SmartPlanner – Data driven governance and risk management of project portfolios

Background The construction sector in general, and municipalities in particular, are facing significant challenges in managing multiple simultaneous projects that are also becoming more complex.

Some municipalities manage over 1,000 objects and the projects are characterized by challenges such as changed timetables, budget changes and relocation of operations, which creates an extensive need for information.

There is a lack of systematic and continuous management of project portfolios within the construction sector in general and not least within the municipalities.

Important decisions about risk management by prioritizing and allocating resources are made without or with a weak data base and risk analyzes often take place through human interaction and without machine learning.

One problem is that there is a lack of a dedicated platform where continuous updates on schedules, budgets, risks and deviations are visualized and where different businesses can access this information.

Purpose, target group and delimitation There are digital systems today that manage individual projects and that can be aggregated to visualize the portfolio.

However, these systems have shortcomings, which means that they are rarely implemented within an organization or implementation takes time.

Within this project, the tool SmartPlanner, a digital tool for the construction and real estate industry, which collects and processes project data and visualizes the project portfolio, is further developed.

The tool facilitates project prioritization, decision-making and analysis of data, reduces administration and increases learning for the entire organization.

SmartPlanner will be easier to implement than existing solutions as it does not need to replace existing systems, but project data is retrieved e.g. via open API. The target group for the project is decision makers and project managers with several projects in their portfolio.

In a larger perspective, indirect decision-makers are also included as a large number of businesses and people in ongoing operations are affected by construction projects for new or development of existing premises.

Investigation of the target group and its needs, together with mock-ups with clear functions, have already been developed.

The purpose of this project is thus to develop a functioning POC (Proof-of-Concept) at TRL level 4/5 to enable continued development and testing in close collaboration with a pilot in the form of a municipality.

This delimitation means that at this stage we prioritize building a basic working model that builds on previously produced Mock-ups. However, a simpler user interface is needed. Additional functionality is to be explored and potentially developed in later phases.

Implementation The project is carried out through a structured process with an iterative, agile way of working, where insights and feedback from testing and evaluation are used to continuously improve the prototype.

The project is carried out in seven defined steps (Design and systematization, Data model, Identification of critical factors, Development of prototype, Testing with data, Evaluation, Business model).

Project organization consists of project manager, system architect, expert in portfolio management and full stack developer.

Results and effectsThe expected result is the creation of a digital tool, aimed at streamlining the assessment of status and risks for individual construction projects and aggregating data into a portfolio.

This will fill an important gap in project portfolio management and risk assessment by offering an overview that today is often missing.

At higher technology maturity levels (TRLs), the tool, using machine learning techniques for risk assessment, can present complex data in a user-friendly and visually accessible way.

This enables not only more informed decisions about priorities between different project sizes but also an integrated risk management for an entire project portfolio. By providing t