Project Management – an Artificial Intelligent (Ai) Approach

Copyright © 2020 by Kim Hin David HO.

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CONTENTS

Preface

Acknowledgements

About the Author

Introduction

Chapter 1    Estimating Construction Demand in Singapore: Potential of Neural Networks

Chapter 2   Artificial-Intelligence Modelling (AIM) of Hong Kong Real Estate—Principles and Concepts

Chapter 3   Energy Resiliency and Sustainability Assessment—Principles and Practical Considerations

Chapter 4   A Fuzzy Discounted Cash Flow Analysis for the Real Estate Investment Project

Chapter 5   Examining Fuzzy Tactical Asset Allocation (FTAA) as an Alternative to Modern Portfolio Theory (MPT) Asset Allocation for International and Direct Real Estate Investment

Chapter 6   A Novel Approach of Designing the Knowledge-Based Urban Development (KBUD) adopting the Agent-Based Model (ABM)

Chapter 7   The Conclusion

Endnotes

PREFACE

Over 100 years ago, this was a mud-flat, swamp. Today, this is a modern city. Ten years from now, this will be a metropolis. Never fear.

—the first prime minister of

Singapore, Lee Kuan Yew, 1965

This modern project management (MPM) book is a unique and novel treatment of project management from artificial intelligence (AI) that entails data analytics, deploying neural networks, fuzzy logic, and genetic algorithm(s); and data visualisation, deploying agent-based modelling (ABM) in, for example, the knowledge-based urban development (KBUD). AI is a technology that replicates activities that would normally require human involvement. We have deployed macros in Excel to automate our daily repetitive tasks. We can treat it as the simplest example of AI. Similarly, we did use AI technology in our day-to-day tasks as in modern project management. Unlike before, organisations are now deploying project managers as facilitative leaders rather than directive managers. Such deployment puts more of the decision-making on the team and empowers them to deliver results faster. AI can help project managers transition to facilitative leaders by taking over routine tasks like performance reporting.

The potential impact AI will have on MPM and business can be enormous, to enable them to be nimble. Yet AI is purely dependent on precise and reliable data in and data out. Thus, we need the human input of the project manager to rectify or provide the data. The argument for MPM from an AI approach lies not so much with cost efficiency and effectiveness but primarily with urban sustainability. There is growing support for the argument that when MPM is competently and properly conducted, it adds substantially to the project real estate value, the rental yields, and the sustainable and comfortable built environment.

This MPM book discusses the discipline of project management in modern light and how it can be competently adopted by design engineers, urban planners, project managers, quantity and real estate surveyors, public and private real estate developers, architects for both new and existing developments, as well as scholars.

Happy reading.

Yours sincerely,

Professor Ho, Kim Hin / David

Singapore

August 2020

ACKNOWLEDGEMENTS

The author wishes to extend his most sincere appreciation to the School of Design and Environment, under the highly able deanship of the provost and chair Professor Lam, Khee Poh, of the National University of Singapore. The same wish is extended to the University of Hertfordshire in Hatfield, UK. These two tertiary institutions of higher learning and research are the globally leading universities, inspiring and encouraging both modern and contemporary project management.

ABOUT THE AUTHOR

Dr Ho Kim Hin / David is honorary professor in development economics and land economy, awarded by the UK public university, the University of Hertfordshire. He retired end of May 2019 as professor (associate) (tenured) f/9/9rom the National University of Singapore. Professor Ho spent the last thirty-one years across several sectors, which include the military, oil refining, aerospace engineering, public housing, resettlement, land acquisition, land reclamation, real estate investment, development, and international real estate investing. He spent six years in the real estate career as part of the executive management group of Singapore Technologies at Pidemco Land Limited and as part of the senior management team of the Government of Singapore Investment Corporation’s GIC Real Estate Private Limited. Seventeen years have been spent in the National University of Singapore at the then School of Building and Estate Management, the Department of Real Estate, School of Design and Environment, where his research expertise is in two areas. First is international real estate in the area of risk-return behaviour behind international real estate investing in direct and indirect real estate. Second is urban and public policy analysis involving real estate, sea transport, public housing, land, and land use. Schooled in development economics and in land economy at the University of Cambridge, England, he has effectively extended these disciplines to examine his two expertise areas. Apart from being well versed in econometrics, his quantitative interests include real estate demand and supply, investment and finance, artificial intelligent modelling in real estate, and system dynamics modelling for real estate market analysis and public policy analysis. He is a member of the Royal Economics Society (UK), academic member of the National Council of Real Estate Investment Fiduciaries (U.S.), fellow of the American Real Estate Society (U.S.), member of the American Economic Association (U.S.), and member of the Economic Society of Singapore and the Singapore Institute of Management. He holds the degrees of master of philosophy (first class honours with distinction), honorary doctor of letters, and the doctor of philosophy from the University of Cambridge, UK. He has published widely in top international journals and conferences and in chapters of international academic book publishers. Dr Ho has written seven major books (including this book) and undertaken many consultancies and funded research projects. He has written a total of about 275 published works (with ninety-one in peer-reviewed, reputable international journals). He is an editorial board member of the Journal of Economics and Public Finance, Real Estate Economics journal, Journal of Property Research, Journal of Property Investment and Finance, Journal of Real Estate Finance and Economics, the Property Management journal, and the International Journal of Strategic Property Management. He has published widely in conferences, finance, chapters of international academic book publishers, undertaken many consultancies, and funded research projects. He is an immediate past governor of the St Gabriel’s Foundation that oversees nine schools in Singapore and a district judge equivalent member of the Valuation Review Board, Ministry of Finance, Singapore, and the Singapore Courts.

INTRODUCTION

Modern Project Management (MPM)—

an Artificial Intelligent (AI) Approach

This MPM book is a unique and novel treatment of project management from artificial intelligence (AI) that entails data analytics, deploying neural networks, fuzzy logic, and genetic algorithm(s); and data visualisation, deploying agent-based modelling (ABM) in, for instance, the knowledge-based urban development (KBUD). AI is a technology that replicates activities that normally require human involvement. We have deployed macros in Excel to automate our daily repetitive tasks. We can treat it as the simplest example of AI. Similarly, we did use AI technology in our day-to-day tasks as in modern project management. In fact, AI is more than just simple tasks such as speech and facial recognition or GPS navigation. You can find the quickest travel route to your destination avoiding traffic jams. All such applications are powered by AI. They combine satellite data with current traffic patterns to give you the best route, which is changing and so do work trends. Unlike before, organisations are now deploying project managers as facilitative leaders rather than directive managers. This puts more of the decision-making on the team and empowers them to deliver results faster. AI can help project managers transition to facilitative leaders by taking over routine tasks such as performance reporting.

First and foremost, AI-operated machines can help save organisations money because they don’t have to outsource work to contractors. AI will enhance the productivity of employees with higher-quality work and decentralise remote operations. Online workflow platforms are gaining popularities because they give the organisation a larger pool of talent to choose from with flexible working hours. At the same time, artificial intelligence can monitor the process flows to ensure proper protocols are followed, checking resource availability in assigning tasks, providing real-time reporting, and curating self-paced study programs.

Second, AI-powered platform can monitor interactions to facilitate better communications, knowledge transfer, and the likelihood of miscommunication and customer feedback. It is of utmost importance because the relationship between the buyer and the seller has fundamentally changed. The fast and the first is the new formula to win. If you want to win, bring the product to the market first that meets customers’ needs. Let AI do the interaction with the customer to figure out what product your customer will want next, while you may focus on developing the product.

Third, while mots projects are unpredictable, AI can help monitor the performance of such projects and allow us to make decisions based on accurate information. AI can help us predict deadlines that might be missed, team member productivity, emerging risks, and quality levels. These predictions will help to calculate the probability of delivering on time, under budget, assignment of the best suitable team member to a specific task, compressing schedules if needed, and response to particular risk. Many decisions have to be taken by leadership and sponsor before the finalisation of the project charter and official approval of the project. Reviewing things like initial versus ongoing investment, make versus buy, risks versus rewards, economic trends and forecasts can take longer time. AI can assist by providing realistic data based on intelligence from similar past projects. Also, it enables senior leaders to select projects that have a higher likelihood of success. Another aspect of initiating projects that AI can help with is making better predictions. Banks are already using AI system for credit scoring to calculate the likelihood you’ll pay your loans on time. AI emphasises predictive planning, comprehensive estimation, and automated resource matching. This enables one to streamline our planning and make better AI data-driven decisions rather than relying on our gut instincts. If we take this a step further, AI can add even more value by acting as our digital assistant augmenting everyday planning activities like collecting requirements from stakeholders, tracking assumptions, and archiving business documents and agreements.

Fourth, and if required, AI enables one to find the root causes of deviations from the KPIs and point them out. We can easily identify which KPI needs greater attention with more intelligent data and a real-time view of what’s happening on the project. AI conducts 24/7 real-time monitoring and performs predictive analysis and forecasting. Once these insights from past projects are combined with information from current projects, an early warning system can alert with the preventative action. AI introduces structure to the closing process by conducting 24/7 real-time task closing rather than periodic task closing.

The punch line is that we need to anticipate and embrace AI rather than fear it. It is true that some professions and industries are being impacted by AI. For example, the legal profession is currently dealing with digital disruption. AI is imparting lawyer skills by taking over tedious tasks like conducting due diligence on decisions from past cases and applying algorithms to predict the likelihood of winning future cases. Another example is online customer service. Your 80 per cent queries are directly handled by chatbots without any human intervention. The potential impact AI will have on project management and business can be enormous, to enable them to be nimble. Yet AI is purely dependent on precise and reliable data in and data out. Thus, we need the human input of the project manager to rectify or provide the data.

References

CMMI. 2011. CMMI for Development: Guidelines for Process Integration and Product Improvement. Old Tappan, NJ, USA: Pearson Education.

PMI. 2013. A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 5th ed. Newtown Square, PA, USA: Project Management Institute (PMI).

Chrissis, M. B., M. Konrad, S. Shrum. 2011. CMMI for Development: Guidelines for Process Integration and Product Improvement, 3rd ed. Boston, MA, USA: Addison-Wesley Professional.

PMI. 2013. A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 5th ed. Newtown Square, PA, USA: Project Management Institute (PMI).

Ho. 2020. Forthcoming Book, Project Management—a Holistic Approach, Partridge Publishing Co Ltd. Bloomington, IN 47403, USA.

CHAPTER 1

ESTIMATING CONSTRUCTION

DEMAND IN SINGAPORE: POTENTIAL

OF NEURAL NETWORKS

The aim of chapter 1 is to demonstrate the capability of the state-of-the-art technology of neural network solutions to provide an understanding of the variables of construction demand and to forecast the level of this demand. As the demand for construction is a derived one, this reflects inter-sectoral linkages and associated overall growth with construction activity. The ability to anticipate construction demand in aggregate terms and with respect to various segments offers the opportunity to plan for the expected mix of demand and to affect any necessary strategic restructuring or fine-tuning of the construction industry. As these have been understood in Singapore, efforts are made to predict levels of construction demand. Existing construction demand forecasting models developed for Singapore have conventionally relied on techniques based on statistical analysis to understand the various influencing factors and the relationships amongst them. These models are complex and cumbersome. They are constrained by real-world problems that make it difficult