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IMPLEMENT MANUFACTURING DATA MINING TECHNIQUES (42 HOURS)

Data mining techniques are increasingly important for data-intensive manufacturing operations as the industry faces a number of challenges such as equipment and material condition variations, trial-and-error in process parameter setting, product quality inconsistencies, low capability of root cause discovery, process performance prediction and process parameters/recipe auto tuning. By applying data mining techniques, a company can improve its product quality and manufacturing productivity.

This WSQ course aims to provide a good understanding of the fundamentals of data analytics and data mining techniques for different manufacturing applications. Participants will learn techniques for advanced clustering methods for product quality management, correlation modelling, and data pattern methods for root cause analyses and neural networks for process performance prediction.

Why This Course

Designed specifically to meet Singapore’s industry demand
Highly practical and intensive
Latest knowledge and up-to-date technology
Case studies highlighting industrial applications
Expert trainers in the field with industrial experience

Who Should Attend

This course is designed for operation directors and managers, production/process engineers, R&D engineers and IT support staff working on processes, production and quality improvement in manufacturing industries such as precision engineering, aerospace, automotive electronics, semiconductor, oil and gas, pharmaceutical and medtech.

Webinar

Watch our webinar video for "Minimising Design of Experiments (DoE) for Process Optimisation Using Data Mining Approach" held on 7 May 2021 for more information about SIMTech's Data Mining Courses.

Watch Playback

What You Will Learn

Fundamentals of Data Mining

Introduction to data mining concept and applications in manufacturing
Process correlation modelling and data pattern analyses through statistical methods
Advanced data clustering technologies for anomaly detection and classification
Process performance prediction using artificial intelligence (neural networks, etc)

Case Studies by Grouping Projects Using Real Production Data

Data preparation

Problem statement
Technical challenges
Project objectives
Data collection and pre-processing

Data analysis

Major factor identification by correlation coefficient analysis
Correlation modeling by multiple regression method and root cause analysis
K-means clustering and pattern based regression modeling
Correlation modeling by fuzzy neural networks method for quality estimation
What-if predictive analysis for process improvement & DOE design
Project conclusion

Improvement plan

Identifying yield improvement areas
Production/process improvement plan

Upon Completion of this Course

Electronic Statement of Attainment (SOA) certificates will be issued by SkillsFuture Singapore (SSG) to participants who have attended and attained competency in the Singapore Skills Framework training modules.

Participants must also meet the following criteria to receive their SOAs:

Achieve at least 75% course attendance;
Take all assessments; and
Pass the course.

Click here to learn more about the WSQ SOA Certificate.

(Sample) Statement of Attainment: SSG SOA

Pre-Requisites

Applicants should possess a degree in any discipline or a diploma with a minimum of 3 years of related working experience.
Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.
Proficiency in written and spoken English.

Full Course Fee

The full course fee for this module is $4,000 before funding and prevailing GST.

Nett Course Fee

International Participants	Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders	Employer-sponsored and self-sponsored Singapore Citizens aged 40 years and above (MCES) ²	SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders) (ETSS) ¹
$4,320	$1,296	$496	$496

All fees are inclusive of prevailing GST.

Please note that fees and funding amount are subject to change.

Long Term Visit Pass Plus (LTVP+) Holders

The LTVP+ scheme applies to lawful foreign spouses of Singapore Citizens with (i) at least one Singapore Citizen child or are expecting one from the marriage, or at least three years of marriage, and (ii) where the Singapore Citizen sponsor is able to support the family.

All LTVP+ holders can be identified with their green visit pass cards, with the word ‘PLUS’ printed on the back of the card.

¹ Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) scheme (ETSS), subject to eligibility criteria shown below.

SMEs that meet all of the following eligibility criteria:

Registered or incorporated in Singapore
Employment size of not more than 200 or with annual sales turnover of not more than $100 million

SME-sponsored Trainees:

Must be Singapore Citizens or Singapore Permanent Residents.
Courses have to be fully paid for by the employer.
Trainee is not a full-time national serviceman.

Further Info: This scheme is intended for all organisations, including non-business entities not registered with ACRA e.g. VWOs, societies, etc. Only ministries, statutory boards, and other government agencies are not eligible under Enhanced Training Support for SMEs Scheme. Sole proprietorships which meet all of the above criteria are also eligible.

² Under the SkillsFuture Mid-Career Enhanced Subsidy (MCES) for employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above.

Singaporeans aged 25 years old and above are eligible for SkillsFuture Credit which can be used to offset course fees (for self-sponsored registrations only).

For more information on the SkillsFuture funding schemes you are eligible for, please visit www.ssg.gov.sg

How course fees are calculated

TYPE	CATEGORY OF INDIVIDUALS
	International Participants	Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders	Employer-sponsored and self-sponsored Singapore Citizens aged 40 years and above	SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders)
	FUNDING SOURCE
	Not applicable for SkillsFuture Funding	SkillsFuture Funding (Baseline)	SkillsFuture Mid-career Enhanced Subsidy (MCES) ²	SkillsFuture Enhanced Training Support for SMEs (ETSS) ¹
Full Course Fee	$4,000	$4,000	$4,000	$4,000
SkillsFuture Funding	Not Applicable	($2,800)	($3,600)	($3,600)
Nett Course Fee	$4,000	$1,200	$400	$400
8% GST	$320	$96*	$96*	$96*
Total Nett Course Fee Payable to Training Provider	$4,320	$1,296	$496	$496

* Based on 30% of Full Course Fee

SkillsFuture Enterprise Credit (SFEC)

The SkillsFuture Enterprise Credit (SFEC) was first introduced during the Singapore’s Budget 2020 and then recently expanded to a larger group of employers in Budget 2022. It encourages employers to invest in enterprise transformation and capabilities of their employees. Eligible employers will receive a one-off S$10,000 credit to cover up to 90% of out-of-pocket expenses on qualifying costs for supportable initiatives, over and above the support levels of existing schemes.

Submission of SFEC Claim

Employers are required to submit their SFEC claims manually via its microsite if they fulfil any of the following scenarios:

Employers have sent their foreign employees (excluding foreign employees who hold Long-Term Visit Pass Plus) for training on a SFEC eligible course;

Employers have sent their Singapore employees for courses conducted by SkillsFuture Singapore (SSG)’s appointed CET Centres and they are not eligible for additional enhanced course subsidy, such as Mid-Career Enhanced Subsidy (MCES) and/or Enhanced Training Support for SMEs scheme (ETSS);

Employers have sent their Permanent Resident employees and/or employees who are Long-Term Visit Pass Plus (LTVP+) Holders to attend courses conducted by SSG’s appointed CET Centres and they are not eligible for the Enhanced Training Support for SMEs scheme (ETSS).

Employers can only submit the claims for SFEC after their employees have completed the training course(s). Click here to download the SFEC Claim e-guide.

NOTE: You will require a Corppass account with "EPJS_User" role assigned prior to login to your account to manage your claims.

If you are unsure whether your company is eligible for SFEC, you may submit an enquiry to Enterprise Singapore (ESG) via here for their response.

Contact Us

For technical enquiries, please contact:

Dr LI Xiang,
Email: xli@SIMTech.a-star.edu.sg

For general enquiries, please contact:

Knowledge Transfer Office,
Email: KTO-enquiry@SIMTech.a-star.edu.sg

Collateral

Download BROCHURE

Registration

SIGN UP NOW

Please register for this course through our Course Registration Form.
For the first question, please select Qn1: Modular Programmes
Applicants will be placed on our waiting list if the course does not have an upcoming scheduled intake.
Once the next intake is confirmed to commence, SIMTech will contact the applicants to share the class information.

Schedule

Module	Skills Course Reference Number	Training Period (Click on dates to view schedules)	Registration Status
Implement Manufacturing Data Mining Techniques (42 hours)	TGS-2020506158