What is the Importance of Analysis of Time Series Data? Mark Skallet Weighs In
As OSIsoft’s Senior Product Support Engineer Mark Skallet specializes in Asset Framework and Analytics software, which allows companies to run analytics on the data generated by their manufacturing processes. One such type of data is time series data, which has its own distinct characteristics and, when analyzed, offers its own distinct benefits and insights.
To answer the question of time series data’s importance in manufacturing processes, Mark Skallet defines time series data, its applications, and the many benefits of its analysis.
What is time series data?
According to Mark Skallet, time series data consists of collected data points that have a timestamp and a value and each stream of timestamp-value pairs is uniquely stored and associated to the asset or device that generated that data.
Rather than noting the overarching shifts in the manufacturing process, time series data records all values generated by a device (or those deemed important or unique enough to keep by an administrator) as they come in. This key characteristic is ultimately what grants time series data its robust versatility and importance, especially in the realm of monitoring and adapting manufacturing processes. Mark Skallet, for example, finds time series data integral in assessing and maintaining effective manufacturing processes.
Applications of time series data analysis
According to Mark Skallet, the central application of time series models yields the two-fold usage goal of both understanding the underlying forces that produced the collected data as well as using this information to reduce waste and improve process efficiency.
Its overarching goal in the context of manufacturing processes is to deliver insight on the health of the process as well as the health of the assets operating within the process. Using this information, businesses can identify bottlenecks, assess process efficacy, and monitor a system’s overall operations. This is no easy task given the potential millions and millions of data streams each with data frequencies in the sub second range, but if this huge amount of data can be appropriately analyzed, opportunities for finding room for improvement are near endless.
Advantages of Analyzing Time Series Data
When analyzed, the data collected by time series models can be analyzed to deliver meaningful, detailed statistics regarding the overall health of a manufacturing process. It may also allow for the observation of correlations not otherwise visible. As highlighted by Mark Skallet, effective analysis of this form of data provides a myriad of potential insight; it can, for example, improve the rate and/or effectiveness of production. It is also known to help manufacturers identify and remedy ineffective processes potentially saving millions. In turn, this serves to reduce waste and boost productivity, thereby cutting costs and raising profit margins.
In many cases, the analysis of time series data has also helped manufacturers identify and address impending production and maintenance hiccups before they even occur, eliminating the risk of delays and future setbacks. In short, time series data analysis is crucial in helping manufacturers improve their production processes, remedy future problems before they arise, and generally facilitate higher outputs and more efficacious operations.
