MULTISYSTEMIC MODELING TO IMPROVE FORECAST ACCURACY IN CONSTRUCTION MANAGEMENT

Abstract

Process modeling and simulations provide indispensable support during project preparation, construction process planning, and execution. Realistic correlation modeling in construction management and/or economics is crucial to inform meaningful, beneficial process simulations. Inductive approach to reflecting reality in a (computation) model enables prospective consideration of the object or process under study and related analyses. This method aids decision making in construction management related projects and contributes to developing new computation models. Linking the management of chances and risks to construction process modeling is thus an essential tool for systematically making and implementing decisions in construction management and economics. Forecast-based conclusions regarding future developments or events require computation models to be developed based on hands-on experience and practical feasibility whilst relying on sound theoretical assumptions, required abstraction levels, simplifications, and considering their mathematical implementation. The resulting model is linked to model objects for which it was developed and to which it is applied. Model objects may include entire buildings or structures, contract sections, or individual structural components, such as those for which construction processes and logistics as well as construction times and costs are determined. Monte Carlo simulations are applied to systematically account for uncertainties. Derived models should be closely related to the aspects and societal challenges of interdisciplinarity, simulation, and digitization. This paper outlines the requirements that a multisystemic model should fulfill to increase forecast accuracy.