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The purpose of this paper is to develop an integrated balanced scorecard (BSC) analytical hierarchy process (AHP) approach for supply chain management (SCM) evaluation. It aims to measure SCM performance from the following four perspectives: finance, customer, internal business process, and learning and growth.

The BSC is developed in the paper based on an extensive review of literature on SCM performance measures, supported by AHP analysis.

The paper develops a BSC for SCM evaluation and proposes a method to prioritize the different performance levels in any organization using AHP methodology. It also suggests from the view of different decision levels and overall performance measurement that is the best BSC perspective.

The integrated BSC‐AHP methodology developed in this paper provides useful guidance for practical managers in evaluation and measuring of SCM in a balanced way.

This paper proposes a balanced performance evaluation system for SCM. While suggesting BSC, different SCM performance metrics have been distributed into four perspectives. Different performance levels are highlighted and preferred BSC perspectives are suggested.

The purpose of this paper is to review the fundamental concept of supply chain management (SCM) and discusses the facts that a road to success in the process of design, development, implementation and operation of a supply chain (SC) is the identification of superior strategies and clear objectives. To understand important SC strategies for a complete success, main strategies need to be identified. The literature of SC is filled with a wide range of strategies applied successfully across various enterprises that reviewed briefly in this paper.

The paper provides key strategies of SCM, and discusses the fact that the vision for the excellent SCM can be built on principles as such as speed, quality, cost, flexibility, quality leadership, customer focused, collaboration, and integrated information system.

To make the excellent SCM successful, management must be committed to high standard of performance including competitive lead times to customers, significantly reduced inventories, world‐class product quality, and reduced process and product complexity.

Because a better management of production system is related to the full understanding of the technologies implemented and the system under consideration, the excellent SCM system including its three As are discussed and metrics used to measure performance are elaborated.

Sharing knowledge with business partners is a challenging issue as firms need to share their valuable know-how assets with individuals or other companies out of their organizational boundaries. As supply chain management (SCM) deals with various stakeholders, firms face difficulties with privacy and ownership when they share their know-how with suppliers or business partners. This study introduces blockchain technology as a mediator in improving knowledge sharing (KS) practices in supply chains.

The data have been collected from surveys with 116 experts working in blockchain start-ups and organizations, and the authors used structural equation modeling for its analysis.

The results show that two features of blockchain technology, namely transparency and security, have the highest impacts on mediating knowledge sharing impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of SCM, speed is highly improved when blockchain technology is used for knowledge sharing. Their study provides guidance for managers on how to improve SCM performance through KS, which is empowered by a blockchain system.

The authors’ findings help organizations to improve supply chain actions, improve innovation, enhance competitive advantage and increase the speed of relationships in the supply chain. The research also contributes literature by analyzing the key factors showing how knowledge sharing structure may be improved by blockchain technology which would be helpful for both academics and practitioners.

The purpose of this paper is to analyze the characteristics and performance of the meat supply chain by focusing on developing a model for measuring the meat supply chain's performance in the province of Isfahan, Iran.

Using a combination of literature review, Delphi approach and case study research, the paper examines part of the meat supply chain that consists of three industrial slaughterhouses, two cold rooms, three factories and more than 20 supermarkets and it then presents a framework to assess the performance of the industry in the region.

The methodology suggests indices for strategic and tactical levels in a meat industry as a case study. The proposed framework for the performance measurement of the chain was applied in strategic and tactical levels in which the ranking of indices are also among the achievements of this study. Results show that there are six main criteria required to measure the meat industry's performance.

Literature shows no record of an integrated measurement system for the entire food supply chain where indicators are combined into a performance function to assess the overall performance of the industry.

Additive Manufacturing offers much potential for industry, but at the same time is likely to have a significant impact on supply chain theory and practice. To-date there has been limited empirical work exploring the effect of Additive Manufacturing, and this study aims to provide a detailed appraisal of how supply chain integration, supply chain performance and firm performance may be affected by the adoption of Additive Manufacturing. These are critical factors for supply chain management, but have received little quantified attention to date.

A theoretical model is developed from a detailed review of the literature, from which a quantitative analysis is performed. Using data collected from 124 automotive manufacturers in European Union and the partial least square- structural equation modeling method, the research examines the relationships among different dimensions of supply chain integration, supply chain performance and firm performance from the perspective of Additive Manufacturing adoption.

The findings indicate that Additive Manufacturing adoption positively influences supply chain performance and as a consequence, firm performance. In addition, supporting the resource-based view perspective, the results show a positive indirect effect of supply chain integration on the supply chain and firm performance improvements, enabled by the Additive Manufacturing adoption. This helps to explain some inconsistent findings in previous research regarding the impacts of supply chain integration on performance.

The results of this study support the view that Additive Manufacturing can make a positive contribution to the supply chain, but this is not achieved solely by the technologies alone. Many of the traditional activities of supply chain management (i.e. integration) are still needed when using Additive Manufacturing, and research needs to understand whether Additive Manufacturing adoption will necessitate changes to the way these traditional activities are undertaken. Building on the findings of the current study, much more work is therefore needed to understand how operations within the supply chain may be changed, and how this may affect the integration and performance of the supply chain.

This study provides quantitative evidence to show that the adoption of Additive Manufacturing has the potential to affect both firm and supply chain performance. This is significant for those companies considering the adoption of Additive Manufacturing, and may serve as a valuable insight in the strategic decision-making process. For those already using Additive Manufacturing, this study serves to underline the potential for firm performance to be influenced, by focusing on improvements to their production strategies and policies.

This study provides an initial insight into some fundamental supply chain concepts within an Additive Manufacturing context, which have received very little research attention. It develops a novel conceptual model, and through a large-scale industry survey provides quantified evidence of the impact of Additive Manufacturing on the supply chain. To date, much of the supply chain research is exploratory and qualitative; the quantitative evidence presented in this work, therefore, makes an important and original contribution to both research and practice.

The purpose of this paper is to present supply chain metrics and to propose a fuzzy-based performance evaluation method for lean supply chain.

To understand the overall performance of cost competitive supply chain the paper investigates the alignment of market strategy and position of the supply chain. Since lean is applicable in many supply chains, the authors propose a set of metrics to evaluate supply chain performance. Moreover, the paper uses a fuzzy model to evaluate the performance of cost competitive supply chains. Fuzzy is an appropriate model method when uncertainty is present. It also allows modelling of a significant number of performance metrics across multiple supply chain elements and processes. Competitive strategy can be achieved by using a different weight calculation for different supply chain situations.

Research provides optimal metrics for lean supply chains. The proposed method can measure the performance of lean supply chains using a fuzzy approach and competitive strategies.

The metrics which have been selected to measure the performance of lean supply chains is particularly applicable for high volume, low-price products.

By identifying optimal performance metrics and applying performance evaluation methods, managers can predict the overall supply chain performance under lean strategy. By identifying performance for each metric they can also categorize the existing performance and optimise them accordingly.

This study provides a performance evaluation method for supply chain managers to assess the effects of lean tools and competitive strategies.

Nowadays more than ever, supply chain management has gained a tremendous amount of attention from both the academic and practitioner communities. It provides a revolutionary management philosophy to cope with the increasing business competition and economy globalization. However, there exist many puzzles, especially in structure mapping and analysis, and performance measurement. The objective of this paper is to propose a process‐based approach to mapping and analyzing the practically complex supply chain network. Via this approach, a process‐based performance measurement system is proposed, in which a method called performance of activity is used to identify the performance measures and metrics.

The COVID-19 pandemic era has severely hampered the economy over the globe. However, the manufacturing organizations across all the countries have struggled heavily, as they were among the least who worked on online mode. The organizations are adopting various innovative quality methodologies to improve their performance. In this regard, they are adopting the Sustainable Lean Six Sigma (SLSS) concept and Industry 4.0 technologies to develop products at a faster rate. The use of Industry 4.0 technologies may reduce material movement and supply chain disruptions with the help of smart intelligent systems. There is a strong synergy between SLSS and Industry 4.0 technologies, resulting in an integrated approach for adoption. This study aims to develop a framework that practitioners can use to adopt Industry 4.0-SLSS practices effectively.

This study portrays 31 Industry 4.0-SLSS practices and 22 performance metrics identified through a literature review to improve the manufacturing supply chain performance. To compute the weights of these practices, the Robust Best–Worst Method (RBWM) is used. The Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method is used to rank performance metrics.

According to the RBWM results, “Process Development Practices (PDP)” are first among the major criteria, followed by “Organizational Management Practices (OMP)” at second, “Technology Adoption Practices (TAP)” at third, “Strategy Management Practices (SMP)” at fourth and “Executive Management Practices (EMP)” at fifth, whereas the PF-CoCoSo method resulted in the performance metric “On time product delivery” ranking first.

The identified practices have the potential to significantly improve the performance of the manufacturing supply chain. Practices that encourage a sustainable manufacturing supply chain and the usage of emerging technology will benefit organizational effectiveness. Managers can assess performance using prioritized performance metrics.

During the COVID-19 pandemic era, this is one of the unique attempts to provide a framework to improve the manufacturing supply chain performance. This study integrates and identifies Industry 4.0-SLSS practices and performance metrics for enhancing overall performance.

The purpose of this paper is to introduce a hybrid framework (suppliers, inputs, process, output and customers+define, measure, analyze, improve and control (SIPOC+DMAIC)) aimed at improving supply chain management (SCM) process dimensions in a supply chain (SC) network.

Based upon the critical review of literature, process dimensions (average outgoing quality limit (AOQL), average outgoing quality (AOQ), process Z, defect per million opportunity) critical to SCM performance were identified. A framework consisting of three phases, i.e., design, implementation and results has been conceptualized and a case from paint industry is investigated. Implementation framework makes use of SIPOC model and Six Sigma DMAIC methodology. The goals of the study were achieved by using Six Sigma tools such as brainstorming sessions; root cause analysis, histograms, statistical tools such as control charts and process capability analysis.

Authors made an attempt to propose a conceptual framework for improving process dimensions in a SC network. It is observed from the results that selection of appropriate strategies for improving process performance based upon experiences, and use of statistical tools by cross-functional teams with an effective coordination, guarantees success. Metrics such as AOQL shows the maximum worst possible defective or defect rate for the AOQ. Process Z helps to know about sigma capability of the process.

The framework so developed is tested in a single company manufacturing batches of paint. The study has important implications for the industry since it tries to integrate SCM process dimensions which would help in successful implementation of SCM practices in firm by following the DMAIC process. The framework enables the practitioners to investigate the process and demonstrate improvements using DMAIC which makes use of statistical tools.

Although process dimensions related to SCM are critical to organization competitiveness, research so far has tended to focus on supply chain operations and reference model, balanced scorecard, total quality management, activity-based costing, just in time, etc., but in literature hardly any description of the SIPOC-DMAIC model to improve SCM process performance is provided. The use of statistics in DMAIC provides better insight into the process performance, and process control.