1. Align with the client’s specific requests
👉 Clients might have specific requests for data processing → they will have an impact on which module format you should opt for.
Common requests
Suppliers or other custom EFs (outside of Greenly’s database):
The client must have paid for their integration, and the EFs should have been validated by the R& M team
These EFs must be added to the EF Manager as company-specific EFs before you can retrieve their IDs
E2E can be used in this case, supplier EFs can be associated via Admeenly
For large accounts:
The best is to validate the EF association to the client’s data with the client → the advanced analysis need to be used for that
💡 For “📚 Review product purchase flagged materials”, check this page:
2. Choose the module that best fits your needs
⭕ 100% automatic processing (E2E module)
To be used as much as possible because it allows client to be fully autonomous from data collection to data processing.
⚠️ Case where this module cannot be used
Client has lines without material or quantity or unitary weight
Example: the client has 80% of the info but some lines don’t have the material → in this case we use an MEF to finalize the analysis but this is not possible in the E2E (100% of the mandatory info are required)
Material given but client lines are too complex (the algo will directly reject them) or with too many different sections (if more than 50 different, manual association with Advanced may be quicker)
Clients provided more than one material in the material section per line (example: 30% PVS, 70% steel) → the Advanced should be used (process below)
Recycled %: the E2E is working well for product with 0% or 100% recycled material; in other cases use the Advanced
If you need to be very specific on the country of the EF for picky clients, the Advanced is easier
If you have client LCAs:
The EFs have been created by R& M with IDs?
Yes → use the E2E and associate manually the EFs to the product on Admeenly (can be long; ask intern help)
No → use the Advanced and do a custom import
Pay attention to:
Client in Y2 or more: the methodology is not the same in Advanced vs E2E
In the E2E, for manufactured products, we add 20% emissions to take into account manufacturingIn this case, rebaseline the base year using the E2E module
Multi entity: EF association of one entity is not based on the EF association of the others → double check the EF association of all entities in each account
🈵 Manual processing (Advanced module)
Some clients will need a study that differs from the traditional one because of the type of data they’ll provide you with
Choose this option only if the end-to-end module cannot be used
3. Data Processing
The goal of an activity-based study is to categorize a client’s data, meaning associating each activity of a specific category to the most suited emission factor.
⚠️ if you are working on a multi-year account, don’t forget to check the Rebaselining section.
🈵 Manual processing (Advanced module)
Guide for the base tool
You need to add 20% Bonus emissions for all the manufactured products present in your advanced analysis. To do so:
Upload the manufactured products on Admeenly, look at the emissions
Create a custom Import file, name it “Bonus 20% manufactured products”
Quantity should be equal to 20% of the number you saw on Admeenly
Unit is equal to UNIT
Purchase category = PRODUCT_PURCHASES
Import it on Admeenly
Continue with the raw material upload
How to extrapolate missing data:
Missing
Name:Fill with unknown and use the category (if provided) to match the EF
Missing / wrong
Unit:Contact PM
Missing
Recycled %:We consider it’s 0%
Missing
Category / Section:Try to replicate the proportion of section in the existing data:
For example, if the existing data has (as % of total weight):
30% “Wood Products”
30% “Plastic Products”
40% “Electronics”,
then your extrapolation on the missing sections would have the same proportion.
So if 10 lines have missing section, 4 of them would then get “Electronics”, 3 of them “Wood Products”, 3 of them “Plastic Products”. For small dataset, doing it manually is fine. For big ones, make a script.Missing
QuantityorUnitary WeightMost of the time, when quantity is left blank but UNITARY WEIGHT is huge (or the other way around), then it’s probably because quantity is 1.
If some quantities / weight have values, but a few other don’t: that’s missing data. Then you have two choice:
Long deadline:
Ask PM for more info & contact client
Short deadline:
Average the quantity of the whole dataset (imprecise but methodologically ok) to extrapolate the missing quantities.
Average the quantities of the category / section and apply it to all row that have this section but missing quantities.
If data has multiple missing field:
Order to follow will be:
Extrapolate Name (Unknown)
Extrapolate Section
Extrapolate Weight
Extrapolate Quantities
⭕ Automatic processing (E2E module)
Process on the SaaS
Upload your file with all mandatory column filled
Before the first step: the algo will match the products with EFs. If matching is perfect, products are automatically validated and the client will not have to review them.
Step 1: Review: client validates categorization for some products (when confidence is low)
Important: client will be asked maximum twice to validate/reject the same products. If they reject twice, the product ends up in Step 2: Categorization-Step 2: Categorization: products rejected twice or not matched at all
Choosing EF: client can choose the right EF in available EFs in the module (Ecoinvent 3.7)
Flagging: if client cannot find the right EF, they can flag the line and provide an explanation. This creates a task “Review product purchased flagged product”. This task is also created if some product has a “% of recycled” different from 0 → association should be done by Greenly (PM or analyst)
Step 3: Double countings: clients can identify double countings directly in the module
Admeenly workflow
When should you use Admeenly?
If you need to use EFs other than Ecoinvent 3.7 (clients are restricted on the SaaS but we can select any EF through Admeenly)
If you need to use EF created for LCAs by R& M
For all the products with a % of recycled that is not 0%
Use the search bar on top right to find the material you are looking for
Select the line where you want to change the EF and click on “Edit EF”
Search and select the EF (by name or ID, filter on unit, database, etc.) then click “Save and close”
How to map the flagged material for the E2E
Go on the client’s profile and open the “Product, Raw Materials & Packaging Inventory” module (E2E name for Product Purchase)
Click on “Log Into account”: this opens another tab to the SaaS profile of the client
Go to: Data > Data Collection > Product, Raw Materials & Packaging Inventory > Categorization > Custom Categorization
If no bug, module status should be “Revision by greenly” and you should see flagged material
For each flagged item, click on the flag to see if the client left a comment
In Admeenly, find the same entry (SaaS E2E aggregates by name, Admeenly not) → select all corresponding products, then click “Edit EFs” and select the most fitting EF
Note: When you assign an EF on Admeenly, you won’t see it directly displayed on the SaaS; refresh the page
Do this for every flagged material
♻️ Rebaselining
The goal of rebaselining is to apply the latest version of EFs to the base year and to ensure that categorization is harmonized with the current year → This way you will be able to study the annual evolution of emissions.
💡 When you do an analysis for a company, you should first check if a similar analysis has been done for the previous years, and check with the deal owner/client if a rebaselining of the data has been planned. It’s very important to make sure that, across years, similar modules were used.
⚠️ Do not proceed to any rebaselining without aligning with the deal owner/your client on the specifics you must take into account (custom emission factors, EF exclusions that differ from the current year, PC override, etc.)
For more information about rebaselining in general, you can check the below sections:
🅰️ Automatic rebaselining
[UPDATE IF NOT TRUE FOR YOUR MODULE]
The algorithm of the E2E module takes into account previously done studies to adapt its categorization suggestions → You can upload the data and follow the module’s process for an efficient rebaselining.
Ⓜ️ Manual rebaselining
👉 Avoid using this section unless the data you have to rebaseline is very little.
🎯 Follow the below steps to process the data:
[TO BE POPULATED]
💡 Try to harmonize categorization throughout the years as much as you can.
4. Review the data
Once data has been uploaded, there’s the “2. Review” section. There, you need to:
Review the expenses that were flagged as potential duplicates
→ the platform will show the expenses that were categorized as XX [TO UPDATE]
→ if not already done, you should review them and flag the ones that are linked to activity data uploaded in the module as duplicatesReview the Data Quality checks
→ Activity data that deviates from expectations will be flagged here. If no checks, it’s all good. If any check shows up, then you should review them and make changes if necessary