Prescriptive analytics assists firms in determining the optimal course of action in order to meet organisational objectives such as cost reduction, customer happiness, and profitability. While determining what to do is an important part of any business, the significance of prescriptive analytics is frequently overlooked. When faced with a variety of options, there is still a tendency to “trust your instinct.”

When faced with the essential question of “what action should we take?” Business decision-makers are frequently confronted with millions of decision factors, limitations, and trade-offs.

By providing tools for constructing and deploying optimization models, which are mathematical representations of business challenges, prescriptive analytics solutions enable accurate decision-making for complicated problems. The models are subsequently solved using advanced algorithms by powerful optimization solvers, which finally provide suggestions to decision-makers.

“What could happen?” is frequently asked before “what should we do?” Predictive analytics can help with this. Advanced algorithms and machine learning are used in predictive analytics to process historical data, “learning” what has happened while revealing previously undiscovered data patterns, interactions, and linkages. The system then generates models that depict the likelihood of various situations or outcomes.

You could believe that a company can get by with just predictive analytics and that prescriptive analytics is merely a “nice to have” feature. That method of thinking, on the other hand, is off the point. According to research by the Economist Intelligence Unit, 70% of company leaders consider data science and analytics projects to be highly significant. However, only 2% of respondents believe these projects have delivered on their promises.

Why? Machine learning-based predictive models generate “actionable insights,” but they don’t specify what actions you should take based on those insights to achieve the best results. In many circumstances, a biased individual follows their “gut instinct.” At best, the outcomes are unsatisfactory, and at worst, they are disastrous. It’s vital to invest in prescriptive analytics if you want to get the most out of predictive analytics.

Here are some examples of how integrating prescriptive analytics to your predictive capabilities might benefit you. Predictive analytics can offer you an estimate of how long it will take personnel and tools to perform maintenance in the manufacturing industry. You’ll be able to build precise timetables after applying prescriptive analytics to determine how much overtime is required. Predictive analytics in retail can foresee an increase in demand due to external factors. Prescriptive analytics can aid in the creation of replenishment plans by determining which warehouse should supply each retail shop to satisfy demand.

Using decision optimization approaches to give prescriptive analytics capabilities has resulted in a wide range of benefits. Millions of dollars in immediate savings, improved customer service, and lower inventory are just a few of the advantages. Prescriptive analytics raises the bar for commercial decision-making. You have the capabilities to predict likely scenarios and integrate these insights into the prescriptive engine, allowing for dynamically optimised decisions with a forward-looking perspective.