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This post summarizes the Performance Loss Monitoring use case covered in the Advanced Analytics 101 webinar in September 2020. In this webinar, the use case was explored by addressing 4 key analytics questions: Why? What data is available? What method(s) can I use with the available data? How do I want to visualize the results? Why? A manufacturing company needs to track performance losses. If engineers are able to identify and quantify those performance losses, the results can be used to justify process improvement projects or to do historical and global benchmarking. The current method to do this involves retroactively wrangling data in Excel. This exercise is very time consuming, so developing a method to automatically generated monthly reports has the potential to save up to 1 week of valuable Process Engineer' time per month. This is time they get back to work on improvement projects and other value added activities. What data is available? For this analysis, two data tags are needed: the target production rate and the actual production rate. What method(s) can I use? Step 1: Identify time periods of lost production To identify the time periods of production loss, I first need to calculate the difference between the target rate and the actual reactor rate. This can be accomplished in the Formula tool. Now, to identify the production losses, I can use the Value Search tool to identify whenever the value of this new signal is greater than 0. Step 2: Quantify the total production loss The Signal from Condition tool can be used to calculate the totalized production loss during each of the Production Loss Events capsules. How do I want to visualize the results? Ultimately, I’d like to create a weekly report that summarizes the production per day of a given week. So in this case, I’d like to create a histogram that aggregates the lost production for each day of the week.

This Use Case became simpler in R21.0.41 with the addition of the now() function in formula. You no longer need to do the first two steps of defining a search window for now and then using a signal to identify the last measured time stamp. Instead you just need to use the now() function to define the capsules and create your condition... //Create conditions representing the last 7, 14, and 365 days $Last7DayCapsule = capsule($now()-7d, $now()).setProperty("Time","Last 7d") $Last14DayCapsule = capsule($now()-14d, $now()).setProperty("Time","Last 14d") $Last365DayCapsule = capsule($now()-365d, $now()).setProperty("Time","Last 365d") condition(370d,$Last7DayCapsule,$Last14DayCapsule,$Last365DayCapsule)