Unscheduled maintenance, whether of capital production equipment, transportation vehicles, or other heavily used assets, is highly inefficient and in addition to the elevated urgent repair costs, it typically leads to interruption in the production line or distribution logistics.
In addition to reducing asset utilization rates through lower reliability and availability, unscheduled maintenance incidents can cascade through the entire production and delivery supply chain introducing significant consequential cost implications from sub-optimal usage of feedstocks, through the elevated energy and utilities consumption to the lack of product availability.
Preventing unscheduled downtime and prioritizing and streamlining repairs with advanced analytics performed on all of process data can thus result in major improvements of the process economics.
With numericcal’s Distributed Intelligence Monitoring Optimizer (DIMO) Platform, the Possibilities Are Endless
DIMO is the only platform to continuously monitors all of the equipment sensory data (both structured and unstructured) providing a real time insight into when maintenance is required.
DIMO’s advanced analytics are based on AI algorithms, which have initially been trained on historical operating data sets, but continue improving, through machine learning, with all of the newly observed data.
DIMO’s dashboard provides clear actionable insights that cannot be observed with conventional techniques allowing the operator to act in timely fashion and prevent failure from occurring and optimize the production line.
Reduced Downtime and Increased Availability
Application of DIMO leads to a step change reduction in the occurrence of unscheduled and unplanned equipment breakdowns as well as significant increase in the equipment utilization. Higher equipment availability, and lower downtime and maintenance costs all contribute to higher business profitability.