Skip to main content
 

BUSI4AM15: Supply Chain Analytics

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type Tied
Level 4
Credits 15
Availability Available in 2024/2025
Module Cap
Location Durham
Department Management and Marketing

Prerequisites

  • Data Science for Business (BUSI4X915)

Corequisites

  • None.

Excluded Combinations of Modules

  • None.

Aims

  • To provide an appreciation of how analytics can provide supply chain managers with decision support
  • To provide knowledge of, and ability to apply, a range of analytics techniques to aid decision making across different supply chain processes

Content

  • Overview of analytical tools used for supply chain decision making
  • Optimization models for supply chain network design
  • Analytical tools for customer segmentation and demand planning
  • Optimal sourcing decisions
  • Inventory Planning and analytics
  • Optimal production planning and scheduling
  • Distribution and logistics planning
  • Overview of supply chain risk analytics
  • Machine Learning applications for supply chain planning

Learning Outcomes

Subject-specific Knowledge:

  • Upon successful completion of the module, the students should:
  • Develop critical understanding of the relevant analytical and scientific techniques to plan and manage supply chains
  • Develop specialised knowledge of the relevant analytical techniques to plan and manage supply chains to improve supply chain performance

Subject-specific Skills:

  • Upon successful completion of the module, the students should be able to:
  • Visualise supply chain data
  • Critically analyse supply chain data using appropriate analytical tools, and develop data-driven recommendations to improve supply chain performance

Key Skills:

  • Effective written communication skills
  • Planning, organising and time-management skills
  • Problem solving and analytical skills
  • Data presentation and visualisation, interpreting and using data
  • Making effective use of communication, information technology and other digital technologies

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • On-site teaching will typically include a mix of taught input, group work and discussion, use of case studies to emphasize real-world applications, and industry-informed sessions. Online learning will be divided into study weeks and will typically include activities facilitated by the teaching team and specially produced resources. Facilitated activities will make use of a range of educational technologies to include digital collaboration spaces and live online sessions. Learning resources vary according to the learning outcomes but typically include: video content, directed reading, reflective activities and opportunities for self-assessment.
  • The summative assessment comprises online assessments and a written assignment which will test students theoretical understanding, their knowledge of relevant techniques and types of analysis and their ability to apply these to a particular, real-world context.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
On-campus workshops (a combination of taught input, groupwork, discussion, case discussions and industry-informed sessions in blocks4Over a 4 day period3 hours12Yes
Online guided learning (a combination of facilitated sessions*, guided activities8Weekly6 hours48Yes
Preparation, reading and other independent study90 
Total150 
*This could cover synchronous live sessions (e.g. Zoom) and asynchronous (e.g. discussion boards, reading activities, video etc.) 

Summative Assessment

Component: Written AssignmentComponent Weighting: 60%
ElementLength / DurationElement WeightingResit Opportunity
Individual Written Assignment1500 words maximum100Same
Component: Data Analysis ComponentComponent Weighting: 40%
ElementLength / DurationElement WeightingResit Opportunity
Data Analysis Exercise 1500 words in total (or equivalent)50Online assessment
Data Analysis Exercise 2500 words in total (or equivalent)50Online assessment

Formative Assessment

The formative assessment serves to encourage students to study regularly and to monitor their learning progress. Students will undertake a series of activities aligned to the module content, which can include group presentations, individual or group reflections on specific topics receiving ongoing feedback on theoretical knowledge and how it is applied. Tutors provide feedback on formative work and are available for individual consultation as necessary (usually by email and video call).

More information

If you have a question about Durham's modular degree programmes, please visit our Help page. If you have a question about modular programmes that is not covered by the Help page, or a query about the on-line Postgraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.