Supply Chain Processes Research Articles

Blockchain for supply chain: performance implications and contingencies

PurposeThis study aims to examine the performance implications of blockchain implementation in the supply chain and explore how blockchain functions and supply chain processes of blockchain implementation moderate the effect on firm performance.Design/methodology/approachUsing 220 blockchain implementations announced between January 2015 and December 2022, we use the event study methodology to estimate the effects of blockchain implementation on the firm value. Regression analyses are conducted to examine the moderating effects of blockchain functions and supply chain processes.FindingsFirst, there is a positive and statistically significant relationship between blockchain implementation in the supply chain and firm value. Second, we find that abnormal returns from blockchain implementation are higher when used with blockchain’s contract automation function and applied in downstream processes, supporting the moderation effects.Originality/valueThe study provides empirical evidence on the effects of the blockchain implementation on firm performance, taking into account the complexity of blockchain functions and supply chain processes. It enriches the current understanding of how blockchain implementation in the supply chain contributes to firm value.

From extraction to end‐uses and waste management: Modeling economy‐wide material cycles and stock dynamics around the world

AbstractMaterial stocks of infrastructure, buildings, and machinery are the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy. While material stock research has proliferated over the last years, most studies investigated specific materials or end‐uses, usually not embedded into an economy‐wide perspective. Herein, we present a novel version of the economy‐wide, dynamic, inflow‐driven model of material inputs, stocks, and outputs (MISO2), and present a global, country‐level application. Currently, MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling, and waste management, as well as 13 end‐uses of stocks. The derived database covers 23 raw materials and 20 stock‐building materials, across 177 countries from 1900 to 2016. We find that total material stocks amount to 1093 Gt in 2016, of which the majority are residential (290 Gt) and non‐residential buildings (234 Gt), as well as civil engineering (243 Gt), and roads (313 Gt). The other nine end‐uses covering stationary and mobile machinery, as well as short‐lived products, amount to 13 Gt. Material stocks per capita are highly unequally distributed around the world, with one order of magnitude difference between low‐ and high‐income countries. Results agree well with similar global country‐level studies. Low data quality for some domains, especially for lower‐income countries and for sand and gravel aggregates, warrant further attention. In conclusion, the MISO2 model and the derived database provide stock‐flow consistent perspectives of the socio‐economic metabolism around the world, enabling multiple novel and policy relevant research opportunities. This article met the requirements for a silver‐gold JIE data openness badge described at http://jie.click/badges.

Be digital to be sustainable! The mediating role of sustainable supply chain practices in triggering the effects of digitalisation on Sustainable Development Goals in the European Union

Research background: European Union (EU) economies need to enhance sustainable supply chains (SSCs) through digitalisation to realise sustainable development goals (SDGs). However, the impact of digitalisation on the SDGs is complex and multifaceted, and the role of SSCs in mediating this relationship is not fully understood. While extant research has delved into the influence of digitalisation on SSCs, predominantly within specific industries or geographic regions, a research gap persists concerning large-N samples and the generalisability of results. Purpose of the article: This study evaluates the role of digitalisation in achieving SDGs along with the mediation of SSCs based on the resource-based view, the triple-bottom-line, and dynamic capabilities theories. Methods: The study has extracted panel data from Eurostat from 2013 to 2022 and evaluated these data through partial least squares structural equation modelling to estimate the association among constructs. The sample covers the EU–27 countries and consists of 270 observations. Digitalisation is operationalised as the use of digital technologies to, for instance, innovate business models, provide new revenue streams and value-producing opportunities, and enhance, streamline, and make supply chain processes more efficient and environmentally sustainable. SSC practices encompass the design, planning, execution, and monitoring of supply chain activities to create value, optimise resources, and minimise environmental impact. Findings & value-added: The findings indicate that digitalisation has a positive association with SDGs and SSC practices. The study also noted that SSC initiatives play a mediating role between digitalisation and SDGs among the EU economies. These interesting insights could direct policymakers to enforce the initiative of digitalisation in SSCs to boost SDGs in EU economies. Although the literature on digitalisation and SSCs has gained significant attention, this is the first study in these domains.

LIVE MRP (Material Requirements Planning) Impact on Supply Chain

Live Material Requirements Planning (Live MRP) refers to an advanced, real-time approach to managing and optimizing inventory, production, and procurement processes. Unlike traditional MRP systems that update periodically (e.g., daily, weekly), Live MRP integrates dynamic data from across the supply chain and production processes, providing instantaneous updates to material needs, production schedules, and procurement activities. This capability allows companies to respond more swiftly to market fluctuations, production disruptions, and changing customer demands, enhancing operational efficiency, reducing lead times, and improving decision-making

Blockchain in Enterprise Resource Planning Systems: A Comprehensive Review of Emerging Trends, Challenges, and Future Perspectives

Abstract This research paper conducts a systematic review of the integration of blockchain technology with Enterprise Resource Planning (ERP) systems, a burgeoning field at the intersection of advanced technology and business management. The primary objective is to bridge the knowledge gap by synthesizing existing literature, providing insights into the current state, challenges, and potential of blockchain in enhancing ERP systems. The study follows a structured methodology involving four key stages: Literature Retrieval, Literature Screening, Biblio-metric Analysis, and Content Analysis. A comprehensive literature search in the Scopus database resulted in the identification and analysis of 250 scholarly articles, narrowed down to 66 relevant publications. The findings highlight a significant trend towards decentralized ERP solutions, driven by blockchain’s promise of enhanced security, transparency, and efficiency. Key areas impacted by blockchain integration include supply chain management, accounting, security, traceability, purchasing, and process automation within ERP systems. The bibliometric analysis reveals a growing academic interest in this domain, with a focus on scalability, data security, and interoperability. However, the integration faces challenges such as technical complexity, cultural resistance, and regulatory uncertainties. The study concludes that while blockchain presents transformative opportunities for ERP systems, realizing this potential requires overcoming substantial technical and organizational hurdles. Future research should concentrate on developing scalable, interoperable blockchain solutions, tailored to specific industry needs and compliant with regulatory standards, thereby facilitating widespread adoption and maximizing the benefits of blockchain in ERP systems.

Stakeholder-led proactive risk assessment of medication safety in the pharmaceutical supply chain

Abstract Introduction In 2022/23, 1.08 billion medications were dispensed to patients using NHS England, at a cost of £19.04 billion1 . However, Elliot et al (2019)2 estimate every year there are 237 million medication errors at some point of the medication process, with a high associated cost. Increasingly, media is reporting medication shortages of critical medicines3 that can contribute to errors of omission. Medication errors suggests system, tasks and processes should be explored to understand if they have been poorly designed. This paper is a pre-Brexit study that examines the pharmaceutical supply chain with stakeholders. Aim To explore risk to medication safety in the pharmaceutical supply chain using qualitative interviews of stakeholders across the supply chain and proactive risk assessment method SHERPA (Systematic Human Error Reduction and Prediction Approach). Method 1. Qualitative interviews with process models System process models were developed (by literature, field observations and interviewees) and employed to elicit stakeholder perceptions about risk to medication safety in 31 qualitative interviews. Participants included pharmacists, wholesalers, pharmaceutical industry representatives, regulators and policymakers. Ethical approval was granted by Cambridge LREC to include healthcare professionals and 6 patients. Interviews were audio-recorded, transcribed and thematically analysed using N-Vivo. 2. Focus group for SHERPA Six stakeholders participated in the SHERPA focus group4 , including pharmacists and representatives from pharmaceutical manufacturing and wholesale industries. Interview participants were invited and those that agreed, attended. The SHERPA template was completed by the group. The session was audio-recorded, transcribed and thematically analysed using N-Vivo. Results The system models led to a greater understanding of the current system ‘as-is’ and created an overview of the supply chain. SHERPA is a validated method and provided the rigour to risk assess the supply chain process and identify sources of risk. However, the error mode terminology required adjustment to healthcare. Across all supply routes, the main source of risk was failure to supply medicines that caused errors of omission and the risk of counterfeit medicines. Failure to supply resulted from shortages that take place from trading activities that result in stockpiling or exporting of medicines. A lack of robust regulation and parallel imports increased the risk of counterfeit medicines. Stakeholders highlighted deficiencies in the supply chain and poor performance of stakeholders, as medication safety concerns. In order of frequency, the deficiencies include fragmented supply chain; complacency by stakeholders; poor communication and integration across supply chain; and end-user often regarded as the pharmacist, rather than the patient. Discussion and conclusion From the findings, strategies emerged as possible solutions. These included communication of shortages in each component of supply chain; robust counterfeit strategies; use of technology (automation/digital tools); and re-design tasks and processes in dispensaries and wards. Staff training, education, and revalidation of stakeholders is vital. Key limitations in this study include biases that can occur from recruitment strategy and small sample size. The SHERPA method posed terminology problems. In conclusion, this multi-method approach reveals risk and possible solutions to improve safety and reduce cost in the NHS. It is clear this study needs to be repeated to understand the impact of Brexit and current reconfiguration of NHS.

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts. In this context, this study explores the role of digitalization in reducing carbon emissions in the natural resources sector supply chain. Furthermore, it examines the role of supply chain mapping as a mediator in the relationship between supply chain digitalization and supply chain decarbonization efforts, providing a novel perspective on how technology can intersect to foster environmental sustainability. This research targets the metal sector supply chain, a significant contributor to global carbon emissions. The data for the study was gathered through a questionnaire administered to 246 employees from various levels of the metal industry in China. We employed Covariance-based Structural Equation Modelling (CB-SEM) using AMOs for analysis. Our results show that level digitalization of supply chain processes in the metal sector has a profound and direct impact on its supply chain decarbonization. Digital technologies facilitate more efficient resource use, improved energy efficiency, and the adoption of cleaner production methods. Importantly, the study identifies supply chain mapping as a crucial mechanism through which digitalization exerts its positive influence on decarbonization. Through detailed mapping, organizations can gain visibility over their entire supply chain, identifying carbon hotspots and opportunities for improvement that digital solutions can address. The novelty of this study lies in its systematic exploration of how supply chain mapping serves not just as a tool for transparency and efficiency but as a strategic facilitator of the beneficial impacts of digitalization on decarbonization.

Artificial Intelligence applications for Information management in sustainable Supply Chain Management: A Systematic Review and future research agenda

In a Sustainable Supply Chain (SSC) context, information management offers a unique perspective on the digital economy and information management. Artificial intelligence (AI) is developing into a more robust digital field to facilitate quick information access and intelligent decisions in expanding commercial contexts. These days, Supply Chains (SC) would crumble without robust information systems. Applying AI and information management is crucial in determining the direction of sustainable supply chain management (SSCM). A systematic literature review (SLR) of the use of AI in SSCM is conducted in this research. The authors can identify crucial factors of the present literature using bibliometric and network analysis. AI is essential to the SSC to address sustainability challenges and manage the large volumes of data produced by numerous industrial processes. In the corpus of research that is already accessible, there is currently no comprehensive and bibliometric analysis of the potential for AI techniques for information management in SSC. Scientific publications were analysed from an objective point of view. Based on our results, we have drafted a proposal for an AI supply chain framework. Researchers, policymakers, and SCM practitioners may all benefit from the approach. This study is the first to analyse AI applications for information management in SSCM. In consideration of this, organizations are now exploring AI capabilities to improve operational efficiency and innovate their processes. This will assist industry people in understanding how AI methods support SC processes in their optimization to attain sustainability in SC practices.

A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

Visualizing Walmart's Supply Chain Management: A Case Study on Detailed Warehouse Management Practices

Supply chain management (SCM) plays a critical role in the success of modern businesses. Visualization techniques have become increasingly important in improving these processes. These methods have become increasingly significant in recent years, offering new ways to understand and optimize complex supply chain processes. This study focuses on how Walmart uses visualization in its supply chain management to boost efficiency and decision-making. This paper examines the integration of technologies like real-time tracking and data analysis into Walmart's practices, discussing both the advantages and challenges they bring. Walmart has embraced visualization technologies to streamline its SCM processes and make more informed decisions. Furthermore, the paper explores how visualization can help with inventory management, demand forecasting, and logistics optimization in Walmart's supply chain. Combined with relevant literature and case studies, it sheds light on how visualization enhances supply chain operations, offering insights for academics and industry professionals alike, that give good suggestions to the market.

Prioritizing Performance Indicators for the Circular Economy Transition in Healthcare Supply Chains

AbstractThe emergence of the circular economy (CE) paradigm is a key driver in steering industrial sectors towards sustainability. The formulation of a robust circularity strategy depends on a comprehensive assessment, which is closely related to the overarching goal of managing the transition to circularity. The initial phase of transition within the healthcare sector reinforces the need to seamlessly integrate CE principles. It is therefore imperative to define and carefully monitor indicators that will help assess and improve sustainability, operational efficiency, supply chain resilience and stakeholder satisfaction, including both consumers and patients. The primary objective of this research is to prioritize relevant performance indicators that will be critical in measuring the effectiveness of the transition to a circular economy within the healthcare supply chain. To achieve this objective, the study employs the Analytic Hierarchy Process (AHP), a well-established methodology known for its effectiveness in solving complex multi-criteria challenges. The AHP is used to prioritize performance indicators that facilitate the management of the circular economy transition in the healthcare supply chain. The focus is on improving the quality of care, optimizing delivery efficiency, and emphasizing environmental sustainability within the healthcare paradigm. The methodological framework includes the identification of key main criteria covering environmental, social, economic, and logistical dimensions. These criteria are further refined with the inclusion of 14 sub-criteria, resulting in a list of 16 key performance indicators (KPIs). Additionally, the study proposes an alternative business model advocating for the implementation of the Circular Supply Chain Operation Reference Model (circular SCOR). This model offers a perspective for rapid change in supply chain processes towards circular transition within the healthcare system. The final stage of the research involves verifying the results through sensitivity analysis and correlation mapping of the indicator results with the Circular SCOR model. This process helps organizations refine their performance measurement protocols, enhancing their capacity for effective evaluation and strategic decision-making within the circular economy framework. This comprehensive approach enables healthcare organizations to proactively contribute to a more sustainable and environmentally conscious future while maintaining a focus on quality care for patient well-being.

Systematic Analysis of Packaging Production in the Electric Motors Industry: A Multi-Criteria Approach through the SAPEVO-M Method

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes.

The application of multimodal AI large model in the green supply chain of energy industry

With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry.

INTELLIGENT AUTOMATION IN SUPPLY CHAIN OPTIMIZATION

Intelligent automation (IA) is transforming supply chain management by integrating advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and the Internet of Things (IoT). This paper explores how IA optimizes supply chain processes, enhances operational efficiency, and drives strategic decision-making. By analyzing the impact of intelligent automation on various supply chain functions, including inventory management, logistics, and demand forecasting, this research highlights the critical role of IA in achieving agility and responsiveness in increasingly competitive markets. Additionally, the paper discusses the challenges organizations face in implementing IA solutions and provides insights into best practices for successful integration. The findings underscore the importance of leveraging intelligent automation as a key driver of supply chain optimization in today's digital landscape.

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

Data analytics for working performance analysis in production management

RFID technology has found widespread application in supply chain processes. Previous research has primarily focused on managing products or stocks, with limited attention given to analysing workers’ performances through RFID data. This paper proposes a model for analysing employee performance using RFID data in production management. Key Performance Indicators (KPIs) are defined and utilised to process, analyse, and visualise the data through various analysis tools to develop the proposed model. Comparisons are conducted to evaluate employee performance between different groups based on the defined KPI. The model is validated by testing an independent data set, demonstrating its effectiveness in analysing existing data. Predictably, the model has the potential to reduce supervisory and associated costs in case-like applications.

Process Research Methods for Studying Supply Chains and Their Management

ABSTRACTProcesses are fundamental to supply chains and their management. Yet, traditional research approaches to supply chain management (SCM) reflect only a limited understanding of process, offering accounts that overlook the constitutive role of dynamically interrelated processes and how their interplay over time shapes the trajectories of supply chains. This article argues that a process‐philosophical perspective is better suited as a starting point for identifying, analyzing, and interpreting the fluid and interwoven processes of supply chains and their co‐evolving environments. Drawing on examples from SCM research, the article offers insights into the nature of process‐thinking and process‐theoretical assumptions, including the analytical choices and challenges entailed in process research. Besides providing methodological guidance, the article highlights how process research methods equip SCM scholars with a powerful lens for studying transformational issues in this field, including sustainability, resilience, and the use of digitalization and technology.

Blockchain-enabled traceability systems for supply chain quality management: empirical insights from pharmaceutical manufacturers

PurposeThe emergence of blockchain technology has the potential to bring about transformative changes in various industries, with supply chain management being a prominent domain of application. This study investigates the strategic performance benefits of using blockchain-enabled traceability systems for improving supply chain quality management.Design/methodology/approachThe present study employed structural equation modelling to analyse data obtained from 200 practitioners working in Indian pharmaceutical companies.FindingsThe results reflect that blockchain-enabled traceability systems have a positive impact on multi-tier quality governance (MQG); supply chain process alignment and coordination (SPAC) and quality centric collaboration (QCC); all of which are the facets of supply chain quality management. Furthermore, when examining the impact of blockchain-enabled traceability systems on firm quality performance and economic performance, the mediation role of SPAC and QCC was confirmed, whereas the mediation role of MQG could not be established.Practical implicationsThe study’s empirical insights offer practical guidance for pharmaceutical manufacturers, regulators and other industry participants seeking to harness the potential of blockchain technology for creating resilient and transparent supply chains that uphold product quality and safety standards.Originality/valueThese findings underscore the significance of blockchain-enabled traceability systems in revolutionizing supply chain quality management practices to achieve superior strategic performance in the pharmaceutical sector.