A few weeks ago, I wrote a posting about how fleet routing applications could be improved by using “real-world” traffic data, particularly data on the true average speed per hour of vehicles traveling on specific road sections at different times of the day and week. GPS navigation device vendors like TomTom have this data, which routing solutions can now use to create better routes.
Tim Pigden, Managing Director at UK-based routing software vendor Optrak, responded to my posting and mentioned other possibilities. His point is that if you analyze how vehicles operating in a multi-stop environment actually spend their time, you will often find that there is greater variability associated with vehicle load and unload times than with road speeds. “And while most people have a reasonable idea of road speeds and can [make] a reasonable approximation, even if not as accurate as the example you give, they can be way off in their estimates of loading or unloading times.”
Again, GPS-enabled devices could provide this type of data. TomTom Work, for example, can track four events: when a truck arrives at a customer location; when unloading has started; when unloading has ended; and when the truck departs. The middle two events–when unloading starts and ends–depend on the driver clicking the right icon on their device. Thus, those two events could be subject to driver exaggeration. However, if telematic sensor data is available–e.g., a sensor on the backdoor of the truck that indicates when the door is opened and closed–those events could be tracked more accurately.
The point of having data on those events is to model the unloading process. The routing software will work better if its model includes customer orders, order lines, products, and product packing hierarchies (pallets, roll cages, etc.). This would allow the routing application to understand that at this customer site the driver is unloading three roll cages, at this other site the driver is just taking the top two layers off a pallet located in the first bay, and so forth.
This kind of solution will be more difficult to implement in certain industries. For example, in a Direct Store Delivery scenario, a driver often needs to get checked in by a store manager and they may have to wait to get their attention. This wait time can vary significantly. In contrast, fuel delivered to gas stations is often unsupervised and the driver does his work late at night when the station is closed.
Optrak has not yet implemented such a solution. The company is anxious to work with a customer where this degree of granularity would generate ROI, which probably means a customer with a large fleet and greater variability around the types of trucks, quantity of goods delivered, and the types of conveyances it uses.
Analysts can’t help but think of the future and imagine what is coming around the corner. But there are plenty of low hanging fruit opportunities available right now. If a company has a fleet of more than 20 trucks, existing routing and mobile resource management (MRM) solutions will provide a very good payback. These next generation products may prove most useful to companies with large fleets.
