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LOGT9404 Design and Management of Logistics Systems Main Assignment 2026-27: Efficient vs Effective Digital Supply Chains | TU Dublin

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Module LOGT9404 Design and Management of Logistics Systems (DMLS)
Institution Technological University Dublin (TU Dublin)
Level Master’s (logistics and supply chain management)
Assessment Main assignment 2026-27: AI output + 1,200-word revised report + 500-word reflection
Weighting 70% of the module
Deadline Sunday 1 November 2026, 23:59 (Brightspace)

The LOGT9404 main assignment compares two companies with opposite supply chain priorities, one built on efficiency and one on effectiveness, and asks how digital transformation supports each strategy. You generate a first draft with GenAI, then rewrite and correct it in a 1,200-word theory-based report on lean, agile and hybrid strategies, and finish with a 500-word critical reflection on what the AI got right and wrong.

Main Assignment: The Role of Digital Transformation in Boosting Supply Chain Efficiency and Effectiveness

Supply chain management has moved from a back-end operational function to a strategic enabler of business success. In an era of e-commerce dominance, global interconnectedness and heightened customer demands, companies are using technology, data analytics, automation and sustainability initiatives to make their supply chains more responsive, efficient and adaptable.

Your task is to compare and analyse two contrasting companies (retailers or manufacturers) whose competitive advantage has been driven by different strategic priorities in their supply chains:

  • one company that primarily pursues market leadership through an efficient supply chain (cost reduction, process optimisation, speed); and
  • one company that predominantly pursues effectiveness (responsiveness, customer value, flexibility, service differentiation).

The comparison should highlight how efficiency-oriented versus effectiveness-oriented strategies shape digital supply chain design and outcomes. The assignment asks you to use GenAI as a tool to critique, refine and extend, grounding its output in supply chain theory and course concepts (AI Assessment Scale Level 4 – AI-assisted editing; all AI-generated content must be cited).

What You Must Submit

  1. Original AI output: a screenshot of the exact prompt used and the full AI-generated document (PDF acceptable). Non-submission of the full original AI output results in zero for the assignment.
  2. Revised theory-grounded report (1,200 words ±10%) – 75%: expand, correct and enhance the AI content using theoretical frameworks, supply chain principles and course material, focusing on how each company applies lean, agile or hybrid strategies and the impact on its supply chain.
  3. Critical reflection (max. 500 words) – 25%: how the GenAI output supported or hindered your analysis, its strengths and limitations, and how your own theoretical knowledge improved it.

AI may not be used for the revised report or the critical reflection.

Requirements

  • Company websites and reports alone are not enough; wider reading on supply chain theory, the companies and their industry is required.
  • APA 7th referencing with no fewer than 10 relevant academic references.
  • The title must name both companies with their focus, e.g. Zara (effective) and Walmart (efficient).
  • Report format, 12-point Calibri, Arial or Times New Roman, with front cover and contents page, submitted as a Word document on Brightspace.
  • Value: 70% of the module. Deadline: Sunday 1 November 2026, 23:59. Late penalty 5% per day for the first week; 0% after one week.

Marking Criteria

  • Presentation – report format, intelligibility, clear structure (max 5)
  • Comprehensive theoretical understanding – relevant material, use of theory, full and accurate referencing (max 30)
  • Association of concepts with other supply chain management topics – wider reading (max 15)
  • Ability to relate concepts and theories to examples – link between theory and practice (max 25)
  • Critical evaluation of the main arguments – logic, coherence, originality (max 25)
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