AI in logistics

Find out what AI is, how it works and what opportunities it offers companies in the fields of logistics, intralogistics and supply chain management!

WHAT IS ARTIFICIAL INTELLIGENCE?

 

First and foremost, Artificial Intelligence is a collective term which combines a number of sub-disciplines of computer science and mathematics. The overarching goal of the methods researched therein is to solve complex tasks, many of which could only be solved by means of human cognition. Some of the best known methods in this area are Machine Learning, Deep Learning, neural networks and Natural Language Processing (NLP). In addition, each of these fields of research is comprised of numerous other methods. Thus, one of the biggest practical challenges when dealing with AI is to correctly identify the problem at hand and to find a suitable method for its solution. The basic question in the development of Artificial Intelligence is, to put it simply: Can computers be made to imitate the human brain?

FACTS ABOUT ARTIFICIAL INTELLIGENCE IN LOGISTICS

A study by INFORM and LOGISTIK HEUTE reveals some interesting figures about Data Science & AI in the logistics industry.

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About 90% of those surveyed hope that AI will improve their market position.

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However, only 26% state that they actively employ AIin their logistics processes.

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The reason: 54 % of the employees lack the required specialist knowledge, with only 12 % claiming to possess good knowledge of the field.

“One example highlights how striking the advantages of Artificial Intelligence in intralogistics can be: In a store, 7.5 percent of articles are unavailable because of manual ordering due to gaps in the shelves. The error rate drops to five percent when special AI software is put in place to provide recommendations to a human dispatcher. If the possibility of corrections by humans is abandoned and the warehouse and logistics tasks are completely turned over to an autonomous Artificial Intelligence, the error rate drops to just 0.5 percent”.

– Joachim Bengelsdorf / diyonline-Magazin –

POTENTIALS & OPPORTUNITIES OF AI IN THE LOGISTICS INDUSTRY

 

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.

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:

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Predictive Maintenance

With the help of predictive maintenance methods, you can detect errors early on.

Quality Analytics

Analyze your Quality Data to discover correlations and derive metrics.

Arrival Forecast

Use statistical models to predict and track the expected arrival time of vehicles at the POI

Demand Forecasting

Machine learning models let you predict your clients’ transportation needs.

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IoT / Connected Devices

Collect Data from your fleet in real time for condition monitoring or predictive maintenance.

Optimization

Use mathematical optimization models to improve the allocation of transport resources.

WHO AND WHAT IS BEHIND [at]

With the help of Data and Artificial Intelligence, we enable our customers to constantly change and adapt in the digital age.

We empower our customers to develop their own strengths and accompany them on their way with our [at] Data Journey.

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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 at MAN

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

Preventive identification of faulty parts

  • Identification of faulty supplier batches through a generic data model
  • Visualized tracking of conspicuous supplier batches in QlikSense
Predictive Maintenance with Data Science

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.
AI IN LOGISTICS

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%

[at] DATA JOURNEY

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.

AT Data Journey

References