Course Code: SCM511
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Synopsis

Supply chain management decisions are becoming increasingly data-driven. SCM511 Supply Chain Analytics covers the application of data analytics techniques to identify and solve complex supply chain problems and improve overall performance. Students will learn to make decisions at different levels of a multi-tier supply chain by leveraging technology and analytics. The course aims to expose students to the risks and uncertainty inherent in supply chain management and how to handle them. It will help students to solve problems in inventory management, demand management and network design as well as improve the efficiency of an organisation’s supply chain. Using real-world examples, students will learn to identify and optimise a supply chain using analytical tools.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Introduction to Supply Chain Analytics
  • Data Collection and Data Visualisation
  • Uncertainty and Risk
  • Descriptive Models
  • Predictive Models
  • Prescriptive Models
  • Inventory Analytics
  • Newsvendor Model
  • Demand Analytics
  • Forecasting for Demand Analytics
  • Network Design
  • Travelling Salesman Problem

Learning Outcome

  • Analyse the role of data analytics in supply chain management
  • Estimate the risks and uncertainties present in supply chain management
  • Improve the way inventory is managed
  • Collect data required for data analysis
  • Construct forecasting techniques for demand management
  • Solve the newsvendor problem
Part-time Programmes / CET Courses

Objectives

A. Knowledge and Understanding (Theory Component)

By the end of this course, you should be able to:

  • Analyse the role of data analytics in supply chain management
  • Estimate the risks and uncertainties present in supply chain management
  • Improve the way inventory is managed

B. Key Skills (Practical Component)

By the end of this course, you should be able to:

  • Collect data required for data analysis
  • Construct forecasting techniques for demand management
  • Solve the newsvendor problem


Who Should Attend

This course is designed for logistics and supply chain management professionals who want to build capabilities in supply chain analytics and data-driven decision-making. It is ideal for those seeking to apply analytical techniques to improve the performance and effectiveness of supply chain operations.

 

Relevance of Course to employment/upskilling/reskilling

The course aims to expose students to the risks and uncertainty inherent in supply chain management and how to handle them. It will help students to solve problems in inventory management, demand management and network design as well as improve the efficiency of an organisation’s supply chain.


Admissions pre-requisite

  • Bachelor’s or Equivalent
  • Knowledge of Logistics or Supply Chain Management will be an advantage

 

Schedule

TimeAgenda
Seminar 1
Supply Chain Analytics, Data Collection, and Visualisation
19:00 – 19:30 Foundations of Supply Chain Analytics
19:30 – 20:05 CRISP-DM framework
20:05 – 20:20 Break
20:20 – 20:35 CRISP-DM framework
20:35 – 21:15 Discussion & Sharing: Data Engineering in CRISP-DM Process Production Data
21:15 – 21:35 Data Collection and Data Visualisation
21:35 – 22:00 Discussion & Sharing: Supply Chain Data Visualisation
Seminar 2
Uncertainty, Risk and Descriptive Models
19:00 – 19:30 Uncertainty & Risk in Supply Chain Management
19:30 – 20:05 Supply Chain Risk Analysis & Model
20:05 – 20:20 Break
20:20 – 20:35 Background of the Case Study
20:35 – 21:15 Discussion & Sharing: The Case of Serum Institute of India
21:15 – 21:35 Descriptive Models
21:35 – 22:00 Discussion & Sharing: Supply Chain Data with Issues
Seminar 3
Predictive and Prescriptive Models
19:00 – 19:35 Predictive Model in Supply Chain Analytics
19:35 – 20:15 Predictive Model: Decision Tree
20:15 – 20:30 Break
20:30 – 20:45 Prescriptive Analytics
20:45 – 21:05 Application of Prescriptive Analytics in Supply Chain
21:05 – 21:20 Background of the Case Study
21:20 – 22:00 Discussion & Sharing: The Case of Brazos Valley Food Bank
Seminar 4
Inventory Analytics and Newsvendor Model
19:00 – 19:40 Inventory Analytics and Newsvendor Model
19:40 – 20:20 Inventory Analytics Methods
20:20 – 20:35 Break
20:35 – 20:50 Background of the Case Study
20:50 – 21:15 Probabilistic Models: Discrete v.s. Continuous
21:15 – 21:30 Activity 2: Value of Partnership in Transitioning to a Circular Supply Chain
21:30 – 22:00 NewsVendor Model and Examples
Seminar 5
Demand Analytics and Forecasting Methods
19:00 – 19:40 Introduction to Demand Analytics
19:40 – 20:15 Classification of Demand
20:15 – 20:30 Break
20:30 – 20:45 Introduction to Demand Forecasting
20:45 – 21:10 Time Series Data and Forecasting Methods
21:10 – 21:25 Background of the Case Study
21:25 – 22:00 Discussion & Sharing: Fargo Health Group
Seminar 6
Network Design and the Travelling Salesman Problem
19:00 – 19:30 Introduction to Network Design Problem
19:30 – 20:15 Regional Facility Configuration
20:15 – 20:30 Break
20:35 – 20:50 Background of the Case Study
20:50 – 21:15 Discussion & Sharing: Marico Ltd. – Distribution Network Optimization
21:15 – 21:30 Travelling Salesman Problem
21:30 – 22:00 Assessment

 

Assessments

  • Class participation (PCT)
  • 1 Final Assignment (ECA)
  • 1 Individual Assignment (TMA)

 

Trainer info

Dr. Zhu Siying, Provost’s Chair and Senior Lecturer at Business Analytics Programme, School of Business, SUSS. She obtained the Bachelor of Science degree in Maritime Studies with First Class Honours at Nanyang Technological University, Singapore; and the PhD degree in the School of Civil and Environmental Engineering at Nanyang Technological University, Singapore. She was a Post-Doctoral Research Fellow in the Department of Civil and Environmental Engineering at National University of Singapore. Her research interests include big data, machine learning, transportation safety, and transportation network analysis, and maritime studies. She has published more than 20 peer-reviewed journal articles, primarily as first or co-first author, in journals such as Transportation Research Part A: Policy and Practice, IEEE Transactions on Intelligent Transportation Systems, Accident Analysis & Prevention, etc.

 

Course Completion requirements

  • Attendance: Participants must achieve at least 75% attendance.
  • Assessment: Participants must sit and pass all prescribed assessments and submit all course work stipulated in the section Assessments.
  • Evaluation: Participants are required to complete course evaluations conducted by SUSS and SSG at the end of the training.
Students who achieve at least 75% attendance and meet the course completion requirements will be awarded a Certificate of Completion. For those who do not meet the course requirements, a Certificate of Participation will be issued instead.

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1Enhanced Training Support for SMEs2 (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Rate (B) 0%70%70%70%
SSG Grant (70%) (C)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (C) = (D) $3,168.00 $792.00 $792.00 $792.00
Total nett course fees payable including GST (E)$285.12 $71.28$71.28 $71.28
SSG Enhanced Grant (F) -   -   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (D) +  (E) - (F) = (G) $3,453.12 $863.28 $335.28 $335.28
1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singapore citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.

 

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]