IB9HS-15 Operations Analytics
Introductory description
This module is designed to introduce the student to the ideas of modelling and analytics and to their relevance for management.
Module aims
This module is designed to introduce the student to the ideas of modelling and analytics and to their relevance for management. The emphasis is upon the student being a critical consumer of quantitative and quantitative information and modelling. It is not intended that the module will turn the student into an experienced modeller but that it will teach the student to understand the importance of, and benefits to be gained from, modelling and analytics for management. It is also intended that the student should be able to have sensible and meaningful conversations with specialists who are experienced modellers. Furthermore, it is intended that the student should be able to ask relevant and pertinent questions of such specialists and comprehend their replies. Therefore the module is concerned with the context and process of modelling rather than with the technicalities underlying specific modelling ana anlytics approaches.
To become a critical consumer of modelling and analytics it is necessary to consider some approaches in some detail. Therefore the module considers a variety of approaches that are commonly used in a business and management environment. However, it is not intended that the focus will be solely on the mechanics of those approaches but upon the interpretations to be placed upon the output of the models and upon the usefulness of such output to management.
As a further guide to the situations where such models may be used, and how they may be employed, the module considers a number of case studies based on applying the techniques in actual business situations.pplying the techniques in actual business situations.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The module will cover a range of topics of which the following are illustrative:
Why managers use models
Working with case studies
Exploratory data analysis
Visualisation
Sampling
Regression analysis
Forecasting
Simulation
The modelling process
Optimisation
When something changes: Sensitivity analysis
Validation & verification of models
Critical review of case studies
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the contribution that modelling and analytics techniques can make to organisations.
- Demonstrate a comprehensive understanding of the purpose of modelling and analytics, and identify the areas in which it can be applied
- Demonstrate a comprehensive understanding of the value of investigating business problems using modelling and analytics
- Critically analyse modelling work undertaken in organisations.
Indicative reading list
Core texts:
Wisniewski, M. (2020) Quantitative Methods for Decision Makers (5th edn) London: Pitman
Pidd, M. (2009) Tools for Thinking: Modelling in Management Science (3rd edn) Chichester, UK: Wiley
Illustrative journal articles:
Bollapragada, S.; Markley, R.; Morgan, H.; Telatar, E.; Wills, S.; Samuels, M.; Bieringer, J.; Garbiras, M.; Orrigo, G.; Ehlers, F.; Turnipseed,C.; Brantley J (2018) A Novel Movement Planner System for Dispatching Trains. INFORMS Journal on Applied Analytics 48(1):57-69.
Esquejo N.; Miller, K.; Norwood, L.: Oliverira, I.; Pratt R.& Zhao, M (2015) Statistical and optimization techniques for laundry portfolio optimization at Procter & Gamble, Interfaces 45(5): 444-461
Gifford, T & Gremley, R (2019) Chassis Leasing and Selection Policy for Port Operations. INFORMS Journal on Applied Analytics 49(4):239-248.
Vigo, D.; Caremi, C.; Giordini, A.; Bosso, S.; D'Aleo, G. and Beleggia, B. (2014) 'SPRINT: Optimization of Staff Management for Desk Customer Service Relations at Hera' Interfaces, 44, 5, pp. 461-79
Winkenbach, M.; Roset A & Spinler S (2016) Strategic redesign of urban mail and parcel networks at La Poste, Interfaces 46(5): 445-458
Subject specific skills
Identify and assess operational situations where modelling and analytics tools may be applied.
Specify methods of modelling and analytics in operations such as exploratory data analysis, sampling, visualisation, regression analysis, forecasting, optimisation, sensitivity analysis and simulation.
Recognise and interpret the requirements for, and benefits and pitfalls of, applying modelling and analytics tools
Transferable skills
Written communication.
Work in groups to solve problems cooperatively.
Study time
| Type | Required |
|---|---|
| Other activity | 27 hours (36%) |
| Private study | 49 hours (64%) |
| Total | 76 hours |
Private study description
Private study to include preparation for lectures
Other activity description
This module will be split as two hours face-to-face workshops and one online lecture hour per week. The lecture hour may be live, or may be prerecorded, or as asynchronous tasks with either online or face-to-face support
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Group presentation | 20% | 15 hours | No |
Reassessment component is the same |
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Assessment component |
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| Class participation | 10% | 7 hours | No |
Reassessment component is the same |
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Assessment component |
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| 2 hour examination | 70% | 52 hours | No |
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Reassessment component is the same |
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Feedback on assessment
Assessments are graded using standard University Postgraduate Marking Criteria and written feedback is provided. Overall percentage marks are awarded for examination performance and general examination feedback is provided to the cohort.
There is currently no information about the courses for which this module is core or optional.