AC457      Half Unit
Data Analytics for Management Control

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Pre-requisites:

Students must have completed AC415 before taking this course.

Assumed prior knowledge:

The course is designed for students with a good working knowledge of elementary statistics and regression analysis.

• The course is focused on the study of the quintessential role of management control in decentralized organizations. Our focus will be on the measurement and evaluation of the performances of organizational entities and their managers. Management accounting at this level of analysis is an integral part of companies’ management control systems.

• Specifically, we discuss what it means to have an organization be in control, what alternatives managers have for ensuring good control, and how managers should choose from among various control system alternatives. Then we will focus on each of the elements of financial control systems, which provide the dominant form of control in most decentralized organizations. These elements include financial target setting, performance measurement and evaluation, and the assignment of various forms of incentives.

• Short outline of the content of the course:

1. Introduction to management control systems (MCSs) and the role of data analytics in designing and evaluating MCSs
2. Analyzing and evaluating action control systems (“internal controls”)
3. Analyzing and evaluating result control systems
4. Choosing between management control alternatives
5. The planning role of budgets: business stress testing
6. Analyzing and evaluating risk management systems
7. Analyzing and evaluating incentive systems
8. Capital budgeting: business models and resource allocations
9. Selecting performance measures and setting performance targets
10. Using data analytics to prevent and detect fraud

30 hours of lectures in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

The course consists of three-hour sessions delivered in the Winter Term, with a reading week in Week 6.

Case analysis / study weekly

Weekly cases to be discussed in class. Students are expected to read the cases in advance and prepare for in-class discussion.

This course draws on the key textbook in this subject matter – Merchant, Kenneth A., and Wim A. Van der Stede [2023], Management Control Systems: Performance Measurement, Evaluation, and Incentives (Pearson, UK), fifth edition.

Additional materials (including, and importantly, several datasets) and readings will be available on Moodle. However, the professor commits to keeping additional or optional readings to a minimum. This is deliberate in a course using “data analytics” as a key theme because there is an infinite amount of “stuff out there” that may be useful. It’s “all yours”. But deciding what is and what is not reliable is a key skill in today’s world of “information overload”, “fake information”, and “alternative truths”. Evaluating the quality of the "evidence" and "citations" brought to bear in the class presentations and discussions is a secondary learning outcome.

Course participation (20%) weekly

Report (80%) in Winter Term Week 1

This component of assessment includes an element of group work.

Students are required to hand in four group reports (which will be assigned using an algorithm), each counting for 20% of the grade for the course, thus 80% in total, where the remaining 20% will be based on class participation.

Groups must consist of between 3-5 members, without exceptions to these limits. Each group member will have an opportunity to formally evaluate their peers at the end of the term.

For those who the algorithm has required a submission (which will be determined by the end of the first week of term for the remainder of the term), group reports are due by noon on the day before class.

Group reports cannot be longer than 3 pages of core text that stands on its own and contains the key analysis of the problem, how it was addressed, the recommendations, and conclusions. Exhibits, appendices, and tables (which do not count towards the 3-page limit) can, and must, contain further detail, properly presented, about the data analyses and analytics the students have performed to support their report, giving the reader the confidence that the inferences, recommendations, and conclusions are based on robust analyses, though it should not necessarily be expected that the reader will study these materials in great depth (or at all).

The groups who must deliver a report will also have to be ready to present their work in class. Everyone else in class should be ready to discuss and constructively challenge the findings, assumptions, or propose alternative analyses that could have been considered. This will be the basis for each student’s individual class participation grade (20%).

Grading class participation motivates class participation, and having highly interactive sessions helps the learning process. Active class participation encourages students to be well prepared, and thus, to become active, rather than passive, learners. Participation provides students with the opportunity to gain from the experiences and talents of everyone in the course. Moreover, class participation helps students improve their oral communication skills. This is particularly important in a quant-based course because data analytics should not be seen as an alternative to problem solving but instead complementary to decision-making processes in real-world business settings. Class participation evaluations will be based primarily on the quality of the participation in classroom discussions.