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Data-Driven Forecasting for Operational Processes

Machine Learning & Data Science for Automated Decision-Making

We develop robust ML models and data science solutions and seamlessly integrate them into your systems and processes. Our applications automate data-driven analyses and support operational decision-making, designed for companies that want to use machine learning in production, not just experiment with it.
15–30 minutes | no obligation | free of charge | actionable next steps
Agentic AI visualisiert: Autonome Intelligenz in einer digitalen Gehirnarchitektur

When is Machine Learning the right fit?

Do one or more of the following apply to you?
  • Decisions must be made regularly based on large volumes of data
  • Review processes are time-consuming or prone to errors
  • Results must be transparent and auditable
  • Probabilities, rankings, or scoring systems drive your processes
  • Analyses or evaluations should be automated
If so, machine learning is very likely the right approach.
Machine learning is not the right choice if decisions can be reliably defined using clear rules or if the available data is insufficient.

Typical Machine Learning Use Cases

  • Sales and Demand Forecasting

    • Improved production and inventory planning
    • Reduced overstock and stockouts
    • Better control of purchasing and sales activities

  • Risk Scoring and Case Prioritization

    • Automated evaluation of applications or transactions
    • More efficient review processes through prioritization
    • Transparent and auditable decision-making

  • Anomaly Detection in Production Data

    • Early detection of disruptions
    • Reduced unplanned downtime
    • Continuous monitoring of large data volumes

  • Recommendation Systems

    • Personalized product or service recommendations
    • Increased relevance of offers
    • Data-driven personalization of customer interactions

  • AI-Driven Process Control

    • Real-time optimization of process parameters
    • More stable production workflows
    • Reduced energy and material consumption
Implementing these use cases requires methodological expertise, integration capabilities and experience running models in production environments.
100 %
recommendation rate*
35+
years of project experience
130+
AI experts
* based on Europe’s largest independent user survey (BARC, 2025)

Structural Improvements Through Machine Learning

Machine learning doesn’t just improve individual analyses. It enhances the quality and speed of entire decision-making processes.
Better Decisions
  • Transparent, explainable and consistent
  • Hidden patterns in large datasets become visible
  • Decisions are based on clear, data-driven logic
Faster Decisions
  • Automated evaluations reduce manual effort
  • Processes become scalable and reliable
  • Every decision follows consistent logic
Machine learning evolves from an analytical tool into a core component of operational management.

From Idea to Production ML in 5 Steps

Our services are modular and cover the entire journey, from identifying a viable use case to running a stable production model.
Not every use case requires the same approach. We select the method that is technically sound, economically viable, and fits seamlessly into your system landscape, if needed, combining ML with other AI methods and proven software engineering practices.
In a short, no-obligation consultation, we’ll determine the best approach for your specific needs.
Assess Your Machine Learning Potential

Let’s assess your use case in a structured way

In a no-obligation initial consultation (approx. 30 minutes), we’ll determine whether and how machine learning can be effectively applied to your specific challenge.
Appointments are typically scheduled at least one week in advance.
Information on data processing can be found in our Privacy Policy.
Preferred Appointment Date
Benefit from Our Experience

Why HMS for Machine Learning?

Machine learning only delivers value when models are both business-relevant and technically integrated. This is exactly where we excel.
End-to-end ML expertise: from use case to production
Strong integration of data science, software engineering, and MLOps
Experience in complex and regulated environments
Focus on production-ready, long-lasting solutions—not isolated PoCs
Seamless integration into your systems and processes
You don’t get a demo, you get a production-ready decision system.
Proven Customer Satisfaction (BARC)
Our clients value our work, as confirmed by Europe’s largest independent Data & Analytics survey (BARC, 2025):
100%
would recommend us
100%
confirm project success
96%
rate our technical expertise as outstanding
... explore HMS study results
Assess Your Machine Learning Potential

Our Success Stories

Learn more about custom machine learning solutions from real client projects. These examples demonstrate how we develop, validate and integrate models to deliver reliable, long-term business value.

Sales Analytics Platform

Consumer Health

Bayer Vital integrates top-tier customer management with modern technologies to quickly respond to changing market demands and differentiate itself from competitors. By leveraging analytical potential, the knowledge lead is further expanded, enabling data-driven measurement, optimisation and control of sales and marketing processes in the sales channels.

Benefits:

  • Rapid response to market changes
  • Differentiation from competitors
  • Data-driven optimization of sales and marketing processes

Enhancing the Customer Journey in Online Sales

E-Commerce

HMS optimized the customer journey for Verivox in the online sale of complex products through detailed data analysis and evaluation of various touchpoints and methods.

Benefits:

  • More precise budget allocation
  • More efficient distribution of marketing resources
  • Improved sales success
Case Study anschauen

Innovating Clinical Research: AI for Secondary Clinical Data Analysis

Healthcare / Life Science

We supported Novartis in exploring the potential of machine learning in clinical research. By analysing study data with machine learning and explainable AI, we identified unknown correlations and improved the predictive power for treatment outcomes and safety profiles.

Benefits:

  • Discovery of unknown correlations
  • Improved prediction of treatment outcomes
  • Optimised identification of critical features through explainable AI
Case Study anschauen

Cloud-native Statistical Computing Environment at Boehringer Ingelheim

Life Science / Pharma

HMS replaced a 25-year-old central SAS-based system with a modern, cloud-native Statistical Computing Environment (SCE) for analyzing clinical studies for Germany's largest pharmaceutical company.

Benefits:

  • Integration of the SCE web app with SAS Studio under SAS Viya for seamless workflows
  • System design with AWS services for optimal data security and access
  • Automated validation strategy to simplify regular release deployments
Case Study anschauen
Visualisierung einer vernetzten Datenstruktur über einer Person, die auf einem Laptop arbeitet – Symbol für automatisierte Datenintegration und digitale CRM-Prozesse.

Automatisierte Datenpipeline für eine optimierte CRM-Strategie

HMS entwickelte eine skalierbare ELT-Pipeline, um Kundendaten täglich zu aktualisieren. Die Lösung liefert Echtzeitanalysen über ein Dashboard und unterstützt Geschäftsentscheidungen in über 40 Ländern. 

  Der Kundennutzen: 

  •  Tägliche Datenaktualisierung für präzise CRM-Entscheidungen 
  • Automatisierung reduziert manuellen Aufwand 
  • Skalierbare Lösung für zukünftige Erweiterungen 
Read Case Study

Sales Analytics Platform

Consumer Health

Bayer Vital verzahnt erstklassiges Kundenmanagement mit modernen Technologien, um schnell auf veränderte Marktanforderungen zu reagieren und sich vom Wettbewerb abzugrenzen. Durch die Ausschöpfung analytischer Potenziale wird der Wissensvorsprung weiter ausgebaut, was eine datengetriebene Messung, Optimierung und Steuerung der Vertriebs- und Marketingprozesse in den Absatzkanälen ermöglicht.

Der Kundennutzen:

  • Schnelle Reaktion auf Marktveränderungen
  • Abgrenzung vom Wettbewerb
  • Datengetriebene Optimierung von Vertriebs- und Marketingprozessen"
Read Case Study

Next-Level Clinical Research: AI for Secondary Clinical Data Analysis

Healthcare / Life Science

Wir unterstützten Novartis dabei, das Potenzial von Machine Learning in der klinischen Forschung zu erkunden. Durch die Analyse von Studiendaten mit Machine Learning und Explainable AI identifizierten wir unbekannte Zusammenhänge und verbesserten die Vorhersagekraft für Behandlungsergebnisse und Sicherheitsprofile.

Der Kundennutzen:

  • Entdeckung unbekannter Zusammenhänge
  • Verbesserte Vorhersage von Behandlungsergebnissen
  • Optimierte Identifikation kritischer Merkmale durch Explainable AI
Read Case Study

FAQ – Machine Learning in der Praxis

Machine learning is particularly well-suited when decisions are made regularly based on data, processes need to scale, or complex relationships between influencing factors must be identified.

Machine learning is less suitable when:

  • there is very little data or the data is unreliable
  • simple rules are sufficient
  • decisions are made infrequently and manually

The key factor is whether machine learning fits the business and technical requirements of the use case. In some situations, reporting tools or tailored systems for data preparation and business logic implementation may be sufficient alternatives.

Generative AI creates new content such as text, images, or code and is particularly suited for conversational or creative applications.
Machine learning identifies patterns in data and generates predictions, scores, or decision models. The input data may include structured tables as well as images or audio files.

For clearly defined evaluation and forecasting tasks, specialized machine learning models are often:

  • more accurate
  • more stable
  • more reproducible
  • more cost-efficient to operate

However, the right choice always depends on the specific use case.

The amount of data required depends on the use case and the specific problem. There are also specialized methods that allow robust models to be trained even with smaller datasets.

What matters most:

  • Data quality
  • Structure
  • Domain relevance

It’s not about “more data,” but about having the right and consistent data.

We systematically assess your data in terms of quality, completeness, and relevance before deploying any model in production.

Typical phases include:

1. Use case validation and goal definition
2. Data preparation and exploratory analysis
3. Model selection, training, and domain validation
4. Integration into existing systems
5. Monitoring and continuous improvement

The goal is always to deliver a production-ready solution—not just a one-off analysis.
Yes. With the right methods, models can be documented and validated in a transparent and auditable way.
Explainable AI provides insight into key influencing factors and supports traceability. Reproducible training processes and clearly versioned model artifacts ensure that results can be replicated at any time.
The combination of explainability and reproducibility forms the foundation for auditability and regulatory compliance.
Regulatory requirements are taken into account from the very beginning—starting with system architecture and model design.
A model only creates value when it is reliably integrated into existing processes and systems.

This includes:

  • Integration via well-defined interfaces
  • Versioning and monitoring
  • Stable MLOps processes
  • Clearly defined operational responsibilities

Machine learning is not a one-time project, it is a long-term component of your system architecture that requires ongoing operation and maintenance.

Portrait von Christoph Bergen
Christoph Bergen
Team Lead AI/ML

Ready to get started?

In a no-obligation initial consultation (~30 minutes), we’ll assess whether and how machine learning can be effectively applied to your use case.
Appointments are typically scheduled at least one week in advance.
Assess Your Machine Learning Potential
Information on data processing can be found in our Privacy Policy.
Preferred Appointment Date
© 2024 – 2026 HMS Analytical Software
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