WM9QA-15 Supply Chain Digitisation and Data Analytics
Introductory description
In this module, students will learn how digitisation transforms supply chain management, using advanced technologies and analytics to challenge traditional practices. The module fosters critical thinking, innovation, and ethics, equipping students with the skills to enhance supply chain performance, resilience, and ethical practices management through data analytics to optimise operational efficiency and enhance transparency and resilience.
Module aims
The overarching aim of this module is to empower learners to develop expertise in data analytics to analyse and link diverse supply chain data, enabling strategic data-driven decisions to enhance performance, resilience and sustainability. The module is built on practical application and aligned with current and future industry needs; it equips students to harness various data sources, deploy advanced analytical techniques, and leverage cutting-edge digital technologies like AI, blockchain, and IoT. Through experiential learning, students will master decision support tools, develop actionable insights, and communicate them effectively to stakeholders. Ultimately, this learner-centred module will enable students to succeed in complex, dynamic, data-driven supply chain environments. The design has been mapped against the course-level learning outcomes.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- Introduction to data analytics in the supply chain
- Data collection and preparation
- Advanced Data Analytics for Supply Chain Applications (Diagnostic, Predictive, Prescriptive)
- Digital Technologies in Supply Chains (Blockchain, AI, IoT, and Digital Twins)
- Designing Dashboards and Data Visualisation Tools for Supply Chain Decision-Making
- Developing Data-Driven Decision Support Systems and Collaborative Problem-Solving in Supply Chain
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate comprehension of technologies shaping supply chain digitisation, including but not limited to blockchain, artificial intelligence, machine learning, and the Internet of Things (IoT).
- Align digital strategies with organisational goals, manage change effectively, and foster innovation within the supply chain context
- Evaluate the choice of analytical tools depending on the specific needs and scale of the complexity of supply chain analytics tasks
- Apply data analytics techniques to solve complex supply chain challenges to enhance operational efficiency, mitigate risks, and capitalize on emerging opportunities.
- Present and communicate complex data insights and analytical findings and suggest actionable recommendations to diverse stakeholders in a supply chain
Indicative reading list
- Abdey, J., 2023. Business Analytics: Applied Modelling and Prediction. London: SAGE Publications Ltd.
- Bhattacharya, R. and Bhattacharyya, A.M. (2020) Supply Chain Analytics: Strategies, Models, and Solutions. Boca Raton, FL: CRC Press.
- Knaflic, C.N. (2015) Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, NJ: Wiley.
- Kuhn, M. and Johnson, K., 2013. Applied Predictive Modeling. New York: Springer.
- Liu, K.Y. (2018) Supply Chain Analytics: Concepts, Techniques, and Applications. New York, NY: Wiley.
- Mandl, C. (2020) Procurement Analytics: Data-Driven Decision-Making in Procurement and Supply Management. Cham, Switzerland: Springer.
- Rahimi, I., Gandomi, A. H., Fong, S. J., & Ülkü, M. A. (Eds.). (2020). Big data analytics in supply chain management: Theory and applications. CRC Press.
- Robertson, P. W. (2020). Supply chain analytics: using data to optimise supply chain processes. Routledge.
- Tipi, N. (2021). Supply chain analytics and modelling: Quantitative tools and applications. Kogan Page Publishers.
- Vandeput, N. (2021). Data science for supply chain forecasting. Walter de Gruyter GmbH & Co KG.
View reading list on Talis Aspire
Subject specific skills
Data integration, advanced analytics, technology utilisation, dashboard design, strategic assessment, digital transformation understanding, data-driven decision-making, deep industrial knowledge, awareness of key practices and principles, understanding of industry structure and future challenges
Transferable skills
Critical thinking, problem solving, data handling and analytical proficiency, collaboration and teamwork, effective communication, digital literacy, adaptability, and sustainability awareness.
Study time
| Type | Required |
|---|---|
| Lectures | 15 sessions of 1 hour (10%) |
| Seminars | 15 sessions of 1 hour (10%) |
| Online learning (independent) | 30 sessions of 1 hour (20%) |
| Private study | 30 hours (20%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
Students will be encouraged to explore the reading list which includes essential and recommended reading material. Students will be encouraged to utilise the CPD tools and range of resources from the CILT and CIPS websites as part of their student and affiliate memberships. Students will be encouraged to trial out various data analytics and visualization software.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Data analytics and visualization case study | 30% | 18 hours | No |
|
Students will be given a case study with a data set and asked to develop a predictive model and visualise the analysis and results |
|||
Reassessment component |
|||
| Reflection on data analytics case | Yes (extension) | ||
|
Students will be asked to write a reflection on the tools and techniques developed for the case study. |
|||
Assessment component |
|||
| Report analysing supply chain predictive model | 70% | 42 hours | Yes (extension) |
|
Students will be given a case study specific to an industry with a supply chain data set. They will be asked to prepare data, analyse it, develop a predictive model, and interpret the results linked to supply chain digitisation strategies. The question will have multiple sections, enabling students to develop a deep understanding of predictive analytics linked to supply chain digitisation strategies and their applications in supply chain management. |
|||
Reassessment component |
|||
| Report analysing supply chain resilience resub | Yes (extension) | ||
|
Students will be given a case study specific to an industry with a supply chain data set. They will be asked to prepare data, analyse it, develop a predictive model, and interpret the results linked to supply chain digitisation strategies. The question will have multiple sections, enabling students to develop a deep understanding of predictive analytics linked to supply chain digitisation strategies and their applications in supply chain management. |
|||
Feedback on assessment
Students will receive formative feedback during the 4-week teaching block through scheduled opportunities such as group feedback sessions, individualised support during office hours, and asynchronous feedback via email or discussion forums. Written feedback will be linked to each learning outcome, incorporating clear, constructive, and actionable understandings. Additional tools, such as video or audio feedback options, will accommodate different learner preferences. Students will also be encouraged to reflect on their progress through self-assessment tools and peer feedback activities, fostering a supportive and collaborative learning environment.
Courses
This module is Core for:
- Year 1 of TWMS-H1SD Postgraduate Taught Supply Chain and Logistics Management (Full-time)