Introduction - Responding to emergency call demand with predictive analytics.
Triple Zero Victoria (TZV, formerly ESTA-000) is the critical point of contact for all emergency calls in Victoria, Australia, serving a population of over six million inhabitants. With the vast scope of its services, they faced the challenge of accurately forecasting call demand to optimize resource allocation and budgeting across its service lines.
Challenge - Predicting the (seemingly) Unpredictable in Emergency Services
TSV needed to anticipate the volume of emergency calls it would receive to efficiently manage its operations. This forecasting was essential for effective budgeting and resource allocation, ensuring preparedness and responsiveness in critical situations.
Project Info
Client
Triple Zero Victoria
Service
ML/AI
Industry
Emergency Services
Solution - PAG addressed this complex problem by:
- Developing a sophisticated Bayesian statistical model to predict the number of Triple 0 calls over the short to medium term, taking into account various influences, such as demographic, climate, socio-economic factors.
- Embedding this model into a bespoke software application that updates predictions in real time with incoming data, offering TZV a dynamic tool for ongoing decision- making.
- Enabling the software to generate detailed reports and conduct scenario analyses, allowing TZV to effectively plan and prepare for fluctuations in service demand.
Outcome - Financial Efficiency and Enhanced Resource Management
By leveraging the model and software solution provided by PAG, Triple Zero Victoria achieved significant cost savings across its operations.
In addition, the data-driven insights gained from the model have empowered TZV to show the impact of external (unforeseen) factors on its operations, demonstrating the value of evidence-based forecasting in operational and financial planning.
Conclusion
PAG's bespoke econometric modelling, and its subsequent delivery via an intuitive web application, has provided TZV with a powerful tool for demand forecasting, proving instrumental in saving costs and enhancing the allocation of resources. This solution has not only strengthened TZV’s operational capabilities but also underscored the critical role of data-driven decision-making in emergency services management.
