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16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
DECISION SUPPORT BY COMPUTER
AIDED FACILITY MANAGEMENTSPACE ALLOCATION
Thomas Madritsch
International Benchmarking Institute, University of Applied Sciences FH KufsteinTirol, Austria;
University for Health Sciences, Medical Informatics and Technology, UMIT-HALL, Austria
ERES Conference 2009Stockholm
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
► Lack of transparency in many companiesneed to optimize operating costs / FM cost
► Demand: CAFM tools from simple information to multifunctional decision support tools
► Space allocation big challenge for FMHardly assisted by IT
Aim
► Illustrate the cutting edge relevance CAFM as decision support tool
State of the art
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Benefits of CAFM ImplementationSurvey from 150 Companies in D-A-CH (May 2005)
enhancement of planning possibilities
enhancement of decision
making
enhancement of resources
availability
reduction of planning
faults
reduction of the administration
costs
0% 10% 20% 30% 40% 50% 60%
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
IT-supported decision support
for space
allocation and optimization
Example for decision support
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Sympathy / Attraction Proximity
Aversion / Repulsion far off
?
24! = 620,448,401,733,239,439,360,000 possible variants (permutations)
Let‘s assume 1 ms computing time per variant 19,674,289,755,620 years for finding an optimal seating
How to Arrange a Seating Plan for a Family Celebration ?
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
2 Possible Seatings out of 24!
Which one will result in more harmony?
How to Arrange a Seating Plan for a Family Celebration ?
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
How does FM achieve ideal allocation efficiently?Surfaces optimally charge to capacity
calculate
plan
optimize & save costs!
analyze
€!
Example for DSSOrganisation move in new building
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Selection floors, offices, spaces
back
Example Data Input
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Adjusting
Communication- relations
back
Input of relations/frequency
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Forced brown and purple due to higher
communication
3. Floor
4. Floor
5. Floor
6. Floor
adjustable reservations for selected areas
Consideration short ways, if necessary over
Stairs and elevators
The occupied space surface is appropriate only 3.72% over theoretical. minimum need
Computing space planning
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Basement
Ground floor
1st floor
4th floor
3rd floor
2nd floor
Scenario 1: City Administration Building
Project Results
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
Example:
By surface compression of only 3% e.g. 100.000m ² (= 3,000 m ²)
Savings renting costs ( 11, - €/m ²month) 396.000, - €/a
Savings operating expenses (3,30 €/m ²month) 118.000, - €/a
Savings 514.000, - €/a
Computer Aided Real Benchmarking
16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm
► DDS Large area of growth
► Simulating real estate processes to support decision processes
► Controlling efficiency of REM
► Higher degree of transparency – web based (any time and place)
► Support Workplace management (FM&REM&PM&..) higher productivity
Conclusion: Decision Support DDS