Data Science for better competitiveness
In times of big data, topics like artificial intelligence and data science are everywhere. But how can you use them for your own economic benefit?
Data science is about extracting hidden knowledge from data to make decisions in the right places and simply processes.
Through our expertise and the intelligent software’s variety of application areas, you can increase your productivity, reduce your costs and, above all, save valuable time.
Find out what our approach is and develop opportunities and potential from your challenges and data.
Example use cases give you an insight into the many possible applications for data science.
PIKON data science lab and AI consulting – our services at a glance
We custom train artificial intelligence to solve your problem. Flexibility, expandability and adaptability are our top priorities. Through our experience with business processes, organisational development and business intelligence, we are very familiar with the many data sources in companies. We use open source frameworks for AI applications. We have experience with different cloud providers and adapt to the infrastructure in your company. As a result, the use of artificial intelligence does not require large investments in infrastructure.
AI Consulting
We advise you and find a data-driven solution to your problem using AI with and without SAP.
AI Operations
We advise you and find a data-driven solution to your problem using AI with and without SAP.
AI Products
We offer customised solutions to automate processes.
Our expertise for your success
With our team of data scientists and mathematicians, we create real added value for your company. In times of digitalisation, data is abundant. The important thing is to use it skilfully to build competitive advantages. We support you in this – find out how in a direct exchange.
Our approach to the project
In our projects we like to work closely with your department and data owners in an agile way, because you know your requirements and data best. To practice the first throw of an AI application, we like to work agile and build a prototype. Data preparation and transformation are among the most time-consuming steps. We therefore build data pipelines that can be reused for other applications. Once a prototype delivers the desired results, we find the perfect structure to make the application productive in your company. This may be on a cloud platform, as a packaged Docker application or as a service hosted by us.
Our Toolset:
- AI applications: Python, R and SQL / SQLScript programming languages
- Code and model versioning: Git
- Different cloud platforms (GCP, Azure, Azure Databricks, AWS, SAP Data Intelligence)
- Data sources (SQL-DW, NoSQL, distributed data sources like Spark/Hive).
- Container-based services (Docker, Kubernetes)
Typical use cases
Every industry and every company has unique challenges. Our use cases show you how you can counter these with AI to generate the most value.
Our team of data scientists and mathematicians can help you with these issues, but also with very individual needs.
What's your use case?
- Would you like to know how artificial intelligence can help solve a specific problem in your company?
- Would you like to automate time-consuming processes?
- Are you interested in forecasts to simplify your planning?
Typical use cases of Data Science
Intelligent master data cleansing
Master data plays a major part in many business processes. This may be customer and supplier data, but also the materials that are used in the company. If the database is built up over many years by many different employees, the quality and homogeneity of the master data can suffer and at the same time have huge potential. The process of cleansing master data often involves a lot of manual work and an immense amount of time for specialist staff. We have therefore addressed the issue of partially automating this process.
- Do processes sometimes run inefficiently in your company due to data quality? Data is used in the company’s daily processes and can be used to generate insights and automation.
- Do analytical requirements fail because data quality distorts forecasting models? We can start together where the data does not meet the desired analytical requirements due to poor quality or hinders processes.
We train AI models for you to speed up data cleansing through automation. Read here how we intelligently clean up master data during a CRM migration (only available in German) by training artificial intelligence.
AI-supported planning
Planning has a significant impact on a company’s success. The more precisely we plan, the better we can respond to customer requirements, optimise stock levels or even make production more efficient. Forecast values can therefore help optimise the planning process. Insights can be drawn from the company’s data as a result. External factors can also be included in the planning. This also has the potential to increase the frequency of planning to more accurately support value creation.
Imagine not having to spend two labour-intensive days planning at the end of each year but being able to create a new plan at any time with just a few mouse clicks.
Are you familiar with planning sometimes being a “guessing game” and the real numbers differing from the planned values? This is where artificial intelligence offers potential that you can exploit. With AI trained for your company, incorporating all the relevant data, your planning process is supported by predicted suggested values.
Just suppose you want to plan each month instead of planning for the year. AI support saves you time, allowing you to carry out the planning process more often.
Quality inspection in production
The quality of products is always important. It is better to identify possible quality defects at an early stage to save costs. The quality inspection can be supported really well by artificial intelligence. Algorithms can be used to identify small variations in sensor data as quality defects. Large quantities of image data can also be analysed efficiently and quickly to detect production faults.
Do you use artificial intelligence to inspect the quality of your production? Manual quality inspections can be supported with AI models based on sensor and image data. This allows quality defects to be identified at an early stage and supports quality management.
We developed AIQinspect with the Zentrum für Mechatronik und Automatisierungstechnik (Centre for Mechatronics and Automation Technology). This is used to inspect the quality of rivets in aircraft production.
Cost variances in the project
When costs are planned for projects, the costs may differ from the actual project costs incurred. Artificial intelligence can assist people to identify indicators of cost overruns at an early stage in order to initiate countermeasures and avoid losses. Do the costs of your projects deviate from the project planning? Variances between project planning and actual costs can play a major role in project business. If the planning is too tight, the margin dwindles and the sales price may not cover the costs incurred. If the margin is too large, there is a risk of losing an order because competitors may make better offers.
Artificial intelligence
- Can analyse cost variances in detail and optimise them based on data.
- Cost variance risks can be forecast and dealt with at an early stage.
We were able to use AI to forecast cost variances and identify causes at SIEMENS Energy.
Contact us
Our Data-Science expertise for your success! Don’t hesitate to contact us.