Machine Learning For Supply Chain Analytics And Operations Management* (40 Hours)

*This is a non-WSQ module.
Machine Learning for SCA

Introduction

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:

  • Sale Order
    • Hidden trend and patterns in demand
  • Sales + delivery (demand) order
    • Order fulfilment performance
  • Purchase + delivery (supply) order
    • Supplier performance
  • Purchase + delivery (supply) order + sales order 
    • Operational uncertainty

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.

What Our Trainee Says

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.

Key focus areas or applications:

  • Data analytics fundamentals
  • Data visualization
  • Supervised learning
  • Unsupervised learning
  • Decision analysis
  • Supplier/customer profiling
  • Demand pattern discovery
  • Order lead-time analysis
  • Demand forecasting
  • Managing demand uncertainty

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 and/or ARTC if they meet the following criteria:

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

Note: Trainees will have to bear the full course fee upon failure to meet either one of the criteria.

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 course is $5,000 before funding and prevailing GST.

Nett Course Fee

International
Participants		 Singapore Citizens aged 39 years and below, Singapore Permanent Residents and LTVP+ Holders Employer-sponsored and self-sponsored Singapore Citizens aged 40 yrs and above (MCES) ² SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders (ETSS) ¹
$5,450 $1,635 $635 $635
All fees are inclusive of GST 9%.
Please note that fees and funding amounts are subject to change.
Long Term Visit Pass Plus (LTVP+) Holders

The Long Term Visit Pass Plus (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 Enhanced Training Support For Small & Medium Enterprise Scheme (ETSS)

The ¹Enhanced Training Support for Small & Medium Enterprises scheme (ETSS) supports company-sponsored participants with up to 90% course fee subsidies.  To qualify, both the employers and trainees must meet the following eligibility criteria.

  • Eligible Small & Medium Enterprises (SMEs) must meet all of the following conditions:
    • Registered or incorporated in Singapore
    • Employment size of not more than 200 employees, or annual sales turnover of not more than $100 million

  • SME-sponsored trainees must meet all of the following:
    • Singapore Citizen or Singapore Permanent Resident
    • Course fees are fully paid for by the employer
    • Trainee is not a full-time national serviceman
Further Information:
This scheme is intended for all organisations, including non-business entities not registered with ACRA, for example, Voluntary Welfare Organisations (VWOs), societies, etc. Only ministries, statutory boards, and other government agencies are NOT eligible under the Enhanced Training Support for SMEs Scheme. Sole proprietorships which meet all of the above criteria are also eligible.
² SkillsFuture Mid-Career Enhanced Subsidy (MCES)

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

Eligible Singapore Citizens can receive higher subsidies of up to 90% of course fees.
SkillsFuture Credit (SFC)

All Singapore Citizens aged 25 years old and above are eligible for SkillsFuture Credit (Opening Credit), which can be used to offset course fees (for self-sponsored registrations only).
SkillsFuture Credit (Mid-Career)

The SkillsFuture Credit (Mid-Career) is only applicable for the full A*STAR SkillsFuture Career Transition Programme (SCTP) enrolments, and cannot be used for standalone or individual module sign-ups.

How Course Fees are Calculated

  CATEGORY OF INDIVIDUALS 
International Participants Singapore Citizens aged 39 years and below, 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)
TYPE  FUNDING SOURCE 
Not applicable for Funding Support SkillsFuture Funding (Baseline) SkillsFuture Mid-career Enhanced Subsidy (MCES) ² SkillsFuture Enhanced Training Support for SMEs (ETSS) ¹
 Full Course Fee  $5,000 $5,000  $5,000  $5,000 
Funding Support Not Applicable   ($3,500) ($4,500) ($4,500)
Nett Course Fee $5,000  $1,500 $500 $500
GST 9%  $450 $135* $135* $135*
Total Nett Course Fee Payable to Training Provider  $5,450  $1,635 $635 $635
* Based on 30% of Full Course Fee

About the Trainers

Ms Yan Wenjing
Mr Tan Chin Sheng
Dr Wen Rong
Ms Yan Wenjing
Ms Yan Wenjing
Principal Research Engineer,
A*STAR SIMTech

Ms Yan Wenjing is a Principal Research Engineer at SIMTech with more than 20 years of experience in system design, development and implementation for supply chain and enterprise information management and analysis. She has led and participated in both research and industry projects covering several areas of supply chain management: production planning and scheduling, demand forecasting, inventory planning, logistics planning, and data analytics. She holds a master degree in engineering.

Mr Tan Chin Sheng
Mr Tan Chin Sheng
Lead Research Engineer
A*STAR SIMTech

Mr Tan Chin Sheng has been with SIMTech since 2012. Some of his works include simulation studies for analysing the impact of business disruption in manufacturing systems and the application of genetic algorithm for sales order allocation to multiple factories and layout planning of manufacturing facilities. He is also a part-time PhD student at the Nanyang Technological University, Singapore, where he researches on the potential synergy between machine learning and metaheuristic optimisation for better operational planning.

Dr Wen Rong
Dr Wen Rong
Senior Scientist
A*STAR SIMTech

Dr Wen Rong is a Senior Scientist in SIMTech whose expertise lies in data analytics, data mining, data visualization, as well as image data processing and pattern recognition. He has R&D experiences working with industry companies helping them from problem identification to proper technical solution development with machine learning and deep learning technologies.

Registration

Sign up now

  • Please register for this course through our online form: Course Registration Form for Public Classes.
  • For the first question, please select "Modular Programmes (Standalone Modules)".
  • Applicants will be placed on our waiting list if the course does not have an upcoming scheduled intake.
  • When the next intake is confirmed, a confirmation email with payment information will be sent to applicants to finalise their participation.

Collateral

Download brochure

Machine Learning for Supply Chain and OMA

Contact Us

Technical Enquiries General Enquiries

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

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

Schedule

Module
 Skills Course Reference Number  Next Intake(s)' Training Period
(Click on the dates to view their schedules)		 Registration Status 
  • Machine Learning for Supply Chain Analytics and Operations Management (40 hours)
 TGS-2020503195 PM 2 Sep 2026 - 4 Nov 2026
SIGN UP Now

The Sep 2026 intake is postponed. We will publish a new schedule soon.

Note: A*STAR SIMTech and A*STAR ARTC reserve the right to change the class/schedule/course fee or any details about the course without prior notice to the participants.

Announcement:

  • From 1 Oct 2023, attendance-taking for SWDA-approved/SWDA-funded courses must be done digitally via the Singpass App. More information may be viewed here.
  • Participants will be provided with digital course materials when attending our courses. Please note that printed copies will not be available.
Sessions
FD: Full day
AM: Morning
PM: Afternoon
EVE: Evening
 

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