Prescriptive analytics help businesses identify the best course of action, so they achieve organizational goals like cost reduction, customer satisfaction, profitability etc. While figuring out what you should do is a crucial aspect of any business, the value of prescriptive analytics is often missed. There is still an inclination to “go with the gut” when looking at an array of possible scenarios.
Read on to understand what prescriptive analytics is, how it relates to predictive analytics, and why they are critical to businesses today.
What is prescriptive analytics?
As business decision-makers deal with the critical question of “what action should we take”, they are often grappling with millions of decision variables, constraints, and trade-offs. Prescriptive analytics solutions like IBM Decision Optimization enable accurate decision-making for complex problems by providing tools for building and deploying optimization models that are mathematical representations of business problems. Powerful optimization solvers then solve these models using sophisticated algorithms and deliver recommendations to decision-makers.
The result? You can get guidance on the actions you should take to meet objectives, such as achieving cost reduction, customer satisfaction, profitability and operational efficiency.
Predictive and prescriptive go together like birds of a feather
Before “what should we do?” there’s often “what could happen?” That’s where predictive analytics comes in. Predictive analytics use advanced algorithms and machine learning to process historical data, “learning” what has happened while uncovering unseen data patterns, interactions and relationships. Then it creates models that show the likelihood of scenarios or outcomes.
You might think that a business could get by with just using predictive analytics and that prescriptive analytics is a “nice to have” add-on. However, that way of thinking misses the mark. An Economist Intelligence Unit report says that 70 percent of business executives rate data science and analytics projects as very important. But only 2 percent say that these same projects have delivered on their promise.
Why? Predictive models delivered by machine learning provide “actionable insights,” but they don’t say what actions you should take based on those insights for the best outcomes. In many cases, a biased human goes with “their gut.” The results are usually not optimal at best and disappointing at worst. To truly benefit from predictive analytics, it’s critical to invest in prescriptive analytics.
Here are some examples that shed some light on the value of adding prescriptive analytics to your predictive capabilities. If you are in the manufacturing sector, predictive analytics can give you an estimate of how much time it will take employees and tools to do maintenance. After using prescriptive analytics, you’ll know how much overtime is necessary so you can generate detailed schedules.
In retail, predictive analytics can forecast a demand surge caused by external circumstances. Prescriptive analytics can help build replenishment plans to decide which warehouse should supply to each retail store to adequately meet the demand.
And finally, if your business is an airline or another part of the travel and transportation industry, you can take demand forecasts and use prescriptive analytics to build out optimal fleet plans and crew schedules.
Why your business needs decision optimization and prescriptive analytics
According to an INFORMS press release, finalists for the Edelman award for Achievements in Operations Research and Management Science have achieved wide-ranging benefits by using decision optimization techniques to deliver prescriptive analytics capabilities. Benefits include millions of dollars in direct savings, better customer service and lower inventory. Prescriptive analytics takes business decision-making to the next level. You have the tools to predict likely scenarios and integrate these insights into the prescriptive engine so that decisions are dynamically optimized with a forward-looking view.
IBM customer Fleetpride is a real-life example of a business deriving value from prescriptive analytics. Fleetpride sells parts and provides services for heavy-duty trucks and trailers. They built a model that uses historical shipping data to predict the shipping orders per warehouse by day, week and month. They apply decision optimization to the model to determine the optimal action for dealing with customer demand on any given day, including staffing and inventory placement.
As Fleetpride demonstrates, prescriptive analytics enables you to transform data and predictive solutions into real, fact-based, unbiased courses of action. You no longer have to rely on intuition. Instead, advanced analytics, statistical modeling and a decision engine can solve complex your business planning, scheduling, pricing and inventory challenges, along with a host of other problems that are beyond the capabilities of spreadsheets and the human mind. As a result, your business can be poised for greater success and competitive advantage.