16.01.2009© Thomas.Madritsch@hsk-edu.atERES 2009 Stockholm

DECISION SUPPORT BY COMPUTER AIDED FACILITY MANAGEMENT

SPACE 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 ProximityAversion / 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

AdjustingCommunication- 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, - €/aSavings 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