How Can Prescriptive and Predictive Analytics Work Together?

Predictive analytics identifies patterns in data to forecast future events. It can predict cyberattacks, imminent machine failures, supply chain disruptions, and price fluctuations—all before they occur.

Prescriptive analytics, on the other hand, can guide decisions based on real-world objectives and constraints. It’s particularly valuable when dealing with conflicting goals and multiple limitations, such as budget constraints, time, and product availability.

Together, however, these two technologies create a synergy that transcends traditional boundaries, enabling businesses to predict challenges and make informed decisions.

Examples of Predictive and Prescriptive Analytics Working Together

In Supply Chain Management

• Prediction: Machine learning predicts potential supply chain issues with specific suppliers in a region.
• Decision: Mathematical optimization determines the least costly way to reduce shipments, considering various constraints from the supplier. This combination allows for proactive and cost-effective management of supply chain disruptions.

In Cybersecurity

• Prediction: Machine learning predicts who will launch a cyberattack before it happens.
• Decision: Mathematical optimization assigns investigators based on the threat level and other factors. This collaboration ensures that the right resources are allocated to deal with potential threats.

In Life Sciences

• Prediction: Machine learning predicts which experiments are likely to succeed.
• Decision: Mathematical optimization considers constraints like cost and researcher availability to decide which experiments to perform. This synergy ensures that resources are allocated efficiently to the most promising research.

In Internet of Things (IoT)

• Prediction: Machine learning predicts potential asset failures.
• Decision: Mathematical optimization determines the best time to shut down a line, considering factors like cost and customer shipments. This collaboration minimizes disruption and financial impact.

In Investment Strategy

• Prediction: Machine learning predicts investment opportunities.
• Decision: Mathematical optimization allocates limited cash across investments, considering constraints on investment amounts. This combination ensures optimal investment strategies.

In Customer Engagement

• Prediction: Machine learning predicts customers’ propensity to buy.
• Decision: Mathematical optimization decides how many coupons to offer to maximize revenue or profit. This synergy allows for targeted and effective customer engagement.

By uniting the predictive capabilities of machine learning with the decision-making power of mathematical optimization, your business can create solutions that are not only intelligent but also optimize decisions in the most intelligent way.