The context for the Operations Analytics module is operations management in complex, interconnected, data-driven environments. Operations Management deals with the design, optimisation and management of products, processes, services and supply chains. It makes trade-off decisions with respect to quality, cost, and time in the acquisition, development, and utilization of resources that firms need to deliver the goods and services their clients want.

Leading companies are using data and analytics to drive critical operational decisions, reduce costs and complexity and maximise operational efficiencies. Machine-generated data is one of the fastest growing and most complex areas of big data. It’s also one of the most valuable, containing a definitive record of all user transactions, customer behaviour, machine behaviour, security threats, fraudulent activity and more. 

Operations Analytics is an emerging area of business analytics that provides visibility into business processes, events, and operations. It is enabled by special technologies that can handle machine data, sensor data, event streams, and other forms of streaming data and big data. Operations Analytics solutions can also correlate and analyse data collected from multiple sources in various latencies (from batch to real time) to reveal actionable information. Organizations can act on the information by immediately sending an alert to the appropriate manager, updating a management dashboard, offering an incentive to a churning customer, adjusting machinery, or preventing fraud.

The aims of the Operations Analytics module are:

To provide students with an understanding of key ideas and concepts in operations management, sufficient to provide a context for exploring how leading companies are using data and analytics to improve their operations practices and performance.
 To expose students to the range of operations problems that leading companies are addressing with business analytics, and the results they are achieving.
 To provide students with practical experience of working with the range and types of data, models, platforms and tools used in operations analytics.