This article originally appeared in DVZ, 12 Jun 2018. Translated from the German original by Alex Hoffmann
Transport planning drives the profitability of carriers. Very few carriers have the potential to differentiate via pricing. Thus, costs remain the most important consideration in actively improving profitability.
This is done by making the best use of a private fleet, by procuring the best capacity from subcontractors or, in the case of most carriers, by combining these two. Transport planning separates profitable carriers from unprofitable ones, as it determines the costs of every single transport. Thus, it is surprising how much of it still relies on ancient manual processes.
The principal reason for this is the complexity of transport planning. Carriers have access to a variety of vehicles that carry different types of cargo to many different locations. All of these combinations come with varying models of cost. A necessary task for any planning software is, therefore, to be able to map this complexity correctly.
However, it is not sufficient to meet the minimum requirement. For each carrier, such a process generates a list of thousands of possible transport plans. Comparing those alternatives with each other on an economic basis thus is essential. For carriers relying only on their private fleet or having full control over subcontractors, this evaluation is comparatively easy: for each vehicle, the dispatcher receives a list of alternatives sorted by best contribution to profitability. Based on this, he can then select the best plan based on economic criteria.
If carriers though use independent subcontractors, the planning becomes more complicated, both for the dispatcher and for a software solution. Subcontractors often retain decision-making authority over the acceptance and pricing of freights. They can further benefit from daily fluctuating market prices, especially in today’s market environment. This uncertainty must be included in the planning. It is therefore important to understand which freights should be offered to subcontractors, when, and at what price.
The traditional solution is that a dispatcher identifies appropriate cargos based on his experience and then successively calls up a list of possible subcontractors negotiating pricing on the spot. The achievable pricing increases with each additional call.
There are, of course, simple technical solutions that automate and typically parallelise this. Instead of negotiating with every single subcontractor, everyone is negotiating simultaneously. The first subcontractor who accepts an offer is then the one who receives the contract.
However, this is by no means the only solution alternative, and by far not the best. In particular, this does not address the specifics of each subcontractor and the specific cargo. Modern and innovative methods have to learn from historical interactions with subcontractors: which freights are relevant, how fast are they usually responding to an offer, how aggressive is their pricing?
One of the major promises of modern software is answering those questions. Using machine learning and adaptive process design, the optimal strategy with which a carriers acts - which freight, which subcontractors, when and at what prices - can be optimally catered to each situation.
Too often, planning software has been based on auxiliary key figures: it minimises kilometres or maximises utilisation coefficients. Even if both are well-meaning key figures that successful carriers should keep in mind, this is not the goal of transport planning. The goal is minimizing costs, and subcontractors crucially influence these.