Examples of Prescriptive Analytics

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What is Prescriptive Analytics and why all the buzz? Take away all the hype, and you find leaders struggle to grasp the definition. This video will help you understand everything you know to know. • *Are you ready for prescriptive analytics? Watch this video to find out: • Are You Ready for Prescriptive Analyt... * • **View more videos here: https://www.youtube.com/channel/UCMJG... • *Request a demo of our software here: https://download.riverlogic.com/reque... • So...What Is Prescriptive Analytics? • Gartner defines prescriptive analytics as “the application of logic and mathematics to data to specify a preferred course of action. While all types of analytics ultimately support better decision making, prescriptive analytics outputs a decision rather than a report, statistic, probability or estimate of future outcomes.” • There are two primary approaches to prescriptive analytics. • The first approach leverages optimization techniques, like linear programming. • The second approach leverages computational logic — like predefined business rules — for decision making. • Let’s first discuss the differences between the two approaches to prescriptive analytics. • Optimization runs through all possible options to achieve the best answer. It respects every constraint and objective outlined by the business. • A rules-based approach to prescriptive analytics takes pre-existing business knowledge to make sure any decision respects those embedded rules. It cannot consider solutions outside of the pre-defined options. • Thus, a rules-based approach to prescriptive analytics provides significantly fewer outcome options. However, it is sufficient if the decision is a simple one, with only a few variables and constraints. For most decision-making challenges, however, rules are not sufficient. Especially given the ever increasing complexity of businesses. • For complex planning and decision-making challenges, optimization will always be the best approach. • Companies often use both approaches, depending on the decision-making challenge. • Enterprises that use optimization see enormous benefits ranging from increased service levels and revenue to better understanding risk and reducing costs. • While it used to only be applied in a single function, prescriptive analytics is now being used to support a much broader range of operational, tactical and strategic decisions. • Who Uses Prescriptive Analytics Today and Why? • Financial services are BIG users of prescriptive analytics. We’re all familiar with credit card and loan applications. When we input our information, a relatively simple set of rules determine whether we are approved and, if so, what our line of credit is. • Retail, consumer goods, chemicals, energy and mining are other industries that heavily rely on prescriptive technology. In these cases, it’s particularly applicable because these industries have highly complex processes, several moving parts, conflicting objectives, and constrained resources. • For example, when you’re talking about a manufacturing company with highly complex processes, prescriptive analytics can consider: • The network of production plants • Available resources • Product demand • Processing • Transportation modes like air, rail, and truck • Workforce efficiency • Financial constraints and objectives • And more. • Prescriptive analytics absorbs granular details across functions, and looks at thousands of scenarios in order to develop an actionable, optimized plan. • Prescriptive analytics optimizes within a business silo or across multiple silos. • Take the example of a snack food company that couldn’t keep up with demand for its most profitable product, small potato chip bags. • With dozens of plants across the U.S., the company made small chip bags at each plant and shipped to the nearest distribution center. • Prescriptive analytics helped the company examine all the variables and run scenarios like: • Do all small chip bags have to be made at each plant? • Can we centralize small chip bag production? • Will transportation costs increase? • Can we meet demand? • In this case, prescriptive analytics helped the company create a plan to consolidate production while keeping transportation costs down and meeting demand. • So what other benefits can be seen from prescriptive analytics? • Companies are able to find major cost savings, profit improvement opportunities, throughput rate improvements and risk reduction opportunities. Not only that, companies see major organizational enhancements: processes are streamlined, planning effectiveness and frequency improves, confidence in plans and decisions is boosted, and teams collaborate more frequently. • Prescriptive analytics rises above all other types of analytics by providing the best plan for your business...without fitting your business into a box.

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