Fibre quality and source, computer chip supply, material prices, competitor performance, energy type and costs, logistics … the domino chain is getting more and more complex. AFRY’s Petri Vasara, Dr. Tech., Vice President, and Hannele Lehtinen, Director, say the answer is already here, and needs to be embraced.
The future is unknown, but it was here already yesterday. That can at least be said when comparing what is being discussed about artificial intelligence (AI) and what it can do in some far-off future. Most of what is described has already been done yesterday, and far more advanced AI assistance is possible – imagination and creativity is the limit.
However, tissue and hygiene are very practical and highly physical issues. There is no point hiding what’s done behind complex terminology.
Two points are at the core:
- It’s good to be made of atoms
It is good to be in a biological/physical sector such as tissue. Bits will not clean atoms. Tissue can’t be cyberattacked. However, bits can help when atoms clean atoms. Replacements researched for tissue such as ultrasound or air blasts are either expensive or unhygienic. - What AI can do to help tissue
Wherever there is an action, a process, data or consumers, something can be done – not directly physically, but indirectly. Examples always help.
The disruptive business environment
Since Covid-19 entered our world, nothing has been quite the same. A domino effect can be shown as in Fig.1.
So, pandemic, war, volatility and underlying long-term trends hit. A domino chain of problems spread with computer chip supply, material prices, and global trade flows. Has this or will it have an impact on tissue in Europe?
Pulp and tissue are linked, and we could call the flow of pulp from South America to China the “Celulose Corridor” (in Brazilian Portuguese). China’s economic development has been slowing down, a closing-down of China is also evident – what will the consequences be for tissue?
CASE FOR AI IN TISSUE TODAY: Supply chain domino analysis
The world’s trade flows are no secret, and data even on single company level is publicly available on, for example, each marine shipment to the US, where it came from, what it contains, who made it, which ports it passed through, and how long it took. Doing an AI analysis on the supply chains from China to the US revealed a frighteningly large amount of detail about the vulnerability of individual companies and what ports and producers were indicators to follow in predicting supply chain disturbances. Tissue does not do travel, but pulp does – AI can help in anticipating global tissue market movements.
Where will changes come in tissue and hygiene?
There are several ways to analyse what is coming up – e.g. patents, company activities, and funding. In patents, we have noticed a “calm before the storm” phenomenon. There is activity in patenting in a certain area over a period of years. Then, a calm settles over the area. Often, after a calm of two to three years, items come out on the market. The calm indicates a focus to get the products ready and likewise, significantly linking funding and technology yields. Using AI to analyse global funding (sizes, who does the funding, when, and what type) also helps in predicting movements in markets.
CASE FOR AI IN TISSUE TODAY: AFRY Funding Curve
We developed a combination of tools and visualisations for analysing funding and its implications. Fig. 2 shows what we call the AFRY Funding Curve. It is one of the simpler visualisations from mining funding in hygiene and tissue development, worth around €17bn in the 2000s. We see that a small proportion only is in the early “gearing up” stages, a majority in the main stages and a considerable part in post-Initial Public Offering. This denotes a sector where strong players are able to push things further, both in resources and also in time. This profile becomes even more interesting when benchmarked against other sectors with deep dives – but that is a story for another day.
Customised assistants: TissueBot at our service
Different AI tools have different tasks. Some assimilate large amounts of data (numbers, text, pictures) and form connections so that they can assist the human in thinking above results from tedious and, well, infeasible amounts of work. As an example (which does not even attempt to go too far, but is real): we made one of the assistants we have taught, TissueBot, combine:
a) trend research (Gen Z)
b) patent analysis
c) product creation and
d) marketing.
CASE FOR AI IN TISSUE: Be a combined trend researcher, product developer, patent expert and marketer
We tasked TissueBot with coming up with suitable hygiene/tissue products appealing to Gen Z, and asked it to give patents to use as technology. Furthermore, we asked for naming, logos and “marketing speak”. The outcome, without the illustrations and with patent numbers stricken out, is below:
“ComfortWave Tissues
Softness That Speaks to You
Ultra-Soft Texture: Enhanced by the latest softener concentrates and emulsions (ref. to specific patents).
Customisable Experience: Options to choose your preferred bulk and lint levels (ref. to specific patents).
Innovative Quality: Superior absorbency and durability using nanofibrillated cellulose technology (ref. to specific patents).
ComfortWave Tissues are designed for the modern consumer who values ultimate comfort and personalisation. With ultra-soft textures that provide a luxurious feel, customisable bulk and lint options to express your unique preferences, and cutting-edge nanotechnology for superior performance, ComfortWave Tissues cater to all your needs. Say goodbye to brand loyalty and hello to a new wave of comfort with ComfortWave Tissues – softness that truly speaks to you.
By aligning with Gen Z’s trends and incorporating the latest innovations, ComfortWave Tissues are set to become a favourite in households everywhere, offering a truly personalised and comfortable tissue experience.”
So, what now and next?
This is not a large, detailed chapter – just a pointed entry on the fact that AI is already here, has actually been here since the 1950s, and can already do more than most “visionary” claims say it will do sometime decades after today. We could put in work done on developing new tissue machines and technologies and other items of potential interest, but we hope the point has been made. As a glimpse, an illustration drawn by the writers with AI assistance (after an initial idea and about 20 iterations) for a new tissue technology concept, “QuantumQuilt”.
Why does it seem AI is not used more, since for a while now it has already done all of this? Maybe it is better not to ask, but instead just do.
*“AI analysis” is very generic. We could describe in detail what data was used, what algorithms were used and so on. However, that is not the point in a short overview – and it gets complicated fast.
