Digital-Twin Model Adoption in Malaysian Semiconductor Industry

Published: 11 September 2023| Version 2 | DOI: 10.17632/2sjnjyybkz.2
Mohammed Saeed jawad


The dataset encompasses responses from professionals in the semiconductor manufacturing sector, detailing their insights on the implementation and impact of Digital Twins. Presented in a tabular format, each entry denotes an individual's response, cataloged with timestamps and demographic information, including their role and sector within the industry. The questionnaire delved into various domains concerning Digital Twins, such as data storage efficiency, device interoperability, business implications, and infrastructure maturity. Respondents gauged their agreement on topics using options like "Agree" and "Completely Agree". Preliminary analysis indicates a general consensus recognizing the benefits of Digital Twins, although opinions fluctuate based on roles and individual experiences.


Steps to reproduce

Research Outline: Methodology for Data Collection on Digital Twins in the Semiconductor Manufacturing Industry Objective: The primary aim of this research was to gather insights on the perceptions and experiences of professionals in the semiconductor manufacturing industry concerning the application of Digital Twins. Instrumentation: Survey Platform: An online questionnaire was created using a reliable survey tool. The platform allowed for timestamp recording, ensuring real-time tracking of responses. Question Design: Questions were primarily Likert scale-based, requiring respondents to indicate their level of agreement with various statements related to Digital Twins. Sampling: Target Population: Professionals in the semiconductor manufacturing industry. Sampling Strategy: Convenient sampling wherein links to the online questionnaire were shared through industry forums, company intranets, and via email to specific industry groups. Data Collection Procedure: Distribution: The survey was disseminated through email campaigns, shared on industry-specific forums, and announced in select seminars or webinars focusing on semiconductor manufacturing. Duration: The survey was kept open for responses over a predefined period to ensure maximum participation. Incentivization: To increase response rates, respondents were (if applicable) offered insights into the aggregate findings or entered into a lucky draw for a relevant industry event or seminar. Quality Control: Duplication Check: The survey platform was set to disallow multiple entries from the same IP address. Incomplete Responses: Respondents were required to answer all questions to submit the survey, minimizing incomplete data entries. Data Processing: Data from the survey platform was exported in a tabular format suitable for analysis in statistical software. Basic data cleaning procedures were applied, such as checking for outliers or inconsistent responses. Analytical Tools & Software: For the preliminary analysis, spreadsheet tools were used to gauge basic trends and distributions. For in-depth analysis, statistical software packages were employed to derive insights and correlations. Reproducing the Research: Researchers intending to reproduce or extend this study should use the aforementioned methodology. Access to similar populations and familiarity with the specific industry dynamics is essential. The questionnaire's structure and content should be preserved, although modifications can be made to cater to specific research objectives or emerging industry trends.


Universiti Tun Hussein Onn Malaysia


Manufacturing Engineering, Digital Twin Technology


Ministry of Higher Education, Malaysia

FRGS/1/2021/ICT 08/UTHM/02/1