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Use of KPIs in an Integrated Decision Support System (IDSS) for Energy Efficient District Retrofitting ECODISTR-ICT H2020 (FP7)Project Esra Bektas and.

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Presentation on theme: "Use of KPIs in an Integrated Decision Support System (IDSS) for Energy Efficient District Retrofitting ECODISTR-ICT H2020 (FP7)Project Esra Bektas and."— Presentation transcript:

1 Use of KPIs in an Integrated Decision Support System (IDSS) for Energy Efficient District Retrofitting ECODISTR-ICT H2020 (FP7)Project Esra Bektas and Bart Luiten (TNO)

2 Structure Section 1: Background –the necessity for the IDSS and use of KPIs Section 2: Modelling the Decision Process that the IDSS supports Section 3: Inclusion of KPIs in the IDSS supported decision process

3 1. BACKGROUND

4 Background: Complex Decision Process in Energy Efficient Urban Retrofitting Multi-stakeholder, with a wide range of stakes and priorities Is boosted due to incorporation of both selecting and using different sustainability measures dealing with each stakeholder’s vision, financial capacity, and different performance expectations. Multiple decision levels Multiple time horizons Multiple KPIs Not only rational decisions but intuitive Dynamic process

5 ECODISTR-ICT project ECODISTR-ICT aims to enable local authorities, architects and urban planners planning in making decisions on retrofitting of districts through a new open source software tool. Called the Integrated Decision Support System (IDSS) Prioritise actions in the district and the surrounding area. Integrate goals and stakes of different stakeholders in a single software environment Enable analysis of different scales and different time frames Create a versatile tool with an open structure Facilitate day-to-day work of future users The IDDS refers to a ‘software backbone’ that integrates existing design and calculation modules rather than developing a new ones. The IDSS facilitates decision making through credible input for selection and incorporation of measures in design phase.

6 The IDDS Components a) Graphical User Interface (GUI), b) the Dashboard, and c) The Framework.

7 Critical Observations When such complex, dynamic, iterative –sometimes intuitive process is mediated by computers. There is a probability to end up with a rigid and prescriptive decision model, which does not correspond to the stakeholders’ practices. If/when included, to focus on KPIs scores rather than how can be instrumented for the stakeholders in making choices. --or– a clear decision model but the way missing the way that KPIs are used.

8 The Need regarding the KPIs in the IDDS There is a need to define decision model that the IDSS can support yet to be dynamic, iterative, fit-to-the purpose. There is a need to identify the use of KPIs that stakeholders require and the way to support it through the IDSS. There is a need to define the support via the integrated environment, which synthesizes the calculation modules and assessment modules for selected KPIs.

9 2. The Decision Model for the IDDS

10 Decision Process: Why to model and plan? Having higher chance to establish clear goals and ambition, made a conscious and directed series of choices. Providing a standard of measurement, to direct whether decision maker go towards or further away from the ambition. Enabling to convert values to action iteratively to advance the plan best Clarifying the boundaries To manage the commitments Kepner et al. (1965)Steinitz (2013) Carpenter et al. (2009)

11 Decision Theories Design steps in geo design context of Steinitz (2013) Decision steps defined by Carpenter et al. (2009)

12 The Decision Process for the IDSS

13 ECODISTR-ICT Decision Process Phase A: Defining Ambition

14 Example: Analyse problem High level ambition : Flourished cities… Energy consumption is too high. Buildings are deteriorated. Housing typology is not suitable anymore to the families. Maintenance is (poor and) expensive. Vacant buildings are too many. There is insufficient greenery. There is insufficient facilities for inhabitants. Crime rate is too high. District to be flourished!! Collect data Data collection modules

15 Analyse problem Energy consumption is too high. [Actor1] Buildings are deteriorated.[Actor2] Housing typology is not suitable anymore to the families.[Actor3] Maintenance is (poor and) expensive. [Actor1] Vacant buildings are too many.[Actor4/1] There is insufficient greenery.[Actor5] There is insufficient facilities for inhabitants.[Actor3] Crime rate is too high.[Actor1/3]  Energy-efficiency neutrality  Quality of buildings  Comfort  Flexibility  Easy-Maintenance  Maintenance cost  Attractiveness  Greenery  Sufficiency of facilities  Safety Identify & Select KPIs Assess AS- IS Problem statement Selected KPIs Energy consumption is too high. [City] Buildings are deteriorated. [Investors] Housing typology is not suitable anymore to the families. [Investors/Citizens] Maintenance is (poor and) expensive. [Housing Associations] Vacant buildings are too many.[City] There is insufficient greenery.[Citizens] There is insufficient facilities for inhabitants. [Citizens, Housing Associations] Crime rate is too high.[Citizens] Identify stakeholders Collect data Data collection modules AS-IS data to enable assessment

16 Districts scored XX in aspect KPI’s + AS-IS score Gap between TO- BE and AS-IS KPI’s + values Define TO-BE ambition Assess AS-IS District flourishing; [Input to Program] -Create small-businesses and cultivating economic activities -Create attractiveness to the vacant office stock there -Improve housing stock’s quality (%) -… Assessment modules

17 ECODISTR-ICT Decision Process Phase B: Selecting Best Alternative

18 3. Inclusion of KPIs in the IDSS supported decision process

19 The legend to read the IDSS supported actions in the decision process

20 KPI Inclusion in Defining Ambition Phase

21

22 Conclusions & Discussions on Does the modelled and ‘rationalized’ decision process seem recognizable? How can we ensure the balance should be in proposing rationalized decision process and ‘irrationality’ of the decision makers which is natural and can play great role in making choices? How can we improve our decision model and make applicable for other contexts?

23 THANK YOU.

24 Example: Analyse problem High level ambition : Flourished cities… Energy consumption is too high. Buildings are deteriorated. Housing typology is not suitable anymore to the families. Maintenance is (poor and) expensive. Vacant buildings are too many. There is insufficient greenery. There is insufficient facilities for inhabitants. Crime rate is too high. District to be flourished!! Collect data

25 Analyse problem Energy consumption is too high. [Actor1] Buildings are deteriorated.[Actor2] Housing typology is not suitable anymore to the families.[Actor3] Maintenance is (poor and) expensive. [Actor1] Vacant buildings are too many.[Actor4/1] There is insufficient greenery.[Actor5] There is insufficient facilities for inhabitants.[Actor3] Crime rate is too high.[Actor1/3]  Energy-efficiency neutrality  Quality of buildings  Comfort  Flexibility  Easy-Maintenance  Maintenance cost  Attractiveness  Greenery  Sufficiency of facilities  Safety Identify & Select KPIs Assess AS- IS Problem statement Selected KPIs Energy consumption is too high. [City] Buildings are deteriorated. [Investors] Housing typology is not suitable anymore to the families. [Investors/Citizens] Maintenance is (poor and) expensive. [Housing Associations] Vacant buildings are too many.[City] There is insufficient greenery.[Citizens] There is insufficient facilities for inhabitants. [Citizens, Housing Associations] Crime rate is too high.[Citizens] Identify stakeholders Collect data

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27 Inclusion of KPIs in Analyzing the Problem a)Choose a certified full KPI set (i.e., from BREEAM, Open House, and Super Buildings); b)Select KPIs from existing sets; c)Adding new KPIs (when existing KPIs do not satisfy the specific needs of the stakeholders for the specific case).

28 Inclusion of KPIs in Collecting Data

29 Inclusion of KPIs in Assessing AS-IS situation

30 Inclusion of KPIs in Defining TO-BE Ambition

31 Inclusion of KPIs in Developing Alternatives

32 Inclusion of KPIs in Assessing Alternatives

33 Inclusion of KPIs in Selecting the Best Alternative

34 Decision Levels


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