Moreton Bay Regional Council (MBRC) engaged MOQdigital to assist in navigating the complex world of IoT, data and the Cloud. After initially delivering a framework to assist MBRC in planning and implementing an IoT environment, MBRC elected to begin a series of proof of concepts to show the business the value of sourcing and properly displaying data.
The intent of the project was to inform the suitability of using Azure as its Smart City Data platform, and this was achieved. MBRC is continuing to understand the best use of Azure, and the right architecture to support smart city data, whilst integrating into Council’s existing systems. MBRC elected to approach the proof of concepts in an agile manner. MOQdigital engaged senior stakeholders to elicit high level goals which were broken down into stories, and finally implemented the solution in a series of 3 sprints, each targeted at delivering a specific business outcome.
Initially, high level brainstorming sessions were held with three aims:
Once high-level goals were identified and prioritised, a planning session allowed the business to identify 3 high goals, each of which could be delivered in a 2-week sprint using a cross functional team from our Applications, Data and Analytics team. The main driving factor was to use data already available in the business to show the secondary benefits of that data could be as transformative as the primary use.
The goal of Sprint 1 was to analyse the correlation between the use of office space with external factors such as traffic and weather conditions, school holidays and other factors expected to affect attendance. A building occupancy system already in place logged building access and egress.
The workshops identified a feeling in the business that the deployment of resources to various work orders around the region was inefficient. In Sprint 2, MOQdigital used information from the Work Order Management System to gather historical data about work orders and to investigate whether the process could be streamlined.
The data supported the hypothesis and showed that many resources were often dispatched to nearby locations separately to deal with similar work orders. This uncovered the ability in the business to hugely increase efficiencies, reducing the cost and time to address issues in the region.
Sprint 3 was focused on exploring the potential of predictive maintenance across the business. For example, it was found that public waste bins around the region were not being emptied in a timely manner, leading to rate payer complaints, while other bins had workers dispatched to empty them when they still had significant available capacity. To facilitate this requirement, MBRC and MOQdigital deployed smart sensors in a selection of bins around the region.
Once actual capacity data was flowing in from bins, MOQdigital used Azure Machine Learning to predict the future capacity of the bins, allowing for the system to produce an optimised daily route for waste management technicians to attend only those bins that required emptying. This optimised route was sent via Azure Logic Apps and used to create a work order and route in the Work Order Management System, greatly increasing the efficiency of the workforce, while also increasing the level of service to the community and proving the capacity for integration to existing council systems such as ESRI and Technology one from the Azure Platform.
MOQdigital were able to assist MBRC through their end to end capability across planning, building and running ICT solutions in the Cloud. We were able to leverage our unique DevOps as a Service engagement model to provide value very quickly, delivering each outcome in a 2 week timeboxed sprint. MBRC have now adopted Microsoft Azure as the solution of choice for all future IoT and Data projects, due to the flexible nature of the platform. MOQdigital were able to identify a technical need, implement the capability and deploy a solution during the development cycle, vastly reducing the cost of licensed products, while also reducing time to value and increasing efficiency.
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