Manufacturing Scale-up

Matthew Klippenstein2012 May 22 (for 4D Labs)

P A G E 2

1. about me

2. about Ballard (where I work)

3. various scenarios

P A G E 4

about me

UBC chemical engineer(1992 to, um, 1999)

13 years at Ballard

occasional playwright

avid dogsledder

P A G E 5

“fuel cells are like batteries, with an external fuel pack”

“an electrochemical analogue to the combustion engine”chemical, not electrochemical

P A G E 6

about Ballard

Burnaby-based fuel cell maker

we used to do cars• late 90’s – Daimler and Ford bought stakes

• late 00’s – automotive half spun out

now: everything except cars

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about Ballard

…a stack for every size and setting…

System Integrators /

OEMs

Downstream Customers

Backup Power Supplemental Power

Material Handling

Bus Distributed Generation

1–10 kW 5–25 kW 100 kW MWOutput:

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yes, Ballard was once a tech bubble darling(more on that in a few slides)

“I’m the king of the world!!”

P A G E 9

why I’m there

New mining companies usually go through:

- a speculative phase (stock peaks)

- a development phase (stock sags)

- a production phase (stock recovers)

P A G E 10

why I’m there

The same applies for tech companies.

New mining companies usually go through:

- a speculative phase (stock peaks)

- a development phase (stock sags)

- a production phase (stock recovers)

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why I’m there

Ballard had its speculative phase.

We’re finishing our development phase. (It took a long time)

Production is gradually ramping up. (At last!)

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why I’m there

not many people get to work at a leading company, in an industry, at this inflection point

the fuel cell sector is big enough that it won’t disappear – and small enough that I can still make my mark

admittedly, I’m biased – I’ve been working on this for 13 years

P A G E 13

Scenarios: UDo Research

Q: what are the right metrics to measure?

[MK - I don’t know, but the wrong ones can hurt you]

You’ll hit what you aim for,

but what you aim formight not be

what you want!

P A G E 14

Scenarios: UDo Research

Real-world examples of bad metrics:

Topic Bad Metric Effect

Call Centers time-per-call company reps won’t spend time to resolve customer problems

Dep’t Store sign-up quota for store card

customers get annoyed

CEO bonus stock options CEO pumps up stock price but weakens company, then leaves

You’ll hit what you aim for,

but what you aim formight not be

what you want!

P A G E 15

Scenarios: UDo Research

Real-world examples of better metrics:

Topic Better Metrics Effect

Call Centers rings before pickup

prompt service, happy clients (Southwest Airlines: 3 rings)

Dep’t Store $ sales per ft2

per departmentenlarge departments which bring in the most sales(Wal-Mart)

CEO bonus # options based on operating targets

CEO focuses on operations, not wild & woolly schemes(CN: $ cost per tonne-km)

You’ll hit what you aim for,

but what you aim formight not be

what you want!

P A G E 16

Scenarios: UDo Research

What metrics would be best for UDo?

…I don’t pretend to know

Some possibilities:

total membership size of community

user time-on-site level of engagement

% of heavy users target market

heavy user time-on-site target market satisfaction

$ revenue per user path to profit, with scale-up

% who upgrade “freemium” models

P A G E 17

Scenarios: HMI

Q: how to verify the product design is adequate?

Lab ≠ real world !

P A G E 18

Scenarios: HMILab ≠ real world !

Paraphrasing a friend working on oil sands tailings cleanup: “you can make anything work at room temperature, indoors. But when it’s -40°C and snowing…”

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Scenarios: HMILab ≠ real world !

In the 1980’s a Japanese carmaker had gear shifter problems in the US (but not Japan). Crumbs from burgers would jam the shifter – but since the Japanese didn’t eat in their cars back then, they didn’t test for this!

P A G E 20

Scenarios: HMILab ≠ real world !

Q: how to verify the product design is adequate?

…here, I have some ideas

Could test voice-recognition against:

different accents a few years ago the Telus virtual assistant couldn’t understand east Asian accents many complaints, e.g. from my wife!

people chewing gum

background noise e.g. near construction

multiple voices e.g. kids talking in back seat

& others…

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Scenarios: Cryotonics

Q: how to maximize learnings from first product run?

he who learns fastest, often wins

Toyota?

BMW?

GM?

P A G E 22

Scenarios: Cryotonics

Q: how to maximize learnings from first product run?some suggestions…

he who learns fastest, often

wins

Check tolerances (what can you get away with?)

IKEA furniture is cheap, because it’s made of sawdust, glue, and a topcoat. (Genius!)

They probably test different glue/sawdust ratios to see what range is acceptable (40-60% glue? 37-82% glue?)

Note: ratios might be different, depending on the type of sawdust! (oak, pine…)

P A G E 23

Scenarios: Cryotonics

Q: how to maximize learnings from first product run?some suggestions…

he who learns fastest, often

wins

The more variability you can tolerate in incoming materials, the better.

But reject parts are expensive! Find where this boundary is, add a margin of safety, and then avoid it!

P A G E 24

Scenarios: Cryotonics

Q: how to maximize learnings from first product run?some suggestions…

he who learns fastest, often

wins

Could Cryotonics perhaps test…?

RhInSb ratios how exact does the ratio have to be?

RhInSb batches how repeatable is the alloying process?

wound core quality can Cryotonics products work with cheap wound cores, or are expensive ones needed?

in-house testing varying the above (or other factors) measure how well each sensor works, to see how much effect each factor has, on functionality.

P A G E 25

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?

[MK – I was running out of ideas… ]

P A G E 26

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?

conditions always about the same, everywhere!

temperature gradients and uneven mixing

can really affect chemical processes!

I’m told DuPont had a lot of trouble scaling up Kevlar™…

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Scenarios: Bioplastics

Q: what went wrong with first scale-up run?

…may be unclear to, or interpreted differently by, production technicians.

Instructions that seem clear and comprehensive to experts in a particular field…

P A G E 28

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?some example tools…

Ishikawa (“fishbone”) diagram is a useful way to problem-solve until reaching root cause of an issue.

P A G E 29

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?some example tools…

5 Why’s is self-explanatory:

Why was the product off? (Incomplete polymerization)

Why was polymerization incomplete? (Instructions not followed)

Why not? (Instructions unclear)

Why unclear? (Writer never checked clarity with operator)

Why not? (Didn’t think he had to)

P A G E 30

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?some example tools…

P-diagram (parametric) captures what affects the product.

Noise factors = variation in incoming material, between operators, etc.

Control factors = what you monitor, to keep the finished product OK

P A G E 31

Scenarios: Bioplastics

Q: what went wrong with first scale-up run?some possible ideas…

Could Bioplastics look at…?

lab-scale variability could the big batch just be a low run within the same distribution curve?

prior runs Bioplastics probably did test runs in the scaled-up process; how do those compare?

confirming “knowns” as with the big reactor case, are we making inappropriate assumptions? (e.g. local temps ≠ mean temps!)

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Scenarios: PNA

Q: to outsource, or not to outsource, that is the question…

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Scenarios: PNA

Q: to outsource, or not to outsource, that is the question…

Generic advantages to outsourcing / JV’ing:

you can focus on your core competency

many companies “deworsify” by trying to do R&D and manufacturing and sales and other stuff

manages cash

no need to buy equipment, hire manufacturing folks, lease more space, etc.

P A G E 34

Scenarios: PNA

Q: to outsource, or not to outsource, that is the question…

Generic advantages to going it alone:

slow the spread of trade secrets / know-how

contractors sometimes become competitors!

self-reliant on quality

better integration (hopefully!) from R&D to Production

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Scenarios: PNA

Q: oh wait, this was about batch sizing, wasn’t it?

P A G E 36

Scenarios: PNA

Q: oh wait, this was about batch sizing, wasn’t it?

Advantages to small batches:

stats come fast can quickly do several runs to learn variability of process

losses smaller each bad batch is less expensive (can be important, early on!)

redundancies equipment malfunctions / breakdowns less catastrophic

P A G E 37

Scenarios: PNA

Q: oh wait, this was about batch sizing, wasn’t it?

Advantages to big batches:

much, much cheaper …as long as you’re not troubleshooting scale-up all the time!

go-forward flexibility increases throughput capacity faster (so you don’t turn down orders)

P A G E 38

Scenarios: PNA

Q: oh wait, this was about batch sizing, wasn’t it?some possible ideas…

Perhaps PNA could consider: how similar is this to other industry processes?

if similar processes have been scaled up before, maybe PNA goes big and trust that consultants can help with issues

could process time be reduced?shortening your longest step (often separation) allows you to increase throughput without spending capital

how comfy is PNA with the existing process anyways?PNA may want to wait until it has proven to itself that it has fully mastered the small batch size, before scaling up

P A G E 39

End.

Matthew Klippenstein

matthew.klippenstein@ballard.comhttp://ca.linkedin.com/in/matthewklippenstein