References

Siemens

Predictive Analytics Partner for Predictive Services

T-Systems

Predictive Quality Control

German Rail

Avoiding total damage thanks to predictive maintenance

EnBW: Full Potential For Renewable Energy Usage

Innovative analytics approach enables future-oriented solutions for renewable energy

BASF: Capacity Planning

Capacity Planning: 5 – 10 % Cost Reduction Potential

SAP

1st pure Predictive Analytics Partner in Germany

Bosch

Predictive Quality Analytics in Production

Opel

Self-Learning Predictions for optimizing Power Plant Operation and Energy Trading

Knauf

Root Cause Discovery and Optimization for Quality Parameters in Production

ZF

Self-learning AI Improves Production Quality in Complex Variant Processing

Police

Discovering communication patterns in organized crime

EVO: Machinery Control / Simulation

Heat Recovery saves 25 % of Energy

NTT Facilities

Failure Prediction for Critical Air-conditioning System

LG: Predictive Heating/Cooling

Adaptive Hotel Solution to Minimize Energy Costs

RES-COM

Research project Industry 4.0

Dillinger Hütte

Energy Demand Analysis

Koehler Paper Group

Predictive Quality Analytics in Production

Stadtwerke Saarlouis: Renewable Energy Prediction

Flexible and Precise Predictions despite High Volatility

Bartz Werke

Reduction of Energy Resources in Casting

Bundesministerium für Bildung und Forschung

Research project Industry 4.0

Saarstahl

Logistics Optimization

Netzwerke Saarlouis: Gas Prediction

Twice as Precise than State of the Art Solution

Wittenstein

Predictive Maintenance

DFKI

Research project Industry 4.0

Hager Group

Predictive Building Control

DesigNetz

The Operating System for Energy Transition

Mobil saar

Research project Mobility 4.0

CyProS

Research project Industry 4.0

bpE

Process Analytics

Guided AB

Predictive and self-learning house automation

Bundesministerium für Verkehr, Bau und Stadtentwicklung

Research project Industry 4.0

Vicar

Customer Analytics and Recommendations

Thanks to self-learning Artificial Intelligence, our standard software has proven itself in different industrial branches. On the one hand, it improves our clients operative results. On the other hand, costs over years are reduced significantly, because necessary modifications – caused by changed processes – are understood automatically by Self Learning Algorithms. Thus, there is no need for a Data Scientist to adapt the solution again and again.

Learn more with our videos.

Industries

Artificial Intelligence is required in all industrial branches. Hot topics are often the same, like:

• Reducing waste in production (Predictive Quality)
• Having critical machinery running reliably (Predictive Maintenance )
• Optimizing transportation (Predictive Logistics)
• Reducing energy costs in production (Predictive Energy)
• Optimizing resource efficiency in smart grid (Predictive Energy Grid )
• Using HR, machinery and material in an optimal way (Predictive Resource Planning )
• Managing buildings efficiently (Predictive Building)
• Knowing how to sell best (Predictive Sales)

To give you a better understanding which analytical challenges other industries solve with our self-learning PREDICTIVE INTELLIGENCE solution, some examples are listed here.

Automotive / supplier

Self-learning PREDICTIVE INTELLIGENCE

• Analyses assets like
  • robots (welding, painting, handling, ..), presses, etc.
  for
  • Quality assessments / predictions (i.e. car body welding spots),
    Predictive maintenance (i.e. welding gun, cable package, gear or entire drive train)
• Analyses end-to-end production process
  • for transmissions and other car components
  • to discover root cause of minor quality although interim production steps' specification was always met
• Analyses energy consumption
  • for various machineries or entire plants for heat and electricity
  • to reduce energy costs
    to automatically recommend optimal energy plant operation and optimal energy trade.

Discrete manufacturing

Self-learning PREDICTIVE INTELLIGENCE

• Analyses assets like
  • stamping machines, spindle machines, etc.
  for
  • Quality assessments / predictions
    (i.e. for injection pump production machinery)
    Predictive Maintenance
    (i.e. for cable shoe production machinery)
• Analyses end-to-end production process
  • for products like semiconductors
  • to discover root cause of minor quality although interim production steps' specification was always met
• Analyses energy consumption
  • for various machineries or entire plants for heat and electricity
  • to reduce energy costs
    or to automatically recommend optimal energy plant operation and optimal energy trade.

Process industry

Self-learning PREDICTIVE INTELLIGENCE

• Analyses material like
  • steel, paper, cement, glass, chemistries, etc.
  for
  • Quality assessments / predictions (i.e. for paper or cement quality)
    Predictive Maintenance (i.e. for rolling mill)
• Analyses end-to-end production process
  • for, i.e., plaster products
  • to discover root cause of minor quality although interim production steps' specification was always met
• Analyses energy consumption
  • for complex machineries like cement mill and steel oven
  • to reduce energy costs and
    to find root cause for high energy consumption.

Logistics

Self-learning PREDICTIVE INTELLIGENCE

• Analyses assets like
  • locomotives, rail way wagons, tracks, ...
  for
  • Predictive Maintenance (i.e. motors, air conditioning systems)
• Analyses transportation demands
  • i.e. for material from plant to harbor or for people at station hubs
  • to predict different demands highly accurate for better planning of locomotives, wagons, staff, ...
• Analyses energy consumption
  • for locomotives or e-cars
  • gives recommendations to driver how to reduce energy without getting negative effects on schedule, machinery, ...

Energy grid

Self-learning PREDICTIVE INTELLIGENCE

• Analyses assets like
  • power plants, complex machineries (i.e. steam gas turbine, boilers, machineries to generate renewable energy)
  • for dynamic efficiency,
    for finding influencing factors for inefficient usage,
    for Predictive Maintenance
• Analyses grids (producers, consumers, prosumers, both industrial and private)
  • for highly accurate predictions
  • for steering energy flows in a predictive way
  • to reduce external energy purchase
    to improve energy trade margin
    to use renewable energy most efficiently.

Cross industry

Self-learning PREDICTIVE INTELLIGENCE

• Analyses online shop users and predicts
  • when they will buy again (for targeted newsletter distribution),
  • what they will buy again (for targeted advertisements)
  • what they will return (for avoiding returning goods).
• Analyses capacity in complex processes,
  • like optimal HR allocation of thousands of workers or machinery and material in multi-national construction projects
  • Like planning and renting for thousands of resources, i.e. rail way wagons.