AI & technology
Learn how digital representations can support operational insight and scenario analysis.
Explore how a digital representation of an operating system can help people understand current conditions and investigate possible changes. This module begins with a specific decision, such as improving the flow of work through a service process, and develops the model, data connections and validation needed to support it.
You will distinguish a useful twin concept from a static visualisation or an isolated simulation, then create a scoped operational demonstrator. The work brings together process knowledge, data definitions and scenario analysis. It also examines what the representation leaves out, how often it needs updating and how its predictions should be checked before they influence real decisions.
Explore the focus and practical output of each teaching session. Open individual sessions below, or expand the complete plan.
Choose a measurable question and identify what a digital representation would need to explain.
Practical output: A twin purpose statement and scope.
Compare different representations and clarify the role of operational data in the proposed concept.
Practical output: A representation choice and justification.
Identify entities, activities, queues, resources and boundaries that influence the selected operating outcome.
Practical output: An annotated process and entity map.
Describe how the system changes over time and define the information required to represent those changes.
Practical output: A state-and-event model.
Assess available observations, missing values and refresh requirements without assuming every signal is real-time.
Practical output: A data-source and refresh specification.
Implement a small model of capacity, demand and flow using explicit, reviewable assumptions.
Practical output: A working baseline model.
Adjust model parameters using reference observations and record the rationale for each important assumption.
Practical output: A calibration log and parameter register.
Compare outputs with reference cases and investigate disagreement before using the model for new scenarios.
Practical output: A validation report and gap list.
Update selected model states from a supplied data stream or scheduled file and handle missing observations.
Practical output: A repeatable model-update workflow.
Test changes in demand, resources and process rules while keeping a consistent baseline for comparison.
Practical output: A scenario comparison with practical implications.
Vary uncertain assumptions and show which parameters most influence the recommended operational decision.
Practical output: A sensitivity analysis and confidence discussion.
Present current state, assumptions and scenario results in a format that an operational user can interpret.
Practical output: A decision-support dashboard or report.
Connect the model, data updates and interface, then test a small set of end-to-end scenarios.
Practical output: An integrated operational demonstrator.
Demonstrate the concept and define ownership, maintenance and validation requirements for a limited trial.
Practical output: A trial plan and handover presentation.
Apply the learning
Model a bounded service process, such as handling enquiries or reviewing applications. Use observed or supplied event data to represent workload and capacity, then compare alternative operating arrangements. The project must state which parts are connected, which are simulated and how model outputs have been checked.
Use anonymised, public or suitable synthetic material where workplace information is confidential. The issued assessment brief confirms the required evidence.
Teaching and the relevant course certificate are provided through Kent Business College in the UK. Completion requirements are set out in the module information; attendance alone should not be assumed to meet every assessment requirement.
No. The core project can use supplied process records or simulated observations. Hardware integration is not required to complete the learning activities.
Only if it helps the chosen decision. The core emphasis is a valid operating model and useful data connection rather than decorative visual complexity.
No. The module starts with the simplest adequate representation. Predictive AI is considered where it adds a justified capability to the operating model.
