The Internet of Things is poised to radically change the way companies do business. The evolution of sensors in connected devices and advances in big data analysis will combine to make field service increasingly proactive, highly efficient, and, in many cases, less dependent on human intervention. The business benefits are clear: increased efficiency will lower costs, while intelligent analysis of sensor data also leads to an unparalleled understanding of customer needs which can benefit the topline.
As a field service management pioneer, ClickSoftware has been closely involved in networked field service for many years and in particular in the elements that combine to make up today’s IoT, such as GPS, mobility, business intelligence and big data, and machine-to-machine (M2M) technology. Today, ClickSoftware partners with IoT platforms and practitioners to offer market leaders in a variety of industries the ability to take full advantage of what IoT means for Field Service Management.
The benefits of IoT to Field Service are numerous, but some of the main advantages include:
To learn more about the Internet of Things and its potential to reshape field service, download our new IoT white paper.
One of the most important capabilities that IoT sensors offer organizations today is the ability to provide better visibility, in real time, into the state of assets in the field. Historically, the lack of real time visibility has meant critical operational decisions are delayed, which results in the inefficient utilization of resources and assets.
Better communication and visibility into issues on remote job sites can assure that proper policies and operational and safety processes are followed, and assistance is provided when needed. The simple task of monitoring remote sensor data can often resolve challenging issues quickly and efficiently. Click has a well-established history of dealing with these use cases, such as ATMs and elevators communicating the need for service.
Typically, these sensors communicate through the layers of the IoT stack and generate a ticket into the Field Service Management system. In turn this ticket generates a work task, which can be automatically scheduled based on the characteristics of the case and the resources available.
While unsophisticated devices can signal a need for service in the event of a system failure, richer data from intelligent sensors enable a shift from reactive to proactive service, greatly increasing field service efficiency.
Unsophisticated sensors can readily communicate a health check signal, but, as IoT evolves, more sophisticated data management can be decentralized and devices can communicate more information over the network. Signals evolve from binary (working / not working), into conditional logic (if the number of toasts > 10,000 and heating coil temperature > 140°C then communicate a certain signal), and beyond into much more sophisticated logic. The improved data also gives more insight into the nature of any problems and may suggest more efficient fixes, perhaps by the customer themselves, rather than a truck roll.
Additionally, as sensor data grows, more sophisticated machine learning techniques can be leveraged to indicate a need for service. For example, machine learning models can establish the likely indicators (e.g. usage levels, speed, pressure) submitted from a single device that correlate with an increased probability of imminent failure, based on patterns established in millions of other data points already collected.
These data points could also be external (e.g. operating environment temperature), as well as internal, to paint a holistic picture of all the influential factors. Moreover, additional context can change the nature of a service requirement. For example, the urgency assigned to a proactive maintenance call for a commercial air conditioner may diminish if there are other ‘healthy’ units for the same building. Imagine a fast food location with one beverage fountain ‘ready for service,’ while three others are working properly.
While increased visibility and the efficiency brought about through proactive service both benefit the customer, the data aggregated from IoT networks and service management solutions offers still more value.
Specifically by looking at usage patterns of networked industrial equipment, capital equipment manufacturers can get a better understanding of their customers and develop services that target a particular set of needs.
This increased level of insight enables predictive workforce intelligence, predictive maintenance, preventing failures and seamlessly providing support without requiring the customer to initiate a call.
Information from networked sensors also can help manufacturers gain greater insight into operational realities. For example, if printing machines fail unexpectedly after only 1000 hours’ use, a service solution that aggregates actuals can identify that the same new fuses are a common factor amongst all failing machines. Even without human interaction this can trigger an ad hoc maintenance program to replace the fuses on all affected machines.
In a similar use of the aggregated data, ClickSoftware can analyze failure rates and service contract history. This can help field service organizations understand the ideal times for servicing assets but can also help inform manufacturers of warranty timelines and guidance for customers.
The visibility that sensors give a field service team not only increases work efficiency, it also can positively impact job satisfaction. With employees keen to use their skills to solve meaningful problems, rich data from networked devices gives workers the intelligence up front about what they will deal with, and ensure they have the right tools and skills to deal with it. First time fix rates rise, employees spend less time traveling and the empowerment from IoT increases employee morale.
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