10th — 11th February 2015 – Radisson Blu Hotel, Heathrow Airport, Hayes, London, UK Annual PIMS | 2015 Pharmaceutical Innovation, Operational Excellence & Manufacturing Strategies Summit http://www.pims.gb.com/ www.qbd360.com 10th— 11th February 2015 – Radisson Blu, Heathrow Airport, Hayes, London, UK Annual BIOINNOVATION | 2015 Seamless, Integrated & Innovative BioManufacturing http://www.bioinnovation.gb.com/ 16th & 17th September 2015 | Boston Marriott Cambridge, MA | USA Annual ManuBIO | 2015 Flexible, Integrated & Innovative Biological Manufacturing Excellence USA http://www.manubio.com/ VIDEO WATCH

1. CASE STUDY
contamination of a
bulk manufactu ring
facility
GOAL
To determine the best response to a plant contamination
in a network of production facilities.
• Analysis of a network of production facilities when facing
a contamination event in a single plant
• Model demonstrated the impacts of contamination on inventory levels
of life saving drugs
• Showed best responses for different contamination scenarios to prevent
product stock-outs using other plants in the network
• Analysis guided a large biopharmaceutical manufacturer in strategic
decision making for possible contamination risks.
high level
summary
The lifesaving nature of the drugs requires careful inventory management.
Given the considerable changeover times between drugs in a plant, long-range
planning is necessary to prevent a stock-out of drugs. Campaigns
running from a few months to half a year are slotted back to back to make
the most use of a plant’s capacity.
A major threat to any long-range planning schedule is a lengthy facility
shut-down due to equipment contamination. To mitigate the impacts on
production during a contamination event, excess capacity in a network of
production plants can be utilized. Bioproduction Group was asked to create
a simulation model of a production plant network and determine the best
response to a facility shut-down in order to avoid stocking out of critical drugs.
THE BRI EF
Bioproduction Group’s approach was to develop a detailed facility-network
simulation model. Working closely with plant managers and engineers,
the model was designed to accurately depict the production process of
every facility in the network.
How We Did It
2 MONTH OUTAGE
3 MO. 6 MO. 3 MO. 2 MO. 1 MO. 1 MO.
RESTART
CAMPAIGN JAN 2009 JUL 2009 JAN 2010 JUL 2010
4 MONTH OUTAGE
6 MO. 3 MO.
3 MO. + 3 MO. 1 MO. 1 MO.
PRODUCT 1
PRODUCT 2
PRODUCT 3
CONTAMINATION
REORDER CAMPAIGNS,
USE BACKUP FACILITY JAN 2009 JUL 2009 JAN 2010 JUL 2010
BACKUP FACILITY B
6 MONTH OUTAGE
6 MO.
2 MO.
JAN 2009 JUL 2009 JAN 2010 JUL 2010 FACILITIES
2 MO. BACKUP FACILITY A
2 MO. BACKUP FACILITY B
1 MO. + 3 MO. 1 MO. 1 MO.
REORDER CAMPAIGNS,
USE TWO BACKUP
© Bioproduction Group. All Rights Reserved. 1

2. “When our client says
‘no patient shall go without
treatment,’ it is not corporate
rhetoric: they mean it”
David Zhang, Principal
Results
MOR E INFORM ATION
BIOPRODUCTION GROUP
CONTACT@BIO-G.COM
www.bio-g.com
Through discussions with subject matter experts and key members of the
scheduling department, Bio-G determined several possible reactions to
a facility contamination to prevent product shortages. “When our client says
‘no patient shall go without treatment,’ it is not corporate rhetoric: they mean
it,” says Principal David Zhang. “There is an intelligent way to utilize available
network resources to keep production flowing while avoiding unnecessary
disruption to the supply chain.”
Bio-G’s strategic model was used to evaluate a number of possible
contamination scenarios, depending on the severity of the contamination
event. Low severity (2 month recovery time), medium severity (4 month
recovery) and high severity (6 month recovery) were each considered,
to determine the range of effects on the supply chain network.
The diagram above shows the range of possible responses to the adverse
events, depending on its severity. Backup facilities were used in more severe
contamination cases to ensure stockout would not occur. The Simulation
System aided planners in quickly evaluating the best response in each case.
Bio-G’s strategic model provided a decision support tool that allowed the
quantification and mitigation of risk for strategic decision-making.
The results showed that stock outs would occur with some of the response
strategies considered by the manufacturer. More importantly, this tool was
then used by the client to determine the best responses amongst all possible
scenarios. This approach significantly lowered the time to make a decision,
increasing confidence the manufacturer had sufficient agility to respond
to adverse events.
2 MONTH OUTAGE
4 MONTH OUTAGE
6 MONTH OUTAGE (BACKUP)
BASELINE (NO OUTAGE)
6 MONTH (NO BACKUP)
RELEASED INVENTORY LEVEL (KG)
INVENTORY LEVELS
0 2000 4000 6000 8000 10000 12000 14000
TIME (HOURS)
600
500
400
300
200
100
0
© Bioproduction Group. All Rights Reserved. 2