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Globalisation, trade barriers, unprecedented health crises and geo-political crises have forced organisations to revisit their performance measurement systems (PMS) to better prepare their supply chain against the risk and improve performance in times of crisis. This study aims to review the supply chain operation reference (SCOR)-based PMS and propose a dynamic SCOR-based PMS for supply chain risk management (SCRM).

Due to the need for multi-stakeholder perspectives on SCOR-based PMS for the SCRM, the authors aimed to develop a theory rather than to elaborate upon or test the theory. Hence, the authors adopted an inductive theory-building approach to build research propositions. The authors also gathered 12 semi-structured interviews with knowledgeable managers from B2B international companies.

The findings of the study highlight the challenges faced by the organisations during the implementation of the SCOR-based performance indicators and the positive impacts they have on decision-making and on the continuous improvement strategy of organisations to tackle supply chain risks and improve performance. The findings suggest that the effects of these indicators are more felt during risk management and risk monitoring stages.

Like any other study, this study has some rules, and, thus, the authors caution the readers that they must interpret the findings of the research considering these limitations. The study is based on semi-structured qualitative interviews. The interviews were conducted with 12 knowledgeable managers from France; thus, the insights drawn from the study cannot be generalised to other settings. Furthermore, the samples represent something other than small and medium enterprises. In the future, the samples from small and medium firms can offer a nuanced understanding of the performance indicators for SCRM.

To the best of the authors’ knowledge, this is one of the few studies which has attempted to revisit the SCOR-based PMS in the B2B supply chain for risk management. The study’s findings help expand the SCOR-based PMS literature and offer numerous insights to the management and consultants facing challenges in SCOR implementation.

This paper aims to automate the supply chain operations reference (SCOR) model as an enabler for process‐oriented supply chain business intelligence.

The hypothesis is the following: SCOR model automation is possible using data that is directly extracted from integrated enterprise systems. To test the hypothesis, an alignment product that allows the SCOR model to be automated with information that is directly extracted from the Oracle E‐Business Suite was developed.

In order to achieve the full benefits from the SCOR model, effective business process management and the SCOR key performance indicators (KPIs) must be implemented and used. Unless data collection to support KPI construction is automated, it is difficult to institutionalize the SCOR model as a measurement and benchmarking framework. We have demonstrated that automated support for KPIs is feasible and achievable.

The E‐Business Suite is a single enterprise solution, but we assert that the same procedures could be followed with other enterprise solutions or even applied in a legacy system environment.

The developed solution described in the paper can immediately be applied to the design, development, and deployment of corporate performance management systems.

The purpose of this paper is to develop and validate a scale measurement of supply chain operations reference (SCOR)-related performance indicators and proposed constructs, SCOR-related performance indicators as practices within the Indian manufacturing sector.

A literature-based model on SCOR processes with five constructs and respective performance indicators was empirically validated by using a structured questionnaire. A total of 155 respondents among Indian manufacturing sector participated in this research, and the returned questionnaires were analyzed by using structural equation modeling.

The study established a relationship among the SCOR-related performance indicators and overall supply chain performance indicators (OSCPI). The moderation effect of demographic characteristics, namely, employee size, company age and type of company showed significant differences between SCOR-related performance indicators and overall supply chain indicators.

The scope of the study is limited to specific Indian manufacturing firms. The survey could not represent whole population of manufacturing sector.

The findings assist managers/supply chain practitioners in improving the performance measures identified using the standard framework, i.e., SCOR processes, overall supply chain performance measures as standard practices for Indian manufacturing sector for a profitable and sustainable business growth in global environment.

This research holds a value for suggested practices under SCOR processes and the proposed model for OSCPI, a path finder/performance measurement tool for supply chain professionals in the Indian context.

Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model.

This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level).

“Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability.

In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC.

By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc.

SCOR 10.0, released in late 2010, is the second version of the supply chain operations reference model (SCOR) to incorporate risk management processes, metrics and best practices. Given the paucity of studies that have explored the coverage and integration of supply chain risk management (SCRM) within SCOR, the analysis and suggested improvements for SCRM are designed to enhance SCOR’s collaborative and coordinated management of supply chain (SC) risks. The paper aims to dicsuss these issues.

Critical analysis was used to analyse the coverage and integration of SCRM within SCOR 10.0.

Discrepancies were identified in how SCRM has been incorporated into SCOR, including issues with the hierarchical representation of SCRM processes, metrics, best practices and skills. These may potentially propagate into difficulties in embedding risk management processes within other SC processes, visualizing risk metrics in a SC’s value hierarchy and reconciling SCOR’s SCRM with organizational enterprise risk management.

This paper is limited to theoretical analysis of the coverage and integration of risk in SCOR 10.0. Once the issues identified are remedied, the subsequent suggested improvements require validation through empirical testing.

Despite SCOR’s wide acceptance as a reference model in managing SC operations, there has been no investigation of its approach to SCRM. The analysis addresses this lack of prior investigation by analysing SCRM in the latest version, SCOR 10.0. The paper identifies deficiencies and suggests amendments regarding SCRM’s coverage and integration of SCRM.

This paper revisits the recent work of Chan and Qi which proposed an innovative performance measurement method for supply chain management. While the measurement method has many advantages, it can be unwieldy in practice. This paper aims to address these limitations and to propose a more user‐friendly alternative performance measurement model.

The performance measurement model described in this paper is a combination of two existing methods: Chan and Qi's model and the supply chain operations reference (SCOR) model. To demonstrate the applicability of the combined approach, actual SCOR level 1 performance data and the measurement information from a case supply chain (SC) are collected and processed by Chan and Qi's measurement algorithm.

These two methods complement each other when measuring SC performance.

This paper develops a practical and efficient measurement model that can resolve SC performance problems by incorporating the strengths of two different measurement models to create a synergistic new model.

Study in supply chain performance research on the shipbuilding industry is lacking. The purpose of this research is to study and provide guidelines to improve the performance of traditional shipbuilding supply chains in Indonesia.

The paper develops an empirical study gathered from a traditional shipbuilding industry, its suppliers, and customers. This study consists of three sections: the traditional shipbuilding industry, the suppliers, and the individual supplier scores. The internal and external performances in this study are measured using Supply Chain Operations Reference (SCOR) metrics. The SCOR model identifies five performance measurement attributes, including reliability, flexibility, responsiveness, cost and assets. Instead of using “responsiveness,” this study applies the schedule performance index, and supplements “cost” with the cost performance index in order to accurately reflect the traditional shipbuilding supply chains processes.

By analyzing SCOR metrics in the traditional shipbuilding industry, it has been found that the ideal shipbuilding supply chain metrics are order fulfillment, flexibility, asset turnover and total supply chain costs. The lowest performance metric value in the traditional shipbuilding industry is the cost of goods. Some improvements are proposed to lower the high cost of ship building. An integrated economic ordering system in collaboration with all the suppliers is one of the most effective ways to reduce the cost of the traditional shipbuilding supply chains. The implementation of SCOR metrics enables management to identify the critical issues to improve.

The study applies SCOR metrics to improve the traditional shipbuilding supply chains performance. The study is limited because the data collected are based on one shipbuilding industry only.

To the author's knowledge, this is the first empirical analysis on the implementation of SCOR metrics to the traditional shipbuilding industry. The analysis to improve the traditional shipbuilding supply chains performance can provide managerial insights to other industries.

As supply chains continue to replace individual firms as the economic engine for creating value during the twenty‐first century, understanding the relationship between supply‐chain management practices and supply chain performance becomes increasingly important. The Supply‐Chain Operations Reference (SCOR) model developed by the Supply Chain Council provides a framework for characterizing supply‐chain management practices and processes that result in best‐in‐class performance. However, which of these practices have the most influence on supply chain performance? This exploratory study investigates the relationship between supply‐chain management planning practices and supply chain performance based on the four decision areas provided in SCOR Model Version 4.0 (PLAN, SOURCE, MAKE, DELIVER) and nine key supply‐chain management planning practices derived from supply‐chain management experts and practitioners. The results show that planning processes are important in all SCOR supply chain planning decision areas. Collaboration was found to be most important in the Plan, Source and Make planning decision areas, while teaming was most important in supporting the Plan and Source planning decision areas. Process measures, process credibility, process integration, and information technology were found to be most critical in supporting the Deliver planning decision area. Using these results, the study discusses the implications of the findings and suggests several avenues for future research.

Drawing on information processing view (IPV) theory, the objective of this study is to explore big data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five processes: plan, source, make, deliver and return.

Semi-structured interviews with managers in a triad comprising pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken. NVivo software was used for thematic data analysis.

The findings revealed that BDA capability would be more practical and helpful in planning, delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.

The study informs managers about the strategic role of BDA capabilities in SCOR processes for improved business intelligence.

Adoption of BDA in SCOR processes within PSC is a step towards resolving the challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time business intelligence helping in better health care support through BDA-enabled PSC.

COVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains.

In this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately.

As a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains.

It is expected that this study will contribute to managers and policy makers as it addresses the “new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries.