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Predictive Analytics for Logistics to
Increase Man & Machine Efficiency
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Richard Martens| MD
Chicago, 13th May 2014
Resource Intelligence – Self-learning Analysis & Prediction
Efficiency increase for man & machinery
Discovery: Automatic generation of optimization and simulation model for your specific process Individual: The system propose you an individual solution for the efficiency increasing tightly consider the specific of Your individual approaches or processes Adaptive: Should your processes change in the future, the model adapts automatically to your process
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… then, Resource Intelligence finds the answers for you!
… and their answers are hidden, cut in pieces and incomplete in your data…
If you have questions…
IS Predict & Scheer Group
Employees
2010 - 2014
Turnover (million €)
2010 - 2014
Locations
Pro
f. A
.-W
. Sch
eer
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Visionary, researcher and author of standard works for business information systems
Member of the council for innovation and growth of the German Government
President of the German Association for Information Technology (BITKOM 2007-2011)
Ranked as 2nd most important German IT person (of 100) by Computerwoche magazin (after Hasso Plattner / SAP)
Founder of international software companies IDS Scheer & IMC AG
Sole Shareholder of Scheer Group GmbH
Germany
Australia
Austria
Benelux
France
Great Britain
Rumania
Switzerland
100
50
800
400
Turkey
Ukraine
Capacity Planning Reduce Costs for Rental Rail Wagons
Optimal allocation of required rail wagon / types
Objective: Reduce costs for hired rail wagons due to predictive and demand-oriented inventory management Problem: Many types of rail wagons difficult to plan best fit wagons in entire context Volatile demand difficult to predict required wagons per category Solution: Discovery of patterns and dynamic profiles Data: Wagon demand (history); wagon hierarchy; wagon prices
Cost reduction potential due to foresighted-optimal inventory management and allocation of best-fit rail wagon
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Capacity Planning Reduce Costs for Rental Rail Wagons
Optimal Fleet Structure
Objective: Optimize fleet structure. Problem: Fleet hiring to be done in advance without correct and foresighted know how about future demand Solution: Discover patterns and dynamic profiles for real future demand Data: Wagon demand (history); wagon hierarchy; wagon prices
Cost reduction due to optimal fleet structure planning
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Plannable Energy Consumption for Fleet of Electrical Vehicles
Individual and reliable prediction of charging status to plan range
Objective: Assurance for driver how far he will get with battery capacity Also considering that battery to be charged with renewable energy, generated by himself Problem: No reliable information about charging status available. Thus, no reliable information about possible range. E-car charging status depends on various factors:
Driver´s behavior, way of driving (sportive, …), usage of electricity consumers (air con, …), distance (stop & go, motorway, …)
Solution: Dynamic correlation and causality analysis for relevant influencing factors; Highly accurate prediction of relevant factors and charging status
Highly accurate prediction of relevant individual factors Thus, also highly reliable prediction of battery demand per individual driver
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Amperage
Torque
Charging status
Temperature
Prediction Actual Deviation
Predictive Maintenance Early Discovery of Engine Anomalies
Individual demand-oriented maintenance via anomaly analysis
Objective: Increase efficiency via early information on (future) wear & tear Solution: Discover first and hidden signs when engine does not run efficient anymore; inform technical service when thresholds of anomalies is passed Condition: Individual & cost-reduced analysis per machine without additional sensors Problem: Strongly volatile energy demand, only engine energy data, no access to production data
10 minutes: Engine run without disturbances
10 minutes: 51 disturbances due to breaks
Anomaly Details: No regularity in variable energy demand during disturbance
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Despite high volatility and no knowledge about production data, engine anomalies are discovered between 86% - 100% - only on energy consumption data!
Utilities PV Power Usage
Objective: Run production machinery mostly on PV power, generated by your own Problem: PV power very volatile and difficult to plan; energy demand of machinery also volatile; energy demand does not match energy availability How: Foresighted machinery control via accurate PV power generation prediction Data: Weather (past / forecast) power generation (past)
Accuracy Mar Apr May Jun Jul Aug Sep Oct
O Month 94 % 97 % 94 % 93 % 99 % 96 % 97 % 92 %
O Day 91 % 93 % 92 % 93 % 95 % 95 % 93 % 93 %
Accurate 24 h PV power generation prediction for 1 individual installation
Reduced power costs due to optimal usage of own PV power
Resource Intelligence realizes flexible and precise predictions despite high volatility
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Energy
Reduced costs for energy via more precise 24 h gas prediction
Objective: Plan demand-oriented gas purchase for tomorrow & thus, reduce purchasing costs Problem: Standard load profiles too inflexible for dynamic demand of consumer How: Dynamic load profiles with flexible pattern recognition Data: Historic gas consumptions, weather (past and forecast); no consumer classification
Accuracy Jan Feb Mar Apr May Jun
O 24h (%) 96 89 91 88 86 88
Accuracy Jan Feb Mar Apr May Jun
O 24h (%) 92 90 81 83 67 74
Resource Intelligence ca. twice as precise than state of the art solution with standard load profiles
Resource Intelligence
Typical Solution April
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„Discovery“ Self learning prediction with automatic model
generation Deviation
Average: 8% Maximum: 26 %
Specific forecast External supplier with 12 years experience in area
Deviation Average: 18%
Maximum: 47 %
Why IS Predict?
Common approach
Benchmark on prediction tools in high volatility
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SAP CEO visits IS Predict
CeBIT 2014
SAP CEO Jim Hagemann Snabe
meets IS Predict MD Britta Hilt to inform himself about
Resource Intelligence
ff
Contact:
IS Predict GmbH Scheer Tower | Uni Campus Nord D5.1
66123 Saarbrücken | Germany Phone +49 681 – 96777-200, Fax +49 681 – 96777-222
www.ispredict.com
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