Please put transla on a er English sentence, otherwise I won’t know what Japanese goeswith what English.
Names:
Vish Veerajappa
Head of Sustainability Champions Network
Bill Wilson
Chief Sustainability Oﬃcer
George Halios
Lead AI Developer
Ma Kearney
Sustainability Lead, Insurance
TITLE: Sustainable Value Chain Genera ve AI Toolkit
Most ﬁrms are aware that their climate change impact is mainly dictated by their supplychain.
However, more than half have not started measuring this category of scope 3 emissions. Inturn, they are unable to provide accurate carbon metrics to their customers.
We need to break this vicious circle, and NTT is well placed to help solve this global problem.
Now, there are three methods for calcula ng supply chain emissions, and matureorganisa ons are using them in combina on.
 The best case is where data is provided directly by suppliers, but that’s o en
unavailableWe can subs tute with supplier-level factors – for example using our C-Turtle
pla orm, NTT can automa cally gather corporate emissions data, declared throughCDP

Or we can fall back on a spend-based approach for diﬀerent categories of purchased
goods and services.

Fuzzy matching technology can tackle simple lookup problems that can arise in this process,like ﬁxing supplier names which have been recorded inconsistently.
However, calcula ng emissions based on spend carries a greater challenge: that is, alloca ng
goods into spend categories. And this needs a new approach.
Even asking human beings to analyse invoices line by line can be a challenge, because thequality of informa on on each invoice line can be poor.
However, at NTT DATA UK, we are using OpenAI on Azure to tackle this challenge.
While genera ve AI it isn’t perfect at categorising data, we’ve found it can out-perform ahuman being, because of its embedded general knowledge.
How does it work? Well, we start by sub-se ng the purchased goods and services categories
that are relevant to the client.
In NTT DATA UK for example, we need 108 categories for our own supply chain.

Next, we pre-process the data, before asking a large language model to perform thecategorisa on.
The next step then is to work with the client on hotspot analysis – we ask for product datafrom those suppliers who represent the greatest carbon footprint in the supply chain.
But we want to go further than just helping clients with measurement, because we have to
use that data to actually drive change.
And once again our genera ve AI exper se can help.
So we’ve done our hotspot analysis, let’s think about how we manage suppliers toencourage emissions reduc on.
This is a challenge for many Chief Sustainability Oﬃcers, me included. We’re looking for a
mixture of quan ta ve and qualita ve informa on.
Suppliers provide this in a variety of diﬀerent formats
My observa on is that each organisa on asks the ques ons in a slightly diﬀerent way.
I know that because some of my me I spend with customers when we’re bidding for newwork.
There’s a be er way.
What if we simply judged suppliers based on the informa on they publish?
A er all, transparency is a key principle of climate change regula on in jurisdic ons worldwide.
But ﬁnding the informa on you want from what are called non-ﬁnancial disclosure reports
can be me consuming.
As a case in point, lets take NTT DATA EMEAL’s sustainability report.
It’s as long as Homer’s Odyssey, although this one contains more informa on about theTrojan War.
And that’s partly my fault, because I contributed some of the content.
But Large Language Models have proven extremely successful at extrac ng the necessary
informa on.
And that means supplier engagement programmes deliver rapid results.
Here you can see our applica on in ac on, ﬁnding the required data
As we’ve started working with ﬁnancial services ﬁrms, we’ve realised that supplierengagement is only one use-case for this technology.
It’s also required for scenarios like risk management and investment por olio analysis.
So, we have powerful genera ve AI technology ready to be deployed for suppliermeasurement and engagement.
And since the supply chain is roughly 60% of emissions worldwide, there isn’t a single
ini a ve NTT could be involved with that would have a more powerful impact on climatechange.
Of course, the environmental impact of large language models is greater than tradi onal
digital solu ons.
But with NTT’s exper se in this area, we can for example op mise the number of mes weneed to use the model.
And in March, NTT releases tsuzumi, a Large Language Model that requires signiﬁcantly lesscomputa onal power.
Since I ﬁrst started working in supply chain emissions, we’ve seen many welcome
developments, and the technology we’ve described here will one day becomecommonplace.

However, the toolkit we’ve developed is technology that every large ﬁrm on the planetneeds now.
Thank you for watching.