Development of a decision support tool for automation adoption and optimisation in precast concrete plants : a New Zealand case study : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction Project Management at Massey University, Albany, New Zealand
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Date
2022
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Massey University
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Abstract
In response to the growing demand in the New Zealand construction market, this study aims to develop a decision-support framework for adopting and optimising automation in precast concrete plants, which are increasingly recognised for their numerous benefits. The primary resources required by these plants include labour, equipment, and materials, and their efficient use is essential for maintaining competitiveness. Automation has been identified as a potential solution for improving productivity and profitability in precast concrete manufacturing; however, an appropriate decision-support tool is currently lacking.
The current study commences with a comprehensive literature review, followed by historical data collection, face-to-face interviews, and site observations of precast concrete plants to address this research gap. These methods help identify attributes that affect profitability, leading to developing and validating of a theoretical framework named the Precast Plant Automation System Tool (PPAST) through a case study. The PPAST framework comprises two sequential phases: the strategic phase, which uses the direct rating method for preliminary feasibility evaluation of automation adoption, and the tactical phase, where the AHP method assesses the appropriate automation sequence for the plant.
The study’s main findings indicate that the developed decision support system enables decision-makers to articulate their objectives and attitudes towards risk as they explore the feasibility of automation and formulate an optimal automation strategy. Specifically, the system aids in evaluating the impact of automation on cost and quality and identifying necessary process changes before implementing new technologies. The primary contribution of this research is its novel approach to systematically evaluating alternative automation scenarios in precast concrete production plants.
The results demonstrate that the proposed model is a valuable and effective decision-making tool for adopting and optimising automation in precast concrete plants. This research fills a critical knowledge gap concerning the crucial measurements of precast concrete plant profitability and the absence of an automation adoption tool. The developed framework can be extended to investigate automation adoption and optimisation in other precast concrete plants across New Zealand.
This study's practical implications include empowering precast plants to meet their organisation's profitability measures, thus satisfying stakeholder value propositions. A thriving precast concrete industry will lead to more satisfied clients, attract additional investment, and improve the overall construction industry's quality, productivity, and profitability at the national level. Theoretically, this research contributes a reliable benchmark for future studies by developing decision support tools that facilitate selecting optimised automation methods for precast concrete plants and contributing to theoretical knowledge by establishing an optimised automation decision support method that guides researchers in exploring other avenues for maximising profitability.
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Keywords
Precast concrete industry, Automation, Decision support systems, New Zealand, automation, construction, precast concrete plant, profitability, productivity, decision support framework