Development of a lean six sigma framework for identification and minimisation of inefficiencies in construction projects.

Abstract

Introduction: This dissertation has focused on the systematic identification, analysis, and reduction of inefficiencies within the construction project domain through the utilisation of Lean Six Sigma. With the construction sector facing challenges such as budget overruns, delays, and resource misallocation, the need for effective strategies to enhance project efficiency becomes imperative. The Lean Six Sigma methodology, known for its success in various industries, offers a structured and data-driven approach to continuous improvement. This research adopts a comprehensive approach by incorporating literature reviews, case studies, and empirical data collection to explore the integration of Lean Six Sigma principles within the context of construction project management. The study begins by establishing the theoretical foundation, elucidating the fundamental concepts of Lean Six Sigma and its historical effectiveness in streamlining operational processes. Subsequently, it delves into a thorough evaluation of the unique constraints and complexities associated with construction projects, emphasising the importance of a tailored approach to enhance efficiency. Purpose: The objective of the dissertation is to carry out an extensive examination of the elements that lead to delays in projects and assess their subsequent effects. By comprehending the consequences and origins of these inefficiencies, adjustments were made to the lean six sigma tool to enhance the processes involved in construction projects and strive to reduce these inefficiencies to the greatest extent possible. As a result, both time and cost overruns were minimised, leading to savings in operational expenses. Consequently, the research endeavours to uncover the underlying causes of process inefficiencies and implement lean six sigma tools as effective solutions to address these inefficiencies. Approach: The practical aspect of the dissertation involves the utilisation of Lean Six-Sigma tools and methodologies in a real-life construction project, focusing on the identification of bottlenecks, waste, and unpredictability. To identify inefficiencies, a value stream map was constructed, while control charts were employed to measure variation. To gain a deeper understanding of the factors contributing to process inefficiencies, a fishbone diagram and factor analysis were utilised. Additionally, an EOQ model was employed to predict material lead time, which aids in effective planning. Furthermore, a scheduling and project monitoring tool, namely the CiteOps software, was developed, along with a visual dashboard created using PowerBi, enabling remote tracking and monitoring of project efficiency. The successful implementation of Lean Six Sigma in construction was illustrated through practical examples from previous studies, highlighting the adaptability and effectiveness of this framework. Findings: During the research, it was discovered that project delays and inefficiencies were largely influenced by lead time, delayed order placement, and task corrections. The correlation coefficient of lead time and placement was found to be 0.66, indicating a strong relationship between these factors and their significant impact on the efficiency of the project process. To mitigate long lead times, an EOQ model was implemented to forecast lead time and plan accordingly. The utilisation of project management software, such as CiteOps software, enhanced accountability among team members and facilitated better planning, enabling the timely identification and resolution of delays. By addressing these issues early on, their impact on the project was minimised. It is recommended to continue utilising the software and EOQ model to minimise the influence of factors that contribute to process inefficiency. Research limitations: The data used for the time study is only for the period when the project delays were at their climax and not from when they first occurred. This means the data used in this research may not be an accurate representation of the issue. The data sampled for this project may be insufficient due to limited availability. Originality: In this dissertation, the DMAIC approach was customised by combining the Six Sigma techniques, statistics, and differential equations to better quantify and understand the impact that delays have on the effective time spent on project completion. Keywords: Six Sigma, Lean, Lean Six Sigma, Inefficiencies, Inefficiency Minimisation, DMAIC