Module BUSI4X815: Advanced Forecasting

Department: Management and Marketing

BUSI4X815: Advanced Forecasting

Prerequisites

• Data Science for Business (BUSI4X915)

Corequisites

• None.

Excluded Combination of Modules

• None.

Aims

• To introduce how forecasting can add value to supply chain decision making
• To provide a critical understanding of different forecasting techniques considering the end objective and the available data
• To explore how human judgment and experience can be combined with artificial intelligence based forecasting methods.

Content

• Understand the importance of forecasting for planning and decision-making in various contexts
• Visualisation of time series data for discovering their components and data issues
• Use of different forecasting methods for different types of data
• Naïve, standard and advanced time series forecasting methods
• Combining and adjusting statistically derived forecasts
• Artificial and Human Intelligence for forecasting
• Forecasting Support Systems
• Forecasting for intermittent demand items eg. spare parts.

Learning Outcomes

• By the end of the module, candidates will be able to:
• Develop critical understanding of different forecasting techniques to plan and manage supply chains
• Develop critical understanding about choosing appropriate forecasting methods for a given dataset
• Develop specialised knowledge about how to combine human judgment with statistically derived forecasts
• Develop critical understanding of relevant forecasting techniques to plan and manage supply chains.

• By the end of the module, students should be able to:
• Visualise data for forecasting purposes
• Critically evaluate combining human judgment with artificial intelligence based methods for forecasting
• Be able to analyse supply chain demand data, and develop data-driven recommendations to improve supply chain performance.

• Effective written communication
• Planning, organising and time management
• Problem solving and analysis
• Data presentation and visualisation, interpreting and using data
• Making effective use of communication, information technology and other digital technologies.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• On-site teaching will typically include a mix of taught input, group work and discussion, use of case studies to emphasize real-world applications, and industry-informed sessions. Online learning will be divided into study weeks and will typically include activities facilitated by the teaching team and specially produced resources. Facilitated activities will make use of a range of educational technologies to include digital collaboration spaces and live online sessions. Learning resources vary according to the learning outcomes but typically include: video content, directed reading, reflective activities and opportunities for self-assessment.
• The summative assessment comprises online assessments and a written assignment which will test students' theoretical understanding, their knowledge of relevant techniques and types of analysis and their ability to apply these to a particular real-world context.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

The formative assessment serves to encourage students to study regularly and to monitor their learning progress. Students will undertake a series of activities aligned to the module content, which can include group presentations, individual or group reflections on specific topics receiving ongoing feedback on theoretical knowledge and how it is applied. Tutors provide feedback on formative work and are available for individual consultation as necessary (usually by email and video call).

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University