9 IT projects primed for machine learning

5. Logistics route planning

The travelling salesman problem is a computer science classic: What’s the shortest route between all the places your sales team needs to go on a round trip? Whether it’s getting salespeople to prospects, deliveries to customers or picking the business location that will attract the most customers, routing and travel planning has a big impact on your business. You can use the predictive traffic services in the Bing and Google Maps APIs to create isochrone maps that show you not just distance but travel time, to compare how many customers an engineer could reach in a 15-minute drive from various starting points, or find the best time of day to make deliveries. (Use the preview Bing Maps Truck Routing API to get routings for commercial and service vehicles that are larger than the average car.)

Add in asset tracking and location triggers and you can create your own logistics solution. Or you can make shipping more profitable by quoting rates that accurately reflect your costs, rather than losing margin by underpricing or losing business by quoting too high. Business communications giant R.R. Donnelley used R and Azure Machine Learning Studio to lower the cautious estimates that kept it from winning freight bid by combining historical data with variables like the weather, fuel costs and market conditions to develop a better pricing model. The automated system that generates real-time quotes for a given route is more accurate; the company is already winning 4% more of its bids and expects to quadruple the size of its truckload brokerage business. The same kind of predictive analytics would be useful for any contract bids where you have enough data to build a good model.

6. IoT predictive maintenance

If you wait until machinery breaks to fix it, you have downtime and unhappy customers; if you take systems offline to do maintenance too often, you reduce your production yields. When ThyssenKrup started analyzing the maintenance records from the 1.1 million elevators it installs and services, it discovered that the maintenance window could be quite a bit longer than it was. When the company used Microsoft’s Azure IoT Suite to remotely monitor sensors, predict failures and pre-emptively service equipment, it didn’t just increase customer satisfaction by fixing problems before they caused a breakdown; they reduced costs by fixing more issues on the first visit, and by being able to predict better what spare parts they needed to carry in inventory. Do the same thing with a manufacturing line and you can improve production yields. According to Accenture’s 2016 report on industrial IoT, predictive maintenance could reduce the cost of scheduled repairs by 12%, bring down maintenance costs by 30% and reduce breakdowns by up to 70%.