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SCOR model metrics in SCM

SCOR model metrics in SCM: Increasing efficiency in logistics

by Editorial Office

In our global economy, the management of complex supply chains is crucial to a company’s success. The Supply Chain Operation Reference Model (SCOR model) provides a comprehensive framework for this matter and aims to improve supply chain performance: SCOR was developed by the global non-profit organization Supply Chain Counsil (SCC) and is used worldwide to review and optimize supply chain performance. We explain which parameters the model uses, how it supports strategic risk management and what challenges can arise during implementation.

The five most important categories of the SCOR model

As a holistic, process-oriented reference model, the SCOR model maps the entire supply chain – from the supplier to the end customer. The aim is to reduce the complexity of supply chain processes in SCM through standardization and at the same time improve performance. The model divides the supply chain into six core processes: Plan (Plan), Procure (Source), Manufacture (Make), Deliver (Deliver), Return (Return) and Support (Enable). Various metrics are defined for these processes that enable companies to evaluate and improve their efficiency: for example, supplier lead time is a common performance indicator in the area of procurement, while on-time delivery or order fulfillment cycle time is important for delivery.

With its metrics, the reference model focuses primarily on five different categories to measure the performance of supply chain processes and identify potential for improvement: 

  1. Reliability: This category evaluates the ability of a supply chain to deliver the ordered quantities of goods to their destination – complete and on time – and to fully fulfill the agreements made in advance. The most important key figures include punctuality, the order fulfillment rate and the return order rate.
  2. Responsiveness: This category measures how quickly the supply chain can react to customer requirements and market changes. Important KPIs include the lead time of orders, flexibility in production and the time it takes to process customer inquiries.
  3. Agility: How effectively can the supply chain adapt to change? This category is about fluctuations in demand, the flexible handling of returns or the speed at which new products are introduced to the market.
  4. Costs: Costs also play a crucial role within the SCOR model. The category covers all financial aspects associated with the operation of the supply chain – including transportation, warehousing and production costs. The optimization of these key figures is a central focus of the model in order to strengthen the competitiveness of companies.
  5. Assets: Asset management focuses on the efficient use of resources such as plants, inventories and equipment. KPIs such as inventory turnover and plant utilization are crucial in this context.

The SCOR model primarily addresses the operational performance and efficiency of the supply chain. However, it also takes into account important aspects of risk management and adaptability, which help to safeguard and sustainably strengthen – and therefore improve – the performance of the supply chain.

How the SCOR model supports risk management

Strategic risk management is crucial for companies in order to improve the performance of their supply chain in the long term. Likewise, in this area, decision-makers benefit from using the SCOR model: it serves as a coherent framework for the structured recording and analysis of risk factors along the entire supply chain. By standardizing processes and defining clear performance metrics, the model enables an objective assessment of potential disruptions. Monitoring order fulfillment, order lead time and total SCM costs are examples of KPIs that not only represent performance targets, but also act as indicators of risk sources. A significant drop in these KPIs can indicate challenges, such as delivery delays due to geopolitical tensions or interruptions in production.

Data is evaluated in real time with the help of Advanced Analysis tools: Predictive Analytics and Machine Learning allow for the identification of patterns and anomalies in performance data that could indicate underlying, risky trends. Furthermore, Adcanced Reporting helps to predictively analyse and clearly visualize complex data sets. Logistics experts are enabled to act proactively on the basis of simulations and scenario evaluations.

The SCOR model promotes an end-to-end view of the supply chain. This holistic perspective allows companies to recognize complex interdependencies and identify risk nodes. SCOR-based risk management quantifies the probability and impact of disruptive events and uses this information to create profiles for the development of preventive and response strategies: that way, managers are prepared for emergencies and potential disruptions to the supply chain are minimized.

Challenges during implementation

However, the successful implementation of the SCOR model requires a thorough analysis of the existing structures and processes within a company. In addition to the need for comprehensive data collection and analysis, the adaptation of IT systems is often unavoidable: organization-wide coherence and synchronization between the various processes is required in order to ensure a seamless monitoring and evaluation of key performance indicators in accordance with the model. In particular, the harmonization of interdisciplinary data streams – from purchasing to production to sales – is a complex task. In order to create seamless traceability and transparency of the processes, ERP systems, APS systems and WMS systems, for example, must be integrated in order for a uniform database to be created.

It is also necessary to align operational business with the strategic objectives of the supply chain. Performance indicators such as Perfect Order Fulfillment, Order Fulfillment Cycle Time or Total Supply Chain Management Cost are used in this context. This process requires a deep understanding of the interplay between short-term operational activities and long-term strategic goals in order to maintain a balance between flexibility and efficiency. However, companies are not left to their own devices: External logistics service providers such as Hermes International do not only possess the required expertise in supply chain optimization through years of experience, but also help to seamlessly integrate operational processes with strategic goals.

Conclusion: Using data to strengthen the resilience of the Supply Chain

The SCOR model is an important tool for companies that seek to optimize their supply chains. By standardizing process steps and implementing efficient metrics, it enables a detailed analysis and optimization of operational performance based on comprehensive data. At the same time, by integrating risk management practices, the model provides a solid foundation for strengthening resilience to external disruptions. While the implementation can be challenging, it certainly pays off: companies that use the SCOR model are able to react quickly to dynamic market conditions, continuously adapt their supply chain strategy and secure a competitive advantage in the long term.

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