From project execution to regular production using an operational excellence philosophy

1. From project execution to regular production using an operational excellence philosophy Kim Sandell, Director, Operations Management and Operational Excellence

2. Scope of presentation • Introduction • Balancing of processes • Technology Transfer & Start Up • Planning for sucess • Managing the Start Up • Movingintoregularproduction • Conclusions

3. Introduction…It all goes back to cost of production • A new facility means a substantial investment (150 M€  ??) which gives a large “locked” depreciation costs to go into the production cost • For biotech; materials are less expensive (exceptions; special media components, some resins etc) therefore the potential of impacting production cost is limited • Simplification of processes could have a great impact on cost e.g. • Creating “one batchness” minimises administration, QC costs etc • Getting rid of unnecessary steps • Creating similar processes to go into a multiproduct facility • Staffing; this is a large contributor to the production cost and it is probably here one can make the largest impact if there is a clear philosophy when starting the design of a facility

4. Balancing up and down stream capacity • Is a high fermentation titre everything? • From a production practical perspective the answer is definitely NO • Equally or more important is: • The match of upstream output to downstream capability • The tact which the facility is running at; e.g. if downstream is capable of processing one batch of certain size every 24 h it might be best to get those quantities out of fermentation at the same phase • Large batches also equals larger cost at risk when something goes wrong • Scaling up downstream has practical limitations (e.g. column sizes) • Although; a high titre could help by decreasing the scale of fermentation which in turn could affect investment costs

5. Introducing one-batchness Current process generation Fermentation 1 Purification 1 Final purification Fermentation 2 Purification 1 Fermentation 3 3*QA Next process generation New process QA 2*QA QA Case study: Using process balancing to develop next generation process Including yield improvement process moves from handling 18 batches to 1 batch with the new process

6. Technology transfer • To be able to transfer products from development to manufacturing or between sites there is a need for a well defined process including: • Clear deliverables • Milestones dates on deliverables synchronised with e.g. facility design and construction • ”Who does what” (RACI-matrix) • Up-front agreed expectations from all key stakeholders avoiding the transfer becoming a moving target • A governance process that sets expectations and help solving ”issues” in a timely manner • And… Do not forget about Cleaning development!

7. Even with that, this happens a lot Customerspec Automation Marketing Design Project understanding Documentation Installation Billing Customerneed Support

8. Technology transfer, why is it so hard? Cross functionality • Traditional organisation set up do not support the process flow. Usually we are organised in “silos” which consciously or un-consciously leads to sub optimisation • It could be overcome by setting up a cross-functional structure with one common clear goal. The team should preferably be located together to support the breakdown of the “silos” Operations Quality Control Process develpment Automation QualityAssurance Maintenance

9. When you have the structure in place…It is just to get started • It will all be about: • Planning • Planning • Planning • Buy-in and approval by all stakeholders • Plan the execution • Execution • You must dare to take the time to plan and… If you are an engineer you should plan at least two times more than what you believe is necessary. • After that you must get everyone onboard and accept the plan

10. During the planningphase • Make sure that you gather enough data so that you with some confidence could ensure a successful full scale start-up. • We use extensive risk assessment approaches to determine what to look at in lab and pilot scale • Minimise by: • Data gathering • Operationalphilosiphy

11. At the end of planning • Do wehaveenough information? Lab data Scalecomparison

12. Time to start • Whydoes it not work? • Are the resultsreally different?

13. Structured problem solving • Do it where it happened • Cross functionally • Usetools to structure the investigation, fishbone, 5 why’setc

14. It seems trivial but… • Some of the tools are actuallycontra-intuitiv and you tendfindthings you wouldneverhavefound with ”commonsense” • Use data tools to analyse data and not just in one dimension

15. What happened? What happened here?

16. Be patient… • Usee.g. the DMAIC cycle to solveupcomingissues. DO NOT take short cuts it will kick back later. Define Measure Control Analyse Implement

17. After solving initial issues.Is the process capability good? • Here we have two • options: • Increase the purity • Decrease variation • What would be • easiest?

18. Are wereadyyet? • Working with exellence and continuousimprovementactually by definition means that you neverwill be ready. • However you are increasing the capabilityquite fast during the first few iterations • The one that belives that I am donewillverysoonfindhimselfbeing ”yesterday’s news” I don’t have time, I have to work….

19. Learnings • Clear any uncertainties nothing is “given” until agreed upon with all parties. Spend time interacting and talking to each other, LIVE. • Plan, plan and plan so that the most foreseeable things are handled before starting execution (there will be enough surprises to handle taking care of unknowns). • It will always be hard to show like-to-like from lab to full scale, act accordingly. The lack of data might also hide issues • Never stop to follow up and improve. Tech transfer is just a start of a long journey

20. Potential traps • Planning might easily look at “best case” only rendering issues with resources and execution times • Personal interaction is hard. Especially if you work on a project with the organisation spread over both countries and continents. • Few batches in all scales makes statistical evaluation of data hard

21. Questions? Thank you!