debottlenecking a bulk manufacturing plant
TRANSCRIPT
© Bioproduction Group. All Rights Reserved. 1
CASE STUDYIMPROVING THROUGHPUT
AT A BULK MANUFACTURING FACILITY
GOAL TO IMPROVE THROUGHPUT AT A BIOPHARMACEUTICAL BULK
MANUFACTURING FACILITY BY DETERMINING THE ACTIVITIES
AND RESOURCES THAT CREATE BOTTLENECKS.
Built a detailed process-based discrete event simulation model • of the existing plant operations
This model was integrated with the Bio-G Sensitivity Analysis toolset • to explore performance robustness and improvement opportunities
Analysis showed that the individual activities affected the facility only • if they were a part of critical resource cycles
Approach allowed the manufacturer to improve throughput at the lowest • possible cost by focusing on supporting operations rather than expensive main operations.
HIGH LEVEL SUMMARY
Traditional bottleneck detection methods are performed with deterministic calculations based on point average estimates or scheduled times. While this approach can yield some insights, it assumes that operations always take the same amount of time. Bioproduction Group’s experience in this area is that inherent biological variability means that operations are highly variable, and that delays can ‘cascade’ through a plant.
This was supported by the manufacturer’s previous experience with bottleneck detection and removal. A previous analysis performed by a large engineering consultancy had failed to increase run-rates to the theoretical capacity of the plant, despite a large budget. Bioproduction Group’s brief was to use a model incorporating variability and detailed resource understanding to target areas where improvement efforts should be directed.
Bioproduction Group’s approach was to integrate a process-based simulation model of the plant with the Bio-G Sensitivity Analysis toolset. The toolset was then used to explore where key pieces of equipment or operators in the plant affected throughput. Notably, even equipment with a low utilization could become a bottleneck if multiple requests came at the same time. “It’s impossible to see this kind of bottleneck in an Excel model” says Principal David Zhang. “Bottlenecks in a well-built plant are very difficult to detect.”
Bio-G’s sensitivity analysis focused around making successive changes to activities in the plant to calculate how much an improvement in that activity would affect key performance indicators like run rate. Implementing this approach showed that the exact timing almost all activities were unimportant for throughput: just a few key activities determined overall performance.
THE BRIEF
HOW WE DID IT
© Bioproduction Group. All Rights Reserved. 2
MORE INFORMATION
BIOPRODUCTION GROUP [email protected] WWW.BIO-G.COM
IMPROVEMENT CHART
KE
Y A
CT
IVIT
IES
INH
ER
EN
T
VA
RIA
BIL
ITY
KE
Y P
ER
FO
RM
AN
CE
IN
DIC
ATO
R (
KP
I)
ACTIVITY INDEX
0 10 20 30 40 50 60 70 80 90 100
53
52.5
52
51.5
51
50.5
50
49.5
49
One of the key insights obtained from the sensitivity analysis was that it was not just the main operation that was the cause of bottlenecks. Rather, focusing on the resources themselves – and the cycle of activities they perform – is of vital importance to understanding how to improve bottlenecks. In the example below, shortening the ‘main operation’ time by one hour will have the same impact on throughput as shortening the material transfer, or equipment CIP times. This meant that, rather than focus solely on (expensive) changes to main operation steps, the plant could focus on relatively simple changes to supporting operations.
Focusing on the critical resource utilization cycles in two downstream unit operations allowed Bio-G to make crucial changes to these cycles, reducing their time and allowing higher throughput. The approach allowed the manufacturer to increase the net throughput of the plant almost 15 %, without substantial capital investments in new equipment for main operations.
RESULTS
MAINOPERATION
POSTOPERATION
ANDSAMPLING
TRANSFERMATERIAL TO
THE NEXT MAINOPERATION
EQUIPMENTCLEAN
IN PLACE
EQUIPMENTSTERILIZATION