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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.

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.

Downstream supply chain (DSC) performance metrics provide a standard framework to assess internal performance. DSC performance metrics can also help balance performance tradeoffs among firms. The purpose of this paper is to develop a survey instrument to determine whether observed performance metrics correspond to the literature and to determine if performance metric systems are used to improve inter‐firm performance.

The survey instrument used in this study was based on SCOR performance attributes consisting of: delivery reliability, responsiveness, flexibility, costs, and asset management efficiency. The survey was completed by 73 members of the Council of Supply Chain Management Professionals (CSCMP) consisting of high‐level managers representing US companies.

One factor explained the underlying one‐dimensional structure of the surveyed Supply‐chain operations reference (SCOR) model as an internal metrics system but the authors did not find convincing support for the notion that external performance metrics are used to coordinate external, DSC inter‐firm activities.

A larger sample size would have allowed more insight into the inter‐relationships of the performance attribute variables. Moreover, the sampling plan limited generalization beyond US firms.

Firms used a standardized performance metric system and did not “pick” among metrics. In addition, firms used metrics independently of the decision to coordinate DSC activities. Perhaps they first learn to coordinate the internal performance and later extend to DSC members.

The paper describes one of the few empirical studies of the SCOR model in US industry.

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.

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.

The strategic management of the supply chain encompasses the development and integration of a supply chain strategy and the evaluation of the findings. An integrated approach is required for linking the supply chain strategy with the overall strategic management of the company for it to be successful. In this regard, this paper aims to examine and compare the relative efficiency of two models, namely, supply chain operations reference (SCOR) and the balanced scorecard (BSC) for measuring the supply chain performance.

A quantitative research design is used and 800 individuals were recruited from the industrial sector in the Eastern Province of Saudi Arabia. A three-tier model was used based on BSC, data envelopment analysis (DEA), decision-making trial and evaluation laboratory (DEMATEL).

The findings have indicated that DMUs 2, 4, 6, 7, 9, 11, 13 and 15 were efficient, while the other DMUs were non-efficient. These non-efficient units were not aware of the training and development and learning and growth. The concern presents comparatively poor performance of employees at this stage even this issue was based on system performance and indifference of managers, establishing complexities in the long-term system. The relationships were determined among four dimensions of BSC by identifying the supply chain metrics and applying the DEMATEL approach.

The current study provides empirical support for the DEA network model concerning its preference and improved performance measurement as compared to BSC model.

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 introduce a more practical approach for supply chain performance measurement (SCPM) and to approve the promising role of internet of things (IoT) technologies in SCPM systems.

This is a conceptual paper that includes literature review analysis, designing a new approach for SCPM, and a case study scenario for proving its applicability.

The case study scenario shows that IoT can enhance SCPM, as it has the capability to enable real-time data collection, increase data efficiency as long as enable real-time communication within the supply chain (SC).

The proposed approach can help to develop performance measurement systems and applications enabled by IoT technologies. These systems can be used to monitor, manage, and control the overall SC in real time and in a more integrated and cooperative manner.

This paper provides a structured systems building approach tailored to show how to employ IoT technologies in the field of SCPM. This approach could help in establishing new performance measurement applications, and it is believed that both practitioners and researchers will benefit from it.

Research on supply chain management can be broadly classified into three categories, namely, operational, design, and strategic. While many analytical and numerical models have been proposed to handle operational and design issues, formal models for strategic planning are scarce. The supply chain operations reference (SCOR) model, developed by the Supply Chain Council, is a strategic planning tool that allows senior managers to simplify the complexity of supply chain management. It is firmly rooted in industrial practices and is poised to become an industrial standard that enables next‐generation supply chain management. This paper gives a brief introduction to the SCOR model, analyzes its strength and weakness, and discusses how it can be used to assist managers for strategic decision making.

The purpose of this paper is to present a new framework for designing and implementing simple but effective advanced planning and scheduling (APS) systems in glass container industry by means of spreadsheets.

The conceptual framework for APS system design is developed by integrating principles of end-user computing and mixed-integer programming (MIP). This framework comprises the APS implementation and its integration with enterprise resource planning systems. Such a proposal is applied in a real-world glass bottle manufacturer and its performance assessment is carried out by using metrics of the well-known supply chain operations reference (SCOR) model.

The case study shows that the framework application can improve the responsiveness of glass container supply chain (SC) as well as reduce significantly SC planning costs. A further assessment of the end-user computing satisfaction highlights the general acceptance of the framework.

The proposed framework can assist glass industry managers and practitioners to solve their complex production planning problem since custom solutions are not available for this sector. Another important implication is the possible generalization of the framework given that MIP models for production planning are currently available in the literature for different industries.

On the one hand, the new framework successfully integrates concepts of end-user computing and MIP bridging the gap between theory of aforementioned areas and their practical applications. On the other hand, the paper contributes to the scarce literature on performance evaluation of APS systems and their impact on production system performance. The novel APS gain analysis based on SCOR metrics ensures that the system potential benefits are aligned with the modern concepts of supply chain management.