OnProcess recently attended Field Service USA, where we had many discussions about how to improve service and support using the latest technologies and best practices. Some of the most illuminating conversations happened during the roundtables we chaired on “Digital Business Transformation: Leveraging Analytics to Optimize the Field Service Event.”
The roundtable participants came from manufacturers in a range of industries, from telecom and electronics to industrial and food production machinery. They all felt strongly about embedding analytics into their organizations, but many were struggling with how to do so most effectively.
Three common themes emerged:
Why Use Analytics
- Efficiencies: Identifying process gaps was high on the list. Because there are so many interdependent, siloed functions involved in service events—from customer service agents to parts/labor dispatch to transportation and field engineers—there are a lot of hand-offs, which leads to intelligence gaps, service delays and unnecessarily high costs. By using analytics to identify interdependencies between these silos, problem areas can be highlighted, root causes identified and plans developed to close gaps, eliminate waste and streamline the end-to-end process.
- Predicting Failures: For many, this is the ‘holy grail.’ As one participant noted, a single hour of their product’s downtime costs a customer about $150,000. So, they use predictive analytics to looks for symptoms of degradation and alert them when a customer’s product is nearing a potential failure. The company then proactively communicates to customers, recommending actions to avoid downtime.
Several other participants, however, who are starting to collect data from connected equipment, said they don’t know how to leverage that IoT intelligence. They are earlier in the digitization evolution and haven’t figured out how to embed predictive failure analysis into their processes in ways that would enable them to improve uptime for their customers.
- Competitive Advantage: Several companies talked about how competitive their industries are, and how using analytics can improve their advantage. For instance, not only can machine data help increase uptime, but it can also help field technicians resolve issues faster by identifying root causes ahead of time. These companies highlight their use of analytics to win and maintain customer contracts.
Problems with Data Accuracy
Our roundtable participants said they are not only collecting machine data, several are also gathering data on customer service touches, order history, problem resolution results, and more. However, because a lot of their data is manually captured by agents and technicians, it’s not always accurate. The more keystrokes you make, the more mistakes are possible.
Fears of inaccuracy are holding some of the companies back from truly leveraging data. They’re skeptical that analytics are providing the right insights and, therefore, unlikely to base service actions and pre-emptive course corrections on it. In cases like these, putting in place more streamlined procedures and leveraging automation can make a big difference.
How to Work with the Data
Everyone agreed there’s no shortage of potential data sources or field service use cases. In addition to streamlining processes and predicting/preventing product failures, other ideas included using analytics-driven insights to inform sales efforts (i.e., which customers are likely to purchase add-ons, upgrades, complementary equipment, etc.) and enabling field technicians to handle more jobs per day. But having data and knowing what to do with it is different than knowing exactly which data to analyze, what types of analytics to perform, and how to interpret the results and turn them into actions.
We find that many OnProcess clients are at this stage, where upfront guidance on best practices can make the difference between leveraging data in ways that add real value to your organization and your customers, and having a lot of data that does little good.
What stage is your company in?