It is easy to see why managing mixed fleets can become complex and result in inefficiencies: the fleet is often spread across several operations, sourced from different plant hire companies, tends to be a mix of different manufactures, ages, models, and could have third parties involved in maintaining and operating the machines. Due to the vast number of stakeholders, inequality of data and lack of granularity, it is no surprise that there is limited visibility of what individual equipment are doing at any given time, resulting in a fragmented view of the fleet and its respective operations.
With no consolidated view of the fleet, it is often the case that equipment operates sub-optimally, resulting in unnecessary costs, project delays, and a detrimental impact on the environment. An obvious solution is to use live machine data to highlight inefficiencies and underperformance - after all you cannot manage what you cannot measure.
Historically, accessing live data across the whole fleet was expensive and onerous. However with the proliferation of IoT, telematics and standardisation of APIs, this is rapidly becoming easier. A scaled approach avoids overwhelming volumes of data and enables a clear focus on areas of opportunity and our experience shows the biggest initial impact can be made by focusing on a handful of the most important machine metrics. Once these opportunities have been exploited the metrics can be expanded and new opportunities identified.
The metrics providing the biggest initial impact include: utilisation, idling time, fuel consumption, location, and operating hours. Our customers, across all industries, have used these metrics to identify patterns in operational inefficiencies including: too many equipment onsite resulting in under-utilisation, incorrect equipment used for the job resulting in lower productivity, ineffective site layout resulting in idling and excessive travelling, suboptimal operator behaviour resulting in dangerous, and inefficient utilisation.