Digital technology is rapidly transforming business processes, communication processes, and customer activities—disrupting and destabilizing markets, but also enabling the creation of new ones. (Forbes Insights Team, 2018). This easily accessible introductory module to supply chain analytics will give students important foundation tools and techniques to support decision making in a new digitally disrupted world.
The module is designed for postgraduate students with a basic knowledge of statistics, spreadsheet modelling and data analytics. A fundamental purpose of the module is to enable students to become intelligent readers and creators of supply chain analytics.
The module will be applied in a blended learning environment, with a mix of online and on-campus theory-based lectures, interactive workshops, modelling tutorials and real-world case studies. Content is split into 4 phases; foundations of supply chain analytics; descriptive analytics (the use of data to understand past and current supply chain performance and make informed decisions); predictive analytics (predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time); and prescriptive analytics (identify the best alternatives to minimize or maximize some objective).
Reflecting on the cross functional/industry nature of supply chain analytics, there will be a varied mix of cases, analytical model examples and analogies used during the teaching of the module. Which will include, but not limited to: supply chain management; marketing analytics; financial modelling; consumer segmentation and econometrics.
The theme of the module is built on 3 pillars; Sustainability; Technology; and Business Intelligence.
Foundations of Supply Chain Analytics
Introduction to Supply Chain Analytics. Analytics on Spreadsheets
Descriptive Supply Chain Analytics
Visualizing and Exploring Data. Descriptive Statistical Measures. Probability Distributions and Data Modelling. Statistical Inference and Sampling and Estimation.
Predictive Supply Chain Analytics
Trendlines and Regression Analysis and other Forecasting Techniques. Introduction to Data Mining. Spreadsheet Modelling and Analysis. Monte Carlo Simulation and Risk Analysis.
Prescriptive Supply Chain Analytics
Linear and Integer Optimization and their applications. Facility Location and Vehicle Routing Decision Analysis.
The module will be applied in a blended learning environment, with a mix of theory-based lectures, interactive workshops, modelling tutorials; site visits and real-world case studies. The objective is to ignite students’ prior knowledge in quantitative and qualitative methodologies and execute the module content in a practical environment, placing the student into real world scenarios.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |