The Benefits of Forecasting in Transportation Planning

Posted by Brian Hoey on October 29, 2020

Truck light trail of trainThe police psychic—who uses supernatural powers to solve crimes that would otherwise baffle their colleagues—is a well-established one in tv and film. But what about the supply chain psychic? Imagine a long-running tv show about, say, a transportation planner who knew in advance what orders would be placed by customers and clients, what the different capacity availabilities and prices would be for each mode or lane, and what weather and traffic events would impact which routes at which times. For a very particular subset of viewers, this would no doubt be thrilling to watch. Why? Because with all of that information at the planner’s fingertips, disruptions and the high costs of premium freight would be a thing of the past. It would be a masterclass in cost reduction and supply chain efficiency.

Now, this might seem like a farfetched scenario (both the idea of a supply chain psychic and the idea that they’d make a tv show about one), but it’s not as crazy as it seems. The rise of advanced analytics and artificial intelligence in the supply stream means that, now more than ever, it really is possible to see things like price fluctuations and capacity crunches coming in advance and to take action accordingly. With the advent of transportation forecasting as a complement to existing TMS infrastructure, this can even be operationalized in such a way as to yield significant cost reductions—among other benefits…

 

1. Longer Planning Horizons

Simply put, the more time you have to get a handle on your capacity needs and secure the right routes in the right lanes at the right prices, the more likely you are to avoid the high costs associated with premium freight and last-minute transportation planning. Unfortunately, most businesses only have a few days from the time the relevant order is created to plan transportation flows, meaning that they’re more or less stuck with whatever options happen to be available at the time the demand materializes. If, instead of waiting for the order to come in, you were able to forecast probable orders in advance using an AI-powered forecasting solution, you could suddenly create a much longer planning horizon. In this way, you could secure capacity based on estimated demand and continue refining your plans as the actual shipping date neared. 

 

2. Better Shipping Rates

In the situation we sketched out above, longer planning lead times meant that you had a much wider window in which to secure freight capacity. This means that if the prices and availabilities for a particular lane seem too high in any given week, you can wait and see if options become more favorable the next week. Actually, you can do a lot more than just passively wait to see how pricing shakes out—using the same forecasting technology you use for shipping demand, you can also create lane- and carrier-specific price and availability forecasts. In this way, you get a much clearer sense of whether you’re securing capacity at the most favorable price. As a result, your shipping costs are reduced significantly.

 

3. Improved On-time Delivery

Let’s say you’re shooting for just-in time delivery on a shipment of parts to one of your clients. As competition for freight capacity gets stiffer and carriers get more selective about what they will and won’t carry, your transport options within the traditional transportation planning window become less and less attractive. As such, you find yourself forced to make a snap judgement about which will be more detrimental to your business: locking in an exorbitant air-freight price that will cut drastically into your bottom line because there are no other options that will get the goods to the recipient on time, or giving up on on-time delivery and dealing with the customer relations fallout of a late shipment. It’s a no-win situation: either your bottom line suffers immediately, or you risk a big hit to your bottom line in the future (e.g. if the customer decides that a late shipment is the last straw and decides to source their goods elsewhere).

Now, let’s reimagine the whole scenario with a robust forecasting solution in place. First, you see the order coming in advance—as such, you’re ready to start securing capacity potentially months before the order actually comes in. You use your forecasting solution to track freight availability and pricing, such that you’re able to secure a favorable rate way ahead of time while giving your goods plenty of time to make their way to their ultimate destination. Here, there’s no need to weigh the tradeoffs of high freight costs versus poor customer service, because you’re all but guaranteed to be well-positioned for on-time deliveries.

 

4. Smarter Supply Chain Management

The way we’ve spoken about transportation forecasting so far, you might be getting the impression of a sort of magical black-box that spits out predictions—handy for this use case, but essentially detached from other areas of the value chain. In point of fact, the kind of forecasting workflows we’re discussing represent key building blocks around which to build a smarter, more comprehensive approach to supply chain management.

First of all, these predictions don’t happen in a vacuum. To power accurate forecasts, you first need to provide the forecasting solution with the right data. This means lining up data streams and integrating them into your existing TMS. Once the transportation forecasting solution is up and running, it will do more than just offer predictions: it will offer predictions in a highly-visible environment that creates new levels of transparency for logistics management. Rather than planning transportation in a spreadsheet that only a few people can access, transport planning will now be done out in the open, such that it can be more easily integrated with other functions (like production and sales planning or S&OP processes)—all within an interface that provides clear data about planning parameters and offers the ability to collaborate. This means that chances of misalignments or different departments working at cross-purposes goes down dramatically. Rather than different departments operating in silos, they can all work together with shared IT towards  holistic, synergistic planning and execution. The end result is a smarter, more proactive supply chain that’s less susceptible to cost volatility and other disruptions.

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Topics: Transportation Management, AI