Wednesday, May 9, 2012

What Good is Your Data if You Can’t Find it?

Modern data historians help solve many issues for process engineers and managers. Data Historians provide the ability to access past values of process data, enabling advanced trending, and to accurately provide root cause analysis. Process data comes in two main forms:
  • Analog Values, in which data is represented by a continuous variable (temperate, pressure, level, weights and etc.) 
  • Discrete Values, in which data is from a finite set of values, such as state information (like on/off, opened/closed, high/low) 

Under the hood of a typical historian, the value for each data variable is stored by time. A routine investigation by a process engineer is to answer the age old question:
“There were reports that we were out of quality on machine X last week, what happened?”
The process engineer can query the historian, based upon a time range, to retrieve process data such as temperature or current draw.

At first glance, this time based retrieval method of the historian seems sufficient. But is it? Upon a closer look, several questions might arise such as what was the machine state (was it running during the time queried) or what was the acceptable range for the process variable for the part being manufactured (by recipe, SKU or lot). Without context to the data, trying to find the appropriate data can be difficult, time consuming and inefficient. If the information cannot be found easily, its value is reduced.

When implementing a historian, take the time to consider the context variables that might assist in future queries; some examples are:
  • Machine State (Running, Idle, and Down) 
  • Lot or BatchID 
  • Recipe Class and/or Step 
  • Operator 
  • Shift 
By using the approach of adding context variables along with process variables, answers can be found with a couple of queries to refine the results and transform the data into information. In addition, similar lots can be compared to determine differences.

Savigent’s Historian solution goes one step further by simplifying the retrieval process down to one query leveraging both context and time.

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