An Integrated Framework for Balancing Contractor's Workload versus Capacity using System Dynamics
Creators: Elnady, Ahmed Abdelrady Okasha Mohamed
Publisher: name: University of Alberta Library
Year: 2023

<p>Workload fluctuation is one of the major challenges facing contracting (project-based) organizations because of the rational decision-making model applied to manage it in current practice. The rational model balances the workload with the capacity required at the level of activity, ignoring the influence of other factors. It also fails to consider the nondeterministic nature of these factors. Hence, there is a need for a decision support system that considers holistically the factors influencing workload fluctuation, their interactions, and their nondeterministic nature.Although the system dynamics approach is capable of filling the previously mentioned gaps, previous studies in this area have overlooked the organization-to-industry relationship. One of the important aspects of this relationship is industry demand. Previous studies have typically focused on predicting the organization&#39;s demand, which is not representative of industry demand. While some studies have endeavored to project industry demand by monitoring economic and political variables, these variables are very difficult to track. Hence, there is a need to accurately predict industry demand and integrate its characteristics with a robust decision support system.To fill these gaps, the present study pursues three objectives. The first is to comprehensively identify the factors affecting workload fluctuation in project-based organizations. The second is to identify the characteristics of industry demand in construction and devise a method for predicting future demand. The third is to develop an integrated dynamic model that considers the inherent uncertainties of, and interactions among, variables.To achieve these objectives, a multi-step approach is applied. First, a systematic literature review is conducted to identify the factors affecting the contractor’s workload, and these factors are analyzed using relative usage index and social network analysis. Second, the number of building permits issued is used as the metric to represent construction industry demand. It is analyzed using statistical tools to measure its characteristics such as mean, range, variability, and distribution. Also, the future demand is predicted using various machine learning algorithms such as neural network, Facebook prophet, and gaussian with kernels. Third, the system dynamics approach is applied to link the identified factors with demand features. The proposed model is analyzed using social network and sensitivity analysis by applying Monte Carlo simulation and other statistical tools.The results reveal a gap with respect to the factors used by the expert mental model in managing the organization’s workload and by the dynamic decision support model that is typically employed. For instance, the cycle of owner bid selection, holistic integration, and the effect of both on organizational performance have received relatively little attention considering their importance. Another notable finding is that industry demand behaviour is found to be cyclic and stable and thus can be considered a low- to medium-variability market condition. Seasonality, on the other hand, it found to have a significant effect on demand. Moreover, the results demonstrate that the cyclical structure of historical data can be leveraged to predict future demand with an average error of 10% for stationary, normally distributed and corelated data type, although the error ranges from 7% to 30% in a few cases for this type of data. Finally, the analysis of the integrated model reveals a tight structure in which one variable variation propagates easily and rapidly to other factors. The hub of these propagations is workload, as it is closely connected to the causes and effects of variations, either directly or indirectly through one or more variables. Hence, the analysis provides an influence matrix for workload fluctuations. This research contributes to the body of knowledge in several respects by providing a robust decision support system. First, it takes into consideration the significant causal factors affecting an organization&#39;s performance that are often overlooked in existing approaches. Moreover, the organization-to-industry relationship that is overlooked in existing approaches is considered in this model. Finally, this model considers the nondeterministic characteristics of the factors influencing management decisions.</p>