AI Demand Forecasting* (24 Hours)

*This is a non-WSQ module.AI Demand Forecasting

Introduction

This course provides a structured and practice-oriented introduction to demand forecasting within engineering and operational environments. Participants will learn how to apply data analytics and artificial intelligence (AI) techniques to improve forecasting accuracy and support operational decision-making.

The course covers the end-to-end forecasting analytics lifecycle — from defining business and process requirements, performing statistical data analysis, and developing forecasting models, to evaluating model performance and integrating forecasting outputs into operational planning workflows.

About This Programme

Consisting of three full-day sessions that combine theoretical concepts with hands-on practical methods for data processing, statistical analysis, and demand forecasting, participants will have the opportunity during the course to explore how Industry 4.0 tools and AI-driven techniques can be applied to real-world forecasting challenges.

By the end of the course, participants will be able to:

  • Define AI-driven demand forecasting problems by analysing engineering and operational requirements, including forecast scope, time horizon, decision objectives, and operational impact.
  • Preprocess and analyse demand data using statistical techniques to identify trends, seasonality, variability, and data quality issues that influence forecasting performance.
  • Apply statistical and AI-based forecasting models, selecting appropriate baseline and advanced methods based on demand characteristics and business requirements.
  • Evaluate forecasting performance using statistical metrics and operational indicators to assess accuracy, bias, robustness, and suitability for operational deployment.
  • Develop and compare hybrid forecasting approaches using different model types and understand the benefits of model integration.
  • Interpret AI-driven forecasting outputs to support engineering and operational decision-making, translating analytical insights into actionable planning and optimisation strategies.

Who Should Attend

This course is suitable for professionals involved in demand planning, operations, and data analytics, particularly in industries such as Manufacturing, FMCG, Retail and E-commerce, and Logistics and Supply Chain, where accurate demand forecasting is critical for operational efficiency and service performance. It is targeted for:

  • Demand and supply chain planners
  • Data analysts supporting forecasting and analytics platforms
  • Operations and planning managers
  • Engineers and technical professionals involved in predictive analytics, forecasting systems, or process optimisation

Course Outline

The programme employs the Learn-Practise-Implement™ (LPI™) pedagogy, where fundamental knowledge and principles taught will be reinforced with hands-on practices.

Key focus areas or applications:

  • Defining AI-Powered Demand Forecasting Problems
  • Preprocessing, and Pattern Diagnosis on Demand Data
  • Application of Statistical, Machine Learning, and Deep Learning Models
  • Forecast Evaluation, Bias Analysis, and Scenario Assessment
  • AI Model Trade-Offs Considering Operational Impact and Process Improvement
  • Explainability and Enterprise Workflow Integration

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 $2,400 before funding and prevailing GST.

Nett Course Fee

International
Participants		Singapore Citizens aged 39 years and below, Singapore Permanent Residents and LTVP+ HoldersEmployer-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
$2,616
$784.80$304.80$304.80
All fees are inclusive of GST 9%.
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.

Enhanced Training Support For Small & Medium Enterprise (SMES) Scheme (ETSS)

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.

SkillsFuture Mid-Career Enhanced Subsidy (MCES)

SkillsFuture Credit

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

How Course Fees are Calculated

TYPECATEGORY OF INDIVIDUALS
International ParticipantsSingapore 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)
 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$2,400$2,400 $2,400$2,400
SkillsFuture FundingNot Applicable ($1,680)($2,160)($2,160)
Nett Course Fee$2,400$720$240$240
GST 9%
$216 $64.80* $64.80* $64.80*
Total Nett Course Fee Payable to Training Provider$2,616 $784.80 $304.80$304.80
* Based on 30% of Full Course Fee

About the Trainers

Dr Bao Hongyan
Dr Liu Ning
Ms Serina Zhao Fei
Dr Le Van Dang
Mr Teng Wei Yuen
Dr Bao Hongyan
Dr Bao Hongyan
Scientist
A*STAR ARTC

Dr Bao Hongyan is a Scientist with A*STAR ARTC’s Digital Supply Chain (DSC) Group, where she develops AI-driven solutions for demand forecasting and supply chain analytics. Her work focuses on applying machine learning and data mining techniques to improve forecasting accuracy and support data-driven supply chain planning. Dr Bao holds a Ph.D. in Computer Science from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, as well as an M.Sc. in Electronics and Communication Engineering from the University of Chinese Academy of Sciences and a B.Eng. in Microelectronics from Xidian University. Her research interests include trustworthy AI, adversarial machine learning, cybersecurity, and large-scale data mining.

Dr Liu Ning
Dr Liu Ning
Senior Scientist
A*STAR ARTC

Dr Liu Ning is the Deputy Group Manager of the DSC Group at A*STAR ARTC. He received his B.Eng. from Xi’an Jiaotong University in 2012 and his Ph.D. from the National University of Singapore (NUS) in 2016. Prior to joining ARTC, he served as a Research Fellow at NUS. He currently leads R&D projects and initiatives within the Digital Manufacturing Division, focusing on advanced manufacturing technologies such as supply chain analytics, sustainable manufacturing, data-driven modelling and optimisation, and artificial intelligence.

Ms Serina Zhao Fei
Ms Serina Zhao Fei
Lead Research Engineer
A*STAR ARTC

Ms Serina Zhao Fei is a Lead Research Engineer with A*STAR ARTC’s DSC Group, where she develops supply chain and AI solutions and supports end-to-end supply chain transformation initiatives. She works closely with industry partners to assess supply chain operations and translate business requirements into practical technical solutions and implementation roadmaps. Ms Serina has successfully led and delivered numerous industry projects in recent years. She holds a B.Eng. in Materials Science from Xi’an University of Architecture and Technology and brings over 20 years of leadership experience in highly regulated industries, including medical devices and semiconductor electronics, in Singapore.

Dr Le Van Dang
Dr Le Van Dang
Senior Scientist
A*STAR ARTC

Dr Le Van Dang is a Senior Scientist at A*STAR ARTC. He earned his PhD in Computer Engineering from the Sirindhorn International Institute of Technology (SIIT) at Thammasat University in Thailand. Dr. Dang specialises in process optimisation, algorithm development, and mathematical programming. His work and research interests primarily focus on supply chain resilience, risk management, supply chain digital twin, network optimisation, and AI applications in supply chain management.

Mr Teng Wei Yuen
Mr Teng Wei Yuen
Lead Research Engineer
A*STAR ARTC

Mr Teng Wei Yuen is a Lead Research Engineer with A*STAR ARTC’s DSC Group. He is responsible for overseeing the end-to-end order fulfilment section and specialises in emerging technologies, including blockchain, AI, cloud technology, and mobile app development. Mr Teng holds a M.Sc. in Electrical Engineering from NUS and has played a crucial role in developing digital solutions such as eDataLogger and the Digital Workflow Automation system. These innovative solutions have been successfully implemented in over 90 local companies, resulting in significant productivity enhancements through streamlined forms and workflow processes.

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

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Schedule

Module
 Skills Course Reference Number  Next Intake(s)' Training Period
(Click on the dates to view their schedules)		 Registration Status
  • AI Demand Forecasting (24 hours)
 TGS-2026063878 The schedule for the next intake is still in the planning stage.  
 

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 SkillsFuture Singapore (SSG)'s 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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AI Demand Forecasting