Route Planning: Man vs. Machine

route planning man versus machine

Route Planning: Man vs. Machine

The general opinion in the logistics market is to push towards automated route planning systems. But is this always the best option? Is machine always better than man?

The benefits of automated route planning

The two main benefits of automated route planning systems are that they save time and also focus on a single parameter, such as cost or service.

Manual route planning is time-consuming, especially where large numbers of routes need to be planned and scheduled. Being able to automate your route planning allows the plan to be quickly adjusted to deal with unexpected changes to the schedule, such as unforeseen upsurges in order numbers, or sudden changes to the available resource.

Automated planning can be focused on particular goals that drive efficiency. Perhaps your objective might be to reduce costs by limiting mileage, or to provide a faster delivery service.

Once a route plan is live, it is also extremely easy to alter it, allowing for rapid adjustments to be made at the last minute. Such flexibility is just not so simple or speedy with manual planning.

The drawbacks of automated route planning

Although there are strong positives for automating your route planning, many people overlook the negatives of an automated system.

Automation comes with a degree of complexity. In order to plan complicated routes, the system needs to know all of your intricate requirements. As a result, the system needs to be configured accordingly to take into account various scenarios, such as:

  • Vehicle restriction at delivery points – perhaps you need specific vehicles at certain locations, such as one with a tail lift, or one that’s dock leveller compatible, or a particularly large or small vehicle to deal with the location
  • Driver training restrictions – perhaps certain deliveries can only be performed by a suitably trained driver
  • Perhaps the delivery type restricts the type of equipment used
  • Completing deliveries to allow physical space in the vehicle before making collections
  • Prioritising certain deliveries over others
  • Parking restrictions, booking in slots or complex opening times

On top of defining these rules and settings, every time a time window changes or a new restriction is added, the system needs to be reconfigured and updated. This data maintenance can often be a full time job, especially when you factor in the time needed to keep an address database in a format that is suitable for use by the system.

The lack of human interaction causes its own issues. Logistics operations have to deal with a variety of changing situations, and these cannot be indefinitely configured into a system. Also, automated routing systems like to work to a defined ruleset and therefore cannot “push” the tolerances on a plan when the volumes are high.

It’s difficult too for automated systems to understand trade-offs. Consider a situation where there are two deliveries in an area, but time constraints only allow for one to be completed. The routing system may assess which is the most cost effective delivery to make and leave the other unplanned, but a human may look at the situation from a variety of angles that just aren’t easy to programme into a piece of software. Perhaps one customer is more “important” in some way than the other? Or would one customer still be happy to have their delivery ten minutes late? Have we delivered late to one of these customers before? And so on.

So automated planning systems work to minimise a certain element – whether that’s miles or hours – but how do you quantify customer service? It’s very difficult to programme in good customer service as the priority, and – especially for small businesses – this is often how they differentiate themselves from larger competitors. So for these companies, manual planning is often better so that customer satisfaction can be assured.

It’s not unusual to see a plan that works on paper but just won’t work in real life. A human can often sense check a plan to pick up errors that might be missed by pure system logic. Likewise, a system will base its route only on the parameters provided. In real life, these parameters can often be pushed a bit harder, sometimes for a huge impact.

Most automated route planning systems will create a matrix table that plots time and distance between all delivery points on a map. The programme will then generate multiple different plans based on the constraints in the system, selecting the best one in terms of a specific goal, whether that be reduced mileage or reduced hours. The problem with this method of working is the huge burden on processing power to generate the huge number of different scenarios. The system will run through a set routine to try and find the optimum scenario as fast as possible, but a limit is often put on how long the system is allowed run. This means that a better solution may be available, but the computer has not been allowed to find it yet. The system demands for automation therefore require that expensive machines are needed.

Often costing £100k and more, these route-planning systems are expensive. The constant database management and necessary settings adjustments can also mean very expensive ongoing operating costs. That, combined with the headcount required to maintain both software and hardware, result in a level of efficiency that is often less than it would at first glance appear.

So what’s the solution?

Manual planning is too time consuming and lacks focus on cost. Automated planning has high overheads, can be inflexible with changes in planning requirements and is often very complex or requires a huge amount of data maintenance and configuration. So what’s the solution?

The best course of action for a small or medium-sized business that runs its own fleet is to utilise a system that provides the tools needed to quickly plan routes, but which also allows for manual intervention at the key points. Thus, the system uses tools to automate the parts of the plan that are simple, but simultaneously leverages the human knowledge, expertise, and ability to deal with unusual situations. This focuses the planning process to get the best of both worlds.

This results in low planning overheads, as there’s no data management, complex configuration or labour-intensive manual planning.

We developed the Springboard Delivery app specifically for these sorts of operations. It allows distribution companies to plan and optimise their routes, track their delivery vehicles, and also to capture proof of delivery data using a signature, a photo and geolocation. It combines automated planning with a degree of manual interaction and intervention to produce optimal route plans for small and medium-sized distribution businesses.