Using the right analytics helps supply chain managers sort through huge amounts of data to leverage operations. While Gartner stresses the importance of prescriptive analytics with decision-making, Ventana Research outlines the benefits of employing predictive analytics. We’ll explore them all, and discuss why predictive and prescriptive analytics that incorporate AI and machine learning are game-changers for the future of supply chains that need to deploy big data systems.
Supply chains need analytic solutions to sort through data. Analytics solutions can be prescriptive, predictive, or descriptive.
How can we make it happen?
When advice is needed or specific actions must be taken, you should use prescriptive analytics. These are analytic models based on simulations and optimization algorithms that help businesses reach their bottom line. Prescriptive analytics provide answers to the question, “What should we do next?”
When supply chains use prescriptive analytics, they can quantify future decisions and optimize inventory levels, scheduling, and production. Improving business intelligence also provides scalability.
The benefits of using prescriptive analytics are:
- In some verticals, it helps optimize production and provide inventory insight.
- It helps with the customer experience; businesses can meet demand and reduce stock-outs.
- It ensures supply chains are delivering the correct inventory at the appropriate times.
For businesses that need forecasting insight, demand forecasting and demand planning software can provide assistance with financial planning, allocating resources, and forecasting accuracy. Inventory optimization software can also help with maintaining appropriate inventory levels and shipments.
About predictive analytics (insight)
What will happen?
When you need to fill in the gaps or look into the future, you should use predictive analytics. Forward-thinking businesses use predictive analytics to make their best guess, as no algorithm can provide 100 percent accuracy. Predictive modeling makes predictions about future scenarios based on explanatory variables. Statistics used with predictive analytics are based on data from CRM, human resources, ERP, POS, or other data sets. These are forecasting techniques, estimates, and statistical models that predict future events and assess what-if scenarios like, “What might happen if we do…”
Vanguard Predictive Planning was designed to help organizations handle large amounts of data. It allows for sophisticated analyses that include AI, deep learning algorithms, and machine learning. “Only sophisticated algorithms provide organizations with the means to maximize the value of the mass of data they collect,” cites Ventana Research, which found that 78 percent of organizations rely on predictive analytics.
Descriptive analytics (hindsight)
When you need insight into aggregate levels based on past performance, you should use descriptive analytics, which describe data or give summaries of past raw data. Descriptive analytics is helpful for data mining and aggregation to historically to address the question, “What happened in the past?”
Gartner characterizes descriptive analytics as traditional business intelligence using tables, pie or bar charts, or line graphs. For example, a business may use descriptive analytics to review past inventory sales or inventory stock from the previous quarter.
With this statistical data, you can generate reports to reflect operations, financial records, sales performance, customer/client tallies, inventory levels, and other financials.
Determining the best approach
Unfortunately, some supply chains only look at what has happened (descriptive analytics). Of the three types of analytics, descriptive analytics provides the least value. Businesses should work from the normative stage and use predictive and prescriptive analytics to determine what will happen to unlock true value.
Analysts use earlier analysis from descriptive analytics to gauge benefits. Predictive and prescriptive analytics synthesize machine learning, mathematical sciences, algorithms, big data, and business rules for predictions and final decisions. These approaches are best because you can apply them against different types of data sets that might be transactional, historical, or big data. Analysts use predictive analytics to look at possible future outcomes and prescriptive models to determine the outcome of predicted events.
Harnessing big data analytics can help supply chains extract the data needed for accurate decision-making. By expanding business intelligence, supply chains can reduce costs, raise revenue, and maximize the customer experience. Vanguard Software has been helping organizations combine market-leading predictive and prescriptive analytics for years. The Vanguard Predictive Planning platform is uniquely positioned to deliver easily consumable insights and recommendations for action.