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It is critical to keep field equipment running in order to maximize utilization and performance and by minimizing costly, unscheduled downtime.
Simply, waiting for the failure to occur is not affordable in today’s business operations scene.
However, some parts of the playbook will require somewhat familiarity with data science concepts to be able to follow implementation details.
Introductory level data science skills are required to fully benefit from the material in those sections.
The majority of these problems can be categorized to fall under the following business questions: Predictive maintenance solutions can provide businesses with key performance indicators such as health scores to monitor real-time asset condition, an estimate of the remaining lifespan of assets, recommendation for proactive maintenance activities and estimated order dates for replacement of parts.
It is important to emphasize that not all use cases or business problems can be effectively solved by predictive maintenance.
To remain competitive, companies look for new ways to maximize asset performance by making use of the data collected from various channels.
One important way to analyze such information is to utilize predictive analytic techniques that use historical patterns to predict future outcomes.
In essence, this playbook brings together the business and analytical guidelines needed for a successful development and deployment of predictive maintenance solutions.
The content is balanced to cater both to the audience who are only interested in understanding the solution space and the type of applications as well as those who are looking to implement these solutions and are hence interested in the technical details.