I've often spoke about predictive maintenance in my pieces about the manufacturing sector's applications of big data analytics. So, I figured I'd dig deeper into the subject, and give you a rundown of two processes that data analytics software can support:
- Prognostics: Forecasting specific occurrences based on real-time and historical indications.
- Diagnostics: Determining or analyzing the reason why a problem or a situation has transpired.
According to the National Institute of Standards and Technology, the purpose behind combining data analytics with prognostics and diagnostics is to "significantly increase the efficiency of dynamic production systems". So, how can manufacturers employ qualitative data analysis software to support prognostics and diagnostics, respectively?
"What will transpire if the following tasks are applied to Machines K, M and Y?"
A prognostic method
The goal of a prognostics endeavor is to develop concrete answers as to how assets, operations and other factors will evolve, regress or remain sustainable in the future. "Event A will happen due to Elements C, D and H," is a statement associated with a finished prognosis.
Integrating data analytics into this process obligates personnel to first develop a specific question, NOT a hypothesis, as the latter may cause specialists to unwittingly aggregate data they believe will support their educated guesses. So, it's best to formulate a query such as: "What will transpire if the following tasks are applied to Machines K, M and Y?"
In regard to manufacturing, establishing a reliable, validated prognosis typically involves data scientists and other concerned parties collecting data from equipment, entire facilities, labor reports or other areas that contribute to operational success. The goal is to apply highly sophisticated analytical algorithms designed to regard current and historic behaviors to accurately deduce how the subjects being scrutinized will function in the future.
"In diagnostics, the question you must ask must begin with 'Why?'"
Creating a diagnosis
The objective of a diagnostic initiative is to comprehensively understand why things are the way they are. An event transpires – a machine malfunctions or a facility cannot efficiently scale to meet new time-to-market standards, for example – and a team takes whatever steps are necessary to identify the factors that caused that occurrence. This process includes assessing the behavior of particular elements.
When applying data analysis technology to a diagnostics project, historical information is a key source of interest, although current data shouldn't be ignored. The key is to measure events as they occurred from a date in the past to the present. So, in this respect, real-time data is a necessary component of the project. You may have already caught on to this, but the question you must ask must begin with the word "Why?"
How can manufacturers use diagnostics? Professionals could measure how a company-wide project impacted the performance of its facilities. For example, if a production firm gave each factory 500 solar panels to support electricity needs, data scientists can look for disruptions, support or any other elements they may want to analyze.
An amalgamation of different analytics technologies
Back-end analytics and data visualization are essential components of both diagnostics and prognostics endeavors. While the latter can provide team leaders with a clear perception of conclusions, the former can be leveraged to adjust operations based on those findings.
Technology aside, the most important contributor at play here is the way professionals guide these projects.