Supply Chain Risk Research Articles

A High Energy‐Density, Cobalt‐Free, Low‐Nickel LiNi0.7Mn0.25Al0.05O2 Cathode with a High‐Voltage Electrolyte for Lithium‐Metal Batteries

AbstractCobalt‐free cathode materials have garnered increased attention for applications in next‐generation batteries for electric vehicles, as cobalt is considered to have at a high supply chain risk. Here, the use of a localized saturated electrolyte (LSE) to enable stable cycling of a cobalt‐free, low‐nickel layered‐oxide cathode LiNi0.7Mn0.25Al0.05O2 (NMA‐70) to higher voltages (4.6 V) in a lithium‐metal battery is demonstrated. Compared to the baseline LP57 electrolyte, the LSE extends the cycle life from ≈100 cycles to ≈400 cycles before reaching 80% capacity retention. Visual indicators of cell degradation, such as product deposition, are observed on electrodes cycled in LP57. It is shown that cycling NMA‐70 in LSE reduces the overall active material loss and overpotential growth during extended cycling. This is attributed to the formation of a beneficial fluorinated interphase layer, a lower degree of rock‐salt phase formation, and a reduction in the gas evolution from the cathode surface. The decrease in gas evolution from the cathode cycled in LSE reflects a lower degree of electrolyte reactivity and an overall improvement in the safety characteristics of the cell. This study highlights the importance of a stable electrolyte to enable the high‐voltage cycling of alternative, lower nickel, and cobalt‐free cathodes.

Resilient and sustainable supply chain criteria for performance evaluation: selection and ranking through fuzzy Delphi

PurposeIn the supply chain performance measurement (SCPM) there seems to be no consensus about measures for performance evaluation and suitable criteria from resilience and sustainability paradigms. In this way, this research aims to identify the attributes that a supply chain (SC) should follow to be resilient and sustainable, and then to evaluate their importance according to industry experts.Design/methodology/approachThis study suggests a hybrid approach. The authors identified the most commonly used criteria using literature review, and then applied fuzzy Delphi technique (FDT) with the objective of surveying experts to find the attributes used in practice and asked to assess their relevance.FindingsThe resilient-sustainable supply chain (RSSC) is formed by four dimensions: resiliency, economic, environmental and social. A total of 15 criteria are identified, and the most important are visibility, flexibility, supply chain risk management (SCRM) culture, work conditions and communication.Research limitations/implicationsThis study used a literature review, so it is subject to a time frame, and the criteria could no longer be relevant as the time and business conditions change. Also, the findings may not be completely applicable throughout different industries, and therefore the finding cannot be replicated to other businesses.Practical implicationsThis study will assist decision-makers among other interested parties to construct and/or strengthen an integrated SC that mixes resiliency and sustainability.Originality/valueThis study contributes to the state-of-art by producing a characterization of the resilient and sustainable supply chain for the automotive industry. Also, this research produces a new and holistic framework for resilient and sustainable SCPM supporting the decision-making process.

Recovery from plant-level supply chain disruptions: supply chain complexity and business continuity management

PurposeThe purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.Design/methodology/approachTo test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).FindingsSeveral important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.Originality/valueThis research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.

From the Five Freedoms to a more holistic perspective on animal welfare in the Dutch Animals Act

One of the aims of the Dutch Animals Act is to protect animal welfare. The assumption that animal welfare risks are managed and mitigated by the Act, however, has not been studied before. The aim of this paper is to evaluate whether or not animal welfare risks can be managed adequately under the current Dutch Animals Act and what modifications to the legislation could improve animal welfare in the Netherlands. For that purpose, welfare consequences identified in various supply chain risk assessments from the Office of Risk Assessment & Research (BuRO) were assessed in conjunction with the Dutch Animals Act and related legislation. A distinction was made between means- and goal-oriented legislation. The current Dutch Animals Act uses the “Five Freedoms” to define animal welfare. However, this seems outdated, given that current scientific insight also indicates that positive experiences should be included as an integral part of animal welfare. Currently, most welfare consequences in supply chains are linked to factors that are regulated by Dutch legislation as qualitative, goal-oriented, open standards. Furthermore, there is no species-specific legislation for some of the most common farm animals in the Netherlands, e.g., dairy cattle. By applying the latest scientific insights, both the current Dutch Animals Act and associated legislation can be improved to more appropriately manage animal welfare risks. As suggestions for improvement, we propose that the definition of animal welfare in the Dutch Animals Act is updated, that species-specific legislation for farm animals is developed where not already applicable, and that animal-based measures (ABMs) are integrated into legislation. As amendments to animal welfare legislation at the European level are currently being developed, our proposals to include the most recent scientific insights in animal welfare legislation also hold for European legislators.

Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms.

In today's global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.

Supply Chain Risk Management in a Digital Era: Evidence from SMEs of Clothing Retailers in Australia

With the increased globalisation and disruptions faced by businesses in this digital era and the occurrence of natural disasters such as floods and disease outbreaks in the world, supply chain risks and management of those risks are major challenges for businesses, especially for SMEs of clothing retailers in Australia. This study, hence, is carried out using an exploratory case study research method, and the data have been collected through semi-structured face-to-face interviews with key informants from managerial levels of 20 Australian SMEs of clothing retailing businesses to identify various supply chain risks and their management processes. This study finds five supply chain risks, namely supply risk, demand risk, financial risk, environmental risk, and operational risk, that the SMEs of clothing retailers mostly face in the supply chain. This study also finds that most of the investigated retailers lack a formal risk identification approach, though they informally use the reactive and proactive methods of risk identification. Furthermore, the assessment methods are not well established in most of the participating firms, and supplier monitoring receives more attention compared to their own performance to deal with their supply chain risks. This study contributes to the body of knowledge by being one of the first empirical studies to explore the SMEs of clothing retailers’ supply chain risks and their management processes in the Australian business context, which can add value in guiding supply chain design decisions for SMEs in other sectors.

Big data and supply chain resilience: role of decision-making technology

PurposeAs global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.Design/methodology/approachThis study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.FindingsThe study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.Originality/valueBy introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

A novel grey multi-objective binary linear programming model for risk assessment in supply chain management

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains.

Risk Management Analysis on Microsoft Corporation

The process of detecting, assessing, and managing hazards is known as risk management. It involves identifying risks, evaluating risks, and implementing controls to limit risks. Risk management is an integral part of the overall management plan of any firm. The objective of risk management is to reduce the negative impact of risks on an organization's capacity to accomplish its goals. Financial risks, operational risks, strategic risks, and reputational risks can all have an impact on a firm. Financial hazards consist of threats to an organization's financial health, such as credit risk, interest rate risk, and foreign currency risk. Operational hazards include supply chain risk, technology risk, and compliance risk, which are all associated with the operations of a business. Risks associated with an organization's strategy, such as market risk, competitive risk, and regulatory risk, are categorized as strategic risks. Reputational risks include fraud risk, data breach risk, and social media risk, which are all associated with the reputation of a firm. This paper provides a comprehensive analysis on risk management approaches at Microsoft.

Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic

PurposeAmid the outbreak of coronavirus disease 2019 (COVID-19), supply chains have faltered. This has influenced operational and financial performance and lead to uncertainty in supply and distribution. Therefore, systems measuring supply chain risk and disruption management performance have gained interest. This study explores barriers to supply chain performance measurement during disruptions such as COVID-19.Design/methodology/approachBased on a survey and literature review, the authors formalise the barriers and rank them using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) fuzzy Višekriterijumsko kompromisno rangiranje (VIKOR) methodology. A total of 14 experts in 11 countries were surveyed. Performance measurement is based on a balanced scorecard.FindingsFifteen barriers are identified. Based on DEMATEL analysis, the cause–effect relationships among the four scorecard aspects are explored. The customer axis is revealed as the cause, while the financial, internal business and learning/development are identified as the effect of the supply chain performance measurement. Fuzzy VIKOR calculations show that uncertainty of investment, disrupted cash flows and the bullwhip effect are the most critical barriers to measure supply chain performance during outbreaks such as the COVID-19 pandemic.Research limitations/implicationsThe study identifies and ranks general barriers; additional research is required to differentiate barriers in specific industrial sectors.Practical implicationsThe findings may help develop proactive, resilient supply chain performance strategies to overcome disruptions.Social implicationsPolicy-makers and decision-makers in industrial and service firms can explore these findings to inform strategies for robust supply chains that can resist disruption in risky environments.Originality/valueThis research addresses a knowledge gap in barriers to measure supply chain performance in post-pandemic areas. It is unclear how far firms will measure supply chain performance in terms of learning from disruption patterns, managing financial and customer demand processes in light of COVID-19. This study contributes by explaining the influences among the barriers and exploring them, offering insights from multiple stakeholders.

Influence of supply chain risks on project financial performance

Facing more frequent and severe disruptions, risk management in supply chains (SCs) has received considerable attention and extensive research. Yet, insights on how changes in SC risks (SCRs) influence project financial performance remain underdeveloped. In this study, we investigate the relationships among industry-specific risk, organizational risk, project SCRs, and project financial performance from the viewpoint of marginal risk product (MRP) using longitudinal project survey data. Structural equation modeling shows that project SCR factors completely negatively mediate the connections among industry-specific risk, organizational risk, and project financial performance. The presence of this full mediation implies that industry-specific risk and organizational risk only indirectly negatively influence project financial performance. Further analysis reveals that industry-specific risk influences project financial performance mainly through project internal-process integration risk, whereas organizational risk's influence is mostly through project demand and internal-process integration risks. A direct implication is that managers who want to increase project financial performance by enhancing the management performance of industry-specific risk and organizational risk should also enhance the management performance of project demand and internal process integration risks to magnify their impact. Analysis also shows that project internal process integration risk and project demand risk possess the largest and the next-largest negative MRP effects on project financial performance, respectively, despite both having no indirect effects. Future research should further clarify the nonsignificant mediating effects of project supply risk on industry-specific risk and organizational risk, although possible explanations are provided.

Ripple effect of disruptions on performance in supply chains: an empirical study

PurposeThe present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.Design/methodology/approachA theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.FindingsIt is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.Research limitations/implicationsThis study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.Originality/valueAlthough many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

Achieving textile supply chain reliability through risk mitigation: a stakeholders perspective

The textile supply chains (TSCs) are exposed to unprecedented risks which can disrupt the sustainable process of the manufacturing industries. It is necessary to analyze the impact of risks and to create reliability in the end-to-end supply chain. The aim of this research is to identify the supply chain reliability (SCR)-related risks and proposed a risk mitigation model based on a systematic toolkit of strategies for handling the risks. For this purpose, a total of 10 key SCR-related risks have been recognized and prioritized using a detailed review of the literature and industrial expert’s opinions. A risk mitigation methodology is presented for improving SCR through its relevant criteria, i.e. risks factors, based on the intensity of importance. This study focuses in determining and evaluating the key potential risks through the blend of mathematical multi-criteria decision-making (MCDM) methods namely fuzzy FMEA, fuzzy AHP, fuzzy TOPSIS, and sensitivity analysis. This study develops a novel approach for objectively projecting future supply chain risks that can be managed timely in achieving SCR. This study also contributed to fill the existing literature gap for prioritization of key risks in the TSCs. The results highlight how postponement in supply chain risks is identified as highly prioritized whereas, manufacturing risks are ranked as second highly prioritized, and disruption risk is ranked as the last one. The concerned stakeholders can adopt the proposed structured methodology as a way forward in formulating risk mitigation strategies and tackling the SC risks in achieving the optimal level of supply reliability.

Risk in sustainable construction supply chains: construct development and measurement validation

Although previous studies have focused on different aspects of green/sustainability risk in construction supply chains (SCs) such as identification of risks or the linkage between characteristics of stakeholders and risk assessment, research on this topic is still quite limited. One important reason for this limitation is the absence of valid and reliable measurement of risk, resulting in the impossibility to discriminate between risk constructs. Therefore, the current study is performed to develop and then validate a measurement scale of risk in sustainable construction SCs. The data are collected from a large-scale survey supported by the Japanese government to promote sustainable socioeconomic development for the ASEAN (Association of Southeast Asian Nations) region, with the participation of 283 firms in Vietnam. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are used to test the reliability, convergent validity, and discriminant validity of the risk constructs. The results indicate that all tests strongly support the measurement scale, and seven reliable risk constructs are identified including supply, demand, internal processes, information, financial, time, and environmental risks. This study also presents opportunities for further developing research on risk management (especially the three phases: risk assessment, risk mitigation, and risk monitoring) in sustainable construction SCs.

Dimensions of supply risks in the supply of water: A case of Zet district municipality

The purpose of the study is to ascertain water supply risks in a district municipality, in South Africa. It aims to strike a balance between the risk of water supply and demand within the Zet District Municipality’s capacity for integrated water service delivery. Inductive thematic analysis and in-depth interviews with district administrators were used to collect qualitative data as part of an exploratory case study design. The findings from the interpretive philosophy reveal that district municipalities lack the capacity to deliver water services due to a variety of supply chain risks and augmenting water demand from the community. Due to the detrimental effects of water shortages on livelihoods, the district's supply risk management capacities need to be strengthened. The supply chain operations should mitigate supply risks not just in water supply, but in service delivery as a whole by striking a balance between water demand and supply to address supply risks. The supply risks that are recognised should take into account data sharing, supplier agility, adaptability, dependability, and infrastructure quality. The force field analysis created a platform to identify the forces that provide a change in the water supply.

Blue economy investment and sustainability of Ghana’s territorial waters: an application of structural equation modelling

ABSTRACT Despite the fact that Blue Economy Investment (BEI) contributes immensely towards sustainable use of marine resources for economic growth, improved livelihoods, employment and healthy marine ecosystem, little efforts have been made to encourage sustainable investment in this area. In Ghana, the Marine Pollution Act 2016 (Act, 932) was promulgated to prevent, regulate and control pollution within Ghana’s territorial waters. Meanwhile, the health of Ghana’s oceans keeps deteriorating. This paper aims to develop an integrative model to enhance BEI through Sustainable Supply Chain Performance (SSCP) by integrating competing theories to attract sustainable investment to conserve and make sustainable use of marine resources. We anchored our study on quantitative research approach and a cross-sectional survey data. Our hypotheses have been tested using variance-based Structural Equation Modeling and SMART-PLS version 3.3.1. The study has revealed that organisational factors, technological factors, supply chain risk, green environmental awareness, perceived cost and regulatory environment are significant determinants of BEI. Moreover, BEI significantly drives SSCP. Again, BEI significantly mediates the relation between its determinants and SSCP. The implications include the emergence of an integrated model which could be used to improve marine resources investment and the realisation of Sustainable Development Goals 3, 13 and 14.

Embracing supply chain digitalization and unphysicalization to enhance supply chain performance: a conceptual framework

PurposeThe paper presents a research methodology that could be used to carry out a systematic literature review on the current state of the art of the technological development in the field of the digitalization and unphysicalization of supply chains (SCs). A three-dimensional conceptual framework focusing on the relationship between Digital Technologies (DTs), business processes and SC performance is presented. The study identifies the emerging practices and areas of SC management that could be positively affected by the implementation of DTs. With this in mind, the emerging practices have a high probability to be considered future best practices.Design/methodology/approachA systematic literature review was conducted on DTs in SC management. The methodology used aims to algorithmically and objectively standardize the information incorporated into thousands of scientific documents. Selected papers were analyzed to investigate the recent literature on SC digitalization and unphysicalization. A total of 87 DTs were selected to be analyzed and subsequently grouped into 11 macro-categories. 17 business processes linked to SC management are taken into account and 17 different impacts on SC management are presented. From a set of 1,585 papers, 5,060 emerging practices were collected and singularly summarized combining DT, business process and impact on SC performance.FindingsA unique analytical perspective provided represents an important evolution when trying to organize the current literature on SC management. The widely used DTs in the practices and the most considered business processes and impacts are highlighted and described. The three-dimensional conceptual framework is graphically represented to allow for the emergence of the best combinations of DT, business process and impact on SC performance. These combinations suggest the most promising areas for the implementation of the emerging practices for SC digitalization and unphysicalization. Additional findings identify and define the most important contexts in which Big Data contributes to SC performance.Originality/valueThe research methodology used is offering progress through which to systemize the current practices as well as detect the potential of digitalization and unphysicalization under the three-dimensional conceptual framework. The paper provides a structured proposal for promising future research directions, assuming that the five research gaps as findings of this research could be the basis for prescriptions, as well as a future research agenda and theory development. Moreover, this research contributes to current managerial issues concerning SC management, referred to data and information management, efficiency and productivity of SC processes, market performance, SC relationship management and risk management in SC.

Getreidehandel und Exportbeschränkungen während des Ukrainekrieges

Abstract Supply chain disruptions related to the Ukraine war have led to a decline in wheat exports from Russia and Ukraine to African countries, which has been largely offset by an increase in wheat exports from other countries. However, fears that disruptions in the grain supply chain will be exacerbated by Russia’s use of grain exports as a weapon and the expansion of economic and financial sanctions by Western countries, as well as the threat of the termination of the Black Sea Grain Initiative, have greatly increased risks in the global grain supply chain. Food security in African countries has therefore been mainly negatively affected by rising food prices, which have limited access to food and thus food security.

Financing and Supply Chain Operations Resilience and Juxtaposing: The Role of Finance in Improving the Supply Chain in the COVID-19 Pandemic Environment

Amidst the renewed quest for supply chain resiliency, finance outgrows its traditional role in B2B; instead, finance position itself to become a more integrated partner in supply chain management activities. Areas such as global trade management and supply chain risk management are fluid areas for finance to flourish, coupled with increasing demands for finance to take a more active role in supply chain management. The global supply chain has experienced several disruptions, such as the COVID-19 pandemic, extreme climate change-induced events, natural disasters, conflict, financial crises, recessions, shortages of raw materials for manufacturing, and macroeconomic imbalances; however, futuristic disruptions are already projected by several stakeholders in the logistics and transport sector. Hence, companies must concentrate on building their resilience to all manner of supply chain disruptions. The initial motive of firm management to increase leverage and engage in financial goods is diminished when the economic dilemma of supply chain value is resolved; by so doing, the internal financial situations could facilitate the advancement of enterprise value. Keywords: supply chain, financial service innovations, distribution dimension, COVID-19 DOI: 10.7176/EJBM/15-7-01 Publication date: April 30 th 2023