Prescriptive analytics

Prescriptive analytics is the final phase of the retail analytics. It goes far beyond the forecasts made by predictive analytics models to prescribe the best course of action to maximize the company’s ROI. This type of retail analytics can anticipate changes in demand, consumer sentiment, and supply shocks so that the retailers can make necessary adjustments. For instance, it can suggest retailers the appropriate quantity of a particular product to stock and its selling price.

What is Prescriptive Analytics?

Prescriptive analytics allows you to realistically represent your business or business function—including millions of variables, constraints, and key objectives—and explore key trade-offs in order to determine the best path forward and optimize business performance.

Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics.^[1][2]
Referred to as the "final frontier of analytic capabilities,"^[3] prescriptive analytics entails the application of mathematical and computational sciences and suggests decision options to take advantage of the results of descriptive and predictive analytics. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today.^[4] Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis.


Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation.
Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.

Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation.

Based on prior experiences, the goal of prescriptive analytics is to enable:

• quality improvements;
• service enhancements;
• cost reductions; and
• productivity increases

Unfortunately, the IIoT status quo seems to stop well short of this.

Without a clear path to delivering valuable outcomes, current analytics deliver insufficient value.

“The bottomline: Enterprises must stop wasting time and money on unactionable analytics. These efforts don’t matter if the resulting analytics don’t lead to better insights and decisions that are specifically linked to measurable business outcomes.”
- What Exactly The Heck Are Prescriptive Analytics? by Mike Gualtieri, Vice President, Principal Analyst, Forrester

Don’t Stop With Knowing

Predictive analytics is a popular approach for machine and equipment maintenance. It focuses on improving maintenance schedules based on operational and environmental attributes.

Predictive maintenance (PdM) techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

Predictive analytics provide important notifications. It can provide visibility to information that was not previously available.

But, it provides limited value without a direct tie to a specific outcome.

These notifications also create more work. They trigger a series of phone calls, emails, and other “out-of-band” activities. This contributes to increased downtime and costs.

Knowing that something is about to break (or broken) does not ensure a time or cost effective outcome.

Don’t Get Confused By Other Terms

Industry pundits, analyst groups, and others have their own lexicons. But all with similar definitions. This adds to the confusion.

• Prognostics
• Condition-Based Maintenance (Usage-Based Maintenance)
• Asset Performance Maintenance (APM)
• Reliability Centered Maintenance (RCM)

Whatever you call it. Predictive analytics leaves a huge gap between “knowing” and “doing”.

Enter Prescriptive Analytics

Prescriptive analytics goes beyond knowing.

Prescriptive analytics provides recommended actions based on prior outcomes. A recommended course of action to achieve a specific outcome.

The hierarchy of business analytics looks something like this:

1. Descriptive Analytics = asset, operation, environmental and diagnostic information
2. Diagnostic Analytics = identifies patterns of behavior (importance and urgency)
3. Predictive Analytics = suggests a timeframe for an action
4. Prescriptive Analytics = recommends specific actions

Below is a simple graphic of analytic type’s relative value:

Others have called this prescriptive approach Cased-Based Reasoning.

Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.

Both prescriptive analytics and CBR have the same goal. They provide specific recommendations based on prior experiences and outcomes.

A Great Step Forward

Prescriptive analytics is a critical advancement in analytics. It can improve decision making and processes effectiveness. It helps us get closer to tying outcomes to specific situations.

While it’s not Nirvana for IIoT, it is clearly a step in the right direction.