Most supply chain analytics tell you what happened or what might happen next. But prescriptive analytics takes it further – it’s like having a seasoned logistics expert who not only predicts problems but hands you a detailed playbook to solve them. Think of it as the difference between a weather forecast that predicts rain and one that tells you exactly when to reschedule your shipments, which alternate routes to take, and how to adjust staffing to minimize disruption.

In this article, I’ll look at how prescriptive analytics can transform supply chain management. Indeed, it goes much further than other types of analytics like descriptive, diagnostics, and predictive. Further, I’ll explain why it’s more effective than other decision support and recommendation systems. Also, I’ll show how prescriptive analytics fits into the broader business analytics landscape. Moreover, I’ll explain how predictive and prescriptive analytics differ, but complement each other to enable rapid, informed decisions. By the end of this article, you’ll understand exactly how prescriptive analytics helps supply chain leaders make rapid, informed decisions.

1. Prescriptive Analytics Defined and How It Works.

At its core, prescriptive analytics is a decision-support powerhouse that leverages historical data, knowledge-based inputs, and real-time data. Based on these digital inputs, it uses advanced algorithms to recommend a specific course of action, explain why it is the best, and details how to implement it. Moreover, businesses can use predictive analytics to automate decision flows. Below I’ll define Prescriptive Analytics and describe how this analytical process works.

a. Prescriptive Analytics Defined.

Below is Gartner’s definition of this type of analytics.

“… a form of advanced analytics which examines data or content to answer the question ‘What should be done?’ or ‘What can we do to make _______ happen?’ …”

Gartner

b. An Example of How Prescriptive Analytics Process Works.

Unlike its business analytical cousins, prescriptive analytics doesn’t stop at telling you that your warehouse efficiency might drop 20% next month. No, it can go much further.  For instance, continuing with the warehouse example, it can outline precisely which picking strategies to modify, how to reorganize your storage layout, and calculates the expected ROI for each suggested change. To sum it up, prescriptive analytics operates as a problem-solver that comes with its own action plan. Further, it can either provide a recommended action plan or through automation it can actually implement the recommended solution. See below for a graphical depiction on how prescription analytics work.

Prescriptive Data Analytics Process: A Model of How It Works

“… prescriptive analytics … leverages historical data, knowledge-based inputs, and … uses advanced algorithms to recommend a specific course of action, explain why it is the best, and details how to implement it.”

2. Prescriptive Analytics: More Than A Recommendation Engine or Expert System.

Research organizations and businesses started using the term “Prescriptive Analytics” back in the 1980s. So, this type of analytical tool is fairly new when you consider that human problem-solving itself goes back to the beginning of time. Moreover, this type of problem-solving tool caters more to organizations than just individuals. Additionally, prescriptive analytics is still evolving as it leverages emerging AI capabilities, new methodologies, and increasingly faster computers. To get a better understanding of Prescriptive Analytics, let’s look at how it compares to other problem-solving tools, Expert AI Systems and Recommendation Engines.

Prescriptive Analytics Vs Recommendation Engines or Expert AI Systems

• Expert AI System Compared: It solves Complex Problems, Like a Human Expert. Expert systems are designed using deterministic, knowledge-based rules to provide answers, using a one-on-one human-machine interface. On the other hand, Prescriptive Analytics not only advises on a course of action, but also explains the reasoning behind it and details how to implement the recommended solution.
• Recommendation Engine Compared. It Makes Recommendations Based on Shared Preferences. Platforms like Amazon, Netflix, or YouTube use recommendation engines to give customers recommendations based on their behavior patterns and similarities to people who might have shared preferences. On the other hand, Prescriptive Analytics supports a full spectrum of organizational decision-making scenarios. Plus, it explains why and details how to implement a recommendation. For more details, see Appier’s article, What Is a Recommendation Engine and How Does It Work?

3. Prescriptive Analytics Techniques.

Prescription Analytics is not necessarily tied to any particular analytical model, AI, technique, or methodology. One reason for this is because analytics is an evolving discipline. Also as with any data analytics capabilities, it is dependent on the data available, the type of use case it is supporting, resources allocated, and skills of analysts available. To illustrate, below is a sampling of techniques that could be used as part of a prescriptive analytics capability.

Examples of Techniques Used in Prescriptive Analytics

• Probabilistic Models. For example, use these models to assess financial risks in terms of percentages by estimating the likelihood of various investment outcomes.
• Machine Learning (ML) / Data Mining. In this case, ML can help optimize supply chain operations by predicting demand and prescribing actions to minimize costs and enhance efficiency. What’s more, it learns over time to improve its recommendations.
• Mathematical Programming. For instance, use these techniques to determine the optimal allocation of resources in production to maximize profit while minimizing waste.
• Simulation. Here, analysts can create virtual environments to test different strategies in order to predict and measure their outcomes.
• Logic-Based Models. Lastly, software developers can use these types of logic-based models to automate decision-making processes in complex systems like smart grid management, ensuring efficient and reliable energy distribution.

See Alexandros Bousdekis’ listing, Classification of the Methods for Prescriptive Analytics, for a comprehensive roster of prescriptive analytics techniques. Also, see AIMMS’ article, Prescriptive Analytics technology achieves recommended actions using optimization modeling. AIMMS is also an example of a company that offers a prescriptive analytics tool oriented on supply chains.

“Prescription Analytics is not necessarily tied to any particular analytical model, AI, technique, or methodology. … it is dependent on the data available, the type of use case it is supporting, resources allocated, and skills of analysts available.”

4, How Prescriptive Analytics Fits In With Other Types of Business Analytics. 

Most modern companies are familiar with or use Business Intelligence (BI) dashboards. However, there is a lot more to business analytics than BI dashboards and even prescriptive analytics. Indeed, there is a full range of business analytics tools available for decision-makers. Below, I’ll share with you the different types of business analytics and how these analytics types work together to support superior business decision-making. First, below is a listing of the major types of analytics and the questions they answer for decision-makers.

Business Analytics Types

Also, business analytics types do not need to work in isolation. Especially, with high-speed computer networks coupled with advanced analytics such as AI, they can work together. Think of it as an Analytics Continuum (see chart), where each analytics type empowers the other starting with descriptive and ending with prescriptive analytics. For example with a potential supply chain disruption, descriptive analytics spots the initial signs, diagnostic analytics identifies the root causes, predictive analytics forecasts the impact, and prescriptive analytics composes the response.

For more detailed discussion of different analytics types to include emerging advanced analytics such as Real Time, On-Demand Analytics and AI-Powered Analytics, see my article. A Data Analytics Perspective To Better Empower Supply Chain Managers. Also, for more on The Analytics Continuum, see my article, Exploit The Business Analytics Continuum.

Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results

“there is a lot more to business analytics than BI dashboards and even prescriptive analytics. Indeed, there is a full range of business analytics tools available … and … these analytics types work together to support superior business decision-making.”

5. Predictive vs. Prescriptive Analytics: The Difference and How They Work Together.

Beyond the Forecast: Forward-Thinking Analytics for Supply Chain Leaders

As predictive and prescriptive analytics are both forward looking, I think it is important to better understand the differences between them, and also, how they work together. To illustrate, think of these two types of analytics as two different GPS systems for your supply chain. One tells you there is likely to be heavy traffic on the road ahead (predictive), while the other provides you recommendations on how to take an alternate route (prescriptive). While both these forward-looking analytics sound similar, you can see that they serve distinctly different purposes. 

Also, historically, these forward-looking disciplines have tended to work in isolation, leaving decision-making sluggish and disconnected. This is not something we can afford today. Fortunately, AI and advanced computing power are finally breaking down these walls, allowing both predictive and prescriptive models to not only be future-looking, but future-thinking. Now, these analytics can frictionlessly work together, enabling rapid, informed decisions. For more on how these forward-thinking models can seamlessly predict outcomes and prescribe immediate action, see my article, Beyond the Forecast: Forward-Thinking Analytics for Supply Chain Leaders.

” … think of these two types of analytics as two different GPS systems … One tells you there is likely to be heavy traffic on the road ahead (predictive), while the other provides you recommendations on how to take an alternate route (prescriptive).”

More References on Prescriptive Analytics.

• Prescriptive Analytics FAQs: Gurobi Optimization’s article, FAQs Prescriptive Analytics.
• Prescriptive Analytics in Supply Chains: ThroughPut Inc’s article, Prescriptive Analytics for Supply Chain Optimization: 6 Powerful Use Cases.
• Use of Prescriptive Analytics Within a Decision System: An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes.
• Supply Chain Analytics Types: A Data Analytics Perspective To Better Empower Supply Chain Managers. 

Supply Chain Analytics Types and The Way They Work To Better Empower Decision-Making

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Data Analysis and Decision Science.