MQM3IL09: Experimentation and Optimisation

Module code: MQM3IL09

Module provider: Leadership, Organisations and Behaviour; Henley Business School

Credits: 0

Level: 6

When you'll be taught: Full year

Module convenor: Dr Sinem Bulkan, email: s.bulkan@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: No placement specified

Academic year: 2024/5

Available to visiting students:

Talis reading list:

Last updated: 19 November 2024

Overview

Module aims and purpose

This module forms part of the Henley Executive Diploma in Managing Business Transformation (Improvement Leader Apprenticeship), and as such, sets out to provide the knowledge, skills and behaviours required by Improvement Leaders/Change Managers/Business Transformation managers in today’s world, in relation to Experimentation and Optimisation. The module is designed to meet the learning outcomes of the Improvement Leader Apprenticeship Standard.

The module aims to expand the knowledge and understanding of design and application of experimentation, optimisation, and simulations to deliver business benefits. It also aims to develop the learner’s approach in assessing the organisation’s approach to mathematical modelling and make recommendations for improvement. The module also aims to equip the learners to guide others in the use of appropriate experimentation tools and to build the organisation’s knowledge in terms of mathematical modelling.

Key behavioural skills –professionalism and strategic thinking–are strongly outlined throughout the module.

Module learning outcomes

By the end of the module, it is expected that the student will be able to:

1. Develop knowledge and understanding of design principles and design of experiments.

2. Develop knowledge and understanding of the application of simulation tools.

3. Develop knowledge and understanding of application and interpretation of experimentation and optimisations. 

4. Develop skills to guide others in the use of appropriate experimentation tools.

5. Develop skills to assess the organisation’s approach to mathematical modelling and make recommendations for improvement.

Module content

The module covers the following topics:

• Experimentation (DOE: Design of Experiments, DOE Approaches)

• Optimisation

• Simulation 

• Monte Carlo and Discrete Event simulation, Balanced and unbalanced designs

• General linear modelling (ANOVA: Analysis of Variance results, simple linear regression, multiple regression, ANCOVA: analysis of covariance).

• Digital Transformation / Automation/ Artificial Intelligence

-The topics are specifically linked to the behavioural skills (Professionalism and Strategic Thinking).

-The content should be mapped against the OFSTED requirements (British values / Safeguarding / Prevent / Maths / English).

-The content should acknowledge limitations and challenges to the approaches discussed within the module content.

-The content should address the need to develop a learning culture to encourage experimentation.

Structure

Teaching and learning methods

Teaching and learning takes place through a blended learning approach. The teaching and learning methods comprise a combination of self-study via a range of online materials on the Canvas learning platform, face-to-face workshops with Academic Faculty and Learning Coaches, and interaction with a Learning Coach (face-to-face and online) who supports the cohort throughout the module. Each person participates in a facilitated Action Learning sets either individually or in teams in week 4 of the module.
Materials on Canvas include content on-screen, videos, PowerPoint presentations, journal articles, book chapters, practical activities, e-portfolio, and reflection points.

Study hours

At least 7 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

The work-based projects are not marked. Evaluation of the work-based project leads to a decision of ‘Proceed’ or ‘Revise’.  To gain a ‘Proceed’ the learner must satisfactorily meet the assignment brief requirements.
Any learning outcomes not achieved will be highlighted for the learner, so that it is clear that these learning outcomes should be addressed prior to reaching Gateway for the End Point Assessment (EPA). 
Learners may revise their project as many times as necessary, as they progress through the programme. However, only one resubmission will be evaluated, and feedback provided by the Learning Coach (see reassessment arrangements, below).

Summative assessment

Penalties for late submission of summative assessment

There is no penalty for late submission. However, if learners are at risk of missing the deadline, they are asked to submit an ECF (Exceptional Circumstances Form) requesting a 14-day extension.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

One 1500-word Work-Based Project, for which formative feedback will be provided.

Reassessment

Additional costs