Simulation-based analytics for industrial construction

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The industrial construction sector in Alberta faces numerous challenges, in particular stemming from recent global pressures on resource pricing (Lewis & Jones, 2015). Under this circumstance, the sector needs to enhance its competitiveness especially through improvements related to safety, quality, and productivity, as well as resolving inefficiencies in the supply network. Industrial construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals (Manyika et al., 2011), and supporting enterprise resource planning (ERP) systems. Industrial construction companies, like most manufacturing companies, "do not make good use of all the generated and collected data to improve production system efficiency" (Dean, 2014). They struggle to manage, analyze, and transform data into useful information for improved decision making to improve their competitiveness. Our goal is to introduce a new generation of quantitatively-driven analytics systems that make use of collected data, and are dynamically supplanted with simulation models in real time to enhance the management of the construction production process. Our proposed research approach will build upon our academic strength in simulation and the knowledge of the industrial partners. Specifically, the proposed research aims to develop a simulation-based analytics for construction (SAC) framework to support a new generation of decision support systems (DSS), develop the simulation environment to deploy the framework, and demonstrate its validity through a number of implementations in the industrial sector with industrial partners, in the areas of productivity, safety and quality management, and in the analysis of the industrial construction supply chain.