Robust Supply Chain Research Articles

Feeding strategies for improving ruminant productivity in the post-COVID 19 pandemic era particularly for small holders

COVID-19 has highlighted the need for robust cattle supply chains using local feed resources. Higher Income Over Food Costs (IOFC) are usually achieved when live weight gains are high and the cost of the ingredients are low. There is a need to formulate rations with high metabolisable energy (ME) and crude protein (CP) to achieve the high live weight gain. Rations can be formulated locally by farmer co-operatives, entrepreneurs and local commercial enterprises to take advantage of cheaper prices for local ingredients. To do this, rations need to be altered quickly to take advantage of local fluctuations in prices and availability of ingredients. A recent ACIAR funded project has developed a least cost ration (ACIAR LCR) system to formulate rations to meet minimum ME and CP contents for beef cattle using locally available ingredients. The use of cassava and its various products in combination with protein sources, such as tree legumes and high protein by-products, have markedly improved IOFC.

First Movers in Molecular Detection: Case Comparison on Harnessing Research and Development, Industry, and Entrepreneurship.

The current unprecedented COVID-19 pandemic underscores the importance of diagnostic assays in health security preparedness and readiness. Advancing new technologies for rapid molecular detection of high consequence infectious pathogens is an ongoing challenge that requires ingenuity and vision. Sustainment of a robust supply chain for materials and the logistics of timely product delivery further challenge diagnostic kit and device manufacturers. Business economists often characterize technology companies that discover unique breakthroughs in their field and are first to bring related products to market as first movers. From a market perspective, three first mover characteristics include: having the knowledge and capability to address a unique breakthrough, excellent technological leadership, and the ability to capitalize on the opportunity. Current mainstays for molecular detection include using Taq DNA Polymerase enzyme and fluorescent chemistry for quantitative PCR (qPCR). A newer and promising technology uses CRISPR-Cas proteins for nucleic acid detection. Our panel discussion from the 2020 ASM Biothreats conference, which included members from two prototypical first mover companies, explored their respective corporate experiences. Both companies were selected for the discussion based on their revolutionary innovations and similarities in their research and development, corporate culture and trajectory. One company, established over 20 years ago, became a market leader in the biothreat detection market by advancing air thermocycling qPCR across multiple product families. The second company is a rapidly growing start-up and a scientific pioneer in establishing next generation CRISPR technologies. Here we discuss their technology development, product deployment, and customer markets to draw lessons learned for researchers, end users, and funders.

Comment on “Geographic Diversification of the Supply Chains of Japanese Firms”

The worldwide COVID-19 outbreak has impacted on the domestic and international supply chains of Japanese firms. In particular, internationalized production has been blamed for working as a propagation mechanism in the worldwide spread of negative shocks. Todo and Inoue (2021) extensively review existing studies that have examined the shock propagation through supply chains and what characteristics of supply chains enhance or mitigate the propagation, in the cases of previous crises and the current COVID-19 crisis. Based on their literature review and the data observations on the current characteristics of Japan's supply chains, Todo and Inoue highlight the need for more geographical diversification in suppliers and clients of Japanese firms to build robust and resilient supply chains and improve the firms' performance. In the face of the COVID-19 crisis, there has arisen a renewed argument that shorter and simpler supply chains and reshoring production back to the domestic economy would be less vulnerable to shocks. Against this kind of turning-inward policy argument, Todo and Inoue emphasize the potential benefits from supplier substitutability and international knowledge diffusion through geographically diversified supply chains, which is quite meaningful. Nevertheless, I suspect that geographical diversification of trading partners is not the only way to build a resilient supply chain. A firm's optimal strategy for resilience might not coincide with that for robustness. The concept of system resilience was originated in the ecology literature and was applied to the risk management literature. In the light of supply chain management, resilience can be the ability to resume normal operations after an acceptable period of disruption, while robustness is the ability to maintain operations amid a crisis (Miroudot, 2020). Supplier substitutability appears to be a key to achieving robustness. Maintaining geographically diversified suppliers or alternative locations of production would enable firms to withstand a location-specific supply shock, which leads to the greater robustness. To improve resilience, on the other hand, a firm might rather prefer to develop a long-term relationship with a single supplier, or a handful of suppliers such as keiretsu, as discussed in Miroudot (2020), for example. Maintaining a less diversified but close relationship would enable a firm to receive investment or support from the partner for minimizing the time and cost of disrupted operations and facilitating recovery from the disruption, which leads to the greater resilience. Indeed, Todo and Inoue (2021) touch on the possibility that the keiretsu-type relationships would help improve resilience in the case of knowledge-intensive goods. It would be insightful if Todo and Inoue could elaborate on the implications of geographical diversification for the robustness and resilience of supply chains with more careful consideration of the two related but distinct concepts. In addition, I am skeptical of Todo and Inoue's policy suggestion that the Japanese government should intervene to promote the geographical diversification of suppliers. The diversification of suppliers is not necessarily an optimal strategy from the firm's perspective, depending on whether the firm prioritizes the robustness or resilience of supply chain as well as on the firm's product or industry characteristics. Even if the current level of diversification is less than the optimal level as argued by Todo and Inoue, a government inevitably faces implementational challenges on the intervention and possibly distorts a firm's decision on production locations and sourcing patterns, which could force the choice of an inefficient location. Rather, a government should devote itself to ensuring an environment where firms can build and develop domestic and international supply chains as they need, as a basis for economic growth, which brings greater efficiency, expands the production frontier, and facilitates technology and knowledge diffusion and spillovers. Last but not least, Todo and Inoue's literature review and accompanying discussion focus on the effects on supply chains of supply shocks such as natural disasters. When thinking of the robustness and resilience against the current COVID-19 spread, however, we should be aware that COVID-19 generated both supply and demand shocks. As a result of social-distancing policies, industries relying on the movement of people, such as hotels, restaurants, and passenger transport services, have been impacted greatly through the fall in domestic final demand. It would be worth discussing the robustness and resilience of supply chains not only against supply shocks but also against demand shocks. Does the shock propagation mechanism of supply chains matter to a similar degree and work in a similar way in the case of demand shocks as in supply shocks? The firm's strategy for robustness or resilience of concern also would depend on the source of shocks. Todo and Inoue could discuss on this further.

A decision integration strategy for short-term demand forecasting and ordering for red blood cell components

Blood transfusion is one of the most crucial and commonly administered therapeutics worldwide. The need for more accurate and efficient ways to manage blood demand and supply is an increasing concern. Building a technology-based, robust blood demand and supply chain that can achieve the goals of reducing ordering frequency, inventory level, wastage and shortage, while maintaining the safety of blood usage, is essential in modern healthcare systems. In this study, we summarize the key challenges in current demand and supply management for red blood cells (RBCs). We combine ideas from statistical time series modeling, machine learning, and operations research in developing an ordering decision strategy for RBCs, through integrating a hybrid demand forecasting model using clinical predictors and a data-driven multi-period inventory problem considering inventory and reorder constraints. We have applied the integrated ordering strategy to the blood inventory management system in Hamilton, Ontario using a large clinical database from 2008 to 2018. The proposed hybrid demand forecasting model provides robust and accurate predictions, and identifies important clinical predictors for short-term RBC demand forecasting. Compared with the actual historical data, our integrated ordering strategy reduces the inventory level by 40% and decreases the ordering frequency by 60%, with low incidence of shortages and wastage due to expiration. If implemented successfully, our proposed strategy can achieve significant cost savings for healthcare systems and blood suppliers. The proposed ordering strategy is generalizable to other blood products or even other perishable products.

Sources of Uncertainties in Offsite Logistics of Modular Construction for High-Rise Building Projects

A critical concern in the supply chain of modular construction is offsite logistics that involve complicated module manufacturing, storage, and transportation processes. However, numerous uncertainties exist in a supply chain that could detrimentally affect the performance of offsite logistics and thereby the success of modular construction projects. A systematic exploration of sources of uncertainties (SoUs) in offsite logistics is needed, as they have not yet been sufficiently investigated. This study aimed to identify and categorize SoUs affecting the offsite logistics of high-rise modular building projects in high-density cities. A multicase study method was adopted involving three modular high-rise projects. Data were collected through documentation, site visits, and interviews, drawing on the uncertainty circle model. A total of 30 key SoUs were identified in five areas: the offsite logistics process, the demand side, the supply side, planning and control systems, and the environment. A framework for SoUs was derived, which showed the multifaceted, interconnected, and context-specific features of SoUs and illustrated their probabilities and impacts. Most SoUs arise from offsite logistics processes and planning and control systems. The demand-side SoUs were found to have higher probabilities and impacts. The supply-side SoUs were less probable but could have significant impacts on offsite logistics. Suggestions are made that would allow practitioners to break uncertainty circles and policymakers to promote the development of a robust modular construction supply chain.

Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.

Global supply chains after COVID-19: the end of the road for neoliberal globalisation?

PurposeThrough its impact on both demand and supply, the outbreak of novel coronavirus disease 2019 (COVID-19) has profoundly disrupted supply chains throughout the world. The purpose of this paper is to explore the underlying drivers of the supply chain vulnerability exposed by COVID-19 and considers potential future directions for global supply.Design/methodology/approachThis paper adopts a case study approach, reviewing the automotive manufacturing sector in Australia to illustrate how neoliberal globalisation policy settings have shifted large tracts of manufacturing from the global north to the global south.FindingsThe authors demonstrate the way that neoliberal globalisation policies, facilitated by certain accounting rhetorics and technologies, have consolidated manufacturing in China and Southeast Asia in ways that embed vulnerabilities in global supply chains. The authors present three scenarios for post-COVID-19 supply chains and the accounting techniques likely to garner stronger attention as a result of the pandemic.Research limitations/implicationsThe paper illustrates how certain accounting rhetorics and technologies facilitate neoliberal globalisation, embedding supply chain vulnerability that has been exposed by COVID-19. It also suggests how supply chain accounting may develop more robust supply chains in a post-COVID-19 world and sets out an agenda for future research in this area.Practical implicationsA number of practical supply chain accounting and planning technologies are suggested to facilitate more robust supply chains.Originality/valueThis paper draws attention to the neoliberal globalisation policies that have shaped global supply chains as well as how COVID-19, in concert with other geopolitical trajectories, may represent a watershed moment for global supply chains.

Achieving wealth from bio-waste in a nationwide supply chain setup under uncertain environment through data driven robust optimization approach

Abstract Addressing dual crisis of fossil fuels i.e. environmental as well as gradually decreasing reserves, design of a nationwide robust supply chain network based on bio-energy has been presented in this work. The four echelon supply chain caters the target of blending 20% bio fuels with conventional fuel during 2018–2026. The mixed integer linear programming model considers handling of multiple types of feed sources, products, transport options while performing the techno, economic and environmental analysis of the supply chain to optimally determine the operational and design decisions. The uncertainty in demand, import product price and biomass feed supply has been tackled using data driven robust optimization approach developed using fuzzy transcription of discontinuous uncertain parameter space using machine learning based unsupervised learning methods. To handle a ∼50% increase in overall biofuel demand over the nine-year planning horizon, the deterministic model shows a dynamically changing supply chain with a ∼20% increase in the newly added locations; however, the worst case robust optimization results are reported to be 6% leaner than the results obtained for the deterministic model. The sensitivity analysis of biomass availability on net present value indicates the need of 43% and above biomass feed supply to run such bio supply chain sector to survive.

From Just in Time, to Just in Case, to Just in Worst-Case: Simple models of a Global Supply Chain under Uncertain Aggregate Shocks

Covid-19 highlighted the weaknesses in the supply chain. Many have argued that a more resilient or robust supply chain is needed. But what does a robust supply chain mean? And how do firms' decisions change when taken that approach? This paper studies a very stylized model of a supply chain, where we study how the decision of a multinational corporation changes in the presence of uncertainty. The two standard theories of supply chain are Just-in-time and Just-in-case. Just-in-time argues in favor of pursuing efficiency, while Just-in-case studies how such decision changes when the firm faces idiosyncratic risk. We find that a robust supply chain is very different specially in the presence of systemic shocks. In this case, firms need to concentrate on the worst-case. This strategy implies a supply chain where the allocation of resources and capabilities does not correspond to the standard theories studied in economics, but follow a heuristic behavioral rule called ``probability matching''. It has been found in nature and in experimental research that subjects appeal to probability matching when seeking survival. We find that a robust supply chain will reproduce this behavioral outcome. In fact, a multinational optimizing under uncertainty, follows a probability matching which leads to an allocation that is suboptimal from the individual producer point of view, but rules out the possibility of supply disruptions.

Unlocking Technology Adoption for a Robust Food Supply Chain: Evidence from Indian Food Processing Sector

This paper pioneers the identification of artificial intelligence (AI) enablers like technology feasibility, sophistication, data integrity, interoperability and perceived benefits that can boost operational efficiency of firms in Indian food processing industry. With the food processing industry contributing significantly to do­mestic gross value added and generating an export earning of close to USD 40 billion from agricultural and processed food exports, the study examines the role of AI in overcoming the existing inefficiencies of firms, particularly the small and medium enterprises (SMEs) involved in food processing. For this, questionnaire wascirculated to 500 respondents comprising of IT and supply chain professionals, managers of food processing companies and academicians working in this do main, of which 341 complete responses were received. These responses were then analysed using PLS-SEM modeling, through which the relationship between AI adoption and operational efficiency of firm was established. The study found a significant relationship between AI adoption and operational efficiency. The R square and Q square values substantiate the predictive power of the model used in the study. The research has significant implications for supply chain professionals as technology adoption would boost resilience, integration and transparency of these firms. The study is also relevant for addressing issues pertaining to food security, employment generation, enhancing industrial output and export growth. Policy makers can also get perspectives on harnessing the benefits of AI technology while creating an enabling environment for different supply chain partners.

Circular economy as a key for industrial value chain resilience in a post-COVID world: what do future engineers think?

While the COVID-19 pandemic has led to many disruptions in industrial value chains, the adoption of circular economy (CE) principles appears to be a commendable solution for more robust, resilient, and sustainable industrial supply chains. In this study, the standpoints and visions of two consecutive classes of engineering students – following the course “Circular Economy & Industrial Systems” at the Université Paris-Saclay – are given on how they value CE strategies to mitigate the impact of COVID-19 on industrial practices. Capturing and understanding the viewpoints of the engineers of tomorrow on such a pressing issue is key to train and provide them with the suitable methods and tools to build a more circular and sustainable society. At the end of their eight-week training class, including theoretical background on industrial ecology tools, workshops, and a hands-on project, part of the final exam included a one-hour essay in which the students had to argue their position on the following questions: (i) “Circular Economy as an answer to the COVID-19 crisis?” for the class of 2020, and (ii) “Circular Economy as an answer for green recovery and value chain resiliency in the COVID-19 context?” for the class of 2021. Interestingly, the evolution of viewpoints between the beginning of the COVID-19 crisis (exam conducted in May 2020 for the first class) and one year after (exam conducted in Mars 2021 for the second class) is discussed and illustrated. Also, the answers and insights provided by engineering students on these questions are positioned within the state-of-the-art literature on the topic. Last but not least, key recommendations and challenges on how CE could alleviate COVID-related disruptions and production shortages are synthesized in a SWOT (strengths, weaknesses, threats, and opportunities) diagram.

A demand-based relocation of warehouses and green routing

The circumstantial surge in demand for products, despite meticulous demand forecasting, calls for robust supply chain enhancement. One of the ways to optimize it without altering other facets of the supply chain is warehouse relocation. In this study, the data obtained from an enterprise is fed to the Mixed Integer Linear Programming (MILP) algorithm to determine the case for relocation of warehouses. The objective of this paper is to find the appropriate location of the warehouse and determine the economical route using dynamic programming. The choice of economical routing would help in the conservation of fuel and emission of greenhouse gases as green routing is applied. This work would help the supply chain managers to optimize their logistics design, minimize operating costs and attain sustainability in their supply chain.

WITHDRAWN: A survey on impact of data analytics techniques in E-commerce

Data Analytics plays a vital role in e-commerce. All the e-commerce companies implemented data analytics in their firm. It also helps better stock maintenance, to build a robust supply chain, analyze information to detect fraud, predicts what's in store for you, personalize recommendations for your customers, forecast inventory for the next season, measure your marketing and Personalize the customer's shopping experience. Data analytics used to compete between various e-commerce companies. The analysis is done based on the historic and statically data. From the study, it has been concluded that the characteristics of the data grown and changing day by day. Hence, we need new models and algorithms to collect, store, process, analyze, and evaluate the data in the e-commerce field.

Resilient supply chain under risks: A network and structural perspective

Constant development and changes in the supply chain lead the system to meet various risks. Thus, a proper procedure should be adopted to cope with such issues. This study addresses a bi-objective model to design a resilient and robust forward supply chain under uncertainty and multiple disruptions. The investigated objective functions include minimizing the total cost and the total non-resiliency of the network that are tackled using the e-constraint method. Notably, resilience strategies and two-stage stochastic programming are respectively considered to cope with disruption and operational risks. Ultimately, some random numerical benchmarked examples are applied to the model to confirm the proposed formulation's performance. The results indicate that considering risks in the system leads to increased costs, but it would be profitable in the long term. Notably, a resilient chain can prevent system failure and enhance capabilities to reduce risk exposure costs and damages.

Robust Design of a Closed-Loop Supply Chain Considering Multiple Recovery Options and Carbon Policies Under Uncertainty

Increasing global warming, climate change and stringent governmental legislations are driving industry practitioners and decision makers to implement various strategies to reduce carbon emissions. One of the effective approaches to mitigate carbon emissions is the implementation of closed-loop supply chain (CLSC). The key motivation for considering multiple recovery options in the CLSC is to capture the remaining economic value and to reduce carbon emissions in the collection and recovery operations. Customer's willingness to return used product depends on the acquisition price and nearness to the collection center. This research proposes a deterministic mixed-integer linear programming (MILP) model for a multi-period and multi-product CLSC network under carbon pricing and carbon trading policies consideration. The model includes different acquisition price for returned products and multiple recovery options. Further, the study takes into consideration uncertainty in procurement cost, demand, and quantity of returned products. A robust optimization approach is adopted to address uncertainty in network parameters. Numerical results show that the proposed model captures trade-offs between total cost and carbon emission. Overall, the study reveals that the carbon trading policy incurs relatively lower total cost compared to the carbon pricing policy. Repair and recycling activities in the reverse supply chain contribute significantly to the total cost and carbon emission. This study provide evidence that it is possible to achieve an optimal CLSC network with reduced carbon emission at a moderate total supply chain cost. The proposed model could be used to guide firms to choose an appropriate budget of uncertainty toward achieving a robust supply chain network.

An integrated ANP–QFD approach for prioritization of customer and design requirements for digitalization in an electronic supply chain

PurposeSupply chain efficiency can be enhanced by integrating the activities in supply chain through digitalization. Advancements in digital technologies has facilitated in designing robust and dynamic supply chain by bringing in efficiency, transparency and reduction in lead times. This research tries to identify and prioritize the customer requirements and design requirements for effective integration of supply chain through digitalization.Design/methodology/approachThe key nine customer requirements and 16 design requirements applicable for an electronics company were shortlisted in consultation with the experts from the company and academia. An integrated analytic network process (ANP) and quality function deployment (QFD) methodology has been applied for prioritizing the customer and design requirements. The relative importance and interdependence of these requirements were identified and a House of Quality (HOQ) is constructed.FindingsThe HOQ constructed has prioritized and identified interrelationships among customer requirements and design requirements for effective supply chain digitalization. These findings could be effectively used by managers for planning the objectives on long-term, medium-term and short-term basis.Originality/valueThis study tries to bridge the gap of identifying and prioritizing the design and customer requirements for effective supply chain integration through digitalization. The results could aid practicing managers and academicians in decision-making on supply chain digitalization process.

EU Food System Strengths and Vulnerabilities during Covid‐19

SummaryThe performance of the EU food system during the early stages of the Covid‐19 pandemic is examined in this article. The preliminary evidence from the early lockdown period in March to June 2020 supports the view that, apart from some experiences of empty supermarket shelves in the very first days of lockdown caused by hoarding by consumers, the EU food system has been remarkably resilient. EU farmers, processors and retailers have maintained food supplies to EU consumers and adjusted to the shift in demand caused by the lockdowns in the food service sector. Farm prices fell after lockdowns were introduced but, with some notable exceptions, such as flowers, the fall in prices has been limited. Consumer food prices, on the contrary, increased but this seems to have been a temporary phenomenon. The response to the pandemic has revealed the limitations in the EU’s crisis response mechanism particularly where market support may be needed. The argument of some EU leaders that local food systems should be supported as a way of increasing the resilience of the food system to future shocks does not have empirical support. Policies in the future should support firms in building more robust and resilient supply chains and not undermine food security built on misconceptions about how supply chains performed during the Covid‐19 pandemic.

Robust Water Supply Chain Network Design under Uncertainty in Capacity

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model

Issues such as high fossil-fuel price fluctuations, considerable air pollution, and the realization that fossil sources are not unlimited threaten the global economy. Hence, researchers have been attracted by biomass, especially sugarcane, as a source of renewable energy to produce bioethanol and other biofuels. This research proposes a three-phase robust supply chain network design optimization model to produce bioethanol from sugarcane. Fuzzy integrated data envelopment analysis method is employed to select suitable cultivation lands as supply potential points. The model considers sustainability in its implementation so that the objectives are to maximize the profit, minimize environmental effects, and maximize social performance. Since some parameters are naturally uncertain, a robust possibilistic programming model is proposed, considering the possibility of transportation disruptions. The model performance has been illustrated through a case study in Iran. Finally, the results of the mathematical model have shown that the recommended supply chain design is justifiable. Practical strategic insights include recommendations for locating refineries in seven provinces (Gilan, Kermanshah, Hamedan, Semnan, Khorasan Razavi, Khouzestan, Kihkooliye). These results have been validated using a simulation-based approach, which demonstrates that the proposed model recommends decisions that more suitable than the deterministic model in terms of average and standard deviation of objective values.

Sustainable Agricultural Mechanization in Nigeria in Context of COVID-19

Post-harvest losses and food shortage has remained endemic to most Sub-Saharan African communities. The reality of COVID-19 in these countries has unfurled the weakness of agricultural sector in containing long lasting effect of such natural disaster. In most developed countries, the impact of COVID-19 is minimal on the agricultural sector because of their functional robust food supply chain structure unlike most developing countries. The effort of most African countries in this sector is yet to come to fruition; this will not happen without sustainable agricultural mechanization. This would further enhance food supply chain mechanism in the agricultural sector. In this paper, the impact of COVID-19 on food supply in Nigeria was discussed, as well as the proffered approaches in combating similar future disaster.