Manufacturing Supply Chain Research Articles

Robust ordering, production, and replenishment in a supply chain for innovative products: A two-echelon newsvendor problem with partial information in yield and demand

With partial yield and demand information, we investigate a two-echelon newsvendor problem in a supply chain for innovative product, in which the retailer and the supplier make sequential decisions in ordering, production, and replenishment, respectively. We adopt the minimax-regret criterion to assume the risk-averse nature and deduce the minimax-regret inventory decisions. We explore replenishment (emergency) production strategies for the supplier. Then we propose shortage penalty strategies as well as subsidized replenishment strategies for the retailer so that the supplier can maintain the required supply even under higher replenishment production costs. We further reveal the role of information uncertainty in the minimax-regret decisions and the bullwhip effect. Interestingly, the results show that the penalty strategies do not always lead to less regret for the retailer, and the information uncertainty does not necessarily lead to greater regret for the supplier. Moreover, some schemes to reduce the bullwhip effect are proposed, such as improving the accuracy of uncertain yield, increasing shortage penalty or replenishment subsidy, and decreasing replenishment cost. It is verified that the replenishment strategies can increase profits for both the retailer and the supplier under some conditions. In extensions, we reveal the supplier’s preference for emergency production and external procurement, as well as preferences for information sharing and confidentiality. Meanwhile, we find that decentralized decision-making may even outperform centralized decision-making in some situations when the minimax-regret criterion is adopted to deal with the information deficiency. Finally, the robustness of the replenishment strategies in reducing both enterprises’ regrets is checked.

Heterogeneous products operation decisions of online dual-channel supply chain considering online reviews under different financing modes

This research examines the operation decisions of the online supply chain for heterogeneous products under different financing modes: e-commerce platform financing or bank financing, when manufacturers face funding constraints. Considering the manufacturer’s adoption of differentiated channel strategies when providing heterogeneous products is also considered, and combined with the impact of online reviews on consumer utility, an e-commerce platform online dual-channel financing model is constructed. The research findings are as follows: (i) when the effectiveness of online reviews differs within a certain range, the equilibrium solution exists. If the relative interest rates of the e-commerce platforms and bank change within a certain range, the same financing mode can bring mutual benefits to both the manufacturer and e-commerce platform, resulting in a “win–win” situation. (ii) If the interest rates under both financing modes are the same, the e-commerce platform financing mode has a higher wholesale price, but the difference in retail prices of distribution products depends on the costs difference between the two products, and at this point, the manufacturer will select e-commerce platform financing mode. (iii) At the optimal interest rate, when the cost of heterogeneous products is the same, the e-commerce platform consistently offers a more favorable interest rate compared to the bank’s optimal rate. when the e-commerce platform’s commission and the positive difference in product reviews is large, the manufacturer will choose e-commerce platform financing mode. Under the e-commerce platform financing mode, both the manufacturer and e-commerce platform are willing to provide lower retail prices to attract more consumers.

Vehicle routing in precooling logistics with dynamic temperature-dependent product quality decay

This study focuses on precooling operations for post-harvest fruits and vegetables in smallholder countries, aiming to provide an effective way to reduce product losses in the early stage of food supply chains. In this study, we consider the traditional centralized precooling and emerging mobile precooling to fulfill a series of small-scale and scattered precooling requests, with the goal of minimizing the total operating cost while guaranteeing the product quality of farmers. The resulting problem is a variant of the classic heterogeneous fleet vehicle routing problems with time windows, with the additional consideration of heterogeneous service and temperature-dependent product quality decay. The vehicle routing model is formulated by integrating product quality as a constraint in which a dedicated function is developed to capture the quality dynamics under changing temperatures. We design an improved adaptive large neighborhood search method to solve this problem by identifying a strategy that is able to deal with the interactions among decisions. Experiment results quantify the advantages of managing product quality in the early stage of supply chains for perishable products and provide management insights on conducting quality-based precooling services. Numerical experiments based on small-scale instances of the studied problem as well as large-scale benchmark instances verify the effectiveness and efficiency of the proposed algorithm by comparing it with CPLEX and three state-of-the-art algorithms.

A MILP-based approach to address the production and distribution planning of large industrial gas supply chains

This paper focuses on addressing the Production Routing Problem (PRP) of industrial gas supply chains (SC). The work introduces a Mixed-Integer Linear Programming (MILP) based approach to effectively tackle this challenging problem. The proposed framework aims to increase company profits by optimizing both production and distribution activities simultaneously. For the distribution part, a route generation algorithm is employed to initially generate a set of feasible routes, ensuring that only routes with practical significance are considered. Subsequently, the mathematical model is developed considering only the set of routes generated. The formulation includes decisions associated with plants production levels, plant selection to supply the different consumers, clients to be visited per trip and day, quantity to be delivered and routes to be used. As part of the problem, it is considered that trucks can make several trips per day, as well as trips of several days. To take into account the last feature, the approach accounts for the explicit effect of the lead time on production and distribution decisions. In this way, the work allows for different delivery times for customers included in the same route, according to their inventories and usage levels. The model considers fleet unavailability until they return to the plants after finishing a trip. Finally, two case studies, one motivating and the other industrial size, are presented and solved. The results obtained demonstrate the effectiveness of the proposed approach to solve the case studies in reasonable CPU times.

Global supply chain flow planning for Chinese manufacturing under the BRI: An SFG-DRO method

The Belt and Road Initiative (BRI) has fostered the free flow of commodities and optimal distribution of resources for the global manufacturing supply chain (GMSC), meanwhile confronted with risks and uncertainties. This paper proposes the SFG-DRO method, which can well solve the flow distribution problem of GMSC under the BRI with uncertainties. The proposed method combines trend prediction from the stochastic frontier gravity (SFG) model with 1-Wasserstein fuzzy set-based distributionally robust optimization (DRO). Additionally, this paper integrates the principles of the column and constraint generation (C&CG) algorithm with the particle swarm optimization (PSO) algorithm to handle the above two-stage nonlinear problems efficiently. The results show that the SFG-DRO distribution method significantly reduces transportation costs by 25% and enhances security. By examining flow distribution across three key BRI channels, this paper identifies underexploited markets along the southward channel and substantial market potential with security risks along the northeast sea-land channel, proposing relevant improvement suggestions.

Optimal establishment of processing unit and partner selection problem of perishable product supply chain: apple case study of India

Decentralized supply chain operation in India adds to inefficiency in management and control difficulties for perishable product supply chain (PPSC). One of the important categories of factors that underly the problem in supply chain is the establishment of processing units that results in end – product distribution. This paper presents an optimal establishment of processing unit around production area for apple supply chain as a case study of India. This study proposed amalgamation of mixed integer linear programming (MILP) and colored petri nets (CPN) to model the PPSC. The mathematical model was set up in regard with capacity of processing unit and transportation cost with continuation in partner selection using MILP approach. The simulation model directs the maximization of system performance by running in CPN environment. The values of the results are subjected to transportation cost and production area with estimate of 25% loss during post harvesting such as transportation and environmental variation. The results corresponding to the case study provided an optimal establishment of processing units. Further, the specific partner associated with the facility and transportation cost. Decision-makers can identify the set of partners from the set of facilities dependent upon the transportation cost for further supply of product using a threshold values. Thereby, the model can be used as a decision-making system for processing companies to result in efficient PPSC management. The developed model was tested and validated on case study to demonstrate our approach’s applicability.

Prevalence of Listeria monocytogenes in Milk and Dairy Product Supply Chains: A Global Systematic Review and Meta-analysis.

Listeria monocytogenes, one of the main foodborne pathogens, is commonly found in milk and dairy products. This study aimed to estimate the presence of L. monocytogenes in milk and dairy product supply chains using a meta-analysis based on PubMed, Embase, Web of Science, and Scopus databases. A total of 173 studies were included in this meta-analysis. The pooled prevalence in the supply chain environment was 8.69% (95% confidence interval [CI]: 5.30%-12.78%), which was higher than that in dairy products (4.60%, 95% CI: 1.72%-8.60%) and milk products (2.93%, 95% CI: 2.14%-3.82%). Subgroup analysis showed that L. monocytogenes prevalence in raw milk (3.44%, 95% CI: 2.61%-4.28%) was significantly higher than in pasteurized milk (0.60%, 95% CI: 0.00%-2.06%). The highest prevalence of L. monocytogenes in milk and dairy products was observed in North America (5.27%, 95% CI: 2.19%-8.35%) and South America (13.54%, 95% CI: 3.71%-23.37%). In addition, studies using culture and molecular methods (5.17%, 95% CI: 2.29%-8.06%) had higher prevalence than other detection methods. Serogroup 1/2a and 3a (45.34%, 95% CI: 28.74%-62.37%), serogroup 1/2b and 3b (14.23%, 95% CI: 6.05%-24.24%), and serogroup 4b/4e (13.71%, 95% CI: 6.18%-22.83%) were dominant in these studies. The results of this study provide a better understanding of the prevalence of L. monocytogenes in milk and dairy product supply chains and suggest a potential foodborne pathogen burden.

Optimizing traceability scheme in a fresh product supply chain considering product competition in blockchain era

Fresh product traceability plays an integral role in ensuring food safety. Recently, many firms have introduced blockchain technology (BCT) for product traceability to guarantee product authenticity and consumer rights. However, introduction of the new technology has also intensified the competition among fresh products. Meanwhile, previous studies on traceability strategies only considered the impact of traceability cost and ignored the issue of increased product competition in blockchain era. Driven by these problems, we build a game-theoretical model to analyze the optimization of traceability schemes for both low-end fresh products (LEFP) and high-end fresh products (HEFP) in a fresh product supply chain (FPSC) in a competitive environment. Moreover, this paper investigates the impact of power structure on traceability schemes and identifies the conditions under which one scheme outperforms the others. We find that the cost per unit of product input has no effect on the traceability decision of the FPSC. Nonetheless, the introduction of BCT increases the number of consumers who choose LEFP and raises the selling price and wholesale price of corresponding products. Finally, from the supply chain perspective, it is best to adopt the same traceability scheme for both products. This study sheds light on the use of BCT in traceability can provide enhanced product authenticity and consumer rights while increasing product competition and demonstrates the significance of traceability schemes in the FPSC.

Barriers to integrated lean management-industry 4.0 technologies

Lean management is a well-known manufacturing and service supply chain management philosophy. The various industry 4.0 technologies have also received enough attention because of their capabilities and initiated a debate among practitioners and academicians on integrating both paradigms. Despite the potential benefits estimated in many research studies and reports, the implementation status could be more encouraging in organisations. Therefore, in this study, all potential barriers are identified and analysed. These barriers were identified from existing literature and finalised with the help of experts. Further, ‘Interpretive Structure Modeling (ISM)’ was used to reveal their contextual relationship. Finally, the ‘Analytic Network Process (ANP)’ ranked the key barriers based on their importance. The results revealed that ‘lack of skilled and experienced workers’ and ‘lack of government policies’ are the most critical barriers. Based on the study’s outcomes, policymakers could make a more effective strategy to address these critical barriers. Additionally, organizations can adapt the proposed hybrid approach to address the barriers more effectively.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

Numerical modelling using fuzzy multi-objective optimisation for environmental sustainability in green supply chain manufacturing

Numerical modelling using fuzzy multi-objective optimisation for environmental sustainability in green supply chain manufacturing

The impact of economic uncertainty on corporate trade credit policy: insights from the COVID-19 pandemic

ABSTRACT During the COVID-19 pandemic, firms exposed to the crisis faced simultaneous shocks to product demand and supply chains. As a result, they faced changing dynamics and greater uncertainty for trade credit for both suppliers and customers. Using regression analyses on quarterly data around the pandemic outbreak, we find that US non-financial firms demanded quicker payments from customers and faced shorter payment periods themselves during the pandemic. We also document that reduced trade credit was more pronounced for financially constrained firms and those with lower operating flexibility. Interestingly, government supplier firms’ trade credit remains unaffected by their COVID-19 exposure. These findings support the precautionary motives guiding firms’ trade credit policies in the face of economic uncertainty.

Dormancy of pathogenic bacteria in the fresh produce supply chain

The increased consumption of fresh produce in the last decades has led to an escalation in the number of associated foodborne outbreaks. Various stressful conditions along the fresh produce supply chain can induce bacteria to enter a dormancy state, such as the persistence state and the viable but nonculturable (VBNC) state. Bacteria can develop an increased tolerance to a range of environmental stresses in the dormancy state and return to their normal growing state when conditions become favorable, leading to foodborne illnesses. This review will explore how bacteria contaminate fresh produce along the supply chain and the conditions that can promote the formation of dormant cells. Additionally, various options for detecting and eradicating dormant bacterial cells in the fresh produce supply chain will be discussed.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Green manufacturing, supply chain alertness, supply chain preparedness and manufacturing performance in a developing economy

PurposeThis research analyses the linkages between green manufacturing (GM) and manufacturing performance (MP) dimensions comprising sustainable performance (SuP) (economic [EcP], social [SP] and environmental [ENP]) and operational performance (OP) with supply chain alertness (SCAL) and supply chain preparedness (SCP) as mediators.Design/methodology/approachThis deductive-quantitative approach embraces the explanatory design. It analyses 285 datasets gathered from structured questionnaires via structural equation modelling.FindingsThe study found that GM, SCAL and SCP significantly improve manufacturing firms' operational and sustainable performance. Also, SCP and SCAL partially mediate the GM-MP correlations among manufacturing firms in Ghana, a developing economy.Research limitations/implicationsThe paper is limited to the quantitative methodologies given its relevance in examining causal relationships among constructs. Also, it was conducted within the scope of manufacturing firms in developing economies, specifically Ghana. Despite the limitations, the study's outcomes imply that manufacturing firms can perform well in sustainable and operational aspects if they prioritise green manufacturing practices, supply chain preparedness and alertness.Practical implicationsThis research offers new insights into the significant contributions of adopting the GM practice to MP (SuP and OP). Also, it advocates for more investments into GM, SCAL and SCP to ensure sustainability in today's highly disruptive manufacturing environment, leading to superior manufacturing performance. The study provides relevant directions for policymakers, industry players and supply chain practitioners in adopting GM throughout their production processes to attain manufacturing performance targets.Social implicationsBy advocating for sustainable manufacturing practices like green, the study contributes to a cleaner environment, resource conservation, and ultimately, a more sustainable future. The shift towards eco-friendly production methods can influence public attitudes towards manufacturing and promote environmentally conscious practices.Originality/valueThe study's originality lies in examining the mediation roles of SCAL and SCP on the GM-MP nexus of manufacturing industries in a developing economy, where environmental sustainability and disruptions along supply chains are becoming major concerns.

Financing a Capital-Constrained Supply Chain under Risk Regulations: Traditional Finance versus Platform Finance

Small- and medium-sized enterprises (SMEs) frequently face challenges in obtaining financial assistance from traditional banks. Platform Supply Chain Finance (PSCF) has emerged as a promising solution for financing issues among SMEs, with an added focus on integrating sustainability aspects. This study focused on a two-tier supply chain as its primary research topic to find strategies to enhance supplier financial viability and improve the efficiency and profitability of the main manufacturing enterprise. In this study, we establish three distinct hypotheses corresponding to the three models involving supplier and manufacturer participation, encompassing parameters such as production batch size, pricing, and supply chain profit. First, it examined financing decisions through the lens of core enterprise-led platform finance. Second, it applied the Stackelberg game theory to investigate financing decisions in three distinct modes: traditional finance, platform internal finance, and external platform finance. Suppliers, manufacturers, and banks can be seen as participants in a Stackelberg game. In this game, suppliers act as leaders, making production and procurement decisions first, while manufacturers and banks act as followers, adjusting their behavior based on the suppliers’ decisions. Finally, it performed a comparative analysis of decisions and supply chain efficiency across these modes. When the risk regulation cost coefficient falls below a certain threshold, suppliers are willing to set up their own PSCF and there is an optimal level of risk regulation effort within the interval (0, 1). We compare platform finance with traditional finance and find that the traditional finance model maximizes profits for suppliers, while the external financing model maximizes profits for manufacturers and the overall supply chain profit. Findings provide insights for platforms, suppliers, manufacturers, and banks to implement optimal financing and channel structures to increase their profits and promote the sustainable development of the financial supply chain. In addition, future research on blockchain platform models would be highly meaningful.

Retailer Insights on Amul’s Fresh Milk Products Supply Chain Study in Anand City and Vidyanagar, India

Retailer Insights on Amul’s Fresh Milk Products Supply Chain Study in Anand City and Vidyanagar, India

Fresh product supply chain coordination using vendor managed inventory and consignment with revenue sharing over a finite planning horizon

Fresh product supply chain coordination using vendor managed inventory and consignment with revenue sharing over a finite planning horizon

Optimization of sustainable supply chain for bio-based isopropanol production from sugar beet using techno-economic and life cycle analysis

This study examines the techno-economic and life cycle analysis of bio-based isopropanol (IPA) production from sugar beet, utilizing a Geographical Information System (GIS)-enabled framework. By focusing on the innovative IPA production technology, the research demonstrates the economic and environmental feasibility of converting first-generation biomass into sustainable chemicals through the optimization of the Sugar Beet-to-Isopropanol supply chain. Findings highlight a cost-optimal production capacity of 55,800 mt/year with significant potential for reducing emissions and operational costs. The production cost of bio-IPA is potentially 70 % less than the fossil-derived IPA price. Additionally, the potential profits from bio-based IPA are estimated to be nearly double the market price of its primary raw material, sugar, demonstrating the economic feasibility of converting the first-generation biomass for sustainable IPA production. The study also explores the impact of facility clustering on transportation emissions and costs, revealing strategic approaches to expanding plant capacities in response to increasing demand. This research provides insights for designing sustainable industrial practices using first-generation biomass in the chemical industry.

Implementing Ireland’s Food Vision 2030 within the Fresh Produce Sector: An Investigation of the Barriers and Enablers Using Systems Thinking Principles

The Irish Food Vision 2030 (IFV 2030) seeks to restructure Ireland’s agricultural food system to deliver long-term social, economic, and environmental sustainability in keeping with its commitment to the European Green Deal and the 2030 Sustainable Development Goals. This study investigates the effectiveness of the IFV 2030 in delivering on this commitment across the fresh produce supply chain in Ireland using a combination of data collected from stakeholders through a survey and systems thinking principles. A review of the literature and Foucault’s genealogical methods guided the development of the survey. Systems thinking principles were used to review the IFV 2030 and assess its capacity to deliver large-scale change for a sustainable Irish food system. Barriers to change were identified, including historical patterns of behaviour between EU and Irish policymakers that have repeatedly hindered progress towards large-scale change events. The results demonstrate that successful, long-term change will depend on the involvement and incentives of stakeholders within the supply chain. Continuous stakeholder engagement is required through a bottom-up organisational and supply chain approach to deliver long-term sustainability within the fresh produce sector. Using a systems thinking lens brings benefits through transdisciplinary collaboration and fosters the multiple stakeholder perspectives needed to deliver new ideas and solutions.