 Hello all welcome. So this talk is more about the, so far we have heard about conversations on localized problem solving around hyperlocal problem statements or in the lost mail delivery problems or even the vehicle routing problems in the lost mail. So this talk is more about the background before entering into the lost mail and it will be heavily on the context of the e-commerce or a marketplace kind of scenario. So my name is Senthil. A quick introduction about me. My name is Senthil. I am a product manager in Flipkart. I have worked in several supply chain problems all the way from the very start of Flipkart having its own supply chain till now. A quick update on the flow of the conversation would be like first is about talking about the landscape that we operate. What are the dynamics of the landscape? What are the dynamics of the landscape that is coming outside as a demand and what is the dynamics of the landscape that we operate within the business? Second is about what is the key problem statements in this domain. So there is a bunch of problem statements that we look at it and how the network intelligence has a subject or a concept is well positioned in this overall scheme of things. And finally if time permits we will also talk about an illustration of a particular use case or a particular problem statement and see how we have evolved in that direction. That's a overall part of the overall journey for this conversation. First we'll start with an operating landscape. Step one is from a Flipkart or e-commerce marketplace scenario the categories that we operate is humongous. So these are some of the typical examples that I can quote here is like books which is very different from the way it operate for which we operate. It's very different from the way we operate books to furniture or it could be larger plants or it could be grocery and so on and entering into jewelry and new subjects like that. So the product categories comes with different dynamics altogether. The dynamics in terms of working capital management, the margin that it operates, the consumer demand it generates and the kind of consumer expectation that it generates is very very different. Not only that this product also has very different types of attributes. For example if I want to deliver furniture to a house it's not one product that need to be delivered. It could be multiple parts that need to be delivered or it could be some of the damaged parts that need to be delivered which could be a replacement of a part rather than the whole product. Or it could be some of the products could be fragile for example it's a wall clock. It need to be handled with care. Also the stacking norms of the product differs across the categories and so on and so forth. The managing of products could be in lots or it could be in heaps or it could be high value and inflammation or it could be in hazardous product and so on and so forth. The third dynamic that we talk about is customer segments. The customer segment that we operate is pan India with a variety of customer categories they come from. For example we just launched a plus program which is our loyalty program. So there is a difference between the plus and non-plus customers. There's a tier 1, tier 2, tier 3 customers, there's metro customers, there's millionaires that we operate. There's a women population that we operate with different age groups and so on and so forth. The important subject here is that these customers have different needs when they look at the e-commerce platform and when you intermingle that with the products and its and kind of dynamics the product brings in, the e-commerce platform is pushed to deliver a lot of new services. It's not just vanilla delivery that we do, place an order, get it delivered. It opens up a variety of new supply chain services that is required. It could be schedule delivery, next day delivery or it could be faster speed like same day delivery, slaughter delivery. We talked about some of the in the morning conference we talked about schedule delivery in the last many years. It's also could be installation. We do furniture and other stuff which requires installation, reverse pickups, open box delivery, so on and so forth. It could be a very different thing if we enter into the house and deliver things, right? Compared to something else. So there are very tough supply chain services. Not only that, it also can, since some of the deliveries could be much much longer in lead time, like it could be three days or five days and so forth. The customer needs also change between that order journey from the time of order placement all the way to deliver. He can change the address or it could be changing the delivery location, date, place and so on and so forth, right? So the variety of supply chain service could be intermingled. And finally, to operate all these things, the fulfillment models that we work in a e-commerce marketplaces, very different. It is not always an inventory and we deliver to the location. It is also about sellers managing their own inventory and partnering with the sellers to deliver the products. It could be brands who is managing the inventory for a variety of marketplaces, not only for Flipkart. How do you interplay with them? Are ready to make orders? For example, if we are launching jewelry, it could be ready to make orders that we want to deliver to the customer and so on and so forth. How many channel hyper local? Flipkart is in grocery these days. So we are in hyper local. We are looking at hyper local and how many channel business in a very close fashion, right? So there are multiple fulfillment models that can be operated for this kind of customer demand. All this thing comes with the one important part of demand volatility. What a demand volatility here is that the demand variation that we see in a marketplace scenario or in a e-commerce scenario is very, very high in terms of so it is also induced as well as received. The induced part is that we run a lot of sale events. Recently we finished our big billion days and when we so that the scale that we operate on a big billion is maybe 5x or 6x of the range that we operate in a BA scenario. So it comes with a very different demand volatility into the picture. The second given this operating landscape, right? When we look at supply chain, how does the supply chain look like? We operate hundreds of warehouses with millions of products in the warehouses. We have hundreds and thousands of sellers who is part of the entire e-commerce story for us. These partners not only spread across the country and the kind of customers also across the country. So we have the supply chain here is not catering to a very specific location. It is from all the way from warehouse to seller to any number of pin codes in the country, right? It is from all the possibility from all the 20k pin codes, the source could be at all the 20k pin codes and the customer or the destination could be all the 20k. That's the kind of supply chain network that we are talking about here. So all these things are delivered to people, vehicles and those are the currencies of operations. So if I look at the people part, we manage a large fleet force, co-owned, partnered and so on and so forth. So the number of fleet force that we manage is also humongous with different skill set. It's not a specific, it's not only a delivery skill set. We look at installation, if you look at people etiquette, there's a grocery delivery where we are doing delivery inside the warehouse, inside the house of the customer with the very different things. While a book delivery requires a very different etiquette in front of the customer, right? So the skill set of this field force varies a lot. And second, the next part is about the partners and vendors. The entire supply chain when we connect all this 20k pin codes to all the other 20k pin codes in the country, it's not only a job that Flipkart can march forward, we also need the partners and vendors who can play a very key role in taking us forward in the journey. So Aditya also mentioned about the millions of packets that we deal in a network, right? So if you look at our network at any point, it has more than a million of parcels that's moving around. And from all the sources and all this destination, the routing of this parcel become much more important. We operate across multiple operating methods. So today Jerome was talking about the truck testing. So we operate on more than 1000s in the scale of 1000s of trucks that we manage, bikes, our own large line all vehicles, we also partner with all the cargo movers through flight and train and all those vehicle modes, all the transportation modes is also part of the story. When it's not only about the physics that we move around, there's also a lot of cash that moves around this entire network. It's in tons of, it's in tens of crores of cash is getting dealt every day. It's also closer to a bank supply chain. So that's the amount of cash because of the COD or the cash on delivery nature of the business. That's the amount of cash that we deal in. So the another interesting part is that this also comes with supply value volatility. So for example, a vehicle can get broke down or it could be an external climatic condition that is changing or it could be the absenteeism that we are seeing in a particular location or it could be a festivals and holidays and so on and so forth. So it all brings up the overall supply volatility picture to us along with the demand volatility picture that we saw in the previous slide. What does all indicate to us? It indicates, so here, at this point, I want to take a step back and look at what is network. Network can be defined in various contests. The supply chain network means it is the operator, so all the element that we saw in the previous slide. The sellers, warehouses, vehicles, people, partners, vendors and all the stuff that is included along with our own facilities, delivery hubs, people called dispatch centres and so on and so forth. So all these things form the part of the network. Now if in the, as Flipkart grew up, Flipkart also had a very fast scaling of things. So it is like, when do you employ intelligence? Vis-a-vis how do you fast scale? So that's a trade-off conversation we usually have. So in the initial days when we had this growth, what we did is that we operated mostly from the inventory model. So it's an incoming demand in a variation form. When it comes to us, every order is pushed into a VAROS. Remember we talked about a millions of packets and this is very close to how our VAROS looks like. From VAROS, it has to get sorted all the way to the 20k pin codes that we talked about. So it's a sortation in itself, it's a very huge problem. So sortation, let me give you one snapshot about sortation. It's not about connecting one product to 20,000 pin codes. Every product has different nature. So we talked about furniture, we talked about grocery. Every product has a different level of sortation. Above that we also apply new services to customer. We talked about next-day delivery, we talked about same-day delivery, installation, all these things when they couple together, the sortation in itself is a huge subject that opens up a lot of complexity to us. So if we push orders to a VAROS and we start processing the orders, the orders get piled up, waiting for a transport connection to arrive at some point in time. And when you see the piling orders and when you design a transport connection given a piling orders, your transport network looks much more complex. And if you're doing it instantaneously, it will always lead to a load. It's all in the industry term as LTL, use load truck load, which reduces our efficiency and cost utilization significantly. While we are managing this, we also have implications because of not employing intelligence into this way of working. Which is customers screaming at us and cost increasing exponentially. So there are a few snapshots about how customer concerns come to us. Most of the customers problem is where is my order. And when you look at this piling network and we say where it is stuck and if I'm not able to give you my right answer, he's going to scream at me back every number ten. The second problem is that most of the times also coupled with all the last problem that Aditya talked about, fake attempt and so on and so forth. So managing the entire network is in itself a complex. So then that's the first point I want to do. And second step is that when we step back and look at this entire network, which we talked about connecting all the hundreds of thousands of sellers and along with our warehouse to all the way to the customer. So it's not a push-based model where you get an order, you operate your push into warehouse and process this works. We need to step back and look at what are our push-ins or what are the problem statement that we need to rethink to develop an intelligent module about this. That's Albert Einstein talking about how important is to stay with the problem. It's more about thinking about the problem and getting in the right frame of the problem statement much more important than actually getting into the solving. Solving it could be much more simpler when you think about the problem statement of this kind that we talked about. So let me talk about some of the directional problems that we as an organization started thinking about. Step one, for every order what is the full frame of model that will be rightly operated? Should I engage a marketplace model here or should I engage into an inventory model? How does this frame of what full frame model need to be operated for an order comes out? The second is how should the inventory be reserved in a network? So I have a bunch of warehouses, I have a bunch of sellers, I have a bunch of inventory roaming around in my network. So where should I position my inventory or where should I reserve my inventory for a given order? If I am understanding the inventory reservation part, what should be when should I release the inventory? Should I release immediately like the thing that we saw previously where we saw the inventory get reserved in a warehouse and it can immediately choke into a sortation or it can lead to a very inefficient transportation mechanism. So should the orders be processed in a real-time basis or should we batch it, wait for some information and start processing it? What should be the shipment flow path? So we have a variety of nodes, for example, if I want to connect from Bangalore to Guwahati, what should be the path that I should? Should I stop in Kolkata and go all the way to Guwahati or should I employ a transport from all the way from Bangalore to Guwahati or it should be fly to Guwahati? There are a lot of options that we have, right? So assessing this shipment flow path is much more important for me. And then across we talked about the exception that can happen, which is the variability. For example, a customer can say that I don't want this order anymore. Or you can say that I want the order to be delivered to some other day or it could be a climatic condition playing a role in its part where we need to be answerable to customer in a more deterministic manner, right? So how do we take this exception? How do we build insights about this exposure and answer the customer query in a more predictable manner? And other things would be is that when should it start a transport connection? Should it start a tug immediately from a Bangalore or from a Bangalore virus to, let's say, on its expedition to Hyderabad? Should I start it now or should I start it later? What should be the transfer connection? It opens up a plethora of problems with along with like and other could be is that what kind of flexibilities can I offer to the customer given at given point in time or given point in time of the order journey? So we say this adding to that that could be other problems like which is which is beyond my conversation here will be about forecast demand planning, supply planning, inventory positioning, inventory planning and so on so forth, which is not part of this conversation though. So how our journey started the journey started with building workflows because we are a scaling organization at one point we are our orders are increasing at 10x, 3x, 2x and so on so forth. So one end we are building workflows, we are building our network, we are scaling our network in a very fast manner. So building workflows becomes a critical part but while we are building workflows, the previous problem statement that we talked about orders getting piled up in a big network and you're getting stuck everywhere and your customers are getting a little bit angry about stuff of product not reaching their warehouse every time. So it's about extracting right data. So we've been looking at this problem statement, we've been looking at this problem statement, we've been looking at this problem statement, we started from workflows. We want to instrument a lot of information, instrumentation some of the 친구 we talked about the Los mail, where we have fees traversing what is the Geo data that we are getting, apart from that how are we managing vendor relationship, how is our moment for what they're operating hours, how they model their information exercise? We our journey of modeling in a liter of data across the notes are factors we've talked about in the supply network. Then you know Then building awareness, it's an integrated awareness that become much more important to us. The integrated awareness in the sense that from all the way from loss mail doing, in a loss mail, if you're allocating an FE, how it replicates and come back to when should an order be released in a barrel. So that awareness has to be integrated across the network. And that was one of the key pointed. And then employ some control levers in the network. If I am able to be awareness, I need to be employ some control levers in the network. I need to deliver a product on a particular day, 30 days, 30 products in this capacity. That is some amount of levers and tools need to be provided to him for him to employ it. Otherwise, whatever that I do as an intelligent part will become shallow. Then the next part is about the building the intelligence itself, right? So we'll talk about more on the intelligence. But the overall goal of this theme is first is to become more an orderly and predictable execution. That's a goal that we want to achieve while we solve all these problems. So that we will be able to provide right answers to customer whenever customer walks to us. So now let me think about the logical view of it. So how did we go about building such amount of network intelligence? Step one is that we were, instead of building workflows, we stepped up and looked at building systems and processes in the model systems and processes in the language of lead time and throughput. Every processes in the supply chain is modeled as a service entity and above that lead time and throughput of that is constantly defined. And every time there is a separate team that works on refining this lead time, making it much more efficient and feeding back to the lead time and increasing capacity part of it. Once we have these activities and process lead time in our pocket, the second step is to build this into a customer view. Customer view is the next day delivery, where I was doing an operation of picking and packing a product. How does it reflect as in terms of next day delivery or same day delivery to a customer? Then about the business and customer intelligence need to be coupled. So what services should be offered to what customers, what businesses much more pruned to what services. So it opens up that area for us. And having all these things lead to us in providing right service offerings and pricing to customers. So pricing is a very vast subject we may discuss in subsequent sessions if possible. Here the idea is that having services, understanding customer need, understanding business need will enable us to offer right services with the element of pricing into it. So that leads to customer choices. Now when you look into the other thing that we talked about is about the events and disruption that can happen across the network. So we need to build a real time engine which can publish all the events and disruption that can happen across the network. One could be predictable, one could be unpredictable or it could be schedule like a band or so on and so forth. So all this information comes through us, we started building a logistics inside module which looks at all these disruptions possible in the world and funnel that information to any decision making layer. So this is one important concept, it's about demand prediction. In the context of Flipkart or marketplace, in the context of Flipkart, when we take a decision for a given order, it is an opportunity cost for the upcoming orders. So it is not only taking a decision for that particular order, it has to be treated holistically. I can give you an example for example, let's say we have one product in an inventory and there are two customers walking in at time t1 and t2 and first customer could be a new customer and second customer could be a very loyal customer to us because it's very high on the plus. How do we, if I am taking a decision at t1 for the incoming customer, I would have reserved that order to the customer but may not be the right thing to do. So keeping a demand prediction becomes much more important to us and that's one area where we are investing heavily. All these things lead to generating some real-time fulfillment decisions. In the subsequent slide we talk about, maybe we take a talk one subject and say how the real-time fulfillment decision has been designed. Then once you have a real-time fulfillment decision, you also have to operate those levers that we generated in the network by creating an orchestration across the network. So what are the orchestration elements that you have? You have arrows, you have people working on the floor or you have your lost mail hubs, you have transportation nodes, you have partners and all these people are players in the network. And once you take a decision or a plan, once you create a plan for an order, then you need to orchestrate with these players in the node so that this can be, the goal can be achieved. And that's an example of the execution system. Here could be a Varro's management system or it could be a lost mail management system or it could be a transportation management system or it could be all the way to the inventory planning all together. So this is the area where we call us network intelligence all together. And the execution system, so as we progress in our journey, in the initial days we were heavy on the execution system, modeling those execution systems. And as we scale now and we look at, with the power of more data in our hands, our investment goes heavy on the network intelligence part. So just to give a snapshot of how our systems look like. So there are a lot of building blocks in the entire plethora of FSG technology, which is supply chain technology in Flipkart. You will see a lot of other systems like Varro's and all those things and also you will also see location platform, location intelligence, network intelligence. We talked about logistics inside. We talked about how to get real-time events and funnel that between that we have scheduled and unscheduled. We have people management, like we have a bunch of people who need to be skilled at various levels. So the skill management itself is a very big subject altogether. So all this information gets funneled by various modules and come to a box called real-time fulfillment decision maker. And that's why I called this fulfillment decision cockpit. From there the decision and the plan for an order is created and it comes down through a network orchestrator all the way across to the network. So that's a high-level system quick view of it. So now let me define what is network intelligence and orchestration means. Given that we have this subject in the background. In a simple sense it's a constraint of our planning system. So what are the constraints? We talked about systems, process, lead time, throughput. Those are the constraints that we have here. And being able to plan on knowing this constraint itself is a big subject altogether. The second thing is that the decisions need to be taken in the subject of visibility of the entire network. Of all the nodes, how they are moving, what are the things that's happening around the network. And also a notion of understanding what capacity they can do. If an FE walks into a hub, how many shipments can he handle? If he's a new FE, he could have handled very less number of shipments or if it's a old FE, he can have much more shipments based on location, etc. That's a capacity notion. All this information is much more important in order to take a real-time fulfillment decision. It also goes from iterative planning, there are multiple events and disruption that's happening either from the customer side or from the supply side. So all these events have to be funneled back to in order to take a right decision. So this defines what is network intelligence in English. Now if you flow the orders again, let's say we have this concept of network intelligence and if you want to flow this orders again, how does it usually look like the same kind of variation? It can come with a high level of variation. But what we do is that we don't push orders to the barrows. We hold orders. We hold orders and take a decision whether it need to be filled at a real-time manner or it should be fulfilled in a batch manner. This is done along with the batch order is not only with the orders that is accumulated over till time. It also includes all the future orders that can be predicted based on our all changes that we are doing in the network. The second thing is about once we are able to understand the batch. The example of a batch could be that I pick orders from barrows only for the next transport connection. Or I could pick orders from the barrows only for the particular zonal areas where the products are located very close by so that the picker's efficiency increases. So these are the multitude of signals that goes in before deciding a batch. And once a batch is decided, that goes for a planned order. The barrows executive knows exactly what he needs to pick at what location. And when he picks it forms a story for the entire order path. And the sortation becomes much more simpler because I am able to pick the orders in a very sequence manner. It is for a particular sortation plan. If I look at the overall decision that I made, the decision is made for a overall order. The order scenario considers all the factors. So whenever I pick a product it is going to need for a certain sortation plan that the sortation plan gets much more simplified in this thing. And also since I am taking a holistic decision the routing becomes much more informed and much more educated and I can always achieve a full truck load whenever I need it. The implication is always better customer experience and significant reduction of cost. So that is the story that we are marching towards. And in the next few slides what we will do is that we will see how a barrows of an order get selected or a batch get formed. So inventory we talked about a bunch of problem statement for what a full filament system should take and all the subjects in itself is very deep and we will not have time to cover everything. So I want to talk more about how we do inventory reservation and release in the barrows. So the question is which barrows to select given an order. So the first step we do is that we do not take that point into decision anymore. We hold order we also have the orders that is accumulated till time. We also look at what are the future orders comes in. Then we prioritize among these orders. The prioritization comes from all available barrows and inventory all available barrows and inventory available in this barrows across the seller included. So look at the list of this thing. Identify flow pod choices. So given a batch given this order segments what are multiple choices that is possible. So it is only a divergent problem thinking here. So taking an order we look at all the choices of arrows from the choice of arrows. We look at all the choice of flow pod. So it is getting diverged and then we look at so every pod has a cost as a SLA tradeoff either I can minimize the cost or I can minimize the time to reach the tradeoff across every orders that need that can be taken and this orders has to be treated very specifically because for example if you have ordered an iPhone X in Flipkart you will demand fast delivery as a customer or if you have ordered a t-shirt which could come in 3-4 days you don't wait for a t-shirt to come into your doorstep immediately. So there is order wise and customer wise all the parameters that we talked about the category wise particular order wise particular customer wise gathering this contest enables us to set a price for an order to take in cost versus SLA tradeoff. Then there is risk in the supply chain right so we may have new partners who are new to the system or it could be an evolved partner they give better SLA but 50% of times or it could be a well established partner and they may give a longer lead time but with high reliability so assisting the risk along with that and finally before doing an inventory allocation we also think about whether to allocate the inventory right away or not the case that we talked about if my match is prepared for a future maybe tomorrow I don't have to allocate right away because I can still get more information and decide the later point in time what need to be allocated or it could be the other way around it's a plus customer and I do not want to derail this experience I will allocate the inventory right away for that customer or it could be pre-orders pre-orders is a concept in e-commerce where a book like Harry Potter book getting launched in future customers have placed order right away so I allocate orders right away for them because there is no deviation in his mind in terms of whether we will get it or not now if this concept lead to varro selection how to batch an orders as usual we determine the flow path for an order now the important thing is that once you determine the flow path now there are areas where we have levers to place our transport connection on a network so how do you balance your transfer should be issued from Bangalore should I operate all the south zone south destinations in the morning or should I operate in the evening how do I balance this network once I do the balancing of the network from Bangalore I am connected all the way across the country so if I am balancing the network how will I group this network so that the sortations plan can be devised properly if I am able to do the sortation plan now I do a backward provocation across all so starting from the last mail we think about when the product has to reach the customer then work backwards and figure out what is the latest time I need to release this order inventory into the warehouse once I determine that I also do a forward provocation because releasing at the latest point means there is no queues in the network or no piling up in the network but any disruption can affect this order much more and also if there is no enough demand for a particular day I am making people I say tidal which means that utilization can go down so backward provocation then also we do a forward provocation the goal of forward provocation is to figure out what is the earliest time I need to release an order into the warehouse which includes the goal here is to see how I can maximize the utilization of the current resources that I have and also the minimized risk so this is the pointers of pointing questions to us about how to select a warehouse or how to batch an order in a warehouse all these things lead to us building custom algorithms so we have defined a lot of algorithms in every subject in the next slide we may talk about one area what are the parameters going inside so this has led to us build a lot of custom algorithms in the network intelligence layer now if I think about only modeling warehouse selection as one of the problem statement how did we go about solving that problem we talked about what are the parameters lead to selecting a warehouse now if I think about how will I model this in a real time scenario first is that everything has a business value so what are the operating levers here either it is speed in terms of delivery SLA or it could be cost in the actual opportunity cost that we are looking at and it is also the risk involved in managing any of the product across our network all this thing has to be modeled to a common business value and organization may take a step of increasing the speed so that it increases the conversion speed or reduce cost so that I increase my cm all this parameter so the business value is a point in time or a point in time strategic decision where the prioritization of any of this thing happen and come to a common unit of business value the objective function is to maximize the value of a batch of orders when creating a batch of orders so the maximum value the parameters that we consider is that for example if an order is given an order what is the cost curve of an order if it flow through multiple paths if I decide a path what is the cost of serving the path if I don't decide then if I do if I if I take an order and if I ask the order to wait for some more time what is the cost of losing that order out of the future value of the order so that is a cost curve of an order that we determine and then cost of serving an order determined from the the common one is that I can delay the order as much as possible so that I can increase I can reduce the cost of serving this order but there's a probability that the order can get cancelled or it can derail from my experience the other end of the spectrum is that I can go only looking at order and look at the minimum cost of serving the order there's a commonality between these two points and that determines our minimal cost of serving an order the opportunity cost versus actually serving that serving that order that commonality comes as a cost of an order or flow path once we identify this cost of the flow path we start modeling the risks all associated with this then the second two steps are much more simpler identification of path which is just an orchestration path and identification of arrows which is again following the simple so this is how we took so just to summarize some of the conversation that we had we talked about the landscape of e-commerce we talked about different products that get operated we also talked about the supply nuances what is supply chain network means we also talked about what are the what is the problem with scale when we don't have an intelligent network orchestration so we also define what is the network intelligence and how did we how a network intelligence has a concept sits into overall scheme of things in the technology stack then we also looked at a lot of problems that need to be addressed before serving an order we zoomed into one particular problem which is the arrow selection as such and then we talked about what are the parameters that gets into before selecting an arrows so that's it from my side folks so hi so my question is slightly tangential but I feel like you're the right person to ask is that in terms of the whole supply chain that you described there's also the key element of people management that you touched upon at some point now my concern or the question is that there's regulatory compliance in terms of labour laws and there's also a human resource need in terms of making sure that all of the people working for you are in a good state to work so at a very basic simplistic level if you could tell me how many hours are FE's required to work and how do you cap it like what drives the decision and what other kinds of people or human centered constraints are put into consideration so let me start with the first problem statement that you mentioned about the human constraint that we face our arrows are kept at very different locations it's not part of the center of the city so it's all outside the city just because of the space cost that is involved in it so everywhere for everywhere also that is operating outside there is a working timing that need to be defined as per the compliance law it cannot be 24 bar 7 if it is 24 bar 7 there is a rate the salary increase has to be significantly higher for the remaining part of the day and also there should be transportation solutions should be there for this employees to go up and down and second thing is about the safety nature includes employing medical facilities near the large that we operate and also other laws applies in a virus scenario for example that could be recreational facilities for a longer continuous work not a law but it is our way of encouraging to reduce the fatigue of this employees we also have recreational facilities and other things that we come as part of the layouting of the arrows itself the second is about when you look at the employee timing in the logistics side it's a usual eight hour shift that we manage we have an eight hour shift and there is overtime paid for the remaining two to three hours that we extend as per the laws defined by various geographies of India how do you measure efficiency for example given time so it is about not about the number of products he deliver in a particular day it is about the number of products he can deliver given the time period of day and also it is about the geographical nuances for example as our system knows based on the heuristics we know that AFE can deliver n number of products in a particular geographical area so it is about the location of the address and then we use it as a metric to measure the productivity of the AFE and relating it back to the mornings talk about address failure and geolocation issues whenever there is technology failure in that sense how does that fall upon the AFE or how much does their feedback count and how do you improve upon it or what kind of cost does an AFE have for infrastructure failure may not be the best question should I go ahead Hi, my question is on you spoke a little bit about modelling risk so I wanted to ask on what kind of risks are you looking at and if you can give some detail on how do you attempt to model it like sure so the risk curve twofold to start with one is the product risk itself so if I am taking high value product let's say it's iPhone X and for some reason the information is available that it's iPhone X so it induces any of the person who is handling the parcel to take a hand take his hands on it so there's a risk on the product itself the risk on the product is also under the second dimension of the risk is about the people who is operating that product if I have a high value product let's say I am operating jewelry for hypothetical sake and I am doing diamond jewelry order there is a significant risk involved in moving this gold item or this jewelry item from one point to another point so who are the parties that need to handle this product what are the delivery nodes it has to or what are the nodes it has to go through is based on the risk assessment of the subjects like people as well as the facilities the risk assessment of that the second part is about the risk of people or the facility if you look into it from a risk perspective of people we know the history of people we have a background verification check across all of our executives we also know the tenure of the executives what are the remarks that he got from different transactions that he dealt with so that information is all instrumented using those information we extrapolate and figure out what is the risk of this person handling this product the second is that about the facility or the nature of this location itself if you come to Flipkart for say various geographic location we don't deliver this product high value products are not delivered to various jocular pockets that we identify where we will not deliver the product beyond a cutoff beyond the value of a product we don't deliver the product because the risk on the area itself very high where people know Flipkart employees where they go and vandalize employees and collect the shipments we understand those pockets and we keep feeding into the real-time engine that I talked about we store this information to take any risk related assessment