Moving toward efficient logistics
—Efforts to improve truck loading efficiency
2025.02.18
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Japan’s logistics industry faces many challenges today; a labor force is decreasing with a shortage of truck drivers and an aging and shrinking population, improvements in the labor environment limit the number of hours individual drivers can work, and reduction of CO2 emissions is necessary. The regulation introduced in April 2024 to limit the overtime hours of truck drivers to 960 hours per year has improved working hours. At the same time, more drivers are needed to transport the same amount of cargo as before. The logistics industry as a whole needs to take urgent steps to address these issues; if it fails to do so, it risks being unable to transport needed cargo in the future.
To overcome these challenges, Toyoda Gosei is seeking to increase the efficiency of logistics by utilizing AI, IoT and other technologies. The first step in this has been a new effort focuses on increasing truck loading efficiency.
- A step toward an efficient
logistics system that
reduces the workplace
burden - Truck allocation plans have
depended on human
intuition and know-how
(before improvement) - Cargo load calculation
system using AI and
cameras
(Improvement [1]) - Cargo load calculation
system using smartphone
3D sensor
(Improvement [2])
01A step toward an efficient logistics system that reduces the workplace burden
Toyoda Gosei started a project in 2021 to improve logistics with the aim of dealing with the driver shortage and reducing environmental impacts. The project was divided into four areas corresponding to the flow of logistics operations. Here we describe the first area, “Improving truck loading efficiency” by optimizing truck allocation planning. Efficiently transporting manufactured products requires optimum truck allocation plans. This means determining the best truck size, number of runs, timing and more to achieve efficient shipment and delivery based on parts orders from the automakers who are our customers. Our target is to raise cargo loading efficiency from the current 70% to 85% within FY2025, and build an efficient logistics system while reducing the burden on distribution sites.

■Overall view of logistics improvement project

■Targeted improvements

02Truck allocation plans have depended on human intuition and know-how (before improvement)
In the past, truck allocation plans required much time and manpower. Not only that, but they depended on human experience. There were four steps in making a truck allocation plan: (1) understand the cargo load of each truck, (2) calculate the cargo load for each route, (3) establish the maximum loading volume for each route, and (4) decide the number of runs for the allocated trucks. In calculating the cargo load in (2), a person would previously go to the site, visually confirm the cargo space of the truck, and decide how much could be loaded onto it. In addition to differences in the judgments of each person in charge, there was also a risk that these individuals would be hit by a truck or forklift. Moreover, production sites and distribution centers are dispersed in the central, west and east regions in Japan, and their operating hours also span from early morning until late night. This work was thus a huge burden, and less than 5% of the load status was actually understood.

03Cargo load calculation system using AI and cameras (Improvement [1])
In 2023, the processes of (1) understanding the cargo load, (2) calculating the load for each route, and (3) establishing the maximum loading volume were automated using AI. The cargo spaces of trucks loaded with cargo are now photographed 24-hours with fixed cameras, so that 100% of the loading status can be understood automatically.
Using this data, it has become possible to conduct searches by individual route and instantly identify low volume routes and runs. Based on this information, improvement work to create an optimum transport system is easier to perform, and the number of truck runs has been reduced by about 9,400 annually. Converted to CO2 emissions, this is a reduction of about 760 tons.
This system has been introduced at distribution centers (in the cities of Miyoshi and Ichinomiya in Aichi Prefecture) that account for 60% of Toyoda Gosei product shipments.
■Steps in understanding loading capacity

■Image of reduced number of runs

04Cargo load calculation system using smartphone 3D sensor (Improvement [2])
In 2024, we developed a loading volume calculation system* that uses a LiDAR scanner installed on a smartphone. Since the data processing is completed on the smartphone, this system has the advantage of being simple to carry about and easy to adopt. Going forward, we plan to use this system for delivery routes that do not go through a distribution center, which account for the remaining 40% of Toyoda Gosei products.
*The system can be used with both 4-ton medium-sized trucks and 11-ton large trucks.
■Steps in understanding loading capacity

■Image of reduced number of runs

Voice of leader
Building optimum logistics
To respond to the challenges in logistics, we are undertaking new kaizen activities in a cross-functional manner in the company. One of those efforts is a system to calculate loads using AI and cameras. The Production Administration Division makes proposals from the perspective of “How to add human wisdom and experience in development.” After these proposals are put into operation, we communicate points for improvement in actual practice to the development team as needed. We have established operations for improved loading efficiency and achieved fixed positive effects. Going forward, we will continue to use 3D sensors on smartphones and leverage real-life experience for further benefit in building optimum logistics.
Katsuyoshi Shirai, Distribution Administration Dept., General Manager of Production Administration Division
Voice of developer
Dealing with the worker shortage in logistics and increasing the efficiency of truck transport are issues that must be achieved. I would like to contribute to the digital transformation and improvements in logistics operations through speedy development in areas such as automation of load measurements with AI and 3D imaging of cargo loading with LiDAR (smartphone).
Tsuyoshi Furuyama, Project General Manager, IT Digital Platform Division
Related articles
■Toyoda Gosei Improves Truck Loading Efficiency With the Use of AI, Reducing CO2 Emissions
https://www.toyoda-gosei.com/news/details.php?id=358
■Improving transport efficiency in product delivery with use of smartphone 3D sensors
Toyoda Gosei Develops System for Efficient Calculation of Truck Loads
https://www.toyoda-gosei.com/news/details.php?id=406