Using Data-Driven Analytics to Drive Supply Chain Decisions

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Supply chain analytics isn’t a new concept, but it is one that’s playing an increasingly important role in streamlining and driving risk out of these critical global networks. By definition, supply chain analytics is the process of collecting, analyzing and interpreting data from various points in a company’s supply chain to gain insights and improve decision-making. 

By examining data related to inventory levels, warehouse management, supplier performance, transportation routes, customer demand and other key factors, analytics can both assess and positively impact the process of getting products from raw material to the end customer. 

5 Different Types of Supply Chain Analytics

Not all supply chain analytics are the same, and Shopify breaks the various types down into these five buckets: 

• Predictive analytics. A “weather report” that forecasts future scenarios for the supply chain. It uses statistical models and machine learning algorithms to identify patterns from past data to project upcoming outcomes. “This type of analytics helps you anticipate and prepare for potential challenges or opportunities,” Shopify explains, “staying ahead of consumer trends in the market and the demands of your current and prospective customers.”
  
  
• Prescriptive analytics. Goes beyond identifying what might happen to suggest how to make desired outcomes occur. This process aims to improve supply chain performance for your business by using algorithms and simulation models to suggest specific interventions. As Shopify explains it, “By transforming data insights into actual suggestions, you get detailed recommendations to improve the performance of your supply chain.”
  
  
• Descriptive analytics. Reveals what has already occurred in your supply chain, providing historical information about your supply chain performance and giving you visibility into your supply chain KPIs. “This type of analytic approach quantifies past events and performance metrics,” Shopify says.
   
• Diagnostic analytics. Identifies why specific events or trends occurred in your supply chain. These insights typicallyw come from data mining and correlations, uncovering root causes behind performance variations. By connecting outcomes with their driving factors (“X happened because of Y”), Shopify points out, “diagnostic analytics gives you an understanding of what’s occurring across your entire supply chain.
  
  
• Cognitive analytics. Uses artificial intelligence (AI) and machine learning to process vast amounts of data (both structured and unstructured), uncovering complex patterns in supply chain operations. These trends might be otherwise difficult for you and your team to spot. This process looks a bit like “thinking,” where your tooling learns from new data, continuously improving its insights and recommendations. 

Organizations in search of some or all of these analytics capabilities are helping to drive a multibillion-dollar global market for analytics software. According to OpenPR, the global supply chain analytics market is projected to reach $35.9 billion by 2033—up from $10.8 billion last year—and will post a compound annual growth rate of 17.8% over that forecast period. 

“The global supply chain analytics market is witnessing unprecedented growth,” the company says. “With the proliferation of big data and advanced analytical tools, companies are now able to gain real-time insights into every aspect of their operations. These insights allow for improved decision-making, more efficient resource allocation, and the ability to anticipate disruptions before they impact operations.”

The Future Looks Bright

As the supply chain analytics market continues to grow at a robust pace, OpenPR says it will be driven by factors like increasing digitalization and the need for resilient supply chains. Government initiatives aimed at boosting digital transformation across industries are also expected to have a positive impact on the market. 

“As countries invest in digital infrastructure, companies will be better positioned to leverage advanced analytics to optimize their supply chains,” it says. “Furthermore, the integration of blockchain technology for improved traceability and transparency is anticipated to further enhance the capabilities of supply chain analytics solutions.”