The Definitive Guide to Supply Chain Data Analytics

Global supply chains face vulnerabilities such as natural disasters and epidemics, but also offer opportunities for optimization to improve customer satisfaction and profitability. Companies increasingly utilize supply chain data analytics to gain insights for solving problems and seizing opportunities, enhancing their competitive advantage. Supply chain data analytics analyzes data from various systems to provide visibility into every aspect, from procurement to shipping. This analysis helps identify vulnerabilities and offers solutions to avoid or mitigate problems. The four types of analytics—descriptive, diagnostic, predictive, and prescriptive—offer varied insights, from understanding past performance to forecasting future outcomes and recommending actions. Despite the potential of prescriptive analytics, its complexity means few companies use it extensively, and human involvement remains crucial for deriving actionable insights. Use cases for supply chain analytics include predicting supply disruptions, ensuring quality, optimizing warehouse management, improving logistics, and aligning sales with inventory and operations. By leveraging these data-driven insights, businesses can address vulnerabilities, streamline operations, and enhance decision-making, thereby achieving efficiency and profitability in a complex global market.