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An employee in a green Siemens suit sits at a desk and works on a computer in an industrial environment.

Siemens: AI demand prediction platform for industrial production planning

Project duration: since February 2022

siemens
HomeCase StudiesSIEMENS: AI demand prediction platform
Technologies

Azure Machine Learning, Azure Functions App, Azure Database for PostgreSQL, Azure Monitor
Python: Pandas, FastAPI, standard ML libraries, Opentelemetry, deepchecks, poetry, code-quality tools, Grafana, PowerBI, GitlabCI, Docker

Methods

Kanban, CRISP-ML (Q)

Team

Data Scientists and Software Engineers

Machine learning for time series prediction

Storage costs are a high cost factor in production for an industrial company like Siemens. The AI Demand Prediction Platform, which can be used to predict future demand for products, is intended to open up a way to optimize these costs.

We are developing a machine learning system for timeseries forecasting in production planning for Siemens. In future, production planners in the plants will use this platform as a self-service.

The aim was to be able to make valid forecasts for an initial 100 different products in a first plant. If this works, the entire product range is to be taken into account step by step and eventually other plants in the Group will be added.

  • A long hallway with illuminated, transparent walls that reflect green light. The floor is dark tiled.
  • Two people are standing in front of a large, white technical device, holding a laptop and an electronic tablet.
  • An orange industrial robot is integrated in a green housing and appears to be working on a production machine.
  • A person in green work clothes operates a keyboard connected to a computer screen.
  • A person in green work clothing and a yellow hard hat is working on a complex machine in an industrial environment.
  • A person in a gray shirt works intently on a laptop in a modern technological environment.

Our approach: From proof of concept to productive system

We first built a proof of concept, which we expanded into a system that can be used productively. We developed a training model for each product in order to map the diversity of the products. From the technically and content-wise functioning system, we built the AI Demand Prediction Platform, which is to be used in future as a web application as a self-service in the plants.

A futuristic car in rapid motion is surrounded by luminous and dynamic light strips under an eye-catching

Our challenge: More speed through automated machine learning

The challenge was the large scale of the problems: each product is a time series in its own right. In addition to strong differences in the demand of the individual products, new products can be added or existing products can be discontinued. As it is difficult to optimize a single algorithm for the problem in this environment, we opted for Automated Machine Learning (AutoML).

In the meantime, another plant has been integrated into the platform. In addition, machine learning algorithms that were developed in the specialist departments were integrated in parallel with AutoML as a possible solution. As a result, these solutions are also shared between the specialist departments.

Increased efficiency through AutoML: Automated finding of suitable ML models for different data sets

With AutoML, experimentation with machine learning algorithms is abstracted and thus accelerated. In most cases, existing AutoML solutions contain a selection of different ML algorithms and feature engineering methods that are automatically tested on the given data.
This allows AutoML to find the right model for each data set. This had the advantage that we could find the right model for each product and also integrate new products directly into the platform.

Two people are sitting at a table with a laptop. There is a glass of water on the table and a brick wall with plants in the background.

Our result: a standardized and scalable solution

Thanks to AutoML, we shortened the PoC phase of the project to just a few weeks. We quickly delivered initial and technically good results. As a result, we gained a great deal of trust on the customer side and aroused enthusiasm for the project. This enabled us to work on the automation issues raised and to be given sufficient time to do so.
In the meantime, another plant was integrated into the platform. In addition, machine learning algorithms that were developed in the specialist departments were integrated in parallel with AutoML as a possible solution. As a result, these solutions are also shared between the specialist departments.

Efficient time series analysis with AutoML and individual algorithm development in the Siemens IT landscape

Using AutoML in combination with the algorithms developed in-house, 50 models are trained per time series and the best of these is selected for the prediction. This identifies the best possible algorithm for each time series and improves the prediction quality.

Thanks to the integration into the Siemens IT landscape, new models are trained automatically as soon as the data basis requires it. Forecasts are fed directly into the data lines of the specialist departments. The integration of further plants is being planned.

An abstract illustration shows a human figure made up of geometric shapes in a scheme of pink and purple.
The most important success criterion for this machine learning project is a standardized and scalable solution that effectively integrates the diversity of our products as well as manually planned plants. This synergy, customized by MaibornWolff, shows the true value of the project.
Dr. Daniel Patrick Kilian, Senior Key Expert Data Science & AI, Digital Industries IT at Siemens

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Why MaibornWolff?

As one of the most innovative IT service providers with a great passion for AI, we focus entirely on the project business and individual software development - without our own products. To stay at the forefront, we continuously invest in our team of digital technology engineers and develop digital solutions that are well thought-out, efficient and reduced to the essentials.

Our principle: simplicity instead of complexity. We only develop what is really needed - tailor-made, useful and reliable. Our results speak for themselves. With over 800 large-scale systems and more than 10,000 person-years of experience in high-end software engineering, we are one of the few who can reliably implement even the largest and most complex IT landscapes. Thanks to close partnerships with leading hyperscalers, our customers operate their solutions in today's most modern and powerful environments.

Less technology. Better Business.

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