2045: scenarios for the textile and fashion industry: How will the industry look like in 5, 10 and 30 years time? Scenarios offer research-based insights, and potentially can show how realistic a world is, that looks rather quite different from what we're used to. What if Asia become today's Europe? What if we did not buy to own? What if everyone was a maker?
How can AI help connect the worlds of sustainability/ESG data, and that of company financials?
ESG is typically considered a mere cost - yet: this perception stems chiefly from a lack of integration of mutually beneficial data of these two worlds. With better approaches to cost accounting, to performance analysis, as well as using predictive analysis relate to trends, legislation and asset management, the ability of AI to integrated diverse and complex data sets may precise be the pathway to shift that needle.
The current fashion and textile landscape is fairly limited in terms of what types of materials are being used. Innovation is key, and products such as Tencel, Sorona and Ingeo proof that industry is investing heavily in R&D. Spider Silk may be one of the few natural fibres of the future yet to see it hayday.
Supply chains, as a discipline of expertise, have come out of the hiding and recognise their role in reducing corporate risk. This is notably and specifically the case in fashion and textiles. At the same time, 'design' - not just in the creation room, but in all facets where it impacts the making, delivery and use of a product or service, is increasingly recognised as relevant.
Have you heard about open data? And about open source?
The equivalent of open source in sustainability terms would be an ‘open standard’.
But what would that mean?
The KISS Principle is a design principle that stems from the 1960.
It originated in engineering and its view point is that most systems work best if they are kept simple rather than made complicated; therefore, simplicity should be a key goal in design, and unnecessary complexity should be avoided.
But what about complex systems such as nature?
How simple can we go before oversimplification results in incomplete, or biased data? Before absence of consideration of relevant factors inherently lead to regrettable substitutions? And before we willingly accept that there will be collateral damages to a decision, without knowing (or wanting to know) of what nature and in what order of magnitude these may be?
One example that illustrates where this challenge may rear what is its ugly head: upcoming Swiss political referenda on agricultural practices.
After 10 years in the make, CETI, the European Centre for Innovative Textiles, was finally inaugurated in October 2012. The aim of the research centre is to give the textile industry a platform to research and prototype innovative textiles that can be used in sectors like: Medical, Sport & Leisure, Hygiene, and Protection sectors representing 25% of technical textile manufacturing industry; building and civil engineering that account for 10% of the production; transport making 26% of the market volume (and 15% of the market value) of technical textiles.
How does digitalisation impact and link to corporate responsibility? This is the question we look into in this post.
Combining the two disciplines results in a range of interesting questions. For example: If humans create non-human agents (e.g. in the shape of AI): For what, towards whom are these responsible? And: are they responsible at all - or is it their creator who is?
PASS THE BATON is a new type of recycle shop with two locations, in the center of Tokyo. It cherishes the emotional bound that both, previous and new owner have with the item, fostered by a carefully curated setting.
Barcodes, RFID tags, and QR codes have each introduced a new era of on-product information distribution and acquisition.
In this article I would like to look at a family of digital solution components that many brands and manufacturers will already use and be reliant on, and that – if integrated – could be leveraged to provide full-depth traceability.
In last week’s post I looked at energy companies and their trajectory relative to the Paris Climate Agenda. The insights clearly suggested a mixed picture. A clear point of how important it is to decarbonised the way we fuel our economy and global society.
But that’s unfortunately not all there is to the energy generation picture!
What few people realise: Energy generation requires water. A lot of water.
Not just in the energy generating processes, but also in the extraction of the energy source (coal in particular), and/or the making of the necessary equipment.
Some insights ... illustrated at the example of China.
Could ESG reporting finally become less repetitive and tedious?
AI has the potential to transform ESG reporting by automating compliance tracking, integrating data from diverse and unstructured sources, and streamlining audit preparation. This opens up opportunities to free data and ESG experts from repetitive, tedious tasks. Yet, while AI offers promise, tight oversight remains essential to address challenges like data quality ('crap in is crap out') and system integration.
As companies and countries around the world pursue net zero targets, one big question is: How do you ensure the carbon removal technologies we will need 20 to 30 years down the road are available, affordable and easily scaled?
S&P Global recently published a podcast mini-series on emerging climate technology.
The series not only introduces a range of much hyped about, CO2 saving or CO2 removing technology, but also looks at scaling, the truth of potential impact, and financial viability.It is for this reason that I would like to list the three episodes in this post – and invite everyone to spend the 3 x 20 minutes to wrap their head around these insights.
As part of a workshop given at the Textile Exchange 2013 conference, we ran a small survey among workshop participants in order to find out more about their perception of and experience with Scenario Planning. Here the survey results.
Can AI help us to get to (better) grips with supply chain compliance?
Supply chains are based on fairly complex partnership networks where every link ideally must meet strict efficiency and compliance standards.
Supplier audits and legislation aim ultimately to ensure high standard, it is a not the least highly time demanding task to be successful at. AI offers the potential to support practical solutions for risk assessment, process optimization, and partner evaluation. Commercial providers are already jumping on the band wagon by providing ways to build 'digital twins' of real supply chains – hence opening them up for 'offline optimisation' - and of course highly sophisticated data analytics tools drawing from multiple disjoint data sources.
Overconsumption or ‘simply’ consumption?
Fair resource use, or resource depletion?
Fair share, equal share or acquired share of resources?
Those are questions that pop up when the Planetary Boundaries are being discussed.
“Is Europe living within the limits of our planet?: An assessment of Europe's environmental footprints in relation to planetary boundaries”, published in April 2020 does exactly that: it evaluates and calculates the European performance for planetary boundaries by taking a consumption-based (footprint-based) perspective. This is turn is interesting as it relates environmental pressures to final demands for goods and services.
And the results are ... shall we say: a stark call to action.
At Shirahime, we have worked quite extensively over the last few months on the development of fashion industry scenarios beyond the 2020 time frame, going as far as 2045.
We mentioned for example Shell as one that used this approach to suit their own goals.
Siemens' 'Future Life' video, as presented the The Crystal in London.
A much more interesting approach, and very insightful in terms of methodology, but also how tangible the results are presented, is Siemens’ work on Future Cities
Connecting the present and the past, learning and drawing conclusions from either, is and will remain key to creating a more sustainable fashion industry. So far, learning from the past in particular - in the good and in the bad - has been chiefly neglected. A series of thoughts.
How do you make ‘sustainability' tangible?
The usual answer is – unsurprisingly – a ‘well, it depends’.
Which it evidently does.
Unfortunately, good case studies are extremely rare to come across.
Hence, when I stumbled across such a gem in one of the primary Swiss news papers, I jumped at the opportunity to summarise it for this blog.
AI has the potential to transform corporate responsibility by handling data-heavy tasks like reporting or data and KPI management. It hence can contribute to helping companies 'being less bad'. However, its potential to support professionals and companies in driving real positive impact is still developing. This post introduces AI’s current potenntial in corporate responsibility and sustainability. In upcoming blog posts we'll explore specific applications: in sustainability reporting, supply chain management, and integrating financial considerations with sustainability impact.
