By Sumair Dutta, Vice President Product Marketing for ServiceMax
Predictive analytics is increasingly the bedrock of modern field service but offers so much more.
While investment in IoT devices actually cooled last year, 2023 is expected to see significant growth, according to Verdict research. The global IoT market will be worth a whopping $650bn by the end of this year, driven by a double whammy of rising inflation and a desire for increased efficiency. It’s an understandable approach to growing uncertainty. IoT sensors hold all sorts of insights, and who could blame any organization for wanting to know more about its assets. But the point is, these assets – whether they’re machinery or devices – have a story to tell you. One that you should hear because they hold all sorts of secrets and intelligence that can help an organization plan and grow.
Asking your equipment assets a question may seem far-fetched but comes down to asking the right questions of the data and using intelligent automation for analytics. That means having a data strategy with clear goals as to what the data is expected to reveal. What does your organization want and need from the data and is it geared-up to act on it? While there can be some variation here, depending on the type of industry, the fundamentals are really the same across the board – if an organization wants to be more resilient and efficient, it needs to be able to do more with less, understand its strengths and weaknesses and create strategies that fit the business goals.
“We find that, regardless of industry or region,” says BCG report, The CEO’s Dilemma, “companies that have successfully built organizational resilience have taken a holistic approach. By starting with data and facts, they use diagnostics to help define the tailored, de-averaged solutions that represent a complementary, mutually reinforcing combination of offensive and defensive actions across people and organization levers.”
Knowledge Harvesting for Efficiency and Resilience
In short, asset data can deliver the sort of intelligence required to make a tangible difference to how an organization manages and runs its machinery and equipment. What’s perhaps less well known is that asset data also holds all sorts of intelligence and insights for a company’s entire team of sales, post-sales and service personnel. Using data from assets, as well as field service engineers – something which a Gartner report referred to last year as “knowledge harvesting” – and feeding that data into a field service management system with predictive analytics delivers invaluable intelligence.
Having real-time visibility of assets, analyzing asset performance and predicting maintenance issues can re-shape how field service leaders organize everyday workflows. It leads to more efficient scheduling of work, ordering of parts or replacement of machines and devices. This kind of insight also holds valuable answers that manufacturers can monetize, particularly when it comes to managing customer retention, renewals and service. For example, customers service parts as needed based on the asset’s requirements, and even helping them to upgrade to higher level contracts. Service contract renewals, warranties, upselling and even the development of new equipment can benefit from the insights delivered via existing assets.
Above all, predictive analytics can enable an organization to turn the traditional transactional relationship on its head, by providing the tools to sell business outcomes rather than solely products. This means greater insight into how equipment assets are performing, leading to reduced outages and downtime. While this would naturally mean happier customers, the asset data analytics has additional benefits. It can reveal machine and part performance, which can influence supplier deals and service contract arrangements. It can also impact engineer deployment, training and recruitment strategies.
Knowledge harvesting in asset data is well underway, particularly in the manufacturing and medical device industries. For many organizations, the real challenge will be in making predictive analytics possible. Data silos and legacy technologies continue to restrict organizations from optimizing their service capabilities. This comes at a cost in terms of both IT and service. McKinsey says CIOs estimate that legacy tech can add up to 20 to 40 percent of the value of their entire technology estate.
Transformation remains a challenge for many organizations but with the carrot of predictive asset data analytics, there should be a renewed urgency. The data derived from assets can tell you a much deeper story that you’ve been missing all this time. It has intrinsic value in how organizations, not just service teams, are shaped and run. And at a time when the pressure for efficiency, resilience and customer service are higher than ever, predictive maintenance and analytics can be the best sales resource you never knew you had.