Marcus Stutz, Dell - LCA Sustainable Product Design Europe 2010

Using High Quality LCA Data to
Make Solid Decisions
Markus Stutz
EMEA Environmental Affairs Manager

Some Preliminary Remarks
• Any LCA is better than no LCA
– Do not use data issue as excuse
• Using your LCA results to take decision already huge step
– Now mostly reporting
– Sometimes discussions about LCA results/data resemble “fight the
problem” behavior
• Relying on bad data can lead to “wrong” decisions
– Actions might not be most effective, however still better than doing
nothing
• Good data do not guaranteed good decisions
– Other factors might prevail (cost, customer requirements, technical
features)

Top Level Assessments-Determining Hot Spots
• Ideal to:
– Determine top impacts
– Identify low hanging fruits to improve product/process
– Calculate if a simple change can bring significant improvements
• Two data streams
– Primary (foreground) data
– Secondary (background) data
• Primary (foreground) data
– Bill of materials, logistics, usage patterns
– Assumptions can lead to significantly different results
– Most errors happen with foreground data (personal feeling)
• Secondary (background) data
– LCA data for materials & processes
– Totally OK to rely on generic data for top level assessment
– Databases available are e.g. Ecoinvent, GaBi, SimaPro

Making Solid Top Level Decisions
• Only pick a hot spot when impact is contributing over 10% to
total result
• Scenario analysis:
– Assume error margins of +/- 20% (or more)
– What happens if key assumptions change slightly?
› Usage time, charger plugged/unplugged 24/7 etc.
– If results still indicate same hot spots: Results are robust
• Compare with other studies if trend is in the same direction

Results Desktop PCF (Total Life Cycle)
Hotspot #1: Use
Hotspot #3
(AUS only):
Air transport
Hotspot #2: Component Manufacturing

Results and Decisions
• Use is dominating total impact (80%)
– Actions taken to make products more energy efficient clearly
mandated
– More uniform use of power management advised
› Needs user education & training (!)
– Actions already underway: Energy Star, Energy Smart
• Component manufacturing
– Analysis shows that impact dominated by motherboard and chassis
– Making both smaller (e.g. by choosing small form factor) already reduces
impact
– Initiate cooperation with supplier (if willing/interested)
• Regional assembly or transport by ship, if lead time allows, is a
preferable logistics option

How Did Data Quality Affect Decisions?
• More detailed data would not have yielded in other decisions
• Energy consumption in use, manufacturing of steel chassis and
motherboard laminate as well as logistics are all fairly generic
and well analyzed process with good LCA data and also with no
huge difference from supplier to supplier (if in same country)
• Assumptions on use and logistics have far greater impact than
variations in data for materials and processes

Detailed Assessments
• Comes only after top level assessment
• Ideal to:
– Decide between design/technical options
• Is only one factor in a set of assessment (e.g. cost, availability, yield,
delivery, features)
• Also here two data streams
– Primary (foreground) data
› Bill of materials, logistics, usage patterns
– Secondary (background) data
› LCA data for materials & processes
• Using generic databases for secondary data still ok, if materials/processes
are fairly generic (plastic, painting, etc.)
• For “non standardized” materials/processes (e.g. bamboo packaging)
collecting data strongly suggested

Result PCF Notebook Housing (LCD Cover)

Results and Decisions
• Mg cover clearly highest impacts by far (more than 3x)
• Plastic, steel, Al housing comparable
• Digging deeper:
– Plastic, steel have high (air) transport impact => could be lowered
– Al could be made out of recycled material => impact could be lowered
• Decisions/
– Environmental guys: Plastic or steel
– Marketing guys: Al (follow Apples lead - recyclable)
– Engineering guys: Mg
– Who won?

Housing: The Big Picture
In the big picture any decision on the housing material seems not so relevant …

How Did Data Quality Affect Decisions?
• More detailed data would not have yielded in other decisions
• Manufacturing of Mg, Al, plastic, and steel housing, surface
coating as well as logistics are all fairly generic and well
analyzed processes with good LCA data
• Potentially there could be differences from supplier to supplier
– Yield (!)
– Energy mix (country specific!)
– Processes, e.g. clean rooms for painting
– Transport route (local suppliers avoid air transport)
• More detailed data could have helped suppliers in choosing
better processes
• Would it have made sense?

Conclusions
• Carry out LCA or PCF studies
• Make/propose decisions based on LCA/PCF results
• Pay attention to primary (foreground) data/assumptions
• In most cases generic secondary (background) data is good
enough for standard materials and processes
• Be careful when looking at unique materials and processes
– Data collection at supplier could be advised

Thank you
markus_stutz@dell.com