Easy to use tools with powerful engines for complex decision making.

Inventory optimization

At P2, we have developed software to empower optimal strategic and tactical inventory decisions for your business. From the data you already collect, our technology will provide optimal inventory allocations relative to the potentially millions of micro-decisions involved in managing many SKUs across many locations with fluctuating, seasonal demand patterns.
The software counts on a combination of machine learning and advanced mathematical optimization in order to predict the evolution of future demand and determine the optimal quantities of each product to base at each distinct location, considering the combination of the cost of inventory, labor costs and shipping costs. The benefit of utilizing P2’s software is conservatively estimated to reduce total cost by 10%.
The tool allows companies to:
  • Drastically reduce inventory cost and associated labor and shipping costs
  • React proactively and efficiently to changes in their business landscape, such as supply chain disruptions, cost structure changes, or new product introductions

Custom software tools

P2 builds end-to-end, custom data-driven decision-making tools for companies, leveraging custom predictive and prescriptive analytics algorithms. These tools are designed to have large organizational impact by targeting complex, high-value, and often, recurring decisions, for which a non-algorithmic approach is often either inefficient or even infeasible.
These tools can incorporate robust optimization techniques to ensure that solutions are optimal in uncertain real-world environments. In this way, the decisions made today are close to optimal if the evolution of events is close to that expected, and, equally important, they remain good decisions even if unlikely scenarios occur.
Examples of some recent applications include:
  • Throughput maximization in queueing systems
  • Risk management for minining company considering commodity price uncertainty
  • Prediction of machine and equipment failure and planning of preventative maintenance
  • Maximization of quality of end products of manufacturing
  • Optimal resource deployment