QMS Valuation Tool

QUEST Predictive Valuation Tool for Quality Management Services (QMS)

The QUEST Tool predicts the (financial) value-add of applying Quality Management Services (QMS) to building projects. It has been designed to be easily used by investors and building owners who are looking to assess the value-add of QMS to their building projects in the design phase. Users input answers to 5 questions which have been established to calculate the different risk and cost factors of the building projects; see red text on image below. The first two questions are answered using roll-down menus. The remaining three are numeric responses. Based on user input, QUEST algorithms will predict the investment cost and value-add of different services.

Click here to try out the QUEST Predictive Valuation Tool!

Tool Input Data

The figure to the right shows the requested input questions that users need to answer in order to get a prediction on the value-add of different QMS. The questions are meant to be easy to understand for everyone and can be filled in by non-technical experts within the building projects. This simplifies the integration of QMS in building projects by allowing investors and owners to easily see a prediction of the value-add that these services can have on their projects.

Tool Output Results

The output of QUEST algorithms is presented in the QUEST Tool as a euro per square meter value-add prediction for QMS Quality Management Services (see figure to the left here). QUEST Tool indicates value-add calculations for each QMS based on savings generated. Output adjusts dynamically as Input is modified. As an additional aid, the Tool provides an estimate of the cost of the different QMS for the projects.

The output data is not an offer or a fixed prognosis but rather an indication of cost and value-add that can be expected based on empiric data. It can be used to budget QMS cost in early project stages and to argue for the application of QMS based on  value-add different services.

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Grant Agreement number 846739. The European Union is not liable for any use that may be made of the information contained in this document, which is merely represention the author's view.