This article discusses the many ways in which truck freight is arranged in the US. The author makes the case that load boards are no longer that useful to truckers, and this is quite possibly due to the natural growth in the chase for users, and the users themselves gaming the system. It’s to be expected in our technical world.
Private freight marketplaces are attempts to fix the issues. They have their drawbacks. Another approach is a ‘centralized, reaggregated capacity marketplace’ optimized for integrity and carrier quality.
That’s what Newtrul founded in 2018, is offering. It appears they are offering their service to brokers rather than carriers. They address the carrier quality issue by only signing up carriers that have seven customers they’ve passed compliance checks with.
It’s not clear how Newtrul is doing the aggregation of capacity. Doubtless it is driven by an optimization or AI routine of some sort.
I think these approaches are interesting and useful. They induce some cooperation into a process that was distinctively siloed and labor-intensive previously. Markets will determine who will do cooperation the best.
Wing, a subsidiary of Alphabet (think Google), is pioneering a new delivery model. Drones pick up packages and deliver them via a network of landing pads and charging stations. They can handle multiple deliveries point-to-point without returning to the base. It’s because the standardized landing and charging stations are also near to the start point of their next run.
The CEO indicates that the model is a lot more like a computer message network than a last-mile logistics network. The software they’ve written matches the delivery demand with the available drones. Any nearby drone can pick up and make the delivery, then scout via software for another nearby pickup.
It’s another ingenious solution to the problems of thin demand. If there’s not enough, drones will be unoccupied. Pooling demand for package rides will make the system work better.
Another advantage of drones for delivery is their zero-emission properties. Drones are electric-powered, and emit lots less than local delivery trucks. They are also lots cheaper. There are limits on the size of packages they can carry, but if you look at typical deliveries to an apartment complex, for instance, you see that most packages are small.
Perhaps drone delivery is the future of last-mile package service.
Lora Cecere, the Supply Chain Shaman, has given us once again something to think about– big time. She goes off on some of the current fads in supply chain that she thinks are not worth pursuing.
It’s always wise to listen to Lora. She has a wealth of experience and many years of consulting with top companies to inform her thinking.
Here are some of her dead-end streets that you should be avoiding.
Dashboards
Lights-out planning
Real-time planning
DDMRP (demand-driven material requirements planning)
Forecast Sharing
Sales Forecasting
She believes future applications are adaptive and distributed. Read the article to see what she means, and how those two keywords play out.
The SCOR methodology she claims is useful in devising a new approach to planning. Figure 3 shows an outside-in model of planning for a company.
I’m particularly intrigued by her comments on dashboards. I’ve felt they were more about visibility than improving processes. While some of that is necessary, the real value will be if you can make changes quickly. And most dashboards, I’m afraid, don’t let the operators do that. They look cool but don’t help making critical changes.
Her remarks about real-time planning are also on point. If you rerun too frequently, the plans thrash— they oscillate between one action and another completely different one. There is value in following a consistent pattern and making slow changes rather than swinging back and forth. We saw that in spades during the Covid epidemic. Look at the auto manufacturers who canceled their chip orders and then six months later could not place them again because the capacity had been diverted to other chips that were more profitable as it happened. Acting too quickly caused them anguish that has persisted for three years now. Reruns generate noise that affects partners and destroys relationships.
I also share her skepticism about DDMRP which bases an entire manufacturing progression on orders. It’s better than not looking at orders, but it fails to capture the richness of what might happen in the future. We can point to the same auto manufacturers and see that they have hundreds of cars in an ‘almost-complete’ state while they wait for certain electronics to be delivered.
And she provides some good references on checking how forecasting is improved by getting the customer’s forecast in advance. Statistical tests indicate that for the most part they are not helpful. What characterizes the few that are? That’s worth research.
And finally, salesmen are the worst forecasters. First of all, they’re liars. And the best ones are the worst liars. Why? Especially if they are paid on commission, or via a plan that pays for meeting targets or provides spiffs for beating them, they will try to get the target set low. And for commission sales often they are not guaranteed, as to size especially. So the salesman might predict low-ball, because he’s sure he will get that as a target. Then the big order comes as an extra. But for manufacturing planning, that is not reality. Often big orders do not come exactly when anticipated, so they might not fall at quarter end when the financials are due. Timing is harder to predict than size.
In the past at a mid-size manufacturer, we had better luck eyeballing a steady rate of production from history (moderated by some anticipation of the future market) and then trying to get salespeople to tell us about big orders and when they anticipated them. We laid the big order forecasts on top of the ‘run-rate’ forecast to determine the timing of production. A few days’ change in big order timing didn’t then blow our whole manufacturing plan.
Salespeople are a good source of info about products and customers, but as manufacturing forecasters they are awful. Yet you need to cultivate them to get that key information about their expectations which is valuable.