A new approach for modelling variability in residential construction projects

Abstract

The construction industry is plagued by long cycle times caused by variability in the supply chain. Variations or undesirable situations are the result of factors such as non-standard practices, work site accidents, inclement weather conditions and faults in design. This paper uses a new approach for modelling variability in construction by linking relative variability indicators to processes. The mass homebuilding sector was chosen as the scope of the analysis because data is readily available. Numerous simulation experiments were designed by varying size of capacity buffers in front of trade contractors, availability of trade contractors, and level of variability in homebuilding processes. The measurements were shown to lead to an accurate determination of relationships between these factors and production parameters. The variability indicator was found to dramatically affect the tangible performance measures such as home completion rates. This study provides for future analysis of the production homebuilding sector, which may lead to improvements in performance and a faster product delivery to homebuyers.

Autonomous production tracking for augmenting output in off-site construction

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Optimization of process integration and multi-skilled resource utilization in off-site construction

“…As a result, the construction industry is in continuous need of specialised trades who become scarce resources particularly during boom periods [12].…”

Do Projects really end late? On the shortcomings of the classical scheduling techniques

“…Concerning the second condition, activity duration variability causes that the Critical Path can change its route through the schedule. Recent studies on work flow variability (Arashpour, Wakefield, Blismas & Lee, 2013;Arashpour & Arashpour, 2015) have evidenced that this variability significantly diminishes performance metrics reliability, causes inflated completion times, excessive delays, and results in productivity losses. Also, in presence of duration uncertainty, some indicators like the 'criticality Index (CI)', which measures the percentage of times in which an activity is critical, have arisen, but this has proven not to be a good predictor by itself as to how important an activity is (Vanhoucke, 2012).…”