Unit information: Supply Chain Analytics & Projects in 2019/20

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 - Business School
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

The unit structure offers 30 contact hours, organised as 10 weekly 3-hour sessions. The learning process will be based on a combination of flipped learning style session with e-learning resources, such as podcasts and voice-enhanced power point presentations, predominantly used for theory, knowledge and concepts and class contact hours used to validate that the intended learning has taken place, using quizzes and polls, with class discussion around problematic issues. The session will then incorporate application of knowledge to cases, both in small group and individual work culminating in formative presentations. Where computer analysis can be done using Excel, students will be encouraged to bring their laptops to class; where specialist software is needed, these sessions will take place in a computer lab.

Assessment Information

Formative Assessment (ILO 1,2,3):

Students will have the opportunity to work online on weekly problems that build the necessary analytical skills for tackling larger case-based problems. A mixture of online assessment and materials to enable self-assessment will be provided.

Summative Assessment: (ILO 1,2,3,4,5,6,7 for both assessments)

All students choose: an analytical applied case on a specific project. Students will be in groups of 4 – 6. It is not possible to be more specific as the exact number of students on the unit may not be divisible by a specific integer. Students in a group will be awarded the overall group mark, but individual mark will be weighted based on team contribution as assessed by peers and/or tutors. To deal with free riding, coaching and scaffolding from tutors will be available during the term. The weighted mark is designed to incentivise collaboration. The project will be a maximum of 3000 words.

This accounts for 50% of unit assessment.

Students choose to do one of:

Analytical applied case (up to 1500 words) on an application of predictive analytics (individual)

or

Analytical applied case (up to 1500 words) on an application of prescriptive analytics (individual)

This accounts for 50% of unit assessment.

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. 3^rd 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