Aberdeen reported that 82 percent of companies have experienced unplanned downtime over the past three years and that unplanned downtime can cost a company as much as $260,000 an hour. This is a 60% increase since 2014. Unplanned downtime can significantly impact revenue but it can also negatively influence your reputation leading to lower NPS scores and even renewals. Unfortunately, many field service organizations still only react once an outage has happened. The key to reducing the negatives associated with unplanned downtime as well as a way to differentiate your firm in the market is to move your field service operations to a proactive and predictive stance. This is a new model of service delivery that is similar to how software is now increasingly distributed as a subscription service in the cloud. In this case, manufacturing organizations need to move to a Maintenance as a Service (MaaS) model that takes advantage of IoT, artificial intelligence and machine learning.
The Internet of Things (IoT) is enabling organizations to move from a reactive model to a proactive and preventative one, a key requirement for a MaaS offering. IoT is poised to radically change the way companies do business. For example, the total installed base of IoT connected devices is projected to amount to 75.44 billion worldwide by 2025, a fivefold increase in ten years and Gartner predicts that by 2021, 10 percent of customer-reported issues won’t need an on-site visit to be resolved because of the remote capabilities of the IoT.
The evolution of sensors in connected devices and advances in machine learning and artificial intelligence are combining to make field service increasingly predictive, highly efficient, and less dependent on human intervention. The business benefits are clear:
- Increased visibility into assets improves service efficiency and lowers costs
- Better understanding of customer needs based on actual usage data
- More accurate SLA design and compliance
Many manufacturers have heard about or adopted servitization, or the bundling of services along with your product to increase revenues. However, with the prevalence of IoT sensors coupled with advances in AI and ML, manufacturing organizations can move beyond just offering reactive services to a model that enables true differentiated offerings with a MaaS field service model.
The following illustration provides a good overview of the differences between a servitization model vs a MaaS one with the first diagram would be more akin to a servitization and the second a MaaS model.
As the diagram shows, a MaaS model is a better method because it solves problems before they happen, helping to eliminate expensive downtime. An added benefit, is that the collected data can be used in the product development process to help identify problem areas and move to fix them in future releases.
There is one key component that field organizations will need to move to a MaaS model and that is a FSM solution that can act on warning from sensors in real-time. Many FSM systems do not operate in real-time, eliminating many of the benefits of IoT alerts and platforms. A system that operates in real-time can:
- Receive the alert
- Open a ticket
- Assess the current location and status of all technicians
- Automatically insert the request in the schedule based on overall business priorities
- Schedule the proper person with the correct skill set and equipment
- Reschedule or cancel lower priority work
There are many FSM systems that only update periodically throughout the day, meaning that a dispatcher does not have an accurate view into the location of a technician nor the status of work. In this type of system, with an IoT alert, the dispatcher needs to wait until the system refreshes to get an up-to-date view of which technicians are available to complete the service. This delay can mean costly unplanned downtime, a missed SLA or, worse, not being able to help out quickly enough in an emergency situation.
In order to take your field service organization to the next maturity level beyond servitization, manufacturers and operator of assets are now moving towards a predictive maintenance strategy which also takes into account sensor data from machines. This MaaS approach will allow organizations to develop optimized maintenance and service schedules as well as more detailed failure predictions to reduce, if not eliminate, any unplanned downtime.