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Machine Learning for SCA
This module is also referred to as "Machine Learning for Supply Chain Analytics and Last Mile Logistics Planning". 

*This is a non-WSQ module.

In today’s connected world, machine learning has emerged as a key technologies for improving business operations in organisations. It extracts meaningful insights from raw business data for better decision support & making in the supply chain planning and operations management.

about this programme

This programme focuses on applying relevant machine learning techniques to extract hidden patterns from either commercial systems such as CRM, ERP or excel files containing large volume of transactions data amassed over the years. Some of the examples are listed below:

Machine Learning for SCA_About the pogramme

Who Should Attend

This programme is designed for organisations in the manufacturing and service sectors that have substantial amount of operational and transaction data. It aim to equip professionals with data analytics skill that can be utilized to discover insights for better planning of their supply chain activities. The programme is highly suitable for management officers/ directors and professional who works in the area of supply chain planning and management, logistics planning, sales and marketing function, production, operation or IT.

Course Outline

Course Outline

The programme adopts the Learn-Practise-Implement™ (LPI™) pedagogy. Participants will acquire knowledge through a gradual learning curve, reinforce the knowledge and skill taught by working on hands-on examples that are related to their work, and apply the knowledge acquired to solve their business problems.

Machine Learning for SCA_Outline

The machine learning topics covered in this course are aimed to provide insights for better decision support in the areas of demand planning, inventory planning and profile analysis of suppliers and customers.

Upon Completion of This Course


Participants will be awarded with a Certificate of Attendance (COA) by SIMTech if they meet the following criteria:

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

Pre-Requisites, Full and Nett Course Fees


  • 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 $5,000 before funding and prevailing GST.

Nett Course Fee

Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders
Employer-sponsored and self-sponsored Singapore Citizens aged 40 yrs and above (MCES² SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders (ETSS¹
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

    International ParticipantsSingapore 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)
    Not applicable for SkillsFuture FundingSkillsFuture Funding (Baseline)SkillsFuture Mid-career Enhanced Subsidy (MCES) ²SkillsFuture Enhanced Training Support for SMEs (ETSS) ¹
     Full Course Fee $5,000$5,000 $5,000 $5,000 
    SkillsFuture Funding Not Applicable  ($3,500) ($4,500) ($4,500)
    Nett Course Fee $5,000 $1,500 $500 $500
    8% GST
     $400$120*  $120* $120*
    Total Nett Course Fee Payable to Training Provider  $5,400 $1,620 $620 $620
    * Based on 30% of Full Course Fee

    Contact Us

    For technical enquiries, please contact:

    Ms YAN Wenjing,
    Email: wjyan@SIMTech.a-star.edu.sg

    For general enquiries, please contact:

    Mr CHAI Lai Sing,
    Email: lschai@SIMTech.a-star.edu.sg

    Machine Learning for Supply Chain and OMA


    • 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.


    Skills Course Reference Number  Training Period
    (Click on dates to view schedule)
    Registration Status 
    • Machine Learning for Supply Chain Analytics and Operations Management (40 hours)
    : Full Day  AM
    : Morning PM
    : Afternoon EVE
    : Evening