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Supply chain analytics: definition & important aspects for buyers
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Supply chain analytics in a nutshell:
Supply chain analytics refers to the systematic collection, analysis and interpretation of data along the entire supply chain using advanced analytical methods and tools. For purchasing, this enables data-based decisions to optimize supplier relationships, inventory management and risk minimization.
Example: In 2023, an automotive supplier implemented a supply chain analytics solution that analyzes 50,000 delivery processes per month to predict delivery bottlenecks 14 days in advance with 92% accuracy, thus enabling proactive countermeasures.
Contents
What are supply chain analytics?
Supply chain analytics refers to the application of advanced data analysis methods to processes within the supply chain. By evaluating data on production, warehousing, transport and distribution, it enables companies to optimize their supply chain strategies. The aim is to create transparency, increase efficiency and gain competitive advantages through data-supported decisions.
Core elements of supply chain analytics
Significance for purchasing
Supply chain analytics play a crucial role in purchasing in order to improve procurement processes and reduce costs. By analyzing the entire supply chain, buyers can identify bottlenecks, evaluate suppliers and identify risks at an early stage. This leads to more efficient procurement, better supplier relationships and increased flexibility in the face of market changes.
Supply chain analytics: from manual data analysis to real-time analytics
Building on the theoretical foundation of supply chain analytics, data-supported supply chain optimization is becoming increasingly important in practice. By analyzing extensive data, companies can design more efficient processes and achieve competitive advantages. However, traditional methods are reaching their limits as they cannot keep up with the complexity and speed of modern supply chains. To meet these challenges, a shift towards modern, automated analysis systems is essential.
Old: Manual data analysis
Traditional approach: In traditional supply chain management, data was recorded and analyzed manually. Information from orders, stock levels and supplier communication was maintained in spreadsheets. Analyzing this data was time-consuming and error-prone. Decisions were often based on historical values and personal experience, making it difficult to take current market trends and fluctuations into account. This approach often led to inefficient processes, high stock levels and delayed reaction times to market changes.
New: Supply Chain Analytics
Supply chain analytics: Modern approaches use advanced technologies such as big data, AI in purchasing and machine learning to analyze data along the entire supply chain in real time. The integration of ERP systems, sensors and external data sources creates comprehensive data landscapes. These enable precise predictions of demand, delivery times and potential risk analysis. This enables companies to act proactively, automate processes and base decisions on current data rather than assumptions. This leads to increased efficiency, reduced costs and increased customer satisfaction.
Practical example: Automotive industry
A global automotive manufacturer implemented supply chain analytics to optimize its supply chain processes. By analyzing production data, logistics and market data in real time, the company was able to identify supply chain bottlenecks at an early stage and initiate countermeasures. This led to a 15% reduction in stock levels and a 20% reduction in delivery times. In addition, adherence to delivery dates for customer orders rose to 98%. The targeted analysis of suppliers' quality data reduced the return rate of faulty parts by 25%, resulting in considerable cost savings and improved production quality.
Conclusion on supplier evaluation with supply chain analytics
ConclusionSupply chain analytics is revolutionizing modern procurement through data-driven decision-making. The combination of descriptive, predictive and prescriptive analyses enables precise supplier evaluation, efficient risk management and sustainable cost optimization. While technical challenges such as system integration and data consistency exist, the strategic benefits clearly outweigh them. Companies that successfully implement supply chain analytics gain a decisive competitive advantage through improved transparency and responsiveness in their supply chain.
