Ankit Kulkarni

Process Context
My team also manages the central master data management for 50+ plants today, and this will grow to 70+ plants by 2027. The entire fleet data management is handled by two people in my team.
Every time we commission a new plant or acquire a new plant, we need to align it’s material master with our fleet database, to avoid duplication, planning errors, and wrong spares being introduced into SAP.
In a typical post commissioning and acquisition, we review minimum of 5000+ incoming material records against an existing 345000 item fleet master.
Practically for my team, each item review takes at least 6 minutes without AI.
The AI-enabled Process
To solve this, I built a Python + AI solution using a MiniLM semantic model, combined with rule based checks.
The program setup classifies each incoming item into three categories, Auto, high confidence match to directly map & upload in SAP. Review, ambiguous match, reviewed by the master data team. Reject, no valid match, program generates a new master data creation template for my team, to directly load into SAP.
You can clearly see, AI does not create master data blindly in this case, it recommends, and the team decides.
When We Trust The AI
I have defined clear rules after testing the model for almost 10 days with millions of lines, semantic similarity is high & critical identifiers (model number, size, rating). It checks if descriptions and attributes are complete and consistent. One more rule I have setup is to keep standard, low-risk categories, and excluding verified MRP items, and these items directly flow straight into Auto category & are uploaded without manual touch.
When We Override The AI
Team deliberately does the review when similarity scores are close across multiple candidates, technical digits conflict even if text similarity is high. Then we also look at if item is maintenance critical or safety critical. We jump to the poor descriptions as well.
In all such cases, team’s priority is correctness, not the speed.
 
Safeguards That Keep The Balance
We have built simple controls to avoid blind trust or even excessive overrides,
Strict thresholds for Auto classification, mandatory team’s review for all Review cases, spot audits of Auto mappings, tracking & analysis of override patterns to improve program, and we have clear ownership, AI suggests, Team decides.
Impact In Real Numbers
Now with this program, my team completes 5000 item migration in 10 days in total instead of 2 months.
I have a clear breakdown of 10 days,
Data setup + AI pre-load + first analysis is done in 0.5 day
SAP mapping for Auto category takes 1 day
Manual review is done for Review category in 7 days
New MD setup for Reject category is done in 1.5 days
This has really improved my team’s output and bandwidth, and also reduced the onboarding risk for new plants, and best part is, it is allowing two people to scale this work for our growing fleet.
Bottom Line
I trust AI where signals are strong & mistakes are low impact, I override it where ambiguity or risk is high. As you can see, we are improving the overall process, idea isn’t to remove people from the process, it’s to make sure people spend time only where judgement actually matters.