KTO Color Logo
More

Connect with us

IMPLEMENT MANUFACTURING DATA MINING TECHNIQUES (42 HOURS)

Data Mining Techniques_Banner

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.


What You Will Learn

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

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, Full and Nett Course Fees

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.

    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

    TYPECATEGORY OF INDIVIDUALS
    International Participants Singapore Citizens, Singapore Permanent Residents and LTVP+ HoldersEmployer-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) ²SkillsFuture 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

    Eligible for SkillsFuture Enterprise Credit (SFEC)

    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 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


    Implement Manufacturing Data Mining Techniques

    Registration

    • 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


    Sessions
    FD
    : Full Day  AM
    : Morning PM
    : Afternoon EVE
    : Evening