Gestion prédictive des conteneurs vides pour NileDutch

Mise en place d'un transfert de données sécurisé

Transmetrics offre une intégration VPN de niveau entreprise, qui garantit un haut niveau de sécurité pour tous les transferts de données automatisés quotidiens. Une fois que Transmetrics a terminé le processus d'intégration, NileDutch a pu facilement télécharger ses données. Ainsi, les données historiques d'expédition pour la période 2011-2018 étaient disponibles pour le nettoyage et l'analyse, contenant des dizaines de millions d'enregistrements, y compris des informations sur les itinéraires de réservation et les conteneurs, les scans des conteneurs, les horaires des navires, ainsi que les données de référence des navires et des conteneurs.

Nettoyage et enrichissement des données

L'un des principaux objectifs des transformations de données pour NileDutch était de déterminer quels conteneurs voyagent sur quel navire entre deux ports. Cependant, il n'était pas facile d'extraire cette information des données historiques, car il y avait une certaine redondance dans les différentes tables, et certaines données étaient en conflit. En outre, une grande partie des données provenaient d'EDI d'agents situés dans différents endroits éloignés à travers le monde, ce qui rendait plus difficile le maintien de la qualité des données.

Néanmoins, l'investissement préalable de NileDutch dans la numérisation, la normalisation des processus d'entreprise et un bon TMS ont été des facteurs déterminants pour la réussite des transformations de données et du projet dans son ensemble. Pour préparer pleinement les données à l'analyse, Transmetrics, en collaboration avec les experts en affaires et en données de NileDutch, a conçu les règles commerciales appropriées pour hiérarchiser les informations provenant des différentes tables.

Une fois les règles de gestion en place, l'équipe d'analystes commerciaux et de scientifiques des données de Transmetrics a nettoyé et enrichi les données en utilisant des algorithmes pour combiner l'historique complet des scans de conteneurs avec les mouvements à vide et les réservations. Ce processus en plusieurs étapes a permis de garantir l'exhaustivité des données, qui étaient prêtes à être mises en correspondance avec les horaires des navires afin de déterminer dans quels ports un conteneur s'arrête entre son chargement et son déchargement. Au cours du processus de transformation des données, Transmetrics a également amélioré les données suivantes :

Building and testing the forecasting model

Before implementing AssetMetrics software, the planning process of global repositioning of empty containers relied on the commercial forecasts from the agents around the world, which were mainly based on the bookings and personal business experience, and later were combined into one master forecast. This approach produced satisfactory results, but for the long-term it was not optimal because of its dependency on the manual inputs and human factor. For that reason, NileDutch wanted to introduce an accurate statistical forecast.

Having extensive experience in creating proprietary AI-driven demand models for logistics companies, the team of data scientists at Transmetrics developed a highly accurate forecasting model, which was trained on several years of already cleansed and enriched historical data. Furthermore, AI and machine-learning algorithms were used to continue improving accuracy with the new data coming in. As a result, the forecasting model was predicting the releases and returns of containers for NileDutch’s entire network for the next 12 weeks on a very granular level - per each day, port, ship, and type of container. It proved to be very precise even for the periods, such as the Chinese New Year, Christmas, and the like.

In addition to the automated statistical forecast, Transmetrics and NileDutch developed the functionality that allowed all the agents in various ports to collaborate and manually suggest their predictions (e.g., when acquiring a major customer, which cannot be data-mined from the past). Combining the powerful AI-driven statistical forecast with the years of experience of the NileDutch’s agents enabled Transmetrics to create the optimal forecasting model needed for the optimization module to plan the logistics of empty containers globally.

Building and testing the optimization model

Before partnering with Transmetrics, NileDutch already had in place good processes for planning empty container repositioning, but they were time-consuming since many tasks had to be executed manually with the tools such as Excel. Complex decisions for loading and unloading empty containers to ships, repositioning them to other ports, storing them in other depots or repairing containers were taken based on the extensive experience of the Global Empty Repositioning team of NileDutch.

Such a process was manageable because the company had many containers in reserve, but despite NileDutch having skillful professionals, it is impossible even for the best planning team to calculate the optimal, lowest-cost plan. In order to do that, one would need to consider a huge amount of inputs such as how many containers would be needed in the next 12 weeks in every single port of the network on few different continents, the schedule of each ship, the free capacity of each ship and how it would change over the period, the cost to transport the containers between hundreds of possible routes, the cost to store containers in each depot, how many containers you store at the moment, which of them are within the free tier and for how many you already pay for storage, the gate costs and stevedoring costs in different ports, how many containers your customers will return in each depot over the period and so on. If some of the inputs change, the planning needs to be recalculated. Such calculations are beyond the capacity of a human brain but they could be performed with AI technologies, and that is what AssetMetrics software does.

The global optimization module was co-developed in close collaboration with NileDutch to meet all the requirements of the container shipping companies. The Transmetrics’ team started from analyzing the necessary business rules, inputs and parameters. A major focus was put on an entire set of logistics costs, which must be considered to produce a meaningful optimization.

While working on the costs, Transmetrics’ teams of data scientists and business analysts identified several data topics, which had to be addressed first in order to provide the best inputs for the optimization model: