Supply Chain Operations Reference Model Research Articles

Service Performance Measurement Using the Supply Chain Operation Reference Model in The Events and Travel Industry

Objective: This study aims to measure the performance of supply chain services using the Supply Chain Operation Reference (SCOR) Version 11.0 model in the events and travel industry. Design/Methods/Approach: The Supply Chain Operation Reference (SCOR) version 11.0 framework was employed to measure the performance of supply chain services. The performance indicators were assigned weights using the Analytical Hierarchy Process in Expert Choice Software. The performance achievements were marked using the traffic light system method, and an Action Plan was suggested for the company to improve its performance. Findings: The present study found 17 performance indicators consisting of 4 Plan performance attributes, 5 Source performance attributes, 3 Make performance attributes, 2 Deliver performance attributes, 2 Return performance attributes, and 1 Enable performance attribute. The weighting of each indicator resulted in the weight values for each activity from highest to lowest order, namely, Plan (0.317), Enable (0.297), Deliver (0.225), Source (0.95), Make (0.44), and Return (0.22). The performance measurement using a scoring system with the "larger is better" method and marking achievements through the traffic light system resulted in the company's aggregation value of 78%, indicating that the company still needs improvements in its supply chain service process even though the company's performance achievement is quite good. Originality: This research presents a new perspective on using SCOR-based performance measurement in service companies to become the basis for strategic decision-making and lead companies to achieve their competitive capabilities. Practical/Policy implication: The SCOR framework in every company business activity will provide a comprehensive view of the service supply chain as evaluation material for the company's service supply chain.

Evaluation of the SCOR Model Research

 With the rapid development of logistics in China, the supply chain process operation of enterprises is more and more valued by enterprises, how to improve efficiency and maximize the benefits of enterprises at the same time of cost reduction is the core concern of enterprises. In supply chain operation and management, the supply chain operation reference model, or SCOR model, is a very important tool. This paper provides a brief description of the current status and model introduction of the SCOR model, as well as the SCOR-DS model, which is proposed to be improved based on the SCOR model at the end of 2022, for the reference of model users.

Supply chain process structure diagram for processing unit of Arabica Coffee Kintamani Bali

Supply Chain Operational Reference (SCOR) is an approach that can be used to map processes in the supply chain. Process mapping is very useful for understanding the flow of products, money and information. Process mapping can make it easier to evaluate supply chain management weaknesses. This paper aims to map the process flow using the SCOR approach at the Kintamani Bali Arabica coffee processing unit. SCOR contains a structured hierarchy of goods, money and information flow processes which are described in three levels. Level 1 in the supply chain of the Kintamani Bali Arabica coffee processing unit describes the implementation of the planning process, source, make, and delivery. Level 2 shows the process category which consists of two process categories based on stock for processing fresh coffee and based on order-based processes for processing green bean, roasted and ground coffee. Level 3 shows the stages of process elements in each category. This level starts from the entire supply chain planning process stage, to the customer order delivery process stage. The results of this paper describe the structure of the SCOR model at the Kintamani Bali Arabica coffee processing unit connecting all interrelated product, money and information flow processes.

Analyzing Food Loss in Banana Pepper (Capsicum annuum) Production: Causes, Impact, and Challenges, A Case Study of Large-Scale Farming in the Hot District, Chiang Mai, Thailand

This study aimed to assess both quantitative and qualitative food loss in banana pepper production at the agricultural level. Its objectives included employing the Supply Chain Operations Reference Model (SCOR Model) for analysis, identifying root causes of losses and evaluating quantity and quality. Food loss here refers to diminished quantity and quality due to supply chain decisions and actions. Data collection and analysis included an extensive literature review, compilation of secondary data, primary data gathering via questionnaires and measurements of lost banana pepper yield during harvesting. Qualitative insights were acquired through in-depth interviews and focus group discussions. The study identified weather variability as the primary driver of food loss, with many farmers lacking access to climatic measuring tools. Additional losses, assessed visually, comprised damage from diseases, pests, stunted growth, mechanical issues and other factors, accounting for 13.68, 10.13, 6.95, 4.03 and 7.01 % of the total loss, respectively. Remarkably, only 57.93 % of the produce met quality and sale readiness criteria. The loss of banana peppers was estimated at 55.21 %, equivalent to 1,189.62 kg/rai, resulting in an economic loss of 35,688.58 Baht/rai. The study also noted significant nutrient losses, including energy, carbohydrates, fiber, protein, potassium, phosphorus, vitamin B1 and vitamin C, amounting to 321,197 kcal/rai and 57,102, 38,068, 17,844 g/rai and 3,045,427, 559,121, 487,744, 166,547 mg/rai, respectively. Farmers, while aware of food loss issues, often struggle to quantify them accurately. This study’s findings can raise farmers’ awareness and aid in designing training programs to mitigate such losses. However, addressing the inherent variability and unpredictability of on-farm food loss remains a challenge. Future research should prioritize a comprehensive analysis of banana pepper losses on farms. HIGHLIGHTS Weather variability was identified as the primary driver of food loss, exacerbated by limited access to climatic measuring tools Various factors contribute to food loss, including damage from diseases, pests, stunted growth and mechanical issues, accounting for 68 to 7.01 % of total loss Significant economic and nutrient losses, with a 21 % loss of banana peppers, resulting in an economic loss of 35,688.58 Baht/rai and substantial nutrient losses in various categories GRAPHICAL ABSTRACT

Blockchain sharding for e-commerce supply chain performance analytics towards Industry 5.0

ABSTRACT In today’s competitive and volatile e-commerce markets, effective supply chain performance measurement (SCPM) gradually becomes essential for investigating pitfalls and loopholes in strategic vendor management and business development. Surrounding the atmosphere of Industry 5.0, sustainability and resilience are two primary pillars, which should be further considered in the SCPM beyond merely operational effectiveness and efficiency. This study customises evaluation criteria based on the green supply chain operations reference model. Moreover, a blockchain sharding-enabled performance analytics system is designed, where a real-life case analysis is conducted to verify the system efficacy. Consequently, the supply chain sustainability and resilience can be fostered.

Using DEMATEL, clustering, and fuzzy logic for supply chain evaluation of electric vehicles: A SCOR model

<abstract> <p>The transportation sector is considered among the major sources of greenhouse gas emissions. Given advancements in transportation technology, customers' willingness to reduce carbon footprints, as well as policy incentives, Electric Vehicles (EVs) are becoming an increasingly important part of the passenger vehicle industry. Evaluation of Supply Chain (SC) performance in the EV industry seems to contribute significantly to the enhancement of the operational consequences across the supply chain tiers. The SCOR (Supply Chain Operations Reference) model was designed to help businesses optimize their supply chain operations, reduce costs, and improve customer satisfaction. Although many performance measurement models have been developed in the context of SC, there is no performance measurement model in relation to the EV supply chain based on indicators of customer perceived value (Reliability, Responsiveness and Agility) in the SCOR model. Therefore, we aimed to develop a new method to evaluate the performance of the EV supply chain using a set of critical SC performance evaluation indicators. Multi-criteria decision-making along with machine learning was used in order to develop a new method for evaluating SC performance. We used k-means clustering and fuzzy logic approaches in the development of the new method. An assessment of indicators' importance level was performed using the fuzzy logic approach. The results of the method evaluation show that the proposed method is capable of predicting the performance of the EV supply chain accurately. According to the results, by optimizing their supply chain, companies can improve their ability to deliver products and services that meet or exceed customer expectations, resulting in higher customer perceived value and customer satisfaction.</p> </abstract>

Demystifying the challenges of water, sanitation and hygiene in rural high schools

Basic sanitation is an essential human need to preserve dignity as well as health. However, many South African schools confront challenges in providing a sufficient level of sanitation. This has been exacerbated by the global Covid-19 pandemic. This study employed the Supply Chain Operations Reference (SCOR) model to examine the challenges affecting efficient and effective sanitation supply chain operations in disadvantaged rural high schools. It also assessed the extent to which high schools engage in planning, sourcing and operations processes for integrated supply chain sanitation, hygiene and service delivery. An exploratory design was adopted and thematic analysis was employed to analyse the data collected from 21 participants in high schools in Ngaka Modiri Molema District, and two from the Department of Basic Education in North West Province. The results showed that the majority of schools, especially those in rural areas, confront supply chain basic health sanitation challenges such as a shortage of water and sanitary pads as well as vandalism, with the lack of financial resources identified as a significant challenge. Based on the findings, the study recommends supply chain integration, improved communication with suppliers and the adoption of the SCOR model to enhance the provision of basic sanitation and water

공급사슬네트워크에서 비즈니스 애널리틱스의 영향에 관한 연구

The purpose of this study is to empirically investigate the relationship between organizational business analytics capability and supply chain performance in order to identify the impact of business analytics in supply chain network. Specifically, drawing on literature streams in Supply Chain Operations Reference Model and Information Processing Theory, this study conceptualizes the notion of business analytics capability in supply chain network, develops its structure as a reflective model, and tests the constructs and operational measures in the model. And then this study investigates the relationship between analytics capabilities in the plan, source, make and deliver area of the supply chain network and its performance using business process orientation and information system support as moderators that form the basis of our theory. Conducting an empirical study of 170 Korean manufacturing firms, this study, through the successive stages of measurement model analysis, provides empirical evidence for modeling reflective indicator and construct of business analytics capability in supply chain network. As a result, the construct of business analytics capability is posited to reflect a combination through analytics capabilities in the plan, source, make and deliver area of the supply chain network. And the findings suggest the existence of a statistically significant relationship between business analytics capabilities and supply chain performance. The moderation effect of information systems support is considerably stronger than the effect of business process orientation. These findings provide a better understanding of the areas in supply chain network where the impact of business analytics may be the strongest.

Risk Analysis on the Cassava Value Chain in Central Lampung Regency

The Central Lampung Regency is the center of cassava production in Lampung Province, the Indonesia’s largest cassava-producing province. Cassava has much promise; however, various risks arise throughout the value chain. Risks in the agricultural sector, including cassava farming, are unavoidable and severely impact stakeholders and consumers. This study aimed to identify the risks and the most effective mitigation measures for the cassava value chain. This research was conducted using the House of Risk (HOR) method, divided into two phases: HOR 1 and HOR 2. It involved 286 samples encompassing 263 farmers, six traders, three tapioca industries, eight non-tapioca industries, and six financial institutions. The risks were determined through the Supply Chain Operational Reference (SCOR) model. The results unveiled several priority risks in the cassava value chain, consisting of unpredictable weather changes, scarcity of fertilizers, the absence of farming standards, limited capital, delivery technical problems, price fluctuations, negligence of the workforce, default credit, and lack of customer or farmer knowledge leading to confusion. Furthermore, the risk mitigation actions covered preventing the risks of cassava farming; applying appropriate technology; utilizing production, marketing, and financing system assistance; preventing the risks of cassava trading business; implementing a structured market system; developing access to financing, institutions, and markets; developing marketing infrastructure; developing adaptive farming; monitoring during the credit period; socializing risk management to employees; and providing institutional, financing, and production assistance.

Measuring Supply Chain Performance for Khanh Hoa Sanest Soft Drink Joint Stock Company: An Application of the Supply Chain Operations Reference (SCOR) Model

As Khanh Hoa Sanest Soft Drink Joint Stock Company attempts to satisfy consumer requirements and maintain market share in the salangane nest industry, it is important to monitor the efficiency of its supply chain (SC). This paper identified metrics to evaluate the firm’s SC performance, using secondary data from firm records, primary data from a survey of 200 sales agents, and the Supply Chain Operations Reference (SCOR) model. Production records revealed a manufacturing lead time of 11 days and a production time of 24 h plus 7 days for refrigeration and observation. Order fulfillment delivery times ranged from 13 to 16 days. About 86.0 percent of customers rated the product as good or very good, and 60.5 percent claimed benefits from consuming the product. SC management costs of goods sold were 75.27% of total costs, while production cost was 41.99% of total costs. Total assets increased 16.6% from 2019 to 2021, but profits declined by 32.9% for the same period, while return on assets fell 42.4%. To maintain a competitive advantage, the company should apply information technology to improve SC performance. The results showed that the SCOR model is appropriate for evaluating the performance of this firm and similar business enterprises.

Modelling the Make Process of the Mineral Supply Chain Upstream Segment

The upstream segment of the minerals supply chain (MiSC) is the backbone for achieving the transition to clean energy by securing the supply of minerals. Recently, the MiSC has been disrupted, affecting mineral supply and generating uncertainty among clean energy producers. Supply chain (SC) mapping addresses this uncertainty, providing a holistic end-to-end view. However, MiSC mapping studies mainly focus on the downstream segment, leaving aside the upstream segment. In this context, the fundamental gap is the lack of standardized modelling frameworks that can accurately represent the upstream segment dynamics and, at the same time, seamlessly integrate with the downstream segment. This paper bridges this gap, designing a model for the MiSC upstream segment (MiSCOR). Framed within design science research methodology, MiSCOR is built by adapting two reference models, the Exploration and Mining Business Reference Model (EM) and the Supply Chain Operations Reference Model (SCOR), focusing on the commodity-making process. The MiSCOR demonstrates its applicability and robustness in real operational scenarios of one of the largest copper producers. MiSCOR provides a management tool for practitioners and decision-makers in the MISC’s upstream segment, offering blueprints to throw functional silos, foster collaboration, and a standardized framework that integrates seamlessly with the downstream segment.

Development of SCOR Database for Digitalisation of Supply Chain Customer Feedback Analysis

High-level customer service, with improved quality at a lower cost, is imperative in today's global supply chain, where customers have myriad options across borders. This is in line with the primary objective of supply chain management, which is to enable and deliver high-level customer service with lower costs, reduce lead time, and improve the quality of products/services. The Supply Chain Operations Reference (SCOR) model based on KPI metrics enables an increase in the quality of products/services by monitoring and digitalising involved processes. The current paper suggests the structure of the SCOR database for Supply Chain process improvement by applying the best practices of the SCOR for Business Processes improvement. We recommend the Bayesian Belief Network (BBN) and Case-Based Reasoning (CBR) methods to estimate the influence of these improvements on Supply Chain efficiency through key performance indicators before implementation. Integrating the methods proposed in this article focuses on an approach that minimises supply chain failures, decreases failure elimination time, understands consumer needs, and offers more accurate price proposals and lead times to improve customer satisfaction. Accordingly, the integrated SCOR-based Business Process Modelling (BPM) was not previously combined with BBN to identify the most efficient ways to improve the reliability of a Supply Chain by applying best practices, which impacts the entire supply chain. Our research is limited to SME companies in electronics manufacturing, but our ambition is to develop a universal framework suitable for broader areas. The study aims to analyse how the customers see the company by combining the CDA (Customer Delivery Accuracy) and Customer Complaints measures and find the optimal way to improve business processes by applying best practices. Still, it is limited to SCOR reliability performance metrics.

The Performance Measurement of Mobiya Material’s Supply Chain to Support Special Projects for the UNHCR

The assessment of supply chain performance enables the monitoring of progress in supply chain management (SCM) and serves as a metric for assessing success in maintaining a competitive advantage The objective of this study is to ascertain the configuration of the supply chain flow and evaluate the performance of the material's supply chain at PT SCH, a company operating in the electrical industry. The employed methodologies encompass the Supply Chain Operations Reference (SCOR) and the Analytical Hierarchy Process (AHP), facilitated by the utilization of the Expert Choice 11 software. The supply chain movement at PT SCH encompasses three primary distribution networks, namely inbound, internal, and outbound. The Supply Chain Operations Reference (SCOR) model implemented at PT SCH has three tiers, including five core processes (plan, sourcing, making, delivering, and returning), three performance attributes (reliability, responsiveness, and flexibility), and a total of 19 performance indicators that have been customized to align with the specific requirements of the organization. Upon conducting performance measurements, a score of 75.70 out of 100.00 was obtained, categorizing it as "good."

Configuring Supply Chain as a Strategy of Enhancing Firm Performance: A study on Manufacturing Sector in Kenya

This paper explored the role played by supply chain configuration on the performance of manufacturing sector in Kenya. Kenya’s manufacturing sector has come a long way in contributing to the country’s economic growth and development. However, the past decade has not been favourable for the sector, as it has recorded a significant decline in performance and growth characterized by loss-making, and declining production. With supply chain taking a huge portion of the industry’s operations, it raises the question on the extent to which supply chain of the manufacturing firms in Kenya has been aligned to suite the interests of suppliers, service providers, and customers as the key stakeholders in the supply chain process. The paper was anchored on the Supply Chain Operations Reference (SCOR) model which describes the essence of organizations to map, measure, and optimize their supply chain processes. Informed by a descriptive research design, primary data was collected from 160 manufacturing firms in Kenya using a questionnaire. Descriptive and inferential statistics were used to analyse the data. The findings revealed that supply chain configuration was significant in determining the performance of the manufacturing industry in Kenya. The findings revealed that configuring supply chain through promoting relationship with customers and suppliers played a significant role in reducing the costs of operations, enhancing quality and productivity thus enhancing profitability of manufacturing firms. A recommendation is therefore drawn that for the Kenya’s manufacturing sector to regain its strength and performance, there is need for the management to put more emphasis on supply chain processes by instigating strategies that enhance configuration of the supply chain network.

Blockchain applications for traceability and food safety in agri-food supply chain: cherry product application

PurposeThe primary objective of this study is to investigate the application of blockchain technology (BCT) in the agri-food supply chain, focusing on traceability and food safety.Design/methodology/approachThe study employed a semi-structured interview method with representatives from the cherry supply chain to evaluate their awareness and acceptance of BCT's impact. Additionally, the analytic hierarchy process (AHP) was utilized to determine digital investment priorities in supply chain strategies. By applying the supply chain operations reference (SCOR) model framework to the cherry supply chain, the study aimed to address the question “Which process model is suitable for implementing BCT in the agri-food supply chain?”FindingsThe global agri-food supply chains are characterized by significant food losses, escalating prices along the chain, and food safety risks. Concurrently, consumer concerns regarding food safety, quality and transparency are on the rise. BCT, with its ability to ensure data integrity, immutability, and seamless tracking of chain movements, presents immense potential as a secure infrastructure in the agri-food supply chain traceability.Originality/valueThe developed analytic framework and the study's findings can be adapted to different sectors and different sub-sectors within agri-food supply chains.

Artificial intelligence in supply chain and operations management: a multiple case study research

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies’ competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

Ekologiczny łańcuch dostaw: narzędzia do oceny E-odpadów – globalna perspektywa

When a company's supply chain has achieved a desirable degree of eco-friendliness in all regards, from a sustainability perspective, its performance will be satisfactory. Since the closed-loop e-waste supply chain's operations are primarily focused on achieving sustainability objectives related to the manufacturing, distribution, reusing, and discarding electrical components, it is crucial to evaluate its success in this area. In order to monitor the performance of supply chains and enhance the processes, the supply chain operations reference model offers suggestions and benchmarking tools. In this study, a conceptual framework is illustrated to show how these standards could be used in the E-waste supply chain to link business processes, metrics, industry standards, and technology in order to enhance the relationship and coordination between the supply chain members and to increase sustainability throughout the supply chain. Insufficient attention so far has been paid to the SCOR model's sustainability criteria, according to an assessment of the literature. Consequently, in the wake of portraying the structure of Supply Chain Operation Reference model, we make sense of which credits should be included in Supply Chain Operation Reference so to reflect manageability and which cycles and practices are related with every standard or should be remembered for Supply Chain Operation Reference to lay out the connection between execution, cycles, and practices.

Performance Measurement Supply Chain Management (SCM) Using the Supply Chain Operation Reference (SCOR) Method at PT X

In the world of the fertilizer industry in Indonesia, there are some of problems in the process of planning, procurement and, delivery of materials that adversely affect the company's operations. One approach to fixing this problem is to use a SCOR strategy that is effective in improving the supply chain. PT. X is one of the fertilizer companies whose current condition is that there are problems in planning to meet the needs for production supporting materials so which has the potential to hinder fertilizer production. This is because PT X has not carried out an analysis that hinders demand planning or the supply chain as a whole. Thus, researchers can measure the value of effectiveness in the supply chain to improve targets that have not been achieved. In this research, a scoring method called SCOR was used which included AHP weighting. Fifteen key performance indicators (KPI) were generated based on the five SCOR model indicators used in processing, namely data plan, source, make, delivery, and return. With a final score of the SCM performance level of 86.872, which is included in the Good category. However, this value can still be improved so that it is included in the excellent category, namely >90. So inecessary to identify KPI indicators that have performance values that are not yet optimal, and to make suggestions for improvements to increase PT X SCM performance values.

Supply Chain Management Performance Measurement. Case Studies from Developing Countries

Supply chain management is critical for companies (suppliers, distributors, manufacturers, and retailers) and researchers. Supply chain sustainability is important in Developing Countries. To evaluate the performance of supply chains in companies located in Developing Countries like Albania and Kosovo, this article will examine the findings of the Supply Chain Operation Reference (SCOR) model. Utilizing the SCOR model can improve a company’s chances of survival and rivalry in the market. The selection of Performance Attributes appears to be the standard for measuring performance. By contrast, Snorm De Boer normalization equates the importance of Key Performance Indicators (KPIs), the Analytical Hierarchy Process (AHP) calculates the weighted criteria of KPIs, and Objective Matrix (OMAX) analyzes the measurement results of the KPIs. This article identifies unexplored areas for future research that will be beneficial in the field of supply chain management, such as the creation of supply chain performance metrics, creation of a model that is tightly integrated into the supply chain, and  handling supply chain-related problems and issues.
  

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.