Artificial intelligence in the transport and logistics sector

The transport and logistics industry is facing many challenges. Fuel price increases change the playing field, and the driver shortage affects the growth opportunities of companies in the sector. The green transition challenges to review and improve your own operations. The transport and logistics industry lives in a world of extreme competition, and only 29 percent of companies can find potential growth opportunities for themselves. (Kuljetus- ja logistiikkalehti 4 September 2020.)

Artificial intelligence and technological innovations may contribute to the industry and to the competitiveness of its companies. Examples of applications of artificial intelligence in the transport and logistics sector include demand forecasting, route optimization, autonomous mapping and driving. In long-distance transports, quite a large part of the journeys are carried out with empty cargo, which produces additional costs. With an artificial intelligence-based route optimizing it could be possible to reduce emissions and at the same time increase the earnings.

A company in the industry, Kiitosimeon Oy, participates in AI-TIE clean industry artificial intelligence accelerator, which has helped the company to identify several use cases of artificial intelligence that could generate value for its own business. Kiitosimeon is a company focused on transportation of liquid products. The domestic market areas of the company are Finland, Sweden and the Baltic countries, and partners are for example Neste, Kemira and Altia.

Next, we open the background and first phases of Kiitosimeon’s artificial intelligence story.

Timing is central in artificial intelligence experiments

The company has noticed that in everyday activities there are more and more little employing extra jobs which have had to be done over the years. This no longer seemed efficient, and the company’s processes were wanted to be critically reviewed again. The automation of operations and its many processes has been both thought about and attempted before, but just a few years ago the necessary technology did not yet exist and the artificial intelligence tools of that time were not yet sufficiently developed. For example, for machine vision the situation is nowadays radically different than a few years ago. There are plenty of both free machine vision models and programs and companies that offer complete machine vision solutions.

Timing builds up from several different things, including the company’s personal acquirements to development work and technical tools currently available. In addition, the development activities implemented by Kiitosimeon, which is linked to artificial intelligence, has challenged the company’s own operators, since the processes are close to each other’s and integrated in many ways. The operators had to take part, too, and develop their personal business together with Kiitosimeon. This results in a ripple effect of one company’s artificial intelligence experiments on its stakeholders, partners and other players in the industry.

Future prospects

The development of Kiitosimeon’s artificial intelligence-based use cases has brought up several new trends which will be seen in the future. One of these is that integrations between the client and company will deepen in the future and this will be reflected in, for example, an increase in data sharing. Another important thing is that artificial intelligence development work enables the company to go through its own business processes, evaluate and improve them. Through a few successful artificial intelligence projects, it is easy to expand the use cases and move from the phase of understanding to identifying more applications of artificial intelligence.

At its best, artificial intelligence offers concrete and measurable results and can be seen, among other things, in the efficiency of internal processes and the elimination of error possibilities. With the saving of man-years, experts can move from doing routine work to more monitoring and reporting, as well as to more demanding tasks. Ultimately, the results must be visible in the customer interface: when employees are more satisfied and perform their work efficiently, this is visible to customers, and the number of errors and unclear cases decreases.

Translated from the Finnish by Sari Benford and Petteri Saloranta.