AI in logistics
Learn what AI is, how it works and what opportunities it holds for companies in the fields of logistics, intralogistics and supply chain management!
Data & AI projects for the logistics industry
The entire supply chain of logistics companies generates vast amounts of data every day. Artificial intelligence is predestined to exploit this information. AI can help to redevelop methods and behaviours, for example, to generate proactive processes from reactive ones, and to make concrete predictions for the future instead of guesses and rough estimates in the interest of planning security. Of course, it is also possible to concentrate only on existing processes and to optimise manual as well as already automated processes in terms of time. Moreover, instead of following generic standards, services can be personalised and thus made more customer-friendly.
With the help of data and artificial intelligence, we enable our clients to constantly change and adapt in the digital age. 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. With this enable our clients to develop their own strengths and accompany them on their way.
Projects of our customers
We have already proven our data science and AI expertise in the field of logistics in various projects. Read some of our references on the topic of AI in logistics and transport here. If you have any questions, please do not hesitate to contact us.
Predictive Maintenance @MAN
- Prevention of 92 % of all injector failures
- Reduction of warranty costs
- Reducing convention penalties and securing follow-up orders
Preventive identification of faulty parts
- Identification of faulty supplier batches through a generic data model
- Visualisation of the tracing of conspicuous supplier batches in QlikSense
Feasibility analysis on predictive maintenance
- Evaluation of the available data basis with regard to predictive maintenance projects
- Recommendations regarding data availability to successfully implement predictive maintenance projects.
Order forecast for spare parts orders
- Successful proof of concept and foundation stone for further analyses within 8 weeks
- Preparation of 7 different data sources
- Calculation of over 20 individual models
- Forecast accuracies up to 91 %
Download Whitepaper
- Artificial intelligence in logistics -
Application areas of AI & Data Science in logistics
The use of AI is already having a massive impact on logistics processes. In the following, we present some fields in which artificial intelligence can be used:
Predictive Maintenance
With the help of predictive maintenance methods, you can detect faults at an early stage.
Quality Analytics
Analyse your quality data to find correlations and derive metrics.
Arrival forecast
Use statistical models to predict and track the expected arrival time of the vehicle at the POI
Demand Forecasting
Using machine learning models, you can predict your customers' transport needs.
IoT / Connected Devices
Collect real-time data from your fleet for condition monitoring or predictive maintenance.
Optimisation
Use mathematical optimisation models to improve the allocation of transport resources.
"One example shows how striking the advantages of artificial intelligence can be in intralogistics: In one store, 7.5 per cent of items are not available due to manual ordering because of shelf gaps. The error rate drops to five per cent when special AI software makes recommendations to a human scheduler. If the possibility of human corrections is dispensed with and artificial intelligence performs the warehouse and logistics tasks completely autonomously, so the error rate drops to 0.5 per cent."
- Joachim Bengelsdorf / diyonline Magazine -
Facts about artificial intelligence in logistics
A study by INFORM and LOGISTIK HEUTE reveals interesting figures around Data Science & AI in the logistics industry.
%
About 90 % of the respondents hope that AI will improve their Market position improved.
%
However, only 26 % state that they are AI active in their logistics processes insert.
%
Reason: 54 % of the Employees lack expertise, only 12 % indicate good knowledge.
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Simon Decker
Data & AI Projects Logistics & Transport
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