Tanis Gummy Candy Machine OEE
An industry leading cannabis company was using a Tanis gummy candy production line. Given their high production volumes and capacity constrained operation, the company wanted to ensure they were getting the most out of their equipment.
To better understand how their operation was performing, they decided to implement OEE on this production line. In addition to better managing the production schedule, this upgrade allowed them to track downtime, throughput, and optimize the line overall. As a result, they were able to increase production by 14% without adding additional staff or equipment.
OEE Solution
Ignition Perspective Module
Allen Bradley CompactLogix PLC
Corso Systems’ Production Framework OEE Module
MySQL Database
Seeq Data Analytics Software
Implementation Summary
Corso Systems engineers worked with the client to understand the Tanis production line’s overall operational plan. This included documenting their production scheduling, recipes, estimated throughput for each recipe, operator training plans, maintenance, ingredient staging, and QA/QC procedures. Our team also dissected the PLC code uploaded from the Tanis PLC to build an overall list of downtime reason codes for the production line. Once we had the downtime reason codes and estimated throughput per recipe, we were able to set up the Availability, and Productivity metrics for calculating OEE. Next we integrated their QA/QC database with Ignition to pull in quality information for each batch—and to update the Quality metric for each batch as data was entered. This allowed us to calculate OEE for each batch. Availability and Productivity were calculated in real-time while Quality was updated as quality checks were completed.
Collecting data while running the machine for a couple weeks showed that the machine was not running at full capacity. The operators increased the machine speeds used during commissioning which were lower than full capacity. We also examined the overall production schedule vs. what was being produced, and worked with the client to better understand their changeover process. At the time, changeovers were taking twice as long as they should on average.
By understanding this process, we collectively realized that ingredients were the last piece of the changeover puzzle, after the machine was cleaned and inspected. We put a process in place so that one of the operators involved in inspection—but not cleaning—take the previous batch’s ingredients back to storage, and retrieve the new batch’s ingredients out to be staged. Completing this task while the other operators were cleaning the machinery reduced changeover times back to acceptable levels.
Overall, these small changes resulted in a 14% increase in production on the machine with no hardware, software, or personnel changes required.
Results
Once we increased production on the machine, this project paid for itself almost instantly. Corso Systems incorporated standard OEE calculations in the process which gave the client the ability to view their data in their Seeq Data Analytics suite. Their process engineers now have full visibility into the line.
This project exposed other issues with the production line in regards to recipe management, once we were able to combine the ability to analyze process data and batch data simultaneously. This new information led to another project with Corso Systems to help manage recipes on this line.
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