The course “Advanced Performance Measurement” aims to provide students with state-of-art tools, advanced data analytics methodologies and real-case applications to manage performances in an organization.  

The course contributes to the development of the following capabilities:

• Understand how organizations run performance measurement systems and their overlapping with data analysis;
• Understand and apply proper data analysis techniques to support plan, lead and control activities and processes of organizations considering the different decision makers and challenges within an organization;
• Design solutions to face performance management issues and opportunities, applying data modelling, analysis and visualization techniques, based on rigorous reasoning and modelling capabilities.

At the end of the course, students:

• Will know and understand who are the different decision makers, the different approaches and challenges within an organization when it comes to monitoring performance and defining goals; 
• Will be able to understand what are the enabling technologies to run an advanced performance measurement system and use proper data analytics techniques and tools to improve the planning control cycle in all its phases;
• Will be able to define and monitor different KPIs, to develop managerial reports and dashboards for different decision makers within an organization and to deliver and interpret data insights.

The course is organized in the following units:

• Introduction to the course. Definition of Advanced Performance Measurement
• How performance management and data analysis within an organization relate with:
• different decision makers;
• different models for advanced performance measurement.
• The enabling technologies for performance measurement:
• The digital transformation journey and the mega trends by which companies are reinventing themselves;
• The major technologies which are empowering the decision making process.
• Data analytics tools and techniques:
• Machine Learning: overview of main data analytics techniques;
• Data treatment: cleaning and preprocessing data;
• Supervised Learning: regression and classification;
• Unsupervised Learning: clustering;
• Business intelligence and performance measurement:
• Data analysis within the planning and control cycle;
• Data visualization.
• Risks and ethics of an Advanced Performance Measurement system.

The subjects covered in the course Accounting, Finance and Control are prerequisites for this course.

The exam consists of two parts:

• A project work (weight: 60% of final grade), to assess the ability of the team of students of applying the course contents to real cases. The assignment will be explained during the course and will require the development of a written report and an oral presentation
• A written test (weight: 40% of final grade), to individually assess the knowledge and comprehension of the topics illustrated during the course and acquired by students. It will be possible to take the written test only if the project work evaluation has received a final mark greater or equal to 18/30.

At the end of the course, students will be asked to evaluate in an anonymous form their group companions on a scale from 1 to 5. The goal of the Peer Review is to make sure that everyone actively participates to the activities of the project work. Based on the peer evaluation, one or more members of the group may be given a penalty of "-1” points applied on the final grade of the course.