In our five-part “Work Connected Series”, we will explore the trends that will drive innovation in the field service market over the next few years. In part 2, we will focus on the influences of big data.
1 - Predictive Analytics Help Solve Inefficiencies
Making smarter, data-based decisions is a key innovation focus. With cloud-based analytics, organizations can bring disparate datasets to one place and harness the insights with all relevant information at their fingertips.
- Machine Learning: This can be achieved through machine learning, where data is moved from a “rear-view” (reports that tell you what has happened/what the trends are) to a more predictive “forward-view” which can help with business planning.
- Optimized Workforce: Advanced and predictive analytics offers a tremendous opportunity for field service organizations to better optimize their workforce, which will drive improved utilization and ultimately, higher customer satisfaction. Gartner recently pointed out that data and analytics will drive modern business operations and not simply reflect their performance.
- Technician Scheduling: Scheduling field technicians often drives inefficiencies in organizations, particularly for assets in the field that move from one location to another, such as construction or agricultural equipment. Reacting to unplanned service needs from customers adds additional complexity that is often difficult for schedulers to account for when assigning tasks.
There is a great opportunity to take the volumes of data being captured in real-time, to make smarter decisions in field service which can lead to a different balance of reactive, proactive and predictive support.
2 - Field Service Solutions for Workforce Optimization
Workforce optimization tools can incorporate multiple rules and historical data when making recommendations on job assignments.
- Key Benefit: Optimization tools are not only faster but can often find opportunities for improved utilization that are hidden or not obvious to schedulers who perform manual assignments.
- Impacted Data: The type of data that can improve job schedules include technician location, asset location, previous commitments, working hours, overtime rules/rates and technician skills.
- Potential Outcome: An optimization engine that can perform what-if scenario analysis and make recommendations on which technician to assign to a job can minimize overtime and reduce the impact on previously assigned tasks.
3 - Leveraging Historical Data to Drive Better Outcomes
Other functions to consider is how historical data can be used to drive predictive outcomes.
- Predictive Modeling: Predictive maintenance has long been a dream for many in the field service market, as it offers compelling and potentially transformative business opportunities.
- Access to Data: A key driver of predictive modeling will be access to more data, which naturally is derived from connecting more assets into the service workflow. The road from reactive to preventative, and ultimately predictive, maintenance will be a slow journey, but as more assets become connected, the need to manage the data effectively will be increasingly important.
- Why Now?: Organizations that plan ahead of this trend will be much better positioned to capitalize on it.
4 - Impact of Big Data on Field Service
As field service organizations continue to update and add new solutions, Big Data will ultimately get even bigger. Because of the enormous quantities of data involved, there will be an increased demand for improved, more robust infrastructure for storage, processing, and networking, in addition to analytics software.
- Considerations: The benefits of a cloud-based, end-to-end solution and the analytics derived from it will have a compelling impact on the way organizations make decisions. Business intelligence not only helps in the daily performance of identifying operational efficiencies and managing customer service levels agreements, but it is critical in terms of offering process improvements.
- Industry Outlook: All of this combines to drive better strategic planning, based on historical patterns and predictive analysis, which will drive revenue growth, improved service delivery and better customer satisfaction.
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