Digitizing in Pharma industry – Changeover time was tip in iceberg
Case in point: Pharma company
At a glance
Focus: Digitizing in Pharma industry Changeover time was tip in iceberg.
Challenge: What started as a small task of finding changeover time, led to Batch end activity time and that led to other set up losses faced at start
Approach: Get the Blister machine data, preferably from the main controller.
Impact: The use of Din Standard 8743 to categorized losses.
SmartFactoryWorx
We are a company that has been working hard to bring you the best productivity-enhancing products whether is it is through our automation in secondary packaging or through our path-breaking Efficiency Improvement Solutions.
Our partner companies have been supplying packaging machines, now, in addition, we have a system to help you monitor and improve the productivity on a real-time basis on your Mobile or Laptop. In short, we make your line and machine talk to you directly!
Pharma company
A medium size pharma company, with multiple factories and over 4000 employees, wanted to know about the actual changeover time in its formulation division.
In Pharma industry, changeover from one FGex to other or batch to batch, happens 1 to 2 times a shift. There were no proper mechanism to know how much time was being used for this. Besides that, there are batchend activity which adds to inefficiency
CEO wanted us to put a system so we can give clear idea on the changeover. So what started as small task, led us to find that OEE due to these changeover and batchend activities, was poor.
Business challenge
What started as a small task of finding changeover time, led to Batchend activity time and that led to other set up losses faced at start up.
It was clear from beginning that there is good scope to improve.
Industry 4.0 solution
Get the Blister machine data, preferably from the main controller
External inputs were needed and for that screen was provided.
OEE and MTBF was part of our solution, though it was not asked for. This led to surprise for the management
Workshops to be conducted to clarify the meaning of many KPI that system monitors.
There are 2 main types of machine data. Machine states
Production data
Machine states:
Production data – Each machine infeed count , outfeed count and reject were captured.
Business outcomes
The use of Din Standard 8743 to categorized losses
It made the line losses transparent.
ROI was 42 days for POC so the solution was scaled to more lines.
Key benefits
| Area | Outcome |
|---|---|
| Time & Speed | The changeover time was correctly identified. Over few months the excess changeover time reduced |
| Productivity & Throughput | The OEE was very low. Reasons were identified |
| Operational Benefit | Excess Changeover |
| Time & Speed | Batchend time |
| Operational Benefit | Downstream machine stoppages |
| Operational Benefit | Minor stops |
Technologies used
Change management and implementation
The states of the machine were not clear from sensors, Thus the logic needed to be altered.
Management did not want to enter any data by operator and thus, integration was done with SAP.
The existing AD was used for user log in.
The production data would not match with the final count and the logic needed to be altered to meet that challenge.
A flexibility is needed from solution provider as the source of the data is at times not clear. We did that to meet the needs as defined in the beginning.
Many KPI were not clear to the management and thus workshop helped.
The existing solution was tailored for the application.
SAP Integration was done as the management did not want to enter manual data.
The existing AD was used for user log in and also role based users were defined.
Most important was data science, was done in house by the existing team members.