At this year’s Field Service Forum, Mark Hessinger of 3D Systems and Aquant‘s Chris Caron and Edwin Pahk shared how it was possible for the company not only to resolve issues faster but also to analyze untapped service data to identify patterns and trends for the purpose of moving from a reactive service model to a predictive and prescriptive one.
The increasing need for predictive service
3D Systems is a producer and provider of 3D printers as well as hardware, materials, software, and on-demand solutions for digital manufacturing. The company was founded in 1986 and it has evolved and expanded in 68 countries over time, acquiring expertise in healthcare, dental, aerospace, automotive, and durable goods. Today, the company’s 3D solutions support the digital transformation of their customers on every stage from digitization to design and simulation, manufacturing, inspection, and management.
As more and more customers have started using 3D Systems technology in production, it became increasingly important for the company to revamp its services in order to increase response time. Ensuring system uptime is critical, and for 3D Systems, mean-time-to-repair and mean-time-to-respond are key metrics that they have been trying to optimize. By implementing a quick-to-value AI platform, 3D Systems was able to:
- Address the skill gap within the company by implementing tiered support, thus reducing the time specialists spent on simple problems and unlocking the ability to assign the right people to the right tasks;
- Pull knowledge together and provide its global network of service professionals better access to siloed information that was scattered across many systems as a result of company acquisition throughout the years;
- Onboard new service technicians quicker to get them out in the field solving complex service problems.
Once 3D Systemd adopted the Aquant solution, the company started seeing significant improvements as repeat visits decreased by 39% and parts consumption went down by 62%. For a company that engages the customer from design and prototype to pilot and production runs, this is an important achievement. Especially considering that, when something goes wrong, customers typically try to address the issue themselves. When they ultimately reach out to the company for resolution, they already lost plenty of time and resources trying to fix it themselves.
So, the time to resolution is important for 3D Systems and it’s also just as important for all field service organizations. With this in mind, how can time to resolution be expedited by incorporating AI into the service process as 3D Systems did?
Service intelligence for faster time to resolution
Aquant’s service intelligence platform helps companies solve service problems faster by leveraging AI to develop actionable insights. The solution consists of service AI performing experience mining, meaning that it digs into structured data, such as customer support cases, work orders, or parts information, and unstructured data, such as tribal knowledge or service notes, to mine different aspects of data using a natural language processing (NPL) engine for service. This is then layered with AI and machine learning algorithms as well as an AI and human intelligence interface.
One of the key benefits of leveraging this solution is understanding service behavior through relevant data insights. The NPL engine enables the service intelligence platform to extract observations and actions or symptoms and solutions from service data and map it out so that it can be leveraged to generate informed service decisions. In other words, service AI mines data to generate AI-driven business insights that empower service leaders to make intelligent decisions throughout the service lifecycle, enabling them to:
- Prevent customer escalations
- Implement proactive workforce training
- Mitigate asset risk
- Derive behavioral insights from free text
- Benchmark reporting
All service leaders experience surprise customer escalations, but service AI can help prevent them months before they would occur. Although customer satisfaction surveys might sound good, no angry emails are received, SLA is met, and standard reports come in with no red flags, escalations can still happen and they can be prevented with the right technology in place.
Through the service intelligence platform, companies can access a customer risk scorecard that identifies customers at risk before they even realize it themselves. This solution provides the ability to measure the temperature of the customer and address it upfront while also enabling companies to prioritize customers based on risk scores.
Beyond the customer perspective, issues might get solved faster from the workforce perspective. The workforce performance index allows companies to mine service data and unstructured free text to create an actionable framework around technicians and workers. If training is needed somewhere, the index will signal it, too.
But issues might get solved even faster than that using behavioral insights to understand key trends in service data. This goes beyond the customer and workforce perspectives, addressing the issue directly from the product perspective. Behavioral insights enable companies to identify whether the issue is product-based or behavioral so that decision-makers can devise an informed action plan that optimizes the service lifecycle.
The service intelligence platform has a significant organizational impact, as it enables the better utilization of employee skills, collaboration with engineering teams, and optimized supply chains. This enables companies to become better at planning and improve the availability of their inventory, which also helps companies save costs on spare parts storage.
By getting smarter at servicing customers with AI and deploying AI at any point in the service lifecycle where issues can be resolved, field service organizations ultimately help their customers save costs on unplanned downtime, which in field service, is synonymous with providing an excellent customer experience.