Why do you still have the headache of resource allocation?
Retailers and bulk transporters have a peculiar set-up in terms of transportation, which are Full Truck Loads with few deliveries that take short times. An interesting concept in this process is resource allocation for these businesses. Normally you create, with a basic optimizer, lots of small FTL trips that do one to four deliveries in one to four hours and then come back to pick up the next load/trailer. When you have a process like this, the difficult part is allocating the right resources to the right trips, because of legislation, wear and, of course, drops (where you have to plan the coupling and uncoupling of trailer and trucks).
For businesses with short FTL trips (of one to four hours), the original way of working was putting the highest priority stores/deliveries in a region into the truck and driving. Then, when the same truck was done delivering, it would come back and you would repeat the process. This process faced some difficulties, such as having two very similar trips taking very different amounts of time, because you weren’t really planning on when the truck would be back, and one of the two drivers went to visit his mother on the way because there was time. So, because you weren’t counting on the truck being back, you didn’t tell him off for visiting his mom and you hired another one to do the next deliveries. This lack of planning in the resource allocation creates a lot of unnecessary extra costs for the operation. For that reason, the concept of master routes was created, which means fixing the planned routes of all trucks, so every day of the week, each resource is allocated to a set of trips that were pre-determined.
Master routes and expected volume
Currently, these master routes are being predicted or at least planned based on expected volume. Because the greatest flaw of master routes is that volumes change and for certain businesses lots of customers are being added to the routes every day. This creates overflowing/infeasible trips which leads to bad service. Predicting or using expected volume allows us to have less no-fits, however, only daily dynamic routing can really eliminate this no-fit situation. The master routes also help a lot, because they allow us to make the routes more predictable. Therefore, we can contract the correct number of trucks for each season, week and day, drastically reducing costs.
Why do you need an optimizer to allocate your trucks, drivers and trailers, after having created your trips? This is the main question, and when we have five trucks, five drivers and maybe ten trailers a day, we don’t really need it. When we get to larger numbers, though, this is no longer a trivial question, because drivers have driver’s legislation and trucks and trailers have planned maintenance.
So now you have resource combinations of three resources that need to execute maybe 100 milk-runs or back and forth FTLs and they aren’t always the same. This is where the optimizer comes in, making sure that not only the optimized trips, but also the restrictions of resources are respected. Perfectly allocating resources to your trips allows you to use the exact number of resources you actually need.
Resource allocation with predictive algorithms
Looking forward, the tactical process of creating master routes based on expected volume and the operational process of dynamic routing of real volume will come closer and closer together. There are already companies out there that have dynamic routes and with resources allocated 10 or even 20 days in advance. In the current scenario, once it is getting closer to the date of execution, we have less uncertainty, but if the predictive algorithm gets better, this uncertainty reduces too. So we will know the exact volumes many days in advance and we can plan and negotiate the best rates with transporters and give the best level of service to our customers.
Resource allocation in logistics is a complex process and involves various different restrictions and objectives. It is necessary for businesses that have lots of smaller trips of between 1 and 4 hours and usually work in a 24/7 setting. When in this business, the optimizer is fundamental, allowing you to make maximum use of your resources and leading to a real cost reduction. In the future, when predictive algorithms get better, this will become easier to do more and more days in advance than the current industry standard of d+1, where the planning happens one day before delivery, allowing an even greater level of service for your customers and lower cost-levels for you.
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