Baskar Radhakrishnan: 0:03

You know, you have the data now. But how do you infuse intelligence into this data so that it becomes an actionable insight. It provides an actionable insight for your manufacturers for the customers.

Tom Raftery: 0:20

Good morning, good afternoon or good evening wherever you are in the world. This is the digital supply chain podcast, the number one podcast focusing on the digitization of supply chain. And I'm your host, global vice president of SAP. Tom Raftery. Hi, everyone. Welcome to the digital supply chain podcast. My name is Tom Raftery with SAP. And with me on the show today I have my special guest, Baskar Baskar. Would you like to introduce yourself?

Baskar Radhakrishnan: 0:47

Sure, absolutely. Thank you, Tom, for having me. It's a pleasure having to be in this podcast. So my name is Baskar Radhakrishnan. I am from NTT DATA and part of our manufacturing business on it. So our group is primarily responsible for the business strategy, and as well as the business transformation initiatives. So primarily, we focus on industry for auto supply chain, workforce transformation, and the customer excellence. So these are the primary areas of focus. And I have been with NTT DATA for a little over 15 years now started off as a supply chain and the transformation procurement transformation leader, I ran the practice SAP supply chain procurement practice for a while, then I moved to this current role about three years back. So look forward to the conversation, and thanks for your time as well.

Tom Raftery: 1:40

Thank you. So NTT DATA, I'm assuming there's a big data component to what you're doing. What what is the? I mean, we can all guess, but in your opinion, what is the big benefit of data in supply chain?

Baskar Radhakrishnan: 2:02

Absolutely. So that's data is a very interesting topic these days, right. So as you can imagine, big data is one of the probably the hottest topics in the technology world today, right, the CEO, and as well as the, you know, the chief, Digital Officers and information officers are all tasked to focus on an initiative to exploit the vast amount of data they have within their organization to transform how they do business. So the ability to gain insight into the data, you have make a huge difference today, in everything you do, from the experience you deliver to your customers to the efficiency of your business operations. So obviously, supply chain creates a massive amount of data. In fact, you think of it like a data lake by itself into which information constantly flows from various systems, like including your you know, ERP systems, which includes obviously, the accounts payables and receivables and EDI and business to business, no integration, warehousing, transportation, logistics, and so forth. So and obviously, from your customers and suppliers. So being able to monitor, measure and analyze the data, in a real time environment will deliver the tangible monetary value for your organization. So that's all we're going to discuss today.

Tom Raftery: 3:24

Okay. I mean, it's hugely, hugely challenging for organizations, this sudden influx of data and integrating that data as well. I mean, a lot of these are coming from disparate systems. So how are how are organizations managing this sudden, you know, deluge of data?

Baskar Radhakrishnan: 3:47

Absolutely. So, as you said, you know, applying analytics to a set of data, especially in in supply chain is still relatively new, right, as the technologies for, you know, intelligent analysis, and data visualization explored, it is probably a great time to look at what your business can achieve through a comprehensive supply chain analytics and analytics strategy by itself. So obviously, we'll we'll delve deeper into some of these aspects as we go through the discussion. But at a very high level, I would say supply chain is probably one of the areas that the manufacturers are really focused on today to completely transform the business operations, especially in the manufacturing or automotive retail, or the FMCG, the fast moving consumer goods side of it, and the information technology. So we'll talk about those details. Definitely. It's a very exciting area to you know, focus on when it comes to supply chain.

Tom Raftery: 4:48

Okay, and what kind of key metrics should organizations be watching out for or managing,

Baskar Radhakrishnan: 4:54

right so supply chain essentially has two main purpose Right one as it allows business to identify, diagnose, and correct inefficiencies, or probably reduce the wastage in the supply chain function, right. And secondly, it also enables the business to use supply chain data to identify, prioritize and address business opportunities. So ensuring that you're measuring and reporting on the correct metrics is key to improve the business operations or the business performance. So essentially, if you look at, you know, for the key metrics, you know, the first thing, especially coming out of pandemic, certainly the organizations are looking to create the resilience in their operations, right. And the second thing is, you cannot measure something unless you know where the problem is, right, unless you know where the issues are. So obviously, the operational visibility within the supply chain function has become extremely important. So I see that customers are looking at understanding the risk, understanding the supplier risk, and the let's say, if their suppliers are impacted with any of these events like COVID, or any of the other external events could be weather or it could be something else, or, you know, some kind of political unrest in the other side of the world. So the ones one thing which the customers are really looking at is how do we create a resilience? And how do we where is the risk and what is the second or alternate source of supplier should look for if there is if my tier one suppliers are impacted? So that's the second side of the equation. But if you look at overall from a supply chain perspective, you can apply this in many different areas right, for example, demand forecasting, yes, traditionally, there are lots of you know, ERP systems and the MRP planning systems, which you know, and also the demand forecasting systems which are available. But what is required today is not just rely on your internal data, which is the enterprise data within the data within the four walls, but you also need certain external data, where you can consider that when you do your forecasting process, right. So, obviously, the modern forecast analytics, use existing data to predict the volatility of a particular product line or a stock item. But the best result usually comes from the large pool of data of that covers a number of different data sources, which could be outside of your, the four walls of an enterprise as well. So back to my point, forecast accuracy is another one, the second one is about increasing the profitability of your organization, by reducing the inventory levels or optimizing the inventory levels, that's probably the right word to use, right too. So companies really want to obviously, you know, want to increase the profitability. So there are two ways to go about it, right? One is, obviously to increase the sales, right to increase the absolute revenue, or to reduce your cost and increase your profit margins. So having those analytics in supply chain will significantly impact or give you that result in terms of, you know, understanding your, you know, inventory levels, or monitoring your, you know, company performance, and, and so on, and so forth. So that's the another aspect that you could you could look for, and obviously, the moment you are talking about inventory levels, you know, working capital, you don't want to carry too much of an inventory that will in turn directly impact your working capital, right. So how do you optimize that function of it? And the second function, the other function is, in the risk management side of it, be it on the supplier level, or even the customer? Right? Understanding the customer churn, right, let's say, if I'm going to improve my product cost by X percentage, what is the impact on my customer that is likely to cause a churn? Right? So those are number of parameters that you could look for, and be able to derive insights and take an action and act on that?

Tom Raftery: 8:48

Yeah, the inventory one is always a tricky one, particularly now in times of disruption. You know, people were running very lean up until last year, and then they got cut short, and there were shortages on shelves. So how do you decide now lean, not lean? How lean? Where does all that breakout.

Baskar Radhakrishnan: 9:10

So inventory is definitely a very, you know, complex function, because that ties back to a lot of different parameters within the company, right? You cannot carry, you don't want to carry a stock where you end up running out of stock. And that will also impact your productivity. And at the same time, you don't want to carry a stock that is too much of an inventory, which will also impact your working capital, right. So one of the ways that I have or we are seeing customers today, especially in a very expensive and sophisticated manufacturing environment, you know, the more options you allow customers, I mean, you see this today, the configurable options for the customers to let's say that the hundreds of individual components gets into a product and you're also leading are asking your customers to go and build certain product or build custom products based on several permutations and combinations, right. So the final configuration of a product is not often known until close to submission of the order of that product. So manufacturing companies need to have a significant will end up carrying significant excess inventory on hand to be able to fulfill their orders on time. So over the years, if you look at it, the manufacturing companies have deployed, you know, several Material requirements planning, in other words, MRP software and solutions that supports the planning and automated inventory management. However, most of these inventory software solutions, were not designed to optimize, quote, unquote, optimize inventory levels, by learning continuously from data. That's where it is important. So you know, you have the data now, but how do you infuse intelligence into this data, so that it becomes an actionable insight, it provides an actionable insight for your manufacturers for the customers. So that's where you know, you need an analytics platform like this, which can give you a real time monitoring and notification, right view inventory, metrics in real time, on on any devices to identify any anticipated issues with inventory levels and analyze the root cause get notified when certain KPIs exceed certain thresholds, you know, optimize the stock levels, and get the summary of, you know, summary view of all the inventory items for the operator so that they can go and make an action on time.

Tom Raftery: 11:36

Okay. If people are listening, and they're interested in, you know, enabling a data driven supply chain, one of the best things they can do are one of the things they should avoid, you know, what, what's kind of a good roadmap?

Baskar Radhakrishnan: 11:52

Well, I don't want to go too prescriptive, but at least I can give my you know, guidance, right. So it is like a crawl, walk and run approach you you cannot be running on day one. So this is a journey. So in order to do that, first thing is you need an answer for certain set of questions. Number one is what do I need to measure? Right? And where is my data? That's very important. Who should I be involved in other stakeholders? And finally, What software do I need? And how do I measure success? Right? So these are the basic questions that one would want to be very clear about. So for most organization Analytics has traditionally consist of mainly looking for historical data in spreadsheet, right? There's nothing wrong in that. But it's just that it is a very time consuming and monotonous process. So definitely, you need to have an answer ready for these questions. So let's talk about the steps very briefly light. So for example, you got to identify the problem business problem first, right? Identifying the problem, and the KPI metrics that will be used to analyze and address the problem is certainly the key, right? Perhaps you need to release more cash from your supply chain, or reduce inventory levels, or address challenges you're having, you know, with on time or in full time in full orders, that are affecting your customer relationships, right. So the number one is identify the problem. The second is about, find that data, where is your data, right? Especially talking of supply chain, you know, if you're going to gain an end to end visibility of your supply chain, you need to break down the silos. When I say silos, it could be processes or it could be the data as well. Right? So go out to various departments and ensure that what analytics activities they are going they are currently doing, and find metrics that are currently measuring that that departments are measuring. So understanding where the data source is extremely important as well. So that's step number two, I would also say, choose the right team analytics cannot be done. Just with a single threaded team, you need a multi functional, multidisciplinary team in order to succeed. So in addition to the IT staff, and data analyst, you may need active enthusiastic involvement from different business departments as well. So you definitely need an executive sponsor to support this, ensure that you identify the data owners and the internal data users who actually own the data, and make sure that you include your trading partners as well into this mix. So that's about the data. And then finally, or maybe the step number four is to select the right tools and technology, right. So there are a number of tools and technologies which are available today in the market. You certainly need to have a good understanding or a fairly good understanding of the set of tools that can be used in order to solve this business problem. And step number five is as I said in the beginning of the call, start small and then think big, right? That's always the, you know, strategy that We use stripe. So implementing analytics successfully is not a simple process, right? It takes time and effort to create correct data structure and technical infrastructure to enable the factor analysis. So, as the saying says, right, don't try to boil the ocean, right. So that's very true. When it comes to analytics. And the last but not the least, measure success, right? You must be able to demonstrate the benefit of your analytics initiative. However, it can be tricky to identify the benefits. So especially especially the financial ones, so when your analytics investments are designed to deliver operational areas, such as operational efficiencies, you'll certainly be able to measure the success. So make sure that you identify those KPIs, while showing the financial benefit, will always be the most important way of demonstrating the success of your analytics initiative. So with that, these are the six steps guiding principles or the process that we follow, and all of our most of our engagements and advise our customers as well. We'll take a pause for questions.

Tom Raftery: 16:04

Okay, cool. where to from here. I mean, let's say you're a company now has enabled a data driven supply chain, what's next? What comes after that?

Baskar Radhakrishnan: 16:17

So as I said, In the beginning of the call, you know, Tom, this is going to be this is not the endpoint, right? So this is the journey, you always have 1000s of ways to create, build more innovation and automation capabilities on top of, you know, if you really achieve the state of the ultimate state of, you know, visibility in the supply chain operations. So obviously, the industry is aspiring towards autonomous supply chain functions, right, which is completely touched, let touchless be able to mimic the behavior or water, right, with the supply chain digital twin, we are actually working on a very large initiative on that. So mimicking the behavior of your entire supply chain function with a water for analysis. So once we have the data, once you have the plant forms in the data lake, you have the analytics, the next logical step, you could also look at creating that digital twin of your entire supply chain, and build more automation, right, let's say if I have an equipment failure. So what is the next course of action, you could automatically go create this service request with a platform which can go and you know, schedule a maintenance, right. So and then if there is a maintenance activity, if they are running out of safety stock, then you could go and, you know, automatically initiate the purchase order or requisition process to fix that, and procure that process as well procure that material as well. So the possibilities are endless, but I would look at, as I said, you know, crawl, walk and run. So once you're in that state, then you can certainly look at mimicking the behavior of your entire supply chain function with a digital twin, and be able to improvise that data on top of it.

Tom Raftery: 18:00

So okay, and is that the the future supply chain? Is it kind of autonomous systems working away?

Baskar Radhakrishnan: 18:07

Autonomous, completely touch touchless, there is a concept of, you know, life's out services, right operations, where you make everything work seamlessly, by automatically integrating with the training partners, and as well as your customers, and bring in more automation to it, and make it more intelligent to make decisions. Right. So that's certainly a future. But again, it's a long way to go right in short time, so many companies today want to improve their business decision decisions by applying analytics to their corporate data, right. So it, you know, we still have a long way to go. Because think about it, you're not just looking at the data within the four walls of an enterprise, you also have a lot of reliance on the external source of data as well. So get to that level of maturity will probably take maybe a few more years, but I think the industry is, you know, aspirating towards that right now.

Tom Raftery: 19:01

Okay, super Bosco. We're coming towards the end of the podcast. Now, is there any question I have not asked that you wish I had, or any topic we've not addressed? That you feel it's important for people to be aware of?

Baskar Radhakrishnan: 19:15

No, I, I think we covered pretty much. So in order to sum up, right, so we spoke about different approaches, you know, the data sources, and then the team readiness, and then ability to gather data from different sources and then build a fully autonomous supply chain function. And along the way, obviously, there are certain steps that you no one would want to take to achieve that state of reality. But that's what we spoke. I think we pretty much covered everything so

Tom Raftery: 19:45

far. Okay, super. That's great. If people want to know more about yourself or about NTT DATA or about any of the topics we discussed it in the podcast. where would you have me direct them

Baskar Radhakrishnan: 19:58

so you can reach out to them, I will share my email ids and my coordinates. You know, as a subsequent as a follow up to this conversation. You know, feel free to reach out to me for any questions related to supply chain or any of the transformation initiatives around supply chain. We are more than happy to help with this journey.

Tom Raftery: 20:17

Right. Great. Baskar. That's been really interesting. Thanks a million for coming on the podcast today.

Baskar Radhakrishnan: 20:23

Thank you, Tom. It's a pleasure talking with you and with your audience and pleasure to be here. Thank you.

Tom Raftery: 20:29

Okay, we've come to the end of the show. Thanks, everyone for listening. If you'd like to know more about digital supply chains, head on over to sa p.com/digital supply chain or for simply drop me an email to Tom Raftery at sa p.com. If you'd like to show, please don't forget to subscribe to it and your podcast application of choice to get new episodes as soon as they're published. Also, please don't forget to rate and review the podcast. It really does help new people to find the show. Thanks. catch you all next time.