Analytics has become a critical tool in supply chain management for improving business processes, reducing costs, increasing efficiency, and enhancing customer satisfaction. There are different types of analytics that supply chain professionals can use to derive insights from data and make informed decisions. In this article, we will explore the different types of analytics in supply chain management.
Descriptive analytics is the simplest form of analytics that is used to describe past events and analyze historical data. It is the foundation of analytics, and it is used to gain a deeper understanding of what happened in the past. This type of analytics helps in tracking key performance indicators (KPIs) such as inventory levels, on-time delivery, order accuracy, and others. With descriptive analytics, supply chain managers can monitor the performance of the supply chain, identify trends, and patterns, and make informed decisions based on historical data.
Diagnostic analytics is a type of analytics that is used to diagnose the root cause of a problem. This type of analytics helps supply chain managers to analyze data and identify the reason for any deviation from the norm. For example, if there is a delay in the delivery of a shipment, diagnostic analytics can help to identify the root cause of the delay, such as transportation issues, supply chain disruptions, or other factors. This type of analytics is essential for identifying bottlenecks in the supply chain and for making corrective actions to prevent future problems.
Predictive analytics is a type of analytics that uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. With predictive analytics, supply chain managers can forecast demand, optimize inventory levels, predict maintenance requirements, and improve supply chain planning. This type of analytics is critical for proactive decision-making and for identifying potential issues before they occur.
Prescriptive analytics is the most advanced form of analytics, and it involves using data, mathematical models, and machine learning algorithms to identify the best course of action to take. Prescriptive analytics goes beyond predictive analytics, which only identifies the likelihood of future outcomes. It recommends a specific course of action based on data and machine learning algorithms. With prescriptive analytics, supply chain managers can optimize production schedules, adjust inventory levels, reduce transportation costs, and improve the overall performance of the supply chain.
Big Data Analytics:
Big data analytics is a type of analytics that uses large volumes of data to derive insights and make informed decisions. In supply chain management, big data analytics is used to analyze data from multiple sources, including suppliers, customers, logistics providers, and others. With big data analytics, supply chain managers can identify patterns and trends that would be impossible to identify with traditional analytics. This type of analytics is essential for optimizing supply chain performance, improving customer satisfaction, and reducing costs.
Real-time analytics is a type of analytics that involves analyzing data as it is generated, allowing supply chain managers to make informed decisions in real-time. This type of analytics is essential for monitoring the performance of the supply chain and identifying potential issues before they occur. With real-time analytics, supply chain managers can quickly respond to changes in demand, adjust production schedules, and optimize inventory levels.
Let’s say a retail company wants to improve its supply chain operations. They can use different types of analytics to gain insights and make data-driven decisions.
- Descriptive analytics: First, they can use descriptive analytics to understand their current supply chain performance. They can analyze data on their inventory levels, lead times, and delivery performance to identify areas for improvement.
- Diagnostic analytics: Next, they can use diagnostic analytics to identify the root causes of any issues they found in the descriptive analysis. For example, they can analyze data on supplier performance to identify why their delivery performance is poor.
- Predictive analytics: After identifying the root causes of issues, the retail company can use predictive analytics to forecast future supply chain performance. They can use historical data on supplier performance to predict delivery times and inventory levels.
- Prescriptive analytics: Finally, the company can use prescriptive analytics to identify the best course of action to improve supply chain performance. They can use simulation models to evaluate different scenarios and identify the most cost-effective solutions. For example, they can evaluate the impact of increasing safety stock levels to improve delivery performance.