The Building Blocks of Predictive Service: Machine Learning and IoT

10/08/2019 / Digital Transformation / Katia Loboda

Man in a business suit presses hexagonal icon labeled “machine learning” next to other icons representing AI and predictive service.

In this age of remote diagnostics and assisted decision making, field service customers increasingly refuse to accept equipment downtime. They want to buy positive service outcomes—not just equipment and service for it when it breaks.

To build their capacity for delivering better service outcomes, field service organizations (FSOs) are investing aggressively in emerging technologies like field service IoT (Internet of Things), predictive service analytics, machine learning, and artificial intelligence (AI).

Enterprise implementation of AI has grown 270% over the last four years, according to Gartner’s 2019 CIO Survey. Meanwhile, WBR Insights research reveals 96% of FSOs consider IoT and data analysis technologies core parts of their business strategies.

By making these investments now, early adopters can learn more about their customers, expand their service offerings, and ultimately establish a sustainable competitive advantage.

Make Predictive Service a Reality with Machine Learning and IoT

FSOs report insufficient data from products in the field is the biggest obstacle to delivering outcomes as a service (OaaS).

IoT helps solve this problem. Using connected devices, FSOs can remotely monitor every piece of moving and static equipment. Wired equipment can transmit a constant stream of data about temperatures, fan speeds, error codes, and more.

But simply collecting data doesn’t prevent breakdowns. To keep equipment up and running, service organizations need to use that data to forecast potential problems before they occur. AI and machine learning let them do so.

McKinsey & Company define machine learning as intelligent applications “based on algorithms that can learn from data without relying on rules-based programming.”

Essentially, by analyzing large amounts of data, computers can distinguish patterns, draw conclusions, and make predictions. Furthermore, by learning from error margins, machine learning programs become accurate as they process more data.

This technology offers boundless applications. In the field service world, a properly trained machine learning application can quickly analyze large amounts of data from connected devices in the field and accurately suggest preventative maintenance to avert asset failure.

Working in concert, field service IoT and machine learning make predictive service a reality. But machine learning also offers so much more.

Machine Learning Brings Speed, Accuracy, and Efficiency to Service

In addition to analyzing historical and real-time data to make decisions, machine learning can process language. This capability has numerous field service applications.

Scheduling and Route Optimization

By considering multiple data factors, intelligent scheduling systems can help FSOs attend to service calls more quickly and at a lower cost. Astea’s Dynamic Scheduling Engine, for example, uses information about workforce attributes, parts availability, job location, and customer histories to dispatch the most qualified technician to complete a job in the most timely fashion.

Additionally, by analyzing real-time data including job updates and traffic conditions, machine learning can optimize scheduling on the fly to ensure the most efficient outcomes and boost first-time fix rates.

To learn more about the benefits of machine learning for field service, download our machine learning whitepaper.

Parts and Resource Planning

In this age of remote diagnostics and assisted decision making, field service customers increasingly refuse to accept equipment downtime. They want to buy positive service outcomes—not just equipment and service for it when it breaks.  To build their capacity for delivering better service outcomes, field service organizations (FSOs) are investing aggressively in emerging technologies like field service IoT (Internet of Things), predictive service analytics, machine learning, and artificial intelligence (AI).   Enterprise implementation of AI has grown 270% over the last four years, according to Gartner’s 2019 CIO Survey. Meanwhile, WBR Insights research reveals 96% of FSOs consider IoT and data analysis technologies core parts of their business strategies.   By making these investments now, early adopters can learn more about their customers, expand their service offerings, and ultimately establish a sustainable competitive advantage. Make Predictive Service a Reality with Machine Learning and IoT FSOs report insufficient data from products in the field is the biggest obstacle to delivering outcomes as a service (OaaS).  IoT helps solve this problem. Using connected devices, FSOs can remotely monitor every piece of moving and static equipment. Wired equipment can transmit a constant stream of data about temperatures, fan speeds, error codes, and more.  But simply collecting data doesn’t prevent breakdowns. To keep equipment up and running, service organizations need to use that data to forecast potential problems before they occur. AI and machine learning let them do so.   McKinsey & Company define machine learning as intelligent applications “based on algorithms that can learn from data without relying on rules-based programming.”  Essentially, by analyzing large amounts of data, computers can distinguish patterns, draw conclusions, and make predictions. Furthermore, by learning from error margins, machine learning programs become accurate as they process more data.  This technology offers boundless applications. In the field service world, a properly trained machine learning application can quickly analyze large amounts of data from connected devices in the field and accurately suggest preventative maintenance to avert asset failure.  Working in concert, field service IoT and machine learning make predictive service a reality. But machine learning also offers so much more.    Machine Learning Brings Speed, Accuracy, and Efficiency to Service  In addition to analyzing historical and real-time data to make decisions, machine learning can process language. This capability has numerous field service applications.  Scheduling and Route Optimization By considering multiple data factors, intelligent scheduling systems can help FSOs attend to service calls more quickly and at a lower cost. Astea’s Dynamic Scheduling Engine, for example, uses information about workforce attributes, parts availability, job location, and customer histories to dispatch the most qualified technician to complete a job in the most timely fashion.  Additionally, by analyzing real-time data including job updates and traffic conditions, machine learning can optimize scheduling on the fly to ensure the most efficient outcomes and boost first-time fix rates.    To learn more about the benefits of machine learning for field service, download our machine learning whitepaper. Parts and Resource Planning Machine learning also has applications for service parts and inventory management.  By analyzing such data inputs as historical parts usage, scheduled and unscheduled maintenance, season, and current inventory levels, intelligent applications can accurately suggest optimal resource levels. They can also make parts allocation and storage suggestions—for instance, which parts should be stored at the depot and which ones in a technician’s vehicle?  FSOs using integrated intelligent parts and resource planning software report the following benefits:  Improved supply chain management, which leads to better informed decisions about what to purchase and when.   Optimized inventory levels, which reduce standing inventory costs.   Streamlined reverse supply chain operations, which make returns and warranty claims much faster and easier. Customer Service   Customers’ inability to get their complaints heard and questions answered efficiently is one of their biggest pain points. It’s also a major factor in determining customer loyalty.  Chatbots with machine learning capabilities can now receive customer calls and either resolve issues directly or pass calls on to a human customer service agent who’s already been informed of the case. Relying on an intelligent customer service application to first gather information and customer records and even propose solutions, your customer service agents can address requests from a position of knowledge and reduce time to resolution.  Better Service Means Better Business Machine learning and field service IoT are vital tools in the transition to predictive service models.  Although most FSOs are still moving from reactive to proactive service, these technologies’ early adopters are already starting to reap such benefits as:  More holistic understandings of how customers use equipment, so they can not only offer personalized service but also forecast and address problems before they happen.   Stronger data, which helps marketing and sales teams make compelling use cases that speak to customer pain points.     More reliable service, thanks to real-time data insights and assisted decision making.   Direct improvements to the bottom line resulting from expanded service offerings, more efficient operations, and extended customer lifetime value.   By leveraging these emerging technologies, your FSO can improve the customer experience, reduce operational costs, and gain a foothold as a best-in-class service company.  Want to find out more about how to transition to predictive service? Download the Astea Emerging Technologies eBook.Machine learning also has applications for service parts and inventory management.

By analyzing such data inputs as historical parts usage, scheduled and unscheduled maintenance, season, and current inventory levels, intelligent applications can accurately suggest optimal resource levels. They can also make parts allocation and storage suggestions—for instance, which parts should be stored at the depot and which ones in a technician’s vehicle?

FSOs using integrated intelligent parts and resource planning software report the following benefits:

  • Improved supply chain management, which leads to better informed decisions about what to purchase and when.
  • Optimized inventory levels, which reduce standing inventory costs.
  • Streamlined reverse supply chain operations, which make returns and warranty claims much faster and easier.
Customer Service

Customers’ inability to get their complaints heard and questions answered efficiently is one of their biggest pain points. It’s also a major factor in determining customer loyalty.

Chatbots with machine learning capabilities can now receive customer calls and either resolve issues directly or pass calls on to a human customer service agent who’s already been informed of the case. Relying on an intelligent customer service application to first gather information and customer records and even propose solutions, your customer service agents can address requests from a position of knowledge and reduce time to resolution.

Better Service Means Better Business

Machine learning and field service IoT are vital tools in the transition to predictive service models.

Although most FSOs are still moving from reactive to proactive service, these technologies’ early adopters are already starting to reap such benefits as:

  • More holistic understandings of how customers use equipment, so they can not only offer personalized service but also forecast and address problems before they happen.
  • Stronger data, which helps marketing and sales teams make compelling use cases that speak to customer pain points.
  • More reliable service, thanks to real-time data insights and assisted decision making.
  • Direct improvements to the bottom line resulting from expanded service offerings, more efficient operations, and extended customer lifetime value.

By leveraging these emerging technologies, your FSO can improve the customer experience, reduce operational costs, and gain a foothold as a best-in-class service company.

Want to find out more about how to transition to predictive service? Download the Astea Emerging Technologies eBook.

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