From a predictive to a prescriptive solution ABC Trucks uses failure predictions as input data to a decision optimization model. This model considers  multiple alternatives for truck servicing simultaneously. It can also include service center capabilities (not  all service centers are able to deliver all types of services), capacities, availability of service parts,  distances, drive times, weekly trips, costs, customer contract agreements, priority customers, and other  data, all within the same model. The decision optimization model, which takes advantage of the advanced scheduling capabilities of IBM  Decision Optimization engines, provides a prescriptive solution that maximizes not only the truck up-time  of the entire fleet, but also overall revenue while respecting contractual agreements with the clients.  To provide the best decision for each specific truck, the decision optimization model must take into  account simultaneously all the ABC Trucks across all the clients who have opted for this service, and all  the ABC Trucks service stations with their current and already planned availability. This example covers using decision optimization for product quality inspections in a manufacturing  company. Tom is responsible for product quality inspections in a manufacturing company. Tom’s team of quality  inspectors makes daily checks on both the quality of the manufactured products and the material provided  by suppliers. Detection of defects is carried out by random sampling and followed by manual or laboratory  analysis. The quality team’s business goal is to detect as many defective products as possible before they are  delivered to customers or used as inputs in production lines. The VP for manufacturing and sales has ordered immediate action to reduce customer complaints due to  defective products, and a reduction in the number of production disturbances due to poor quality of  materials used in input. Although Tom’s team and the laboratory team have been working harder and doing overtime, the number  of detected defective products has not increased significantly. The rate of detected defects is too low.  Random sampling is clearly not enough. Historical data from the results of past product quality inspections on manufactured and supplied material  lots, together with data records (physical and chemical characteristics, where and when a product is  produced, the material’s supplier, and so on), make it possible to build a machine learning model. This model can predict the likelihood that a given lot has defective items. Using this information, Tom can  now assign quality inspections and tests on specific lots to his team and the lab daily depending on their  availability. With this predictive solution, the rate of detected defective products and materials is  significantly improved when compared with results obtained previously with random sampling. However, each morning’s list of suggested quality control checks is too long to complete by end of the  day. Therefore, Tom puts them in decreasing order of likelihood of presence of defective items and  assigns them to his team, starting from the top of the list. Example 2 – Product quality control The problem A first predictive analytics solution 5 IBM Decision Optimization and Data Science