Module Details
| Module Code: |
MGMT8071 |
| Title: |
Supply Networks Optimisation
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Long Title:
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Supply Chain Networks Optimisation
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 1 - 2020/21 ( September 2020 ) |
| Field of Study: |
3450 - Business & Management
|
| Module Description: |
This module is designed to examine the use of data and technologies to problem solve, and optimise supply chain decision making.
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Distinguish between Linear and Non-Linear models |
| LO2 |
Formulate 'live work scenario' problems in the supply chain into a mathematical model |
| LO3 |
Identify decision variables, objective function, constraints, and non negativity restrictions for all linear models as they relate to supply chain activities |
| LO4 |
Solve the (supply chain network) linear programme by geographical methods |
| LO5 |
Solve the (supply chain network) linear programme with the use of computers and software |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
| Indicative Content |
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Supply Chain Networks
The evolving commercial nature of business is examined in the conext of supply chain management. Global supply chains; outsourcing and back-sourcing; customer demands and expectations; forward and backward integration; competition and competitive advantage; are all examined in regard to the extent of global supply chain networks and the supply chain architecture that supports demand.
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Supply Chain Optimisation
The competitive nature of markets; resources and capabilities frameworks; optimal use of resources; lean and agile supply chains as a source of competitive advantage. Modelling supply chains to optimise returns to both the customer and the provider/manufactuirer of goods and services.
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Formulating desicion making models
This is a three step process (formulation, solution, and intrepretation). Develop clear and concise problem statements with appropriate variables. Manipulate the model to optimise solutions. Vary data and model parameters for optimal results.
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Application of linear programming models.
Supply chain architecture analysis. Reconfiguration of storage layouts and workforce. Shelf space optimisation in warehouse and retail scenarios. Optimisation of delivery routes and use of distribution centres.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Introduction to Supply Chain modelling and linear programming |
Every Week |
1.50 |
1.5 |
| Lab |
Contact |
Application of mathematical models to supply chain problems |
Every Week |
0.50 |
0.5 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
No Description |
Every Week |
5.00 |
5 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
2.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lab |
Contact |
Application of mathematical models to supply chain problems |
Every Week |
0.50 |
0.5 |
| Lecture |
Contact |
Introduction to Supply Chain modelling and linear programming |
Every Week |
1.50 |
1.5 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
No Description |
Every Week |
5.00 |
5 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
2.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Balakrishnan, N. and Render B. J., Stair, R. and Munson, C.. (2017), Managerial Decision Modeling With Spreadsheets, 4th. Pearson Education Inc. Publishing as Prentice Hall., [ISBN: 9781501515101].
| | Supplementary Book Resources |
|---|
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Law, A.. (2014), Simulation Modelling and Analysis., 5th. McGraw-Hill Education, New York, [ISBN: 9780073401324].
| | Recommended Article/Paper Resources |
|---|
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Greenwood, A., Hill, T., Saunders, C.
and Hold, R.. (2016), Modelling and Analysis of intermodal
Supply Paths to enhance Sourcing
Decisions., Proceeds of the 2016 Winter Simulation
conference, Arlington, Virginia, USA.,
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Hokey, Min and Genguy, Zhou.. (2002), Supply chain Modelling: Past Present and
Future., Computer and Industrial Engineering, 43 (243-249,
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Li, B., Tan, K. and Tran, K.. (2016), Traffic Simulation Model for Port
Planning and Congestion Prevention., Proceeds of the 2016 Winter Simulation
conference, Arlington, Virginia, USA.,
-
Kulkarni, K., Tran, K., Wang, H. and
Lau, H. (2016), An effective Gate System operations for
a Multi-purpose port using
simulation-optimisation., Proceeds of the 2017 Winter Simulation
conference, Las Vegas, Nevada, USA.,
-
Pedrielli, G., Vinensius, A., Chew, E.,
Lee, L. and Duri, A.. Hybrid Order Picking Strategies for
Fashion E-Commerce Warehouse Systems., . Proceeds of the 2016 Winter
Simulation conference, Arlington,
Virginia, USA.,
-
Rabe, M., Klueter, A. and Wuttke, A.
(2018).. (2018), ). Evaluating the Consolidation of
Distribution Flows Using a Discrete
Event Simulation Tool: Application to a
Case Study in Greece., Proceeds of the 2018 Winter Simulation
conference, Gothenburg, Sweden.,
| | This module does not have any other resources |
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