The Real-World Applications and Business Benefits for Prescriptive Analytics

You’ve undoubtedly heard the buzz about prescriptive analytics. Smart people have probably told you that your company needs to be using such capabilities. In this blog post, we look at real-world applications and, more importantly, how such capabilities can make your company more efficient and more profitable.

In brief, prescriptive analytics uses algorithms to create a flexible performance model of your business that can provide managers insights into the best decision to make in a given situation. It has the benefit of fully respecting the constraints and objectives of the business while also reporting the financial impact.

The dashboard chart below shows the types of business analytics and their role in planning and decision-making processes.

A Framework for Understanding the Value of Differing Types of Analytics From Gartner

Gartner Analytic Value Escalator chart


Prescriptive analytics is not new. Manufacturers have been leveraging such tools to model and optimize functional processes for years. The algorithms (typically linear programming) help solve complex problems that have competing objectives and hundreds to thousands of constraints. Some of the questions that manufacturing managers can use prescriptive analytics to help answer include:

  • What promotions should we run on what products to optimize the profitability of sales, rather than simply driving revenue?
  • What orders should we prioritize to maintain service levels and optimize profits? How should we balance service levels with production constraints?
  • Where should we manufacture which products? How can we best balance the tradeoffs between manufacturing, inventory, distribution and customer service levels?
  • When should we reduce SKUs or introduce new products?

As analytical tools have evolved, the applications for prescriptive analytics have grown for a number of reasons:

1. Availability of more, higher quality data. Cleaner, more diverse and more easily integrated data is now available across business functions, enabling the entire value chain to be optimized.

2. Easier to use. Simpler interfaces make prescriptive analytics less of a “black box” solution that requires specialized knowledge to use. Managers can understand the “why” and “how” of optimisation runs with simple, clean interfaces.

3. Greater sophistication. New decision-support tools can model highly complex problems with hundreds to millions of constraints and variables. Further, it’s easy to adjust assumptions and objectives for rapid what-if and scenario analysis.

4. Expanded focus on the business as a whole. Optimisation models can now address larger enterprise-wide challenges and tradeoffs, including the financial impact of potential decisions.

Prescriptive Analytics Is Now in the Hands of Business Unit Leaders – Not Just Data Scientists.

Expanding on these points, any effective decision-support tool will be more beneficial if it’s understood and used by people at all levels of the organization. At the end of the day what matters most to business leaders is financial performance. Prescriptive analytics models can now incorporate contribution margins, activity-based costing, and pro-forma financial statements to help leaders make the best possible business decisions.

For example, consider a North American consumer packaged goods manufacturer.* The company is relatively integrated and develops, manufactures and distributes most of the products it sells. It has built a number of prescriptive analytics models to help executives and managers make better financial and operational decisions.

Examples of the analytics models are outlined below. Some of the business benefits include higher throughput, significant cost reductions and risk mitigation. The models enable managers to respond quickly to unforeseen circumstances with an optimized and feasible plan rather than making gut-feel decisions.

Analytics Models examples


Those two attributes (optimisation and feasibility) are what has been missing from other decision-support tools that prescriptive analytics can provide. Optimized means that the plan maximizes profit across the company. Feasible means that it can readily be implemented. Both are necessary to identify and realise the potential of any recommended course of action.

Hopefully this brief overview has helped explain what prescriptive analytics is, why potential applications for manufacturers are expanding, and the challenges that these solutions can help solve. In our next blog post on this topic we’re going to look at why so many capital investments and process improvements don’t hit the bottom line, and how prescriptive analytics can make sure they do.

We worked with River Logic, a new solutions partner for TBM Consulting Group to develop this blog post. We like River Logic's easy-to-use interface and blend of operations and financial insights to help our clients make better and faster decisions.