Best-in-class maintenance teams are Proactivity needs foresight. Predictive maintenance provides this foresight. Our predictive maintenance solutions rely on a combination of input data from a machine's integrated sensors, a machine manufacturer's expertise and experience, and an increasingly richer base of historic data and events to learn from. The PREKIT AI engine orchestrates probabilistic methods and AI models from the fields of machine learning and deep learning to distill actionable insights from these factors.
Today, an average service team may spend up to 90% of their time fighting reacting to problems. Upon adopting increasingly proactive maintenance strategies, best-in-class teams are able to reduce unplanned downtime by 40-80%, while extending the lifetime of assets by 50%.
Field service may be the busiest and most understaffed department in your company. When made actionable, predictive maintenance data allows you to do more with less - optimized field service planning, remote diagnostics, less speculative removals.
STRENGTHEN CUSTOMER RELATIONSHIPS
The value-added enables machine manufacturers to stay relevant to their customers and extend your participation along their product's lifecycle. Create stable recurring revenues by supercharging your service business with new service packages and predictive sales.
PREKIT AI ENGINE
Developing predictive maintenance capabilities, that are specific in the failure patterns they detect and farsighted enough to optimize service plans to their full extent is a journey. There are no shortcuts, but there is a direct path. Our AI engine - the core technology of the PREKIT platform - orchestrates a selection of state-of-the-art algorithms to continuously evolve the predictive capabilities of a solution to unmatched levels.
Machine User Value
Minimum planned Downtime
Maintenance is prepared in advance and planned optimally
Accurate component-level remaining lifetime predictions allow optimized spare parts inventory
Machine Manufacturer Value
Minimal Capital Tie-Up
Demand-oriented procurement and supply chain management reduces inventory
Minimal Service Effort
Customer-wide planning of service interventions
Reduced risk of failure
Preemptive detection of anomalous operation
Data-driven prioritization of service tasks
Extended foresight and detection of specific failure modes aligns more interventions with maintenance windows
Remote diagnostics reduces on-site interventions
Learnings from machine population
Reduced risk of failure
DATA SCIENCE PROJECT?
Decades of research have been invested into developing condition monitoring best practices and acting on them to implement predictive maintenance strategies. Neglecting these mature technologies by approaching predictive maintenance as a pure data science project is a recipe for failure. In predictive maintenance we use artificial intelligence to add value and as tools, that make solution development more scalable, efficient, and accessible.
PROOF OF CONCEPT?
In predictive maintenance poorly designed PoCs are likely to undermine the technology rather than creating the organization-wide buy-in they intend to. Unambitiously scoped PoCs fail to aggregate significant amounts of field data and produce meaningless or misleading performance metrics. From a purely technological perspective the concept of predictive maintenance has been proven throughout countless applications. When designing a PoC, businesses should pay special attention to building actionable systems by integrating insights into their processes and experiencing the business value of predictive maintenance.