Strongly weakens, the need for long-term practices have never been more critical.
How can we use analytics, be raised by Agentic AI, supporting companies in their green converts?
Age, focus on my blog has been using supply chain Analytics methods and solving tools.
In LogIgreen, the start of the implementation I removed, using these analytical solutions to help retailers, manufacturers, and companies.
In this article, I will show how we can use these available solutions about AI agents.
The aim is to make it easier and quick to use efforts to stand for all the chains available.
The barriers of the green alteration of companies
Since political and financial presses focus away from sustainable, making the green conversion easier and more accessible has never been more emergencies.
Last week, I went to the whole world Freezer The conference, held in our hometown, Paris.
The summit included founders, businessmen and decision makers committed to a better future, despite severe context.
It was a good opportunity to meet with other students and contact the driving leaders changing industries.
In these discussions, one clear message arose.
Companies deal with three main obstacles when driving a sustainable change:
- Lack of visibility of operation,
- Difficulty reporting of savings,
- Challenge creativity and to use all attempts.
In the following stages, I will check how to suggest you Agentic Ai The two victories of these major obstacles:
- Improve reporting to respect regulations
- Accelerate make-up and stable things
To resolve reporting challenges with AI agents
The first step in any steady roadmap to build a basis for reporting.
Companies must measure and publish their current environmental cables before taking action.
For example, ESG reporting is communicated Corporate performance (E)the responsibility of social responsibility (S)The Areas of Rule “ strength (G).
Let's start by dealing with the data preparation problem.
Subject 1: Data collection and processing
However, many companies face important challenges from the beginning, starting with Data collection.
In the article in the past, I introduced the idea of examining a life cycle (LCA) – how to test the environmental impacts of products from the disposal of raw materials.
This requires the sophisticated data pipe to connect to many programs, uninstall green data, process it and save it to the data storage.
These pipes work to produce reports and provide integrated analytics data and business groups.
How can we help non-technical teams that wander this complex situation?
In LogIGREEN, we examine the use of ai agent of Text-to-SQL applications.
Additional amount that Business and Workers are no longer relying Analytics experts to create appropriate solutions.
As a Supply Chain engineer, I understand the frustration of Operation Managers to create support tickets just to release data or calculate a new indicator.
With this AI jent, we provide an analytics-as-a-service experience of all users, allow them to create their demand in a clear English.
For example, we help report groups to make certain incentives to collect data from multiple tables to feed the report.
“Please generate a table showing the sum of Co₂ issuer per day on all delivery from Warehouse XXX.”
For more information on how to use the agent, check this article ②.
Automatic working on analytical analysis flows with AI agents using N8n | Facing in the data statement
Uninstall 2: Report formation
Even after collecting information, companies deal with another challenge: Creates a report in required formats.
In Europe, New The reporting order of the Corporate Sustainability (CSRY) It provides a framework for companies to disclose their environmental, social impacts, and governance.
Under CSRD, companies should submit formal reports to XHTML format.
This text, advertised in detail Esg TaxkomiesIt requires the process that may have a very technology and tend to errors, especially in companies with low data maturity.
Therefore, we examine the use of an AI agent automatically processing this report and provides summary to non-technical users.
How does this work?
Users send their email message.
Endpoint automatically downloads the attached file, make content audits and format, searched for missing errors or lost prices.
The results are then sent to AI agent, producing a clear audit of books in English.
The Agel sends a report and returned to the Supper.
We have developed full-default service in the audit reports that are due to future advisors (Our Customer is a consultation company) that anyone can use without requiring technical skills.
You're interested in using the same solution?
I built this project using the NO-Code platform n8n.
You can find a configured template in the My own N8n profile.
Now that we have examined report solutions, we can go to the spine of green changes: Designing and using sustainable efforts.
Agentic AI of analyzing products for Supply Chain Analytics
Analytics products of sustainability
My focus on the past two years was on the upgrading of the properties, including web apps, APIs and default work hours.
What is meatmap for sustaining?
With my previous feeling, usually starts with a push from higher management.
For example, leadership will ask the service provider to measure the company's release of the Center of 2021.
I was responsible for measuring Scope 3 Issuing of chains.
That is why I have used the method presented in the above-connected articulate.
Once the foundation has been established, a Target defined with a clear deadline.
For example, your managers can commit to 30% reduction in 2030.
The role of the provision department of the provision of the provision where it should be class and use methods that reduce the output of the CO2.
In the example above, the Company reaches 30% of the year n through production opportunities, access, sales and carbon operations.
Supporting the trip, developing analytics products that imitate the impact of different programs, to help groups design higher sustainable strategies.
To date, products were in a position of web applications with a user display and backend connected to their data resource.
Each module provides important understanding to support active decisions.
“Based on the output, we can reach a reduction in the release of 32% co₂ by migrating our factory from Brazil to USA.”
However, the audience is not familiar with the data analytics, communication with these applications still can be very sensitive.
How can we use AIs better to support these users?
Agentic AI of Analytics Products
We now promote these solutions to the empowerment agents AA communicating directly with Analytics and tools through the EPI Enpoints.
These agents are designed Show non-technology users throughout the journey, from a simple question:
“How can I reduce the release of my travel network Co-?”
Ai agent takes charge:
- To make the right questions,
- Connecting to Model to Well-Use,
- Translation results,
- And to provide usable recommendations.
The user does not need to understand how the backend works.
They get the best, guided by a business such as:
“Use a solution XXX for the YYY EURA's investment budget to reduce the reduction of the ZZZ Tons Co₂eq reduction.
By combining effective functioning models, the Apis, and the Directory conducted by AI, provides an analytics-as-a-service experience.
We want to make an analytics of improvements available to all groups, not just technical experts.
Store
Using AI for commitment
Before shutdown, the name regarding reducing the natural components of solutions that we improve.
We fully know the environmental impacts of using llms.
Therefore, the role of our products remains designed Models for use available, designed by US carefully.
Large models of languages (LLMS) are only used when giving more original value, and the more is to facilitate user interactions or change sensitive tasks.
This allows us to:
- Verify the stability and trust: By the same installation, users find constantly outgoing, avoiding a clear moral valuable moral of the AI
- Reduce the use of power: By reducing the number of tokens used in our API calls and upgrading everything immediately to operate as well as possible.
In short, we are committed to creating stable solutions in their construction.
Agents AI is a change of Supply Chain Analytics
To me, AI agents have turned all the powerful Allies to help our customers speed up their stimuli roads.
As I work with the technical intended technology, this is a competitive advantage, because it allows me to give analytics-as-a-service solutions that enable workshops.
This makes it easier for one of the largest companies facing the start of their green conversion.
By Communication Information in Clear Language including To direct users for their trip, Ai agents help Book the gap between the solutions conducted by data and effective execution.
Let's connect to LinkedIn and Sane; I am an engineer for purchase requirements that uses data analytics to develop logistics activities and reduce costs.
In consultation or advice on analytics and extra conversion, feel free to contact me with logean Consulting.
Samir Saci | Data Science and Production
The technical blog centered on data science, personal, automated production, practical research and stability …samis.com
