Developing stronger logistics platforms with AI and intelligent technologies

AI offers to improve complex operations such as yard management. Brett Sayles, CFE Media and Technology
AI can help with complex logistics operations such as yard management. | Source: Brett Sayles, CFE Media and Technology

On average, people have about 87 items of clothing or accessories in their wardrobes. Every morning, they choose their outfits based on the season, weather, planned activities, and mood, along with considering various combinations.

This is one example of the 20,000-plus decisions people make every day, and their selection process weighs multiple factors. Artificial intelligence can do that today and doesn’t forget any decision parameters.

While AI isn’t necessary for deciding what to wear, this example provides an easy-to-understand example of how AI works to which everyone can relate. In more complex operations such as yard management, AI may be able to make decisions that advance the industry as a whole and not just individual players.

Unlike the multitude of wardrobe options for each day, the number of parameters in yard management pales in comparison. It’s no wonder psychology speaks of “decision fatigue” when there are so many options.

What often happens when many decisions need to be sorted is that they are made on autopilot. It can be almost impossible to monitor all factors, dependencies, and consequences. It is better to make precise conclusions based on experience and a subjective selection of the available information.

Perhaps this is why operational decisions are always made in a way that favors people’s own interests rather than taking all perspectives into account. In logistics, freight forwarders traditionally complain about having to align themselves with the requirements of their larger customers.

Instead of focusing on adhering to this one perspective for simplicity, AI now offers the opportunity to deal with such complexities and can optimize yard processes that create efficiencies while benefiting all parties.

Managing yard complexity

Facilities must juggle complex processes throughout their yard operations. For instance, if 1,800 trucks arrive at a large industrial plant in the automotive industry on a daily basis with multiple load/unload points, it could cause a truck to be in the yard for hours for one pickup or delivery.

There could be hundreds of trailers in the yard at any one time. Yard managers need to know registration processes, gate checks, management by the plant, and adherence to or reallocation of certain time slots. Of course, they must also coordinate personnel and resources at the various ramps and dock doors for each trailer.

All of this should happen in coordinated sequences, ideally considering the plant’s internal processes, requirements, and priorities. Even medium-sized shippers or consignees may not be expecting 1,800 trucks a day, but they still have dozens or hundreds of trucks and trailers to manage logistically. Combined with the decisions of the facility managers, dispatchers, and other parties, this can reach an unmanageable level of complexity.

For example, what happens if a truck arrives at the gate an hour early despite having booked a time slot? Do the employees at the gate then have all the information about the processes in the plant to be able to make this decision in an effective manner? Usually not. Such decisions can affect subsequent processes.

There may be bottlenecks later at the scales or at one of the ramps to be approached. This could cause delays with not just one truck, but with several vehicles.

In practice, delays and other schedule changes are the rule rather than the exception. The probability that the employees at the gate will make the best possible decision in terms of the overall process and planned schedule for the entire day is unlikely.

AI in yard operations — win-win situations on the ramp

AI-based solutions to manage the real-time scheduling changes in yard processes. The examples described above are complex and consider all the decision parameters and work well within mathematical models while being searched by modern algorithms in real-time environments to achieve the best possible course of action.

Optimization is no longer just a synonym for “improvement” it means that the optimal choice in a given situation will be achieved in part because it is being measured against the real conditions of a decision-making situation. Trailer yards benefit from AI-based algorithms.

Depending on the destination loading point, the AI automatically optimizes the coordination of the trailer to be parked in the yard, and organizes the trailer call-off to the destination loading point according to the rules stored in the system such as a trailer must be available again after 24 hours and assigns a transport order to the most suitable internal yard driver with the trailer call-off.

Not only can routine decisions be automated, but complex data analyses can also be carried out to enable well-founded strategies and run-through scenarios to convert ad-hoc changes into a solid, reliable plan. In particular, new intelligent technologies can create synergies between companies, leading to genuine win-win situations.

For instance, when AI reduces truck throughput times at the yards through clever ramp control and the determination of optimal sequences, it is not only the companies served that benefit from efficiency and cost reductions (lower demurrage charges).

Thanks to greater robust processes in yard management, more precise forecasts can also be achieved with determining when trucks will be back on the road.

Using synergies through software

What does this look like in practice? If a company uses an AI-based planning system for truck arrival and ramp control, a booking platform for time slots can also be connected to it. This allows haulers to book their desired time slots while giving dispatchers complete transparency. If certain timeslots are no longer available, alternatives can be automatically generated. In the case of AI-based systems, the algorithms calculate five suitable suggestions that dispatchers can use to adjust their route planning at an early stage.

Of course, this also makes manual follow-up phone calls for rescheduling material arrivals obsolete and reduces the amount of paperwork, as freight documents, for example, are digitally transmitted when the order is created. Not only will the gate-in-process become faster, but from a production plant perspective, the planning of material arrivals and capacities will become much more concise.

On the day of delivery, forwarders can see the status of their truck at any time by accessing the respective platform: arrived at the gate, at the loading point, loading or unloading, etc. The AI systems provide information about delays or premature processes, and time slots can be booked when planning a tour, which saves freight forwarders further planning effort.

While we have only discussed synergies between companies to this point, AI naturally offers enormous potential in the seamless integration of inbound, in-plant, and outbound logistics, as well as synchronizing logistics and production processes. The industry is beginning to see the real advantages of intelligent technologies with synergies like these.

Editor’s note: This article was syndicated from Automated Warehouse sibling site Plant Engineering.

Written by

Justin Newell

Justin Newell is CEO at Inform Software Corporation.