Guest Speaker:
Transcripts
Welcome to Tech Lyceum, where we cover all the ideas shaping enterprise transformation. My name is Neerja. Today we are discussing why MRO master data is the hidden level for manufacturing performance. It sounds like a level waiting to be unlocked.
Now, in manufacturing efficiency conversations often focus on supply chain and production, but there is a less visible layer that has a significant impact, and that is MRO Master Data.
Joining me today are two guests with two unique perspectives on ground client reality and domain expertise. I'm here with Neerav Gupta, who leads data and analytics and MDM at Birlasoft, with over 30 years of experience in multiple industries in transforming their data by infusing quality and governance in their journeys towards next gen readiness. Hi, Neerav. Excited to talk to you today.
Now, in manufacturing efficiency conversations often focus on supply chain and production, but there is a less visible layer that has a significant impact, and that is MRO Master Data.
Joining me today are two guests with two unique perspectives on ground client reality and domain expertise. I'm here with Neerav Gupta, who leads data and analytics and MDM at Birlasoft, with over 30 years of experience in multiple industries in transforming their data by infusing quality and governance in their journeys towards next gen readiness. Hi, Neerav. Excited to talk to you today.
Listen On
Speaker – Neerav - 01:14
Hi, thank you so much for this opportunity, Neerja.
Hi, thank you so much for this opportunity, Neerja.
Speaker – Neerja – 01:16
We are also joined by Abhinav Srivastava, who leads Birlasoft strategic manufacturing and industrial accounts, helping operations-intensive enterprises to drive large scale transformation. He creates scalable road maps that deliver measurable business value. Abhinav, great to have you here today. Thanks for joining
We are also joined by Abhinav Srivastava, who leads Birlasoft strategic manufacturing and industrial accounts, helping operations-intensive enterprises to drive large scale transformation. He creates scalable road maps that deliver measurable business value. Abhinav, great to have you here today. Thanks for joining
Speaker – Abhinav - 01:39
Thanks, Neerja. Good to be here.
Thanks, Neerja. Good to be here.
Speaker – Neerja - 01:41
So, we'll dive straight in. And Neerav, my first question is for you. Why is MRO master data now emerging as a strategic priority for manufacturers?
So, we'll dive straight in. And Neerav, my first question is for you. Why is MRO master data now emerging as a strategic priority for manufacturers?
Speaker – Neerav - 01:54
So, thanks for the question. Let me start with the definition of MRO. Okay, MRO means maintenance repair org. items, so I'll give you an example. The example being like any car company, for example, a car or engine making company, or a truck making company, they have the service repair or separately more than their finished goods. So, what happens is, if your car is stuck somewhere and your truck is stuck somewhere, they have a repair factory separately from where the actual the maintenance van would come, and they would actually have all the items that are needed to repair your car immediately. That's just a basic example. So, but this goes across the manufacturing industry in a big way. The point here is, for a long time, this MRO data has been treated as a supporting function, which is important, but not very strategic. But what the clients have now understood that if they don't maintain and plan this inventory correctly, with all the attribution, with all the details, then they will be not making better money. They can make more money, that's what this all is all about, but also plan it correctly. So that's changing because today they are focused on optimizing working capital, improving uptime, and driving faster decisions.
All of these depend on great and reliable data, specifically for items, which is products for their product, which is needed for this maintenance org, so if parts are duplicated, poorly described, or difficult to locate, these organizations end up carrying more inventory, access of inventory, reordering parts unnecessarily, or delaying any maintenance, which I give you an example of. So, MRO data is no longer just operational, is becoming a core level for performance, cost control, resilience, and so this is a change which is coming across the industry.
So, thanks for the question. Let me start with the definition of MRO. Okay, MRO means maintenance repair org. items, so I'll give you an example. The example being like any car company, for example, a car or engine making company, or a truck making company, they have the service repair or separately more than their finished goods. So, what happens is, if your car is stuck somewhere and your truck is stuck somewhere, they have a repair factory separately from where the actual the maintenance van would come, and they would actually have all the items that are needed to repair your car immediately. That's just a basic example. So, but this goes across the manufacturing industry in a big way. The point here is, for a long time, this MRO data has been treated as a supporting function, which is important, but not very strategic. But what the clients have now understood that if they don't maintain and plan this inventory correctly, with all the attribution, with all the details, then they will be not making better money. They can make more money, that's what this all is all about, but also plan it correctly. So that's changing because today they are focused on optimizing working capital, improving uptime, and driving faster decisions.
All of these depend on great and reliable data, specifically for items, which is products for their product, which is needed for this maintenance org, so if parts are duplicated, poorly described, or difficult to locate, these organizations end up carrying more inventory, access of inventory, reordering parts unnecessarily, or delaying any maintenance, which I give you an example of. So, MRO data is no longer just operational, is becoming a core level for performance, cost control, resilience, and so this is a change which is coming across the industry.
Speaker – Neerja - 03:47
Thank you, Neerav. It helps to have this context, and I'll follow it up with you, Abhinav, from your experience with manufacturing clients.
Q: How does this problem actually show up on the ground?
Thank you, Neerav. It helps to have this context, and I'll follow it up with you, Abhinav, from your experience with manufacturing clients.
Q: How does this problem actually show up on the ground?
Speaker – Abhinav - 04:00
See, good question.
I think these challenges show up pretty much every day, and I give you an analogy. Think like in a hospital, right, where doctors cannot find a medicine. What will happen? The whole pretty much hospital shutdowns, right? Same thing happening on the manufacturing floor. Team spends valuable time searching for the part that already exists, like Neerav explained, if a part has been labeled as motor bearing 6205 and you're looking in the system, but in the system it's been saved as 6205 industrial varying, you cannot find the part right, so you are looking for a part which is available in the system, but you're not able to find it, then you're going to go to procurement, you do an urgent procurement because you feel the inventory is low and in the process your maintenance team loses critical time to make an asset overcome the failure and over time what happens these inefficiencies become normalized organization accept delays duplicate inventory access stocks and believe this is business as usual which actually leads to high cost overruns and your efficiency decline, the risk is even higher in regulated industries, especially in aerospace, industrial manufacturing, and life sciences, where traceability and compliance is a critical element, and the poor MRO data increases, and audit exposures, and operation risk, and chances of using incorrect or a non-compliant path, so in summary, it's very, very critical, and any organization should have a good control over it,
See, good question.
I think these challenges show up pretty much every day, and I give you an analogy. Think like in a hospital, right, where doctors cannot find a medicine. What will happen? The whole pretty much hospital shutdowns, right? Same thing happening on the manufacturing floor. Team spends valuable time searching for the part that already exists, like Neerav explained, if a part has been labeled as motor bearing 6205 and you're looking in the system, but in the system it's been saved as 6205 industrial varying, you cannot find the part right, so you are looking for a part which is available in the system, but you're not able to find it, then you're going to go to procurement, you do an urgent procurement because you feel the inventory is low and in the process your maintenance team loses critical time to make an asset overcome the failure and over time what happens these inefficiencies become normalized organization accept delays duplicate inventory access stocks and believe this is business as usual which actually leads to high cost overruns and your efficiency decline, the risk is even higher in regulated industries, especially in aerospace, industrial manufacturing, and life sciences, where traceability and compliance is a critical element, and the poor MRO data increases, and audit exposures, and operation risk, and chances of using incorrect or a non-compliant path, so in summary, it's very, very critical, and any organization should have a good control over it,
Speaker – Neerja - 05:28
Right, Abhinav. So that's what ground reality looks like, right? And I suppose then the question is, and I'll ask you this, Neerav,
Q: How does AI-driven path classification address this in a scalable way. Can you take us through that?
Right, Abhinav. So that's what ground reality looks like, right? And I suppose then the question is, and I'll ask you this, Neerav,
Q: How does AI-driven path classification address this in a scalable way. Can you take us through that?
Speaker – Neerav - 05:44
Sure. Let me take up one minute first to explain to you why we reached to this point to drive this using AI. So, as Abhinav already mentioned, that this is, I mean, I mentioned that, which is important, and Abhinav, we gave you a very great example. The point here is that when it comes to it, when, and we try to classify all these parts manually, that it takes a while. It takes so much work. Every person, even a data analyst, would have to go and search for each manufacturing part number or manufacturer name and search for any attribution, which takes like long time. So, what we thought, let's come up with a with a tool, and everyone is going for AI tools these days, and that's why we thought, let's start with the AI-driven part classification. If we kind of made, I'll use this word, a web crawler for you, a web crawler, but with intelligence in a way.
So, instead of manually searching for a part or their manufacture or their attribution, our tool is actually helping you crawl through the web, go to that web with that manufacturer part with manufacturer site, and then grab that detail. AI basically is fundamentally changing, as has changed over the period for both scale and sustainability of the solution. What has happened that using this natural language processing, we can interpret unstructured descriptions. I'll use this word again.
Unstructured description means that when these parts are actually listed in ERP or enterprise resource planning tools or any database system, their descriptions are not good. That's why we need some more details and something to collaborate the details about them, so this tool can now interpret those unstructured descriptions, standardize naming conventions, identify duplicates, and even record where they are different, they seem different, can still enrich any missing attributes, but the real value that it creates is continuous layer of governance with that. What I mean, governance is how you control the descriptions, the attribution, the technical details, and standards around these items and their attribution. So, instead of one time cleanup, organizations build a system where data is consistently interpreted, validated, and improved over time. That's when the impact becomes sustainable.
I'll give you one more example, that there are multiple of our clients that they actually keep on acquiring more companies, even though this one, this past classification tool for MRO data can be leveraged in their one-time system, but when they acquired the other companies and they can acquire 4/5/6 companies in a year, this tool can have the intelligence and can do this work next time faster every time you acquire a company, because that acquired company comes with the set of data.
So our tool takes that product data as the input and then process it after deduplication by building a taxonomy, adding additional technical and business attribution, and providing suitable values based on the manufactured part number, or just the description with the manufactured name, this helps our clients with a clear understanding of the parts with their attributes and the correct part number, so that they can plan correctly and with their usage and keep the correct inventory for saving their own money.
Sure. Let me take up one minute first to explain to you why we reached to this point to drive this using AI. So, as Abhinav already mentioned, that this is, I mean, I mentioned that, which is important, and Abhinav, we gave you a very great example. The point here is that when it comes to it, when, and we try to classify all these parts manually, that it takes a while. It takes so much work. Every person, even a data analyst, would have to go and search for each manufacturing part number or manufacturer name and search for any attribution, which takes like long time. So, what we thought, let's come up with a with a tool, and everyone is going for AI tools these days, and that's why we thought, let's start with the AI-driven part classification. If we kind of made, I'll use this word, a web crawler for you, a web crawler, but with intelligence in a way.
So, instead of manually searching for a part or their manufacture or their attribution, our tool is actually helping you crawl through the web, go to that web with that manufacturer part with manufacturer site, and then grab that detail. AI basically is fundamentally changing, as has changed over the period for both scale and sustainability of the solution. What has happened that using this natural language processing, we can interpret unstructured descriptions. I'll use this word again.
Unstructured description means that when these parts are actually listed in ERP or enterprise resource planning tools or any database system, their descriptions are not good. That's why we need some more details and something to collaborate the details about them, so this tool can now interpret those unstructured descriptions, standardize naming conventions, identify duplicates, and even record where they are different, they seem different, can still enrich any missing attributes, but the real value that it creates is continuous layer of governance with that. What I mean, governance is how you control the descriptions, the attribution, the technical details, and standards around these items and their attribution. So, instead of one time cleanup, organizations build a system where data is consistently interpreted, validated, and improved over time. That's when the impact becomes sustainable.
I'll give you one more example, that there are multiple of our clients that they actually keep on acquiring more companies, even though this one, this past classification tool for MRO data can be leveraged in their one-time system, but when they acquired the other companies and they can acquire 4/5/6 companies in a year, this tool can have the intelligence and can do this work next time faster every time you acquire a company, because that acquired company comes with the set of data.
So our tool takes that product data as the input and then process it after deduplication by building a taxonomy, adding additional technical and business attribution, and providing suitable values based on the manufactured part number, or just the description with the manufactured name, this helps our clients with a clear understanding of the parts with their attributes and the correct part number, so that they can plan correctly and with their usage and keep the correct inventory for saving their own money.
Speaker – Neerja - 09:09
That's great. You've actually shown us, or rather told us, about the many benefits and use cases of this, and that brings me to this question. And Abhinav, perhaps you can take this one.
Q: When clients start addressing this, what kind of outcomes do they see, and how does it change the conversation?
That's great. You've actually shown us, or rather told us, about the many benefits and use cases of this, and that brings me to this question. And Abhinav, perhaps you can take this one.
Q: When clients start addressing this, what kind of outcomes do they see, and how does it change the conversation?
Speaker – Abhinav - 09:30
I think when the clients specially start adopting AI-driven plant classification, the whole conversation, which was previously inventory management and inventory governance related, it shift to enterprise level efficiency and smart decision making. Right.
I think the first impact of the whole process is standardization, because AI automatically identifies in concession here data classification, it identifies the duplicate misname parts across, and like for example, I give you motor bearing 6205 is. If you have something bearing 26205 AI can recognize both the same component, right? That cleans up the data and create a very single tested view of inventory.
I think the second big change comes in is your optimization with duplicate and obsolete items already been exposed through AI, right? The capital, which is going for emergency procurement at Freedom, it reduces your shortage and improves your inventory terms, and the whole procurement becomes more intelligent, given a very enterprise-wide visibility of the parts, but I think the big change comes at the executive level. The MRO discussion, which was initially started, is tactical about how do we can clean the data, how do we can reduce duplicate inventory, how do we improve maintenance efficiency. I think that discussion becomes more strategic. Executives start talking about how can we build an intelligent supply chain, how do we can improve our operation resilience, how we can optimize manufacturing with end to end, and how we can do accelerate ERP modernization. I think this is the real impact of AI, which is far bigger than just being limited to the parliamentary.
I think when the clients specially start adopting AI-driven plant classification, the whole conversation, which was previously inventory management and inventory governance related, it shift to enterprise level efficiency and smart decision making. Right.
I think the first impact of the whole process is standardization, because AI automatically identifies in concession here data classification, it identifies the duplicate misname parts across, and like for example, I give you motor bearing 6205 is. If you have something bearing 26205 AI can recognize both the same component, right? That cleans up the data and create a very single tested view of inventory.
I think the second big change comes in is your optimization with duplicate and obsolete items already been exposed through AI, right? The capital, which is going for emergency procurement at Freedom, it reduces your shortage and improves your inventory terms, and the whole procurement becomes more intelligent, given a very enterprise-wide visibility of the parts, but I think the big change comes at the executive level. The MRO discussion, which was initially started, is tactical about how do we can clean the data, how do we can reduce duplicate inventory, how do we improve maintenance efficiency. I think that discussion becomes more strategic. Executives start talking about how can we build an intelligent supply chain, how do we can improve our operation resilience, how we can optimize manufacturing with end to end, and how we can do accelerate ERP modernization. I think this is the real impact of AI, which is far bigger than just being limited to the parliamentary.
Speaker – Neerja - 11:10
Yeah, thanks, Abhinav. I think we have an understanding now of what this means for the future of manufacturing, and I will turn to Neerav to give us one final takeaway, especially for everyone listening in.
Q: How do you think this makes a difference in the bigger picture?
Yeah, thanks, Abhinav. I think we have an understanding now of what this means for the future of manufacturing, and I will turn to Neerav to give us one final takeaway, especially for everyone listening in.
Q: How do you think this makes a difference in the bigger picture?
Speaker – Neerav - 11:30
Actually, great question. Because, see, this is exactly why these tools people can, I mean, multiple SI’s or multiple company IT companies are creating multiple tools, but we want to showcase the value of those tools, so because of that I would say that treat MRO data as a strategic asset, not an operational byproduct.
What I mean is that start with understanding your current landscape, your data landscape, and focus on building a scalable governance model, because see, usually I'll connect. This is a very big example. I'm giving you, like, a big in the sense is still in the world. 80% of the companies still don't know where their data is. Honestly, now they don't even have any master data management, even governance around any data, whether it's customer, product, or supplier data. So, first of all, understanding your current landscape is very important, and so what our tool helps you with this not only helps the business with cleansing of your data and building a item taxonomy to begin with, but also it becomes a repeatable process for the clients who acquire other organizations, as I already mentioned to you.
So I will close by the statement that the clients these days are gearing towards understanding their data, especially master data, and putting some governance around them, which is a good point of view for all IT companies like Birlasoft, because now at least they want to see, okay, where their data is, what done, what shape is it, then is it garbage or is it usable? That's where these tools come into play, and we are helping them to monetize their own data, because unless they know how the data is in what shape, they cannot even monetize or plan correctly or optimize their processes. I'll close by that.
Actually, great question. Because, see, this is exactly why these tools people can, I mean, multiple SI’s or multiple company IT companies are creating multiple tools, but we want to showcase the value of those tools, so because of that I would say that treat MRO data as a strategic asset, not an operational byproduct.
What I mean is that start with understanding your current landscape, your data landscape, and focus on building a scalable governance model, because see, usually I'll connect. This is a very big example. I'm giving you, like, a big in the sense is still in the world. 80% of the companies still don't know where their data is. Honestly, now they don't even have any master data management, even governance around any data, whether it's customer, product, or supplier data. So, first of all, understanding your current landscape is very important, and so what our tool helps you with this not only helps the business with cleansing of your data and building a item taxonomy to begin with, but also it becomes a repeatable process for the clients who acquire other organizations, as I already mentioned to you.
So I will close by the statement that the clients these days are gearing towards understanding their data, especially master data, and putting some governance around them, which is a good point of view for all IT companies like Birlasoft, because now at least they want to see, okay, where their data is, what done, what shape is it, then is it garbage or is it usable? That's where these tools come into play, and we are helping them to monetize their own data, because unless they know how the data is in what shape, they cannot even monetize or plan correctly or optimize their processes. I'll close by that.
Speaker – Neerja - 13:18
That's a great note to end on. Neerav Abhinav, thank you for giving us this detailed view, because a tool is only as effective as those who know how to use it well, right? And you've given us that perspective, so thank you for all this knowledge sharing. It was great having you on the show.
That's a great note to end on. Neerav Abhinav, thank you for giving us this detailed view, because a tool is only as effective as those who know how to use it well, right? And you've given us that perspective, so thank you for all this knowledge sharing. It was great having you on the show.
Speaker - Neerav - 13:34
Thank you so much, Neerja.
Thank you so much, Neerja.
Speaker – Abhinav - 13:36
Thank you, Neerja.
Thank you, Neerja.
Speaker – Neerja - 13:37
I'll end on this: MRO master data may not be visible, but it directly shapes efficiency, cost, and decision making. As manufacturing becomes more complex, data integrity will define operational performance. Thank you to everyone listening in for joining us on Tech Lyceum. I'll catch you on the next one.
I'll end on this: MRO master data may not be visible, but it directly shapes efficiency, cost, and decision making. As manufacturing becomes more complex, data integrity will define operational performance. Thank you to everyone listening in for joining us on Tech Lyceum. I'll catch you on the next one.
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