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.
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
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.
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.
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