MACHINE LEARNING FOR SUPPLY CHAIN ANALYTICS AND OPERATIONS MANAGEMENT* (40 HOURS)
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- This module is also referred to as "Machine Learning for Supply Chain Analytics and Last Mile Logistics Planning".
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.
Contact Us
- For technical enquiries, please contact:
Ms YAN Wenjing,
Email: wjyan@SIMTech.a-star.edu.sg
- For general enquiries, please contact:
Knowledge Transfer Office,
Email: KTO-enquiry@SIMTech.a-star.edu.sg
Registration
- Please register for this course through our Course Registration Form for Public Classes.
- For the first question, please select "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 | Next Intake(s)'s Training Period
(Click on the dates to view its schedules) |
Registration Status |
|
TGS-2020503195 | ![]() |
Note: SIMTech and 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.
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- View the full list of modular programmes offered by SIMTech and ARTC.
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