In today's competitive world, real-time data and innovative service methods are vital for field service enterprises to ensure customer delight, increase revenues, and expand profit margins.
The IoT explained
The Internet of Things (IoT) allows machines to communicate with each other (M2M communication). It is built using a combination of networks that comprise of data-gathering sensors, devices, big data, analytics, and cloud computing, which communicate via secured and encrypted channels. Connected devices enable efficient predictive maintenance by constantly providing information on a machine's performance, environmental conditions, and the possibility of failures. IoT can connect machines on the field in order to record incidents in real-time into a semi-intelligent 'Gen-X' FSM system.
Integrating IoT with FSM software applications
Field service organizations always strive to consistently provide the best service experience to their customers, by ensuring immediate repair and maintenance of their equipment and machinery. By collecting data about the machine's health and performance from IoT sensors, organizations can leverage predictive and preventive field service to minimize device downtime.
Three primary traditional FSM challenges
Here are three primary issues that challenge the current reactive scenarios:
• Field technicians execute the job and fix the equipment after the issue is reported. However, the delay can impact business continuity, which in turn affects the operating profit margins
• Adding more field technicians and service trucks to the field comes at a cost and sometimes the increased capacity remains under-used
• Assigning more work to existing field teams can have a negative impact on SLAs and first-time fix rates. Even worse, it can increase the cost of travel and overtime
Essentials of a new-age FSM solution
field service management system that integrates device sensor data,
technicians, customers, and technology is the key to address these issues. It
should function in a predictive and preventive mode with the following
• The FSM process, which includes issue identification, communication, incident creation, scheduling, and assignment can be automated, thereby ensuring zero disruption in machinery operations and no or negligible downtime. This not only increases productivity, but also expands operating profit margins
• Most FSM products can also automate incident creation, scheduling, assignment, and invoicing processes. Using IoT, we can predict upcoming issues based on sensors data analysis and auto-creation of incidents based on preset threshold rules
The workflow of a FSM system with IoT integration
is an outline of the flow of incidents in a typical IoT-enabled FSM system:
1. Data from the equipment's sensors is collected and transmitted, using secured and encrypted channels, to a big data storage
2. Big data management and analytics is used to parse and analyze for refined sensors data
3. The IoT command console is configured with predefined threshold rules to identify errors and monitor the device's health and performance
4. Incidents are auto-created in the FSM system whenever errors are detected
5. Auto-scheduling, routing, and dispatching of field service technicians against the incidents is done based on customer entitlements, location, product, skills required for the job, technician's availability, parts availability, etc. via the FSM system
6. A field technician performs the job at the customer's site; records the effort, parts used, travel time, and any expenses incurred; and then bills the customer
Workflow of Field Service Management application using IoT.
Six Solution benefits
Wind turbines: A case in point of how IoT integrates with FS systems
Failures in wind turbines interrupt power generation leading to lower productivity and higher system downtime, which result in varying energy production and higher operating costs. To maintain profit margins, higher efficiency and uptime are required.
Near-real-time analytics provides data so that FS teams can react faster and address the issues before they become mission critical, thus reducing impact and avoiding downtime.
The wind turbine's sensors collect real-time data that is analyzed and through which, auto incidents are created, service scheduled, and an agent assigned to fix the issues. Wind turbine sensors are also used to continuously collect operating temperature, rotor acceleration, wind speed and direction, and blade vibrations - all of which can be used to optimize the turbine's performance, increase its productivity, and execute predictive maintenance to ensure reduced downtime.
*** Authors: Haresh Sreenivasa and R.N.Sarath Babu **