Amity Institute Of Training & Development

Using AI to build cognitive (futuristic) supply chains Read Time: 38 mins

Speaker: Dr. Rahul S Dogar, Co-Founder & Managing Director, Holisol Logistics Pvt Ltd

About the speaker - Dr. Rahul S Dogar is the Co-Founder and MD of Holisol Logistics Pvt Limited. He has over 20 years of experience in supply chain domain, handling supply chains of Global and Indian Retail and Automotive brands, international and India operations, Strategy, Business Planning, New Set-ups, Business and People Development. Dr.

Rahul S. Dogar: Very well warm welcome to all of you, Friday afternoon. All of us or few must be looking for the weekend now. Before we jump into the topic straight away let me prime it with some challenges that all of us Supply Chain Professionals can relate to, the challenges that we face in our lives. There is an increasing complexity in the supply chain. We all see that there is a race to please the consumer and in that race there are lot of things happening which is making supply chains more and more complex, is a SKU proliferation with a view to provide unlimited choices to the consumer. There are more sourcing options available to us, there are more selling options, multiple channels to sell options that are available to the companies today. And on top of that there is a race for instant gratification which needs to.

Click below to watch the recorded conversation.

 All this is driving a huge complexity in the supply chain which we as supply chain professionals must behave in day-to-day basis. And then on top of that is the accuracy of our forecasts. This has been a question out there for so many years and this remains a question. Everything, all the planning that we do in the supply chain depends on how accurate the forecast is, and when forecast accuracy itself is a question, then you are left to deal with the fluctuations in the forecast and handling them on a day-to-day basis.

Then there is a lack of visibility. We all want to know as to what is happening in our supply chain, where is our inventory, where are our products etc. But because of not all the supply chains being digital or being able to throw out that data and even if that data is coming, the sources of data are quite disparate, you are leading to the lack of real time visibility coming in from the supply chain.

Then of course, there is a bigger question of managing the inventory across the supply chain, allocating the inventory across the supply chain, and hence you know trying to optimise. With these kinds of fluctuations and with these kinds of complexities, it always remains a challenge for a supply chain manager as to how to allocate the inventory across the channels and get the best out of it. And then of course the million-dollar question – Balancing, doing a tightrope walk. All the time to balance the service levels, provide a great product availability and then the costs associated with it. So, this is a balancing act that we as supply chain professionals are doing, day in and day out.

And all these challenges bring in a lot of hustle and bustle in our daily lives. We continue to remain busy throughout the day, we get calls late at night. The events like Covid disrupts and turn our lives upside down with a lot of expectation from everybody for us to be out there and do things and change the scenario and make us still get back to the normal as soon as possible.

So, what's the solution, is something that we all need to think about. Is there something that can be done which can help us make our lives better. That's where the Cognitive Supply Chain comes in. Definitely, there is a lot of work that can be done around the supply chains to make them much more cognitive and what do I mean by cognitive, you can liken it, I always give this example, you can liken it to child growing up. As a child when he is growing up, there is a lot of input that is given to the child, to make the child learn. The parents, siblings, people around it, inanimate objects. Kids are getting lot of inputs as they growing up and then they use that input to essentially build on the experiences of their own and then of course build it into their own learning rather than following what has been told to them. A simple example is that the parents would always tell you not to touch something hot. So, that's a generic statement but over a period, the kid even if the parent is telling not to touch something hot would end up and touch it and over a period build intelligence around it as to how hot can be touched and how hot cannot be touched. And what can we touch with the hand for example, what can be touched with the tongue etc.

So those all become a part of our learning, understanding, and using all that learning and understanding we start predicting, adjusting, and acting. And that is exactly what Cognitive Supply Chains can do, can do with the help of technologies that are available now, with the help of these new technologies, we can start getting all those inputs. We all know that the smartest or the most cognitive kids out here are the ones who are seeking a lot of information through talking to people, through observing things around them, through reading, through having discussions and conversations.

So likewise, if we are able to build our supply chains and give it lot of inputs and use technology to analyse those inputs then we can definitely build supply chains which can almost mimic the human brains and that’s what are the cognitive supply chains. These are the supply chains which can think on their own, which can learn on their own, they can start predicting, they can start adjusting and start acting. And some of the things with cognitive supply chains are that they are highly aware, because of the computational ability that they have using the new technologies, they are in a very heightened state of awareness, they know everything that is happening around them, they have inputs coming in from all the directions, from their internal environment, from their external environment and they don’t need rest. I mean these are the machines. Like human, they don’t need rest, they work 24 x 7, and they have these inputs coming into them 24 x 7 which makes them highly.

Using those inputs, they can anticipate, adjust, and adapt. And then eventually what we believe is that they will also become autonomous, once they have built enough learning of their own that they are able to visualise different scenarios and prescribe that this is the best scenario, this is the best decision to be taken, this is the best action to be taken, they will become autonomous and start taking decisions on their own. So, these are the cognitive supply chain that I am talking about which come handy, which can as Brig Sharma said earlier, can help us can help us improve our performance and most importantly make our lives easier and simpler.

So, the advantages that can come out of building these cognitive supply chain of course help us overcome all the challenges. We can make much more accurate decisions with all these inputs and data coming our way. They can be automated monitoring; we don’t need to be on phone all the time or we don’t need to continue follow up with the stakeholders in our supply chain. There can be automated monitoring, there will be automated alerts that will get generated. We can be much more proactive in the way we work around; we would know what will happen in the coming times, what is it that need should be done today. We can look at optimising our supply chain of course to provide the real time visibility and at the end of the day, it will lead to improved customer satisfaction and of course higher growth for the business with satisfied customers.

So why is that with so many advantages, it’s not happening as fast. So, let's look at the present scenario as it is today. Where are companies when it comes to building cognitive supply chains. About 70% of the companies are still at the descriptive and diagnostic analysis. If we still look at the data and most of this work is happening around the lagging indicators or at the best what is happening at the present. This analysis is done around what is happening currently not with what can happen in future. 15-25% companies have moved to predictive analysis where they are using Sophisticated Predictive Modelling, Statistical Techniques or Mathematical Modelling to be able to say as to what can happen in the supply chain in the future with different factors that can have an impact on the supply chain.

The best thing is that about 5% companies are moving towards is, what we call as Prescriptive Analytics and that's where the full value of the cognitive supply chains kick in. Predictive can still tell us that this is what possibly will happen in future but Prescriptive is telling us that if this is what will happen in the future, what is it that needs to be done, what is it that can be done and that is where these supply chains will work with least human intervention and start becoming autonomous and start acting on their own.

But yes, there are challenges to get there, these are some of them, the biggest one is that there is a shortage of talent. It's not that any of these things are not doable but there are not enough data scientists and AI specialists out there. This is a relatively new field, new skill that people are still learning. So, the availability of professionals is a challenge right now. There are not enough people again to implement and manage. People needed with similar skills with the understanding of the technologies that need to be implemented within the organisations. We also see that a lot of companies work with legacy system that there is a now a way of working with those legacy systems which played a huge cultural roadblock per se within the organisation to accept these technologies and adapt the new technologies. Then availability of data, yes as I said in the beginning, availability of data is a new challenge and not all our supply chains are digitalised. So, the data is available, there are blind spots within the supply chain which make it difficult for us to have the data available for the analytics and cognitive supply chains. And then of course there is also a lack of management commitment.

Not all the companies or all top-level executives are convinced, and it takes a lot for them to be convinced about that this is the future, and this is where we should put our money in. So, these are some of the challenges which are being faced right now which can help our transformation quickly into building the digital and cognitive supply chains. But yes, the prerequisite that we need to have to be able to get there is to first build digital supply chain, this is a precondition. Without having a digital supply chain, you would not be able to build cognitive supply chains.

So, what do we mean by having a digital supply chain? Of course, we all know that what is the physical supply chain. By digital supply chain we mean that we build a digital twin of physical supply chain, we get all the data coming in from physical supply chain, about the product, about where they are, in what condition they are etc in a digital form and then nowadays there are technologies available across, plenty of them available across which can be deployed, or used in the different components of supply chain for us to be able to convert the existing physical supply chains into the digital supply chain. And then once we have the data coming in from these digital supply chain using technologies like big data, AI, machine learning this data can then be analysed and used to convert into the insights, predictive analysis, causal analysis, get the real time visibility, improve the forecast, generate insights etc.

There's a lot that can be done with this data, which can help us to really make our supply chains cognitive. In today’s scenario I would say there is still a lot of action would have to be done by the humans, we are still at early stages, there is still a lot of proof of concept that needs to be built in. And the reason also at this point of time for the human intervention is that there is still a lot of input that needs to be given to these technologies, to these systems for them to become smarter. I would say that they are still infantile, they are still smaller child right now, they are still not adults who can think on their own, they still need that input right now from us for them to become self-learning intelligent and smarter. But yes, in the future we believe that in next 5-10 years these systems will become intelligent enough, they would know what needs to be done and would become autonomous, would start taking their own decisions and would start acting on their own.

How does it work really in the real world - So what happens when we go about building these AI enabled cognitive supply chains is that we tell the system that this is a supply chain decision making framework within which it has to work, coming in from the company's competitive strategy business model, what is their priority etc, there is a framework and this is also, I am not saying that this is a standard thing, this can change for different companies. It really depends on what a particular company wants to achieve but coming in from all that, the system is trained on this framework within which it must work. And then of course the system is told about what are the KRAs, what are the different components of the supply chain, what are the KRAs and KPIs of each of these components and then their huge relationships built as to what gets impacted by what. And how does one thing lead to other into what we call ‘Neural Network’. This is something which is very similar to how our brain functions. These Neural Networks look at the external factors, they look at the internal factors, you find the relationship, causality among the different factors. And then over a period as we go about it this starts becoming stronger and stronger, it's a child now growing up with more and more inputs coming their way.

The child is now growing up and have started learning on its own and that’s where the initial work with the system, the intensity of giving inputs to the system, the intensity of the discipline of transferring our own human intelligence onto the system by the intervention and inputs becomes much more important and much more relevant. If we must derive maximum benefit out of the cognitive supply chain, we can very well have these systems implemented but if we don't really interact with these systems, if we don't really give them the inputs then it’s a dull child growing up and money going down the drain.

So, this is how these systems work, this is how these things can take us towards building cognitive supply chains. Let me further illustrate it with the help of few examples. If they are coming in from the different components of the supply chain, just to highlight as to what kind of benefits have come in from different parts of having used AI enabled certain parts of the supply chain. Right now, I don't have an example from end to end. It's still both in progress, the management wants to see the proof of concept before they put the money on the table. So, these examples are from the company's wherein taken a leap of faith on head and done something in some parts of the supply chain.

This example is from a global retailer where they monitor their sourcing operations, just sourcing operations where goods are being contract manufactured. And these are the examples of insights that the AI enabled system has thrown out. Otherwise you have to take a big team Army or big army of people or analysts to come out with these kind of insights, but using AI enabled system these insights come in within the seconds and they continue keep coming in. So, this is an example of pushing operations of a global retailer where you know they want to monitor what is order cancellation happening, what is the deviation in what was ordered versus what was delivered, house inventory situation and stuff like that and then you know the system goes to the next level, as I said, it is still in the learning phase, it is seeking input from the user of the system using human intelligence to make it more smarter. So, it gives data in support of specific insight, says that ok fine, for the insight number 11, what is the reason that I have brought in this insight. And then further goes deeper down into what is the correlation and what is the causality, why is it happened? And how's it correlated and this is all for seeking the human input, the system has an option for the humans to really intervene and say okay I agree with this I don't agree with this, these are the additional things that need to be seen. These are the additional factors that need to be considered. These are the additional constraints which need to come in etc. and that is all a part of the system, then taking all those inputs and becoming smarter on its own. This is another example of an Indian retailer.

This component is from distribution centre to the store. So here if you look at it, the system is saying that there is a possibility of sales of reduction. It's a predictive insight. And it is also giving as to why this would happen. And what is it that will get impacted. So, SKU wise, which are the SKUs which can possibly face to shortage. Another insight is that there is an opportunity for transport cost reduction because I am seeing that this transporter is charging this much on this specific route. Whereas the other transporters, if I look at other transporters are charging that much and this the mileage stuff like that. Another one is on inventory rationalization here again it’s a predictive and prescriptive insight. It is saying that ok fine I see that so many pieces of 17 SKUs have crossed 45 days and there's a possibility of these going into obsolescence. So, these are the stores, wherein you can push these SKUs and get rid of them or sell them in the next 30 days. So, this is a part of the prescription.

Here again this is a sales loss prevention that, yes, this came in 2019 we had this famous Odisha cyclone coming in, so the system really predicted that because of this cyclone there will be a traffic disruption on NH 16 from this date to this date. And then it is recommending that you should rather build up the stock in Hyderabad and the supply from another Depot will get impacted, beautiful example of predictive and prescriptive both. Likewise, this is another example of how to prevent service level failure.

Next example is about the fraud detection and this is another area where AI can help. So here in this example, what the system has done is that it has studied the ordering pattern of individual customer in normal situation and suddenly the system sees that there is a change in pattern. The way this consumer wash shopping earlier has suddenly changed. And then this consumer is now shopping more frequently, all orders are prepaid and then you know finally what came out was that the consumer is using the same promo code again and again to buy something and what came out was that there was an idea leading to multiple use of the onetime coupon. And this is something which this consumer has been able to figure out and was really hacking the system. And this is another example where the AI help improved the forecast of the fashion retailer.

You see the forecast accuracy improved from 65% to 90% for the regular style, which is very high and 50% to 75% for the seasonal styles been very high. So, what was happening earlier was that this fashion retailer was using time series method for forecasting which was built into the machine learning algorithm. And there were lot many other business specific factors that were brought in the consumer characteristics, consumer segmentation, the price points, the fashioning profile etc. So, this was done in a very specific way for this fashion retailer because they had a very different business model. And that's where the machine learning algorithms was able to do much better modelling and come out with a much more accurate forecast. Of course, the initial savings came out to 1.84 billion dollars.

This next example is of a slightly wider supply chain where the customer had digitalized the upstream supply chain as well. Here the customer wanted in fact the insights coming in from the right from the raw material procurement, going over inventory, capacity allocation within the plant, and within the plant on different lines and there were multiple plants and stuff like that. Here, the benefit also was that the supply chain was even was digitalized even up to the upstream suppliers using good sophisticated ERP systems and there were other systems also being used. So, in a way, there were much better insights that that were coming in and much wider insights that were coming in right from raw material, going over to the inventory and the capacity allocation. So, the system is continuously now here looking at the forecast or the sales forecast deviation or the sales trend or even going up to the social media to see what is trending, what is being spoken about the product of that particular company and predicting as to what can be the impact of those discussions happening. System even went on to sort of look at the competitive data, what are the new models that the competition is launching and what can be the impact of launch of those new models on how we should produce. So, looking at all those factors there was a continuous change in the capacity allocation, which was happening among the plants, and within a specific plant among the lines, all lines could produce all kind of models. So based on these signals coming in from the market, the capacity allocation would get adjusted. And then likewise going back up to the raw material procurement, the whole lot of insights that would get generated and just to make sure that there was seamless fulfilment happening across the supply chain. So slightly wider example of getting insights, AI enable insights from the supply chain. So those were some of the examples hopefully you have been able to understand the use cases right now prevalent I would still say that these are early stages, a lot of work is in progress. But yes, we started seeing the results on the ground. Yes, digitalization per se, is one theme that a lot of companies have picked up, lot of companies have allocated budgets, there is a huge management buy in, at least to take the first step towards digitalizing their supply chain and then of course, once that happens once more and more supply chains, start getting digitalized. I am sure the use of these new age technologies like AI, ML and Big Data also start coming in to get the benefit of the data that is being generated by these digital supply chain. And that’s the next step. Some of the companies, as I said earlier are early adopters and these are the examples coming in from those set of companies who really think that these technologies are must adopt and they will change the way they operate.

This next slide is a little bit on the funnier side but you know at the same time, this is the reality we fast forward to 2025, we would realize that COVID-19 is one event, which really forced, at least, if not, digitalize our supply chain but at least start thinking in that direction and we have seen in the last three four months of this situation that the companies which were digital first or higher up in the digitalization effort, were able to actually quickly adapt. Maybe they change business models or pivot quickly because they were sitting on the data, they were sitting on the data that coming in from their supply chains and were able to quickly analyse the data and say okay fine, this is what we need to do in this scenario and these are the companies who have come out or emerged with us in these situations, a lot of them pivoted, a lot of them started new businesses, a lot of them started doing different things etc to really come out of this situation. And the situation continues but we are seeing that companies which were more digitally adapt have been able to do things quicker and faster.

This is a credo, which I very much like. We live by this freedom within our organization and all of us as supply chain professionals have to start living with this credo, whether we want it or not but this change is coming, the digitalization is going to become a part of our lives. We need to start looking at bringing in more data from our supply chains and start looking at the entire data to help us to improve our supply chain and you know eventually move towards building these cognitive supply chains, the thinking supply chains or intelligent supply chains, you can call them by different names. And within our area of responsibility, within where we are working for the customers, either for the customers or for our own companies. So, this is something that all of us need to get into and become demanding from our suppliers from our ecosystem. That's all from me. Thank you very much, ready to take any questions.

AITD: Thank you very much for such an excellent exposition on cognitive supply chain. You have really simplified, a very highly technical subject, but it is something which we must face that is a future, especially for people in the logistics. We have got some questions from the audience. I will start with the first one.

The first question is:

Which businesses stand to gain the most by adopting cognitive supply chain is it really for the multi brand giants, or normal small and medium who are the businesses which are stood to gain the maximum?

Mr. Rahul S. Dogar: I would say that all businesses, stand to gain from building the cognitive supply chains. Of course, it really depends on the ambition of the leadership and how quickly they want to be ahead of the curve. But yes, if we say that businesses which have a lot of complexity already there and the businesses which are operating at a huge scale, the ones who gain the most at this point of time. Of course, there's always a question of getting the ROI, I would say that technology adoption or technology providers face that question all the time. So yes, in the current scenario I think the businesses, which are scaled up and have a lot more complexity can bring in the ROI fast track.

AITD: - But I am sure as the time goes by, every will reach for it. The next question is from Mr. Aditya Gupta who is in the audience. He asked a question which you have partly answered already, but I would still pose it to you. He asked:

Can you share some examples of predictive and prescriptive analysis which these systems can generate?

Mr. Rahul S. Dogar: I have covered some of the things like the prediction of cyclones coming in Odisha. Every year we have these heavy rains coming in Mumbai. Then there is a lot of stuff happening. So, what is currently happening is that as supply chain professional we still able to look at internal things and do some planning around that. But external events, for example COIVD for that matter, it’s an external event, no body took it seriously in that sense. So, there are a lot of such things happening in the external environment, which can cause disruptions to our respective supply chain. And these new ways technologies can go out and develop a correlation and causality with those events, tell that this is what is coming. This is the buzz that I am seeing somewhere in the news or on the social media. And then there is a possibility that this buzz, this news, this data can have a disruption, on how you plan your supply chain. So, things like the competitive activity for that matter.

What can happen with the weather, where can the floods come, where can the cyclone come, where will be the heavy rains. Things like even for that matter, very high summer I mean, the fashion retailers need that input that, it's going to be 40 degree plus in, let’s say Delhi or Mumbai for the next five months, so don't plan for the monsoon season, produce more of the cotton shirts. So those are the things which can be predicted using these technologies and of course, as they become smarter, they will start prescribing also.

AITD: There is another question which has come from the audience. This is more related to the warehousing. So, he wants to know:

Can you please share some areas where AI can impact warehousing? You have partly done in your presentation, but can you give little more about the impact of AI on warehousing.

Mr. Rahul S Dogar: Yeah, warehousing is one area where you can look at in fact each process in the warehouse and you can find a use case for the AI. So let's say inventory optimization, how do you store inventory within the warehouse, how do you optimise the space, which are the SKUs that you maybe need to store far away because they are not selling as much and which other SKUs you need to store close by and bringing in a predictive element in it. I am not saying this doesn't happen today, it does happen where this ABC analysis is done when you are planning the storage in the warehouse. But are we doing it on the real time basis, are we doing it in a predictive way right now, no.? So what AI can do is to bring in those signals from the market who say that it looks like that these are the SKUs which may sell more in the future, in the next seven days, in the next one month so it makes sense to bring them closer from where the picking can happen faster, so that we are more efficient and we can fulfil faster. We can now fulfil the order faster. It can go even as granular as looking at the data of people working in the warehouse and say okay this person remains absent most of the times on these dates, the team is able to deliver this productivity between these hours of the day, this team is more productive in this, this team is less productive in this, this human being is more absent, so all those things can be built into and can be used to improve the overall efficiency and effectiveness of the warehousing operations. Similar things can be done in the picking strategy where AI can really let you know which picking strategy to use at what point of time, for what kind of products, which picking strategy would be most efficient at any given point of time.

Right now yes we do plan picking strategies but they are most static in nature so you decide in the beginning and then say OK fine this is a picking strategy that we are going to follow for the rest of the life of this warehouse or maybe you change it once in two years but with AI coming in you can make it more dynamic. The whole idea of bringing in AI in the supply chain and using it for predictive and prescriptive analysis is that we can align much better with what is happening in the market. Eventual aim of the AI of the supply chain, eventual objective of the supply chain is to make sure that there is a seamless fulfilment and there is a product available in the market must be sold. So, lot of use cases for that matter.

AITD: Thank you, I think that is very well answered. There is another question which is coming from the audience.

What are the challenges to data integration from various customers in the supply chain are there any legal issues in getting the data, are there any problems one can encounter in data integration?

Mr. Rahul S Dogar: So data integration firstly not all the players in the ecosystem are digitally enabled, not everybody is adopted the technology so they are not using as much data or they not at some of them are not even capturing the data but if they are capturing the data, they may not be capturing it in the digital form and even if they are capturing it in the digital form, they may not be capturing it in the way where you can really exchange the data with someone else. I mean there are lot of companies capturing data for example in excel files and these excel files get exchanged over the emails. So, there is a need to do a real time integration when you go about trying to do all these things. Yes, there are challenges, of course everybody looks at what is the benefit, what is the ROI of doing it and then of course the second question comes is that whether this data can really interact with each other, the systems can really interact with each other. That question is solved with the new technology.

New technologies are very flexible, we can get data from anywhere in any form and still convert it into form that they want to use it in but yes remains a huge challenge. And then there is a legality involved, of course everybody wants the data to be protected, nobody wants their own data to go out. So, they are very strict, security expectations from everybody involved in the supply chain to make sure that their data is properly protected and is not misused. So yes, those things are there, I mean there can be legal issues coming out of it doing it in the right and proper way.

AITD: These are emerging technologies and what is your suggestion for companies who want to adopt cognitive supply chains. How should they approach this adoption especially in the small and medium enterprises with limited budgets? So how should they start adopting the cognitive supply chain?

Mr. Rahul S. Dogar: I would say that there is, as I said, the first step, the first precondition is that you need to digitalise your supply chain and for that to happen you need to create a digital road map. I would say that companies who really intent to do that, need to sit down and see or do in as in analysis as to what is the current state and what is the vision with creating a digital supply chain or a cognitive supply chain, what is it that they want to achieve. Of course this is a comprehensive work that needs to be done within the organisation and then the second most important thing that needs to kick in is a management buy in and that would get displayed by creating a team who would run this within the organisation and then allocating a budget to it. Sort of these two things really communicate whether the management has really bought into it or not. Without this thing will not work.

The third thing that needs to be done is to set the priorities. You may have created a huge digital road map with maybe under things to be done but what is also important is to say that these are the priorities that we need to work with and within those priorities, identify some lighthouse projects. The projects which can bring in early success and can display to the rest of the organisation that if we really adopt this, our life will change, we will be much more successful in future and that's where the cultural shift within the organisation will start coming. Of course changing attitude of the people towards using or going digital is a huge project management, change management task or project in itself which of course needs to come in but yes moving in people showing early successes will really help people to get adopt to the new technologies and go about it. And then of course there is a continuous journey that one needs to follow up yes, the follow up needs to happen from the top management.

AITD: There is a very interesting question which has come up. They want to know:

If a company is a first mover into cognitive supply chain, how does it talk to other companies who are yet to adopt Cognitive Supply Chain, so people are scared I am the first mover while others are still on legacy system. So how does he communicate with others?

Mr. Rahul S. Dogar: So that's a very good question and that is I think one of the biggest challenges out there and that's the reason the example that I gave in were from the components which were more or less within the control of the organisations where the digitalization has already happened and then they were trying to get the proof of the concept of using the AI within that so that there's a larger project or the programme that would be created around it. The fact remaining that the lead must be taken if let’s say we say that OK fine in India we must for creating cognitive supply chains and become a leader in that sense, so the lead must be taken by the large corporations which have a bigger influence in the ecosystem. So, these are the companies who can demand from their suppliers that they must quickly get onto this. We can demand from the service providers like us and then they can of course make it their business criteria that you get to work with us only if you have reached a certain utilization. I think the lead must be taken by some of these big guys.

AITD: There is question about managing this change. Anything new, the human tendency is to resist the change, so how can companies manage that change that comes with adopting cognitive supply chain and specially the cultural aspect, there is a cultural issue involved in adopting a change?

Mr. Rahul S. Dogar: I think, the cultural aspect of it, yes it can be a huge roadblock. There are people who resist change within the organisations. I think this important consideration very much must be the part of the digital road map which the company would create before embarking on this journey. Every company has a different culture but yes considering what is the culture that exists within the organisation, a separate change management project to adopt to this technology, has to be envisaged and then based on what is the culture that exists. Let's say that there is a new age company, which has a team of engineers. They would be early adopters, they would already be working with lot of technology and stuff like that and then we compare it with, let's say a large corporation which is like 60 years old, 50 years old and 40 years old. Of course, people working over there would be less open to adopting the new technology. So, in both the organisations, things must be driven very differently, very specific the culture that exists within the organisation and while going about designing this programme I think it's very important to see the need of the people. I mean largely either there's complacence that is already built in, there's a familiarity and there's an inertia of rest that has got built because you are using certain kind of system for so many years and suddenly you have now to do something new.

There can also be, let's say insecurity that this new technology will come in and I will lose my job. So around those things the plans have to be created, maybe there's a upskilling programme that needs to be created, there should a comfort that needs to be given to these people about job security and stuff like that and I think most important thing is that, you need to have a very strong leadership, top level leadership backing while going about it. Without that I think most of these programmes can fail or they can just be in. We have seen that companies wanted to do it, but their lot of things are in backburner for couple of years, for three years just because leader doesn't think that this is a priority right now. Fortunately, things are changing now with COVID, lot of eyes have opened.

AITD: If we take examples of a few companies in India and abroad who have successfully adopted the cognitive supply chains and what has been their experience as the volatility of the logistics really increased, what has been the biggest gain of these companies?

Mr. Rahul S. Dogar: So, I would say that there are many examples out there as I said in my presentation that we are not here yet, it's a work in progress. We are moving fast in that direction, there lot of companies who per se are early adopters who have really put money in building these supply chains and the biggest benefit that these companies have been able to get out of this is that they have become leaders within their own industry verticals. Amazons of the world, I mean Amazon - there's a lot of work around it. Our home-grown Flipkart does a lot of work around it. I think one of the reasons why Walmart got interested in this is because Flipkart was doing remarkable work with the technology.

Alibaba does wonderful work. There's a lot of good examples from automotive industry also. The biggest benefit that these companies who are actually early adopters, who are really putting their trust and their money into building the cognitive supply chain, the biggest benefit that they get is that they stay ahead of the race and eventually they start sort of giving the direction to the whole industry. Talk of Tesla for example, setting the direction for the whole automotive company, automotive industry per se. So likewise, every industry has few leaders who are up there and putting lot of effort in building the cognitive supply chains.

AITD: Before I conclude and propose vote of thanks I would request you, in next few minutes, have your concluding remarks whatever you want to say in the end. You have answered all the questions I know but we would welcome your summing up remarks before we finish this.

Mr. Rahul S. Dogar: As concluding remarks, I would say that 2 things. I mean two different set of stakeholders here, one is of course for the companies that whether we like it or not, it is coming, it will happen and I think the best thing to do is to adopt it as quickly as possible, put it on the top of your priority. Events like COVID has taught us even more that we need to get there faster. So that’s for the companies.

For the individuals again I would say that I keep meeting lot of people who were little sceptical about it, that it will take lot of time and stuff like that. I would rather say that we need to quickly upscale ourselves, we need to quickly invest in our understanding these technologies, we need to start building a network which can help us to get there quickly because there can be lot of jobs at stake if we don't quickly adapt. I would say that it's not that the jobs would be lost but the companies would like to have you within upgraded skill, you need to be able to understand the technology and how to use it in the most efficient way. So yes, for the companies quickly get onto it and for the individuals quickly upgrade yourselves so that you stay relevant.

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