Data & AI projects
Huge amounts of Data are generated every day throughout every step of the supply chains of logistics companies. This Data is available in both structured and unstructured form. Artificial Intelligence is made to explore and exploit this information. AI can help to develop new methods and patterns of behavior. It can, for example, help generate proactive processes from reactive ones. In doing so doing, it can improve planning reliability by enabling concrete predictions for the future, as opposed to mere speculation and rough estimates. Of course, it is also possible to concentrate on already existing processes by focusing on optimizing the timing of manual or automated procedures. In addition, instead of simply adhering to generic standards, services can be personalized and thus made more customer-friendly.
We have used our experience from over 1,000 projects in the last 8 years to develop a holistic system for Data & AI projects – our [at] Data Journey. An integrated Data Strategy forms the basis and the framework for generating real added value from Data – what we have dubbed Data2Value. Our Data Lab is all about speed! Their main goal is to test Use Cases as quickly as possible – from the concept phase to the prototype using real Data. In the Data Factory, Use Cases are industrialized into finished products. The absolute main focus is on scaling and the sustainable generation of added value – as such, the user is just as much the focus here as well. In our DataOps we continuously operate and maintain your platforms and Machine Learning algorithms.
Reference projects of our customers
We have already proven our Data Science and AI expertise in the field of logistics over the course of various projects. Read some of our references on AI in logistics and transport here. Please do not hesitate to contact us if you have any questions.
Predictive Maintenance @MAN
- Prevention of 92% of all injector failures
- Reduction of warranty costs
- Reduced penalties and securing of follow-up orders
Preventive identification of faulty parts
- Identification of faulty supplier batches through a generic data model
- Visualized tracking of conspicuous supplier batches in QlikSense
Feasibility analysis for predictive maintenance
- Evaluation of the available Data basis with regard to predictive maintenance projects
- Recommendations regarding Data availability in order to successfully implement predictive maintenance projects.
Order forecast for spare parts orders
- Successful proof of concept and groundwork for further analyses in just 8 weeks
- Processing of 7 different Data sources
- Calculation of over 20 individual models
- Forecast accuracy of up to 91%
About 90% of those surveyed hope that AI will improve their market position.
However, only 26% state that they actively employ AIin their logistics processes.
The reason: 54 % of the employees lack the required specialist knowledge, with only 12 % claiming to possess good knowledge of the field.
AREAS OF APPLICATION FOR AI & DATA SCIENCE IN LOGISTICS
The use of AI is already having a massive impact on logistics processes. In the following, we will introduce you to some fields in which Artificial Intelligence can be put to use:
With the help of predictive maintenance methods, you can detect errors early on.
Analyze your Quality Data to discover correlations and derive metrics.
Use statistical models to predict and track the expected arrival time of vehicles at the POI
Machine learning models let you predict your clients’ transportation needs.
IoT / Connected Devices
Collect Data from your fleet in real time for condition monitoring or predictive maintenance.
Use mathematical optimization models to improve the allocation of transport resources.