Table 2

Strengths and weaknesses of MFA.

Strengths Weaknesses
Time, temporality Foresight, the future based on modeling derived from MFA (like IoS models). Deals also with the past (time series since beginning/middle of 20th century) due to the long lifetimes of some goods (e.g. construction, machines, industrial equipment). The IoS model is a phenomenological model, not a model based on a mechanism described in details. Need for more work in this area, based on outside scientific disciplines.
Scale, space Large systems, institutions, large companies or industry associations Too large for most companies. They consider that it is not “their business”!
Scope 1/3 of the Mendeleev table already covered. Missing elements and materials, such as polymers, composites, missing goods and parts. Focus on “pure” elements is also a weakness. Need to take co-elements into account such as alloying/tramp elements in metals, impurities in ores (true at mining, use and recycling levels)
Access to data Somewhere in the datasphere but (for a long time) kept safely inside the knowledge base of a research team. Supplementary materials and open data policies are slowly removing this habit. No commercially or publicly available database, except for MFAc. Therefore, newcomers have a high barrier of entry. Moreover, data reconciliation remains a rather “hidden”, “hush-hush” reality.
Standardization No No
Achievements Foresight studies on future materials need, construction, etc. Not enough estimates of recycling rates nor of stocks (anthropogenic mines, hibernating stock, etc.)
Missing elements Social dimension
Reuse, lean and frugal design rarely studied
Economics (MFC, similar to LCC?)
MFA at a smaller scale, e.g. addressing industrial symbiosis
Integration of MFA as a strategic management tool inside business outfits
Evolutions? Automatic creation of MFAs, based on algorithms, automatic collection of data in the Big Data sphere, Artificial Intelligence, etc.

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