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Unit information: Supply Chain Analytics & Projects in 2020/21

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Unit name Supply Chain Analytics & Projects
Unit code EFIMM0073
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Sheng
Open unit status Not open
Pre-requisites

Nil

Co-requisites

Nil

School/department School of Management
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

Analytics plays four distinct roles in a supply chain context:

  1. To provide input into the supply chain strategic planning process by means of forecasts and predictions relating to potential strategy option. This may be termed predictive analytics.
  2. Once strategy is selected, the operations function must work out a means of implementation. Analytics provides models to advise on optimal decisions in this context. This may be termed prescriptive analytics.
  3. To assess the success or otherwise of any supply chain initiative; and metrics that need to be identified to measure performance. This may be termed descriptive analytics.
  4. How the above analytics can provide useful knowledge for supply chain enhancement purposes and inputs to associated project management activity.

The aim of this unit is to introduce the key concepts, models and computing software tools in each of these three domains and to work through the challenges of successful implementation in real world cases and situations.

A key element will not only be a focus on appropriate choice and implementation of approach, but communication of the output from such methods and software for supply chain improvements and their subsequent project management.

Intended Learning Outcomes

On completion of this unit, students should be able to:

  1. Demonstrate understanding of the main descriptive, predictive and prescriptive analytical models and tools for supply chain and project management
  2. Apply the main descriptive, predictive and prescriptive analytical models and tools available for analysing supply chain and project data
  3. Use computer software for analysing and modelling projects and problems.
  4. Interpret analytical results to form an opinion on a given business question
  5. Demonstrate a critical approach to the selection of analytical tools for a given problems and the opportunities and limitations inherent in both the tool and the application environment.
  6. Distinguish different approaches when communicating technical information, whether orally or written, to different audiences, whether specialist or generalist, strategic or tactical.
  7. Demonstrate the ability to study independently and work within a small group.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions including lectures, tutorials, drop-in sessions, discussion boards and other online learning opportunities.

Assessment Information

Summative 1: 1,000 word individual assignment (50%) Summative 2: 2,500 word group assignment (50%) Formative: weekly quizzes

Reading and References

Students are encouraged to read extensively around their subject to inform their knowledge. Students should draw from a range of sources which may include academic texts and papers, practitioner books and journals, market reports and online sources.

Core Texts

Albright, S.C. and Winston, W.L. (2017) Business Analytics – Data Analysis and Decision Making. Sixth Edition. Cengage Learning.

Maylor, H. (2018) Project Management. Fifth Edition. Prentice Hall.

Recommended Reading

Feigin,G. (2011) Supply Chain Planning and Analytics: The Right Product in the Right Place at the Right Time. Business Expert Press.

Ragsdale, C.T. (2014) Spreadsheet Modelling and Decision Analysis: A Practical Introduction to Business Analytics. Cengage.

Chopra, S, Meindl,P. (2012) Supply Chain Management, Pearson

Anderson, D.R., Sweeney, D.J., Williams, T.A., Wisniewski, M and Pierron, X. (2017) An Introduction to Management Science. 3rd Edition. Cengage.

Following international journals could also provide important information related to new developments in supply chain analytics and projects area:

  • Harvard Business Review
  • Journal of Operations Management
  • Journal of Supply Chain Management
  • Management Science
  • Omega: The International Journal of Management Science
  • Supply Chain Management: An International Journal

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