Archive for category: Start-up-en

The inland container logistics chain suffers from low digitization, which limits organization and communication between the various parts of the chain. To overcome this problem, the start-up DMS Logistics, incubated at Mines Saint-Étienne, is developing a platform to optimize management of these flows of goods. It uses machine learning methods to automate the creation of schedules, reduce congestion at terminals and boost the competitiveness of ports and their logistics networks.

Ports’ ability to streamline the transfer of goods impacts their competitiveness in a sector where the competition is fierce. Yet, one of the major problems for this type of infrastructure is their permanent congestion. This is explained in part by the physical capacity to receive trucks at terminals, but is also due to the lack of anticipation in the exchange of containers with carriers. This results in costly slowdowns and detention charges for every delay. At the heart of the problem is a lack of communication between the various participants in the same supply chain: terminals, road carriers, container parks, ship-owners, freight forwarders etc.

To help overcome this problem, the start-up DMS Logistics seeks to bring together all of these participants through a platform to optimize and anticipate the inland flow of containers. “This is a major market with 800 million containers exchanged every year worldwide,” explains one of the three founders, Xavier Des Minières, a specialist in inland logistics. Using operational data from each participant in the chain, the start-up has successfully optimized the overall supply chain rather than an individual part of the chain. This software solution therefore achieves the goals of the French government’s strategic plan “France Logistics 2025” initiated in 2016 to make national logistics more efficient and attractive.

Digitizing companies that work with containers

“The logistics sector is still little digitized. It is made up of many SMEs with very small profit margins who cannot afford to buy digital tools to manage their operations,” explains Xavier Des Minières. DMS Logistics is solving this problem by equipping these users digitally and adapting to their resources. However, the solution becomes more useful when it groups together all the parts of the supply chain. To do so, the company is targeting the terminals around which all the other inland transportation participants revolve.

DMS Logistics’ solution is a distributed cloud-based SaaS (Software as a Service) platform. It enables users to enter their operational data online: container movement, missions to accomplish or already carried out etc. For participants that have already gone digital, the service connects to their data through an API (Application Programming Interface) protocol. Since it was founded in 2020, the start-up has collected 700,000 container movements. This massive amount of data will feed its machine learning algorithms. “We’re automating three key time-consuming actions: managing operations schedules, making appointments at terminals and communication between partners,” says Xavier Des Minières.

Predicting flows based on data history

Why does this sector need to be automated? In the field, many participants in the chain respond to management difficulties in real time, using walkie-talkies or over the phone. They are constantly dealing with seemingly unforeseen difficulties. “However, we have shown that there is a lot of redundancy in the operational behavior of the various participants. The difficulties are therefore predictable and our algorithms make it possible to anticipate them,” explainsCyriac Azefack, a data scientist at DMS Logistics who holds a PhD in artificial intelligence.

The prediction is even more accurate when it cross-references data from the various participants. For example, carriers can optimize drivers’ schedules based on times for appointments offered by terminals to pick up goods. Furthermore, carriers’ behavior (history of their operations, inventory movement etc.) can be used to identify appropriate time slots for these appointments. Carriers can then access the terminal when it is convenient for them to do so and when it is not crowded. This seemingly simple organization was not possible before now.

An even higher level of optimization can be reached. “Still using carriers’ behavioral data, we identify the drivers and trucks that are most suited for a mission (local, long distance, etc.),” adds Taki-Eddine Korabi, a data scientist at DMS Logistics who holds a PhD in mathematics, computer science and automation. Ultimately, the overall optimization of an ecosystem results in better local management.

Towards the optimization of local logistics ecosystems

DMS Logistics’ solution is distributed in the Ivory Coast and in Marseille, where a team of 12 people are  based. “After 4 months of operations at our pilot facility, we can predict the arrival of trucks at a terminal with a reliability rate of 98% over a week,” explains Xavier Des Minières. For the terminal, this means 15% savings in resources. Moreover, when a port is efficient, it boosts the attractiveness of the entire region. The economic benefits are therefore wide-ranging.

Another key finding: optimizing flow at the terminals also helps the ports in their efforts toward ecological transition. More efficient organization means less unnecessary transportation, a reduction in traffic at ports, and therefore less local pollution. And air quality is improved. 

On the scientific side, research has only focused on optimizing container carriers’ operations since 2015 and on-the-ground information is still lacking. “We’re going to be starting a Cifre PhD with Mines Saint-Étienne which will rely on the use of data collected by our platform. That will allow us to explore this topic in an optimal way and offer bright prospects for research and the logistics business,” concludes Taki-Eddine Korabi.

By Anaïs Culot

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The OSO-AI start-up has recently completed a €4 million funding round. Its artificial intelligence solution that can detect incidents such as falls or cries for help has convinced investors, along with a number of nursing homes in which it has been installed. This technology was developed in part through the work of Claude Berrou, a researcher at IMT Atlantique, and the company’s co-founder and scientific advisor.

OSO-AI, a company incubated at IMT Atlantique, is the result of an encounter between Claude Berrou, a researcher at the engineering school, and Olivier Menut, an engineer at STMicroelectronics. Together, they started to develop artificial intelligence that can recognize specific sounds. After completing a €4 million funding round, the start-up now plans to fast-track the development of its product: ARI (French acronym for Smart Resident Assistant), a solution designed to alert staff in the event of an incident inside a resident’s room.

The device takes the form of an electronic unit equipped with high-precision microphones. ARI’s goal is to “listen” to the sound environment in which it is placed and send an alert whenever it picks up a worrying sound. Information is then transmitted via wi-fi and processed in the cloud.

“Normally, in nursing homes, there is only a single person on call at night,” says Claude Berrou. “They hear a cry for help at 2 am but don’t know which room it came from. So they have to go seek out the resident in distress, losing precious time before they can intervene – and waking up many residents in the process. With our system, the caregiver on duty receives a message such as, ‘Room 12, 1^st floor, cry for help,’ directly on their mobile phone.” The technology therefore saves time that may be life-saving for an elderly person, and is less intrusive than a surveillance camera so it is better accepted. Especially since it is paused whenever someone else enters the room. Moreover, it helps relieve the workload and mental burden placed on the staff.

OSO-AI is inspired by how the brain works

But how can an information system hear and analyze sounds? The device developed by OSO-AI relies on machine learning, a branch of artificial intelligence, and artificial neural networks. In a certain way, this means that it tries to imitate how the brain works. “Any machine designed to reproduce basic properties of human intelligence must be based on two separate networks,” explains the IMT Atlantique researcher. “The first is sensory-based and innate: it allows living beings to react to external factors based on the five senses. The second is cognitive and varies depending on the individual: it supports long-term memory and leads to decision-making based on signals from diverse sources.”

How is this model applied to the ARI unit and the computers that receive the preprocessed signals? A first “sensory-based” layer is responsible for capturing the sounds, using microphones, and turning them into representative vectors. These are then compressed and sent to the second “cognitive” layer, which then analyzes the information, relying in particular on neural networks, in order to decide whether or not to issue an alert. It is by comparing new data to that already stored in its memory that the system is able to make a decision. For example, if a cognitively-impaired resident tends to call for help all the time, it must be able to decide not to warn the staff every time.

The challenges of the learning phase

Like any self-learning system, ARI must go through a crucial initial training phase to enable it to form an initial memory, which will subsequently be increased. This step raises two main problems.

First of all, it must be able to interpret the words pronounced by residents using a speech-to-text tool that turns a speaker’s words into written text. But ARI’s environment also presents certain challenges. “Elderly individuals may express themselves with a strong accent or in a soft voice, which makes their diction harder to understand,” says Claude Berrou. As such, the company has tailored its algorithms to these factors.

Second, what about other sounds that occur less frequently, such as a fall? In these cases, the analysis is even more complex. “That’s a major challenge for artificial intelligence and neural networks: weakly-supervised learning, meaning learning from a limited number of examples or too few to be labeled,” explains the IMT Atlantique researcher. “What is informative is that it’s rare. And that which is rare is not a good fit for current artificial intelligence since it needs a lot of data.” OSO-AI is also innovative in this area of weakly-supervised learning.

Data is precisely a competitive advantage on which OSO-AI intends to rely. As it is installed in a greater number of nursing homes, the technology acquires increasingly detailed knowledge of sound environments. And little by little, it builds a common base of sounds (falls, footsteps, doors etc.) which can be reused in many nursing homes.

Read more on I’MTech: In French nursing homes, the Covid-19 crisis has revealed the detrimental effects of austerity policies

From nursing homes to home care

As of now, the product has completed its proof-of-concept phase, and approximately 300 devices have been installed in seven nursing homes, while the product has started to be marketed. The recent funding round will help fast-track the company’s technological and business development by tripling its number of employees to reach a staff of thirty by the end of 2021.

The start-up is already planning to deploy its system to help elderly people remain in their homes, another important societal issue. Lastly, according to Claude Berrou, one of OSO-AI’s most promising future applications is to monitor well-being, in particular in nursing home residents. In addition to situations of distress, the technology could detect unusual signs in residents, such as a more pronounced cough. In light of the current situation, there is no doubt that such a function would be highly valued.