I certify that this project report entitled "EXAMINE THE READINESS OF ARTIFICIAL INTELLIGENCE IN CONSTRUCTION LOGISTICS". This research aims to investigate the readiness of local industry players for the adoption of AI in construction logistics.
Background of Study
The research methodology used is presented in section 1.5 while section 1.6 sets out the scope and limitation of this research. To deliver a construction project on time and within budget, construction logistics is one of the most important factors.
Problem Statement
However, construction logistics is unpredictable with different variables involved in predicting, storing and transporting the materials. For example, there are hundreds of multi-ton loaders, excavators, dumpers and other heavy equipment involved in construction sites, when any of the construction logistics activities run into trouble, all these equipment can only stand still and wait.
Research Aim and Objectives
Therefore, the following research questions are to be answered in this study.
Research Methodology
Research Scope and Limitations of the Study
Chapter Outline
The biggest challenges in the adoption of AI as well as the current readiness of AI adoption in construction logistics are also discussed in this chapter. The current implementation of AI in construction logistics and the challenges faced in AI adoption in the construction industry are reviewed in Sections 2.7 and 2.8.
Definition of Artificial Intelligence
However, Eric Jonas, assistant professor at the University of Chicago, assessed that "AI is a lie", meaning that the current reality of AI development is rather premature and far from the expectations for AI, and raises many questions, such as whether AI will replace human for job completion, the role of AI for human and its ethics (Webb, 2019). The diverse definitions of AI continue to evolve in defining more specific fields or subcategories, each definition essential to help researchers achieve the goals and guidelines (Lorica and Loukides, 2016).
Structure and Types of Artificial Intelligence
Second, the layer below AI is machine learning (ML), which is the subset of AI as well as a system that can learn by itself (Motalebi, 2020). The example of the current advanced creation of narrow AI is IBM's Deep Blue and Watson's.
Construction Logistics
Refer to figure 2.3 above, construction logistics can generally be categorized into 2 types which are supply logistics and site logistics. However, Moone (2015) described that the concept of construction logistics is not only limited to deliver the construction material to the boundaries of the construction site.
Problems of Construction Logistics in Current Practices
- Cost and Time Overrun
- Impact to people
- Impact to environment
- Health and safety of workers
- Lack of Collaborations
The problems of construction logistics in current practice include cost overruns, impact on people, impact on the environment, worker health and safety, and lack of collaboration. This scenario has caused serious problems in the management of construction logistics and eventually exceeds the time plan and budget due to incorrect logistics solutions (Robbins, 2015; Ekeskär and Rudberg, 2016).
Benefits of Logistics 4.0 in Construction Industry
- Improve Productivity
- Save Cost
- Increased Earnings
- Improve Efficiency and Effectiveness
- Improve Transparency and Flexibility
- Higher Safety
- Environmentally Friendly
On the other hand, AI-controlled network robots can ensure that communication between supervisors and warehouse workers is error-free and simultaneous, in order to respond more accurately to any interruptions or order changes (SSI Schaefer Whitepaper, 2018 ). In turn, to improve site safety and reduce accidents, Volvo Construction Equipment (CE) is collaborating with Colas to develop an integrated AI algorithm innovation that will detect and warn when people enter an area of dangerous (Volvo CE, 2018).
Adoption of Artificial Intelligence in Construction Logistics It is obvious that construction industry has vast potential when the construction
- Automated Guided Vehicles
- Unmanned Aerial Vehicles and Unmanned Ground Vehicles For fully autonomous warehouse inventory, the unmanned aerial vehicles (UAV)
- Autonomous Truck
- Unmanned Tower Crane
- Smart construction lift
- AI-Based Drones
- Predictive Analytics
- AI-powered Jobsite Cameras
- AI Back Office
- AI Developer Kits
The accuracy of voice recognition is high even in the noisy construction site (Ahn, et al., 2018). Then, by learning this large volume of data, the running zones can be determined by the lifts themselves (Ahn, et al., 2018).
Challenges on Artificial Intelligence Adoption
- Algorithm Aversion
- Change of Working Culture
- Lack of Leadership Commitment
- Lack of Employees Commitment
- Privacy Concerns
- Environmental Concerns
In order to apply AI to increase the competitive advantages and value of companies, the leadership and management of companies will be significantly affected (Omar, et al., 2017). On the other hand, AI machine or robots make them feel insecure as they will be replaced to reduce human errors (Omar, et al., 2017).
A large amount of energy or raw materials such as nickel, cobalt and lithium are required by the servers that store big data and run calculations in the cloud. The enormous amount of these raw materials can soon no longer be supported by the Earth.
Summary
The Definition of Research
Research Methods
- Quantitative Research
- Qualitative Research
- Mixed Method / Triangulation Research
- Justification of Research Method
For this research, quantitative research method will be used to investigate the readiness of AI in Malaysian construction logistics. For this research, only quantitative research methods will be used to investigate the readiness of AI adoptions in Malaysian construction logistics. The data collected using quantitative research methods are numerically analyzed and interpreted in the tabular forms in Chapter 4.
Research Design
It was found that there is no research on the implementation of artificial intelligence in Malaysian construction logistics and its readiness by construction actors. Primary data is essentially obtained using quantitative approaches to obtain information or data about the current existing practices of artificial intelligence in construction logistics and their readiness to adopt artificial intelligence. Select "Check the readiness of artificial intelligence in construction logistics" as the research topic - determine the scope of the research.
Research Philosophy
- Positivism
- Critical Realism
- Interpretivism
- Pragmatism
- Justification of Research Philosophy
Different people will come from different cultural backgrounds, will have different meanings at different times, under different circumstances and different social realities will be experienced. Through interpretivist research, richer, new and meaningful interpretations and insights of contexts or social worlds will be created (Saunders, et al., 2019). Moreover, pragmatists recognize that there are different ways of interpreting the world and conducting research, so no single point of view can provide a complete picture (Saunders, et al., 2019).
Approaches to Theory Development and Its Justification
Therefore, the survey was targeted and conducted for various industrial players to study their readiness for AI. In this research, it is not only discussed whether local construction industry players are ready to introduce AI in their practice or not. Their readiness is further explained and discussed by its underlying constructs which are their awareness towards adoption of AI, current level of adoption of existing AI practices and the challenges of adopting AI such as high implementation costs, lack of support from managers, lack of knowledge or technologies and so on.
Methodological Choice and Its Justification
Time Horizons and Its Justification
Data Collection Approaches and Its Justification
Questionnaire Design
Question B1 and B3 focused more on the respondents' perception of the importance and their agreement on the UA adoption functions discussed in construction logistics. Next, C2 is focused on the problems that current construction logistics practices face and that have occurred to organizations. Potential barriers to UA adoption in local construction logistics are identified through the analysis of data collected in this section.
Sampling
- Population
- Sampling Frame
- Sampling Size
- Sampling Method
There are two types of sampling methods which are probability sampling and non-probability sampling (Sekaran and Bougie, 2016). Probability sampling is used where elements of the population have a known, non-zero chance of being selected as sample subjects. Non-probability sampling always creates highly unrepresentative samples, while elements of the population have no probability associated with being selected as sample subjects.
Data Analysis Method
- Shapiro-Wilk W Test
- Cronbach’s Alpha Reliability Test
- Descriptive Statistics
- Inferential Statistics a) Mann-Whitney U Test
In this research, convenience sampling (one of the non-probability sampling methods) is used in this research when respondents are selected based on availability and convenience to investigate the readiness of AI in construction logistics. The selected respondents would provide this research with more comprehensive and thorough information because they have higher level of involvement and particular experiences in dealing with construction logistics. With Mann-Whitney U test, comparison of Section B1 to B4 and C2 is done in this research to reject the null hypothesis in order to reveal the significant differences of the pairs of sample groups in terms of their perception.
Introduction
Respondents’ Background
Response Rate
Shapiro-Wilk W Test
The problem in the current construction practice is cost overrun and the same between the group of "contractor" and "subcontractor". 033 The problem in the current construction practice is cost overrun and the same between the group of "assistant" and "executive". The problem in the current construction practice is a lack of collaborations and the same between the group of "executives" and.
Perception towards the Relevance of Construction Logistics Activities with AI Adoptions
The perception about the relevance of contract process and invoices with AI adoption is the same among the group of. The perception about the relevance of downloading and uploading materials with AI adoption is the same among the group of. The perception about the relevance of delivery and traffic management with AI adoption is the same among the group of.
Agreements towards Statement Related to the Functions of each AI Adoptions in Construction Logistics
Agreements regarding statements regarding AGV functions are the same between the "contractor" group and. 022 Agreements regarding declarations relating to functions. smart construction elevator is the same among the group. Agreements on statements relating to predictive analytics functions are the same across the group.
Perception towards the Organisation’s Plan for AI Adoption in Different Construction Logistics Activities
The perception of the organization's plan for AI adoption in relation to contract and invoices is the same between the group of. The perception of the organization's plan for AI adoption in delivery and traffic management is the same among the group of. The perception of the organization's plan for AI adoption in relation to contract and invoices is the same between the group with.
Perception towards the Organisation’s Plan with Different AI Adoptions in Construction Logistics
The perception of the organization's plan to adopt .. unmanned tower crane is the same among the group. Perception of an organization's plan to adopt predictive analytics is the same among the "doers" group. Perception of an organization's plan to adopt .. predictive analytics is the same across the board.
Agreement on Benefits of Logistics 4.0 in Construction Logistics The results of the agreement on benefits of Logistics 4.0 in construction logistics
Perception on Factor Undermining AI Adoption in Construction Logistics
Based on Table 4.15, most respondents strongly agree that Logistics 4.0 can improve productivity, as well as transparency and flexibility of construction logistics activities. There are 85, 75, and 70 respondents who slightly agree that Logistics 4.0 can provide higher security, improve efficiency and effectiveness, and save cost, respectively. There are 71 respondents who are unsure that Logistics 4.0 can increase revenue, while 71 of the respondents are unsure that Logistics 4.0 is environmentally friendly.
Discussion
- Problems in Current Construction Logistics Practices
- Organisation’s Awareness towards the Benefits of Logistics 4.0 Local construction organisations most concern Logistics 4.0 improves
- Organisation’s Awareness towards AI Adoption in Construction Logistics
- Existing Practices of AI in Construction Logistics
- Challenges in AI Adoption
- Readiness towards AI Adoption in Construction Logistics
Suppliers are considered more relevant to AI in the top two construction logistics activities when compared to contractors (Section 4.6 (i) (a) & (d)). They have a greater awareness towards the development of AI in construction logistics compared to subordinates who have less work experiences. Respondents tend to have the highest awareness of the usefulness of AGV when compared to other nine adoption of AI in construction logistics.
Summary
Introduction
Summary of Background Study
Shortages or problems related to supply logistics or site logistics can create a serious setback for a project and cause unnecessary construction costs and wasted time. Thus, the shortcomings related to current construction logistics practices have increased the importance of AI automation and robotic technologies for traditional construction logistics practices. AI is the next revolution in the world; It is vital for the construction industry to embrace AI technologies and optimize its adoption opportunity especially in the logistics sector.
Accomplishment of Research Objectives
The majority of respondents are aware of and recognize the benefits of Logistics 4.0 and the introduction of AI in construction logistics. However, the actual level of adoption for existing practices of AI in today's construction logistics is low. In conclusion, the Malaysian construction industry is more aware and acknowledging the AI trend in construction logistics as compared to the actual AI adoption practices.
Research Implication
Research Limitations
Also, the convenience sampling method used in this research may not be the best method to collect data. The structure of the population may not be reflected by the structure of the respondents in this study. Therefore, the questionnaire designed in this research may not cover the latest AI developments and approaches.
Research Recommendations
Summary
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