What is Prescriptive Analytics?


Prescriptive analytics is the process of analyzing a situation using data, and recommending the best courses of action to achieve the best outcomes.

Prescriptive analytics makes recommendations based on available data and a given objective. For example, if a truck with a shipment bound for a store breaks down, predictive analytics can inform you of the downstream effects on inventory levels at the store and on possible lost sales. Prescriptive analytics would tell you how best to respond, whether sending another truckload from the DC, transferring inventory from another nearby store, or telling you that the impact is minimal and that you shouldn’t do anything.

One Network's Perspective on Prescriptive Analytics

NEO, One Network’s intelligent agent technology, provides users with prescriptive analytics, intelligent recommendations, and helps them navigate the complex trade-offs inherent in running supply chains.

Your prescriptive analytics are only as good as the data that the algorithms consume. If you are limited to your own enterprise’s data, your visibility to problems, and your options are limited and suboptimal.

Because NEO agents run on the network and have access to trading partner data, they can analyze plans and execution data, to guide users to optimal resolutions. Problems can be identified much earlier upstream and downstream, and they can be solved in many more ways. The wider the domain of data is drawn, the more options there are, and usually the cheaper it is to fix a problem.

For example: A potential stock out can be solved or dealt with in a number of ways:

  • Expediting a shipment
  • Adding a shift at a plant
  • Rerouting supply in transit
  • Shipping from an alternate supply source
  • Allowing the stock out if the impact is low

One Network’s NEO helps by analyzing a vast array of factors relevant to each option, and then prioritizing the options based on the cost and benefits of each.

Within the user interface, users can click on an option to have NEO execute the resolution, or they can click to explore the problem and suggested solution in more detail, and collaborate with other trading partners within the app to decide among themselves which course of action to take.

Because NEO runs on the network, and the network platform supports both planning and execution, NEO can autonomously resolve many of the problems it identifies, such as by triggering new orders when inventory levels run low, rescheduling dock doors for late trucks, etc.

Prescriptive Analytics Resources

AI and Machine Learning in the Supply Chain

AI and Supply Chain Problem Solving

What does it take to effectively address challenges in the supply chain? What’s really required is real-time “always on” planning and execution capabilities that eliminate the information lead times between unplanned shifts in consumer demand or supply capability.


Webinar: 8 Keys to Achieving Success with AI in the Supply Chain

The success of AI in supply chains is heavily dependent on information accuracy, data timeliness, and selection of the correct decision space for AI applications. In this webinar, Joe Bellini explains the basics of AI that supply chain pros need to know, and how they can get the most from their AI implementations.


8 Keys to Success with Artificial Intelligence in the Supply Chain

Artificial Intelligence (AI) can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains. This report identifies the eight fundamentals that need to be in place in order to achieve the dramatic returns that AI is capable of delivering.