouse and distribution center setup, transportation amounts between facilities, and transportation modes of products to be transported. This model, which is a multi-objective multi-echelon green closed-loop supply chain, is a mixed integer linear mathematical model and tries to maximize the joint satisfaction of the objectives with the help of a fuzzy approach using Zimmermann’s minimum operator.

A meta-heuristic-based algorithm for designing multi-objective multi-echelon supply chain network

Recently, practitioners and researchers have concentrated on the use of meta-heuristics in the design of multi-objective supply chain networks. The number of publications in this field reflects this interest. The purpose of this paper is to propose a novel metaheuristic approach for designing supply chain network problems in the case of multi-objective supply chains. The proposed algorithm hybridizes three meta-heuristic approaches; simulated annealing, tabu search, and variable neighborhood algorithms. The proposed algorithm is also combined with a linear programming approach. The purpose of hybridization is to aggregate the approaches in order to benefit from the advantages of these solution algorithms. The features of these algorithms are investigated in this paper for the first time. The most useful characteristics of each algorithm are then utilized for developing the new algorithm. The performance of the proposed algorithm is compared with an exact algorithm, simulated annealing, and tabu search algorithms. The results indicated that the proposed algorithm is comparable to the exact in the case of small and medium-sized supply chain problems. The findings revealed that the proposed algorithm takes on an average of 6.3 min to solve small and medium problems, while the exact algorithm takes 412 min. Moreover, the proposed algorithm outperforms other algorithms with a mean ideal distance (MID) of 13,606,871, a percent of domination (POD) of 0.84, and a computational time of 15.64 min compared to the tabu search algorithm’s MID of 15,574,523, a POD of 0.55, and a computational time of 21.46 min. The simulated annealing algorithm, on the other hand, achieved an average MID of 18,145,931, a POD of 0.40, and a computational time of 30 min. Furthermore, the findings showed that the proposed algorithm is capable of solving complex and large supply chain problems in a reasonable time when exact approaches fail.

Supply chain management with uncertainty in consumer perception of product greenness under an eco-label policy

The urgency of environmental preservation necessitates green manufacturing and supply chain advancements. This research examines a green supply chain problem influenced by eco-label policies, focusing on two prevalent market eco-label types. One allows the manufacturer to determine product greenness, while the other requires compliance with standards set by a non-governmental organization (NGO). We also explore the variability in consumer comprehension of eco-label implications and purchasing behaviors with different eco-labeled products. Through consumer utility and manufacturer profit models, we discover that the manufacturer's production and pricing choices significantly impact consumer behavior. Increased investigation costs may enhance consumer utility through improved product greenness, potentially boosting manufacturer profit via price hikes. However, if investigation costs are minimal, the NGO-label may be rejected due to decreased utility and profit. These insights could help direct supply chains by providing a theoretical foundation for green production decisions and future eco-label policies, whether determined by an NGO or the manufacturer.

Integrating distributed disassembly line balancing and vehicle routing problem in supply chain: Integer programming, constraint programming, and heuristic algorithms

Today, the fast-changing demands of customers force manufacturers to adapt their systems to the variability. As a result of the recent advances in technology and transport systems, most manufacturers have begun to employ more than one distributed facility to respond rapidly to the demands of their customers. In addition, with the emergence of Industry 4.0, information exchange between systems at the same and different levels has become relatively easy. Therefore, effective management of distributed facilities by integrating processes at different levels in the supply chain can provide a significant advantage in adapting the system to customer demand dynamics. Furthermore, recycling waste products and using them in new products have become essential for environmentally friendly production. Therefore, this paper introduces integrated distributed disassembly line balancing and vehicle routing problem first time in the literature. Since the distributed disassembly centers with routing decisions of the vehicles from these centers to the factories have not been considered before, the proposed integrated study will contribute to both industry and the literature. The contribution is not only limited to the proposed integrated problem. Also, novel solution methodologies, mixed-integer linear programming, mixed-integer non-linear programming, and constraint programming models are developed to solve the problem. Besides the mathematical models, a multi-start simulated annealing algorithm is also proposed to overcome the large-size instances due to the complexity of the proposed integrated problem. The comprehensive computational analysis demonstrates that the proposed methods are very competitive in providing good-quality solutions for the problem.

Evaluating supply chain network resilience considering disruption propagation

Amidst the current global disruptions on supply chains, having a comprehensive framework for evaluating supply chain resilience is of utmost importance. However, existing techniques suffer from limitations such as a lack of comprehensiveness and failure to account for the ripple effect, leading to biased assessments. To address these challenges, this study proposes a comprehensive evaluation scheme that quantitatively describes the state of supply chain resilience under disruption propagation. The proposed approach introduces six metrics to assess the absorptive, adaptive, and restorative capacities of resilience. By considering these metrics, a disruption/recovery simulation procedure is employed to evaluate the resilience of three network structures widely found in real-world supply chain problems. The results indicate that networks exhibiting scale-free structure offer enhanced reliability, responsiveness, persistence, and quicker recovery time. To validate the applicability of the proposed approach in assessing supply chain resilience, it is implemented on two real-world supply chain networks. Additionally, this study uncovers the limitations of single-measure methodologies, which overlook the multi-faceted nature of resilience. It highlights the shortcomings of approaches that fail to accurately reflect resilience behaviour in the presence of ripple effects. These findings emphasize the significance of adopting a more comprehensive evaluation scheme, as proposed in this study, to achieve accurate results.

A location-routing problem for local supply chains

This study addresses a local Supply Chain problem by proposing the Collection and Delivery Location Open Routing Problem (CDLORP), a variant of the classical location-routing problem that considers both collection and distribution routes to decide where to locate facilities. Collection routes are responsible for picking up the required raw material from local suppliers, while distribution routes deliver the final product to local customers. This study is motivated by a public initiative that desires to seize the by-product generated by the agro-industrial local sector to produce and provide healthy and nutritional snacks to students in the public elementary school system in Saltillo, Coahuila, Mexico. A node-based formulation and a flow-based formulation are proposed to solve instances of the problem. The formulations are valid for the symmetric and asymmetric cases of the problem. Four families of valid inequalities from the literature are adapted for the problem and are used to implement two branch-and-cut algorithms based on the proposed formulations. Computational experiments using 42 benchmark instances of different sizes (25 to 260 vertices) are performed to assess the efficacy of the proposed formulations and algorithms. The best results are obtained by the branch-and-cut algorithm using the node-based formulation. Finally, a case study is also addressed and optimally solved with the branch-and-cut algorithm using the node-based formulation. The results obtained for the benchmark instances and the case study show that the proposed approach can solve real-life problems to develop local supply chains for new products.

An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly.

A Cyber Secure Medical Management System by Using Blockchain

In the pharmaceutical industry, problems like counterfeit drugs, including vaccines, and their supply chain management problems like transparency, immutability, and traceability exist. In the case of vaccines, it becomes more difficult to standardize and detect fake vaccines because the public has less awareness and knowledge about vaccines. Moreover, the increase in online pharmacies gives more opportunities for counterfeiting vaccines to enter the authentic supply chain management system. We present transparent, immutable and secure vaccine supply chain (TISVSchain), a framework based on blockchain to handle the issues of counterfeited vaccines and vaccine supply chain problems like transparency, immutability, and traceability. Our proposed framework can run both on the private and public blockchain. We have implemented the framework on public blockchain by using remix ide and the smart contracts designed by solidity language run on very low gas cost. We also carried out several experiments by changing the number of nodes and their block time to evaluate the performance of our framework in terms of transaction per second (TPS), gas cost, and propagation delay. Our proposed framework improves the security by using offline unique account addresses in blockchain-based frameworks and improves the overall efficiency of the framework by keeping the gas cost low, finding a way to decrease the number of lost blocks to keep low propagation delay, and keeping high TPS value. TISVSchain shows us promising results to improve vaccine supply chain management’s overall performance, security, and efficiency.

Appropriately limiting quantities of remanufacturing products considering virtual inventory for stabilization of production resources

As environmental problems become more apparent, manufacturers need to balance environmental considerations with economic activities. This is where closed-loop supply chains are gaining attention. However, in addition to demand fluctuations, which are a problem in conventional supply chains, a circular supply chain is unstable in terms of supply, where end-of-life products are collected and reused. This destabilizes not only excess inventory and shortages but also production resources, such as manpower, facilities, and raw materials. This study focuses on the stabilization of the manufacturing system in a closed-loop supply chain. To confirm the dynamic changes in the manufacturing system, we designed a simulation model of a closed-loop manufacturing system and conducted numerical experiments under several scenarios, taking the variation of manufacturing quantity per unit period as an evaluation measure of stability. After showing that unplanned remanufacturing destabilizes the recovery of reusable end-of-life products, we demonstrate that the manufacturing system can be stabilized by appropriately limiting the amount of remanufacturing. However, excessive limits reduce opportunities for remanufacturing end-of-life products and generate adverse economic and environmental impacts. To determine appropriate restrictions, it is necessary to consider the product currently in use by the customer as a virtual inventory and to consider factors such as the quality of the products in the virtual inventory. In the future, we plan to study a system that can dynamically manage remanufacturing quantities based on the status of virtual inventories.

Synthesis of Biomass Corridor in Peninsular Malaysia via Hybrid Mathematical and Graphical Framework

The valorisation of biomass by synthesising a multi-biomass corridor can be an optimistic pathway to solving the growing waste management problem. However, the supply chain problem usually involves a massive number of variables, including the connectivity of the sink source and the selection of a technology pathway. In this work, a “Decomposition Approach” was utilised, wherein a P-graph was incorporated with a conventional mathematical model to reduce the number of variables. Although this type of approach is well established with respect to solving biomass supply chain problems, no previous works have comprehensively considered the effect of the maximum allowable travel distance (MATD) on a supply chain model. A case study in Peninsular Malaysia involving oil palm, paddy, and coconut biomass was conducted using the proposed approach. Moreover, a multiple linear regression (MLR) tool for formulating the cost-correlated function based on the best technology pathway obtained from a P-Graph was incorporated. As a result, the net profit of the biomass corridor was estimated to be USD 0.87 billion, with 1.45 × 107 tonnes per year of biomass being sent to 39 processing hubs over a 20-year lifespan. Furthermore, a sensitivity analysis was also conducted to investigate the impact of several cost-related parameters on the net profit.

Jem Sultan and Venice’s intelligence system: sorting and deploying information in Venice’s ‘letterocracy’

ABSTRACTLike other early modern European states, Venice ruled by correspondence. The letters to and from officials preserved in Venetian and regional archives reflect the structure of the composite state. Just as Venetian rule layered centralized control onto areas of local autonomy, local archives contain series of documents created by governors and captains over centuries of Venetian rule and archived locally, as well as documentation produced by local elites. In Venice itself, letters about daily governance and sensitive intelligence passed through an interlocking system that was designed to manage correspondence about spies and supply chain problems through the same central institutions. This article uses the Council of Ten’s involvement in the dramatic circumstances of the Ottoman Jem Sultan’s rebellion, escape and time as a hostage in the West to examine the particularities of how the Ten gained power through their control of correspondence. Juxtaposing the Ten’s use of sensitive and timely information in a single case with the daily machinery for sending, receiving and archiving correspondence in Venice and in regional archives allows us to understand the internal dynamics of a particular ‘letterocracy’ and how one government council uses information to place itself at the centre of a republican polity.

Ältere: Personalreserve für den sich verschärfenden Arbeits- und Fachkräftemangel?

Abstract Companies are finding it increasingly difficult to recruit not only skilled workers but also staff for simpler jobs. The economic downturn due to the Russian war of aggression on Ukraine and the associated price increases as well as supply chain problems are at best slightly slowing demand. But the bottleneck situation on the labour market could worsen: On the one hand, the transformation of an economy that is increasingly relying on digitalisation and decarbonisation will require experienced specialists. The double transformation is changing the need for qualifications, which could intensify competition on the labour market. On the other hand, demographic developments are leading to a shortage and simultaneous ageing of the labour supply. This brings the employment of older people into focus. If it were possible to keep the generally well-qualified baby boomers in employment longer, the labour and skills gap could be reduced.

Pricing and inventory decision in a two-layer supply chain under the Weibull distribution product deterioration: an application of NSGA-II

Academicians and practitioners have focused a lot of attention on the separate issues of pricing and inventory control in a competitive setting. However, integrating these choices in a competitive environment has received scant attention for deteriorating inventory systems from academics despite being crucial to practitioners. From this perspective, our research focuses on designing a supply chain model with inventory coordination to reflect time systems with improved accuracy and optimal control systems. In this research, we develop a two-layer supply chain model consisting of one manufacturer and one retailer incorporating the inventory classification of the retailer. Price-sensitive market demand and two-parameter time-varying Weibull distribution deterioration have been assumed to develop the mathematical model. First, a collective decision on price and inventory control of a deteriorating product has been evaluated in a duopoly environment. Secondly, to explore the decentralized scenario, we have proposed the NSGA-II algorithm to solve the bi-objective programming problem of the two-layer supply chain. The paper aims to explore product collaborative pricing policies and the inventory decision of the deteriorating item in two-layer supply chain coordination. Finally, numerical research is conducted to execute the centralized supply chain and NSGA-II application in a decentralized supply chain. The research findings can provide valuable insights for members of the two-layer supply chain to make optimal product pricing and inventory scheduling decisions.

Supply chain’s green investment strategy to cope with an entrant threat considering differentiated competitiveness

Facing the strengthening of consumer environmental awareness, we investigate the green investment and green subsidy problem of an incumbent supply chain (ISC) taking into account whether the external manufacturer (EM) encroach. Green investment strategies are explored under three scenarios: no-green investment, ex-ante and ex-post green subsidies by the incumbent manufacturer (IM), and green investment by the supplier. The results show that market size does not influence supplier channel selection and investment decisions. The green investment strategy is significantly affected by investment cost efficiency. Meanwhile, contrary to expectation, the more the investment is, the more willing the incumbent supply chain is to invest. Moreover, under a single-channel format, the incumbent supply chain can’t always achieve Pareto equilibrium. However, in the presence of the supplier green investment, although each green investment scenario can improve the profit, it cannot realize Pareto equilibrium. In addition, when the dual-channel format is adopted, the local areas can achieve Pareto equilibrium under different scenarios. The supplier plays an important role as it holds a monopoly upstream in the supply chain. As a result, the supplier’s green investment generates excellent profit and consumer surplus; however, there will be fluctuations in the optimal strategy of the incumbent manufacturer.

제약회사의 스마트 공급망 관리에 영향을 미치는 요인 및 효과에 대한 분석

With the current surge in information, big data is no longer a distant concept, but is closely related to and close to everyone. With the rapid development of mobile communication and the Internet, big data has gradually influenced and penetrated various fields, resulting in new service and business models. In this study, we focused on the problems in the medical supply chain through a big data background and empirically examined how technology, organization, and environment factors affect the utilization and business effect of big data-based supply chain management systems in pharmaceutical companies. Our results revealed that all three independent variables (organization, technology, and environment) positively affect both the supply chain management of pharmaceutical companies and, furthermore, the overall business effect.. Therefore, the overall business value of a company can likely be realized through dynamic adoption of big data in terms of organizational, technical, and environmental aspects, and that it can respond quickly through various surveys and systematic analysis in the rapidly changing market.

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Environmental concerns prompt the world for a transition to renewable energy sources from fossil energy. Reducing the dependency on non-renewable sources of energy is prerequisite for the sustainable world and less environmental pollution. Biogas energy, which is one of the most important renewable energy sources, is produced by burning organic wastes and can be used in many different fields. In this study, a two-stage approach is undertaken to optimize a biogas supply chain design by incorporating of 30 districts in Izmir. In the first stage, the selection of the most suitable biogas plants is considered by the goal programming approach, which is of great importance to decide the optimal location with high energy potential. The most suitable sites for the biogas plants are obtained as Konak and Narlıdere districts. In the second stage, the location problem of the biogas vehicle charging stations (BVS) for biogas vehicles is handled considering the results of the first stage using mixed integer linear programming (MILP) approach. Computational results demonstrate that it would be more appropriate to establish BVS in 12 districts of İzmir. The model and solution approach are pioneering for supply chain problems and an efficient tool for renewable energy plans.

Weak solution of non-Newtonian polytropic variational inequality in fresh agricultural product supply chain problem

Abstract In this article, we study a class of variational inequality problems with non-Newtonian polytropic parabolic operators. We introduce a mapping with an adjustable parameter to control the polytropic term, which exactly meets the conditions of Leray-Schauder fixed point theory. At the same time, we construct a penalty function to transform the variational inequality into a regular parabolic initial boundary value problem. Thus, the existence is treated with a Leray-Schauder fixed point theory as well as a suitable version of Aubin-Lions lemma. Then, the uniqueness and stability of the solution are analyzed.

A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types

The blood supply chain (BSC) is a complex system with optimization challenges, whose activities impact carbon emissions and the environment, especially with regard to the disposal of expired blood bag waste. This study proposes a new mixed-integer linear programming (MILP) model that considers multiple echelons, multiple blood types, transportation and production emissions, and blood bag shelf life to maximize profit in the blood supply chain system. The model is applied to a real-world case study. The research results confirmed that the model could mitigate the BSC challenges. We also conducted sensitivity analysis of patient demand parameters, blood production capacity, selling price, and shelf life to investigate the impact of the key factors affecting the total profit, costs, carbon emissions, and number of obsolete products.

Optimizing Supplier Selection and Order Lot-Sizing Decisions in a Two-Stage Supply Chain

This paper analyzes different lot-sizing policies for the supplier selection and order allocation problem in a two-stage supply chain. The supply chain consists of multiple candidate suppliers and a single buyer. In this system, selected suppliers produce a product in batches at finite production rates, ship it to the buyer, and the buyer sells it to the market at a constant demand rate. Our goal is to evaluate two lot-sizing policies and select the one that optimizes the supply chain by minimizing the total cost and maximizing supplier efficiency. A bi-objective mixed-integer nonlinear programming (BOMINLP) model is proposed. The first objective consists of the development of a coordination mechanism for supplier selection and order allocation that minimizes the entire supply chain cost, and the second objective comprises a data envelopment analysis (DEA) approach to evaluate the overall performance of suppliers to optimize supplier efficiency. Then, the lot-for-lot and order frequency policies are applied to the BOMINLP model separately to determine the set of selected suppliers as well as the corresponding order quantities and number of orders allocated to each selected supplier per replenishment cycle. Numerical examples that illustrate the solution approach and compare the two lot-sizing policies are provided.