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 use real examples to illustrate the key distinctions between predictive and prescriptive approaches. By the end of this article, you’ll understand exactly how prescriptive analytics helps supply chain leaders make better 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, when appropriate. 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?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.”

Gartner

b. An Example of How Prescriptive Analytics Process Works.

Indeed, 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.

a. Expert System Vs Prescriptive Analytics. 

First, let’s start with a definition of an Expert System.

“… is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries.”

JavaPoint

Essentially, an Expert System replicates human expertise using deterministic, knowledge-based rules to provide answers. Also, an Expert System is classified as Artificial Intelligence (AI) as ii replicates human cognitive capabilities. Moreover, rules-based Expert Systems are unlike AI Large Language Models (LLM) which use statistical pattern recognition and predictive analytics to generate responses. Additionally, AI Expert Systems are normally designed for one-on-one human-machine interfaces. On the other hand, Prescriptive Analytics goes much further than Expert Systems. It not only advises a course of action but also explains the reasoning behind it and details how to implement the recommended solution.

b. Recommendation Engine Vs Prescriptive Analytics.

Below is a definition of a Recommendation Engine.

“… is a system that gives customers recommendations based on their behavior patterns and similarities to people who might have shared preferences. These systems, also known as recommenders, use statistical modeling, machine learning, and behavioral and predictive analytics algorithms to personalize the web experience.”

TechTarget

Most of us are familiar with recommendation engines from platforms like Netflix, Amazon, or YouTube, which are narrowly focused on suggesting products or personalizing content. On the other hand, Prescriptive Analytics supports a full spectrum of organizational decision-making scenarios, going beyond mere recommendations to explain why a particular course of action is best and detailing how to implement it. For more details on Recommendation Engines, 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.

a. Primary Types of Business Analytics and The Question They Answer

Specifically, the major types of analytics include descriptive, diagnostics, predictive, and prescriptive. Further, each of these types of analytics focus on answering a specific question as listed below.

A Data Analytics Perspective To Better Empower Supply Chain Managers

• Descriptive Analytics: What Happened?
• Diagnostic Data Analytics: Why Did This Happen?
• Predictive Data Analytics: What Is Most Likely To Happen?
• Prescriptive Data Analytics: What Action Should We Take?

Also, each of these types of analytics can employ a full range of analytical tools and methodologies to help answer the questions they are designed to answer. At the same time, decision-makers do not necessarily need to use all these types of analytics. It depends on the situation. For instance, a decision-makers or business does not always need the full power of prescriptive analytics. At other times, decision-makers need a full range of analyzes to include descriptive, diagnostic, predictive, and prescriptive. In fact, in some cases a decision-maker will not only need the full range of analytics, but they will need each analytics type to mutually support each other. 

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.

b. The Analytics Continuum: How Different Analytics Types Work Together for Better Decision-Making.

A useful chart for understanding how prescriptive analytics fits within other forms of analytics is the Gartner’s Analytics Continuum depicted below. This chart shows how each form of analytics supports both business decision-making and implementation as well as how they can mutually support each other, tied together by a common goal.

So, Prescriptive Analytics has unlimited potential to empower both business analytics and decision-making. At its best, imagine it as the conductor of an orchestra where descriptive, diagnostic, and predictive analytics are the musicians. For example of how the Analytics Continuum works, let’s take a potential supply chain disruption. In this case, descriptive analytics spots the initial signs, diagnostic analytics identifies the root causes, predictive analytics forecasts the impact, and prescriptive analytics composes the response. For instance, when a port delay threatens operations, the system doesn’t just predict delivery delays. Indeed, it automatically recalculates optimal inventory levels, suggests alternative ports, estimates cost implications of each option, and even initiates communications with affected customers and suppliers.

For a more detailed explanation of the Analytics Continuum, see my article, Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results.

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. Examples to Clarify the Differences Between Forward Looking Analytics: Predictive Vs Prescriptive.

Predictive Analytics Types: The Best Opportunities For Supply Chains

As predictive and prescriptive analytics are forward looking, I think it is important to better understand the differences between them. 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-thinking analytics sound similar, you can see that they serve distinctly different purposes. 

To further clarify the differences between these forward-looking analytics, Predictive and Prescriptive, let’s look at some specific examples.

Examples of Predictive and Prescriptive Analytics Working Together

a. Supply Chain Management Example.

In this example, forward-looking analytics, predictive and prescriptive, will do the following:

• Prediction: Machine learning predicts potential supply chain issues with specific suppliers in a region.
• Prescriptive: Mathematical optimization determines the least costly way to reduce shipments, considering various constraints from the supplier. This combination allows for proactive and cost-effective management of supply chain disruptions.

b. Investment Strategy Example.

Below are examples of what Predictive Analytics and Prescriptive Analytics will do in this scenario:

• Prediction: Machine learning predicts investment opportunities.
• Prescriptive: Mathematical optimization allocates limited cash across investments, considering constraints on investment amounts. This combination ensures optimal investment strategies.

c. Customer Engagement Example.

Lastly, in this example, forward-looking analytical types could do the following:

• Prediction: Machine learning predicts customers’ propensity to buy.
• Prescriptive: Mathematical optimization decides how many coupons to offer to maximize revenue or profit. This synergy allows for targeted and effective customer engagement.

So, to summarize how forward-analytics works, one provides a prediction and the other one provides a decision (recommendation) with an action plan. For on how Prescriptive and Predictive analytics can work together, see Gurobi Optimization’s article, How Can Prescriptive and Predictive Analytics Work Together?

” … 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.

• Gurobi Optimization’s link, FAQs Prescriptive Analytics, provides a comprehensive overview of prescriptive analytics.
• ThroughPut Inc’s article, Prescriptive Analytics for Supply Chain Optimization: 6 Powerful Use Cases, for more on prescriptive analytics and supply chains.
• Build In’s article, Top 14 Predictive Analytics Tools to Know, provides an overview to different software vendors that offer predictive analytics tools.
• For an example of how analytics works within a business decision-making framework, see my article, OODA – Enabling Business Agility: The Best Way To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.
• For more discussion on different types of data analytics, see my article, A Data Analytics Perspective To Better Empower Supply Chain Managers. 
• This article, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes,  illustrates how prescriptive analytics would work within a decision intelligence platform.

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.

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