how are y'all doing it's after lunch well we got a fantastic panel coming up shortly to wake you all up and you guys heard today's keynote yesterday s keynotes a lot of good stuff about AI and ml and we are going to show you how a and ml it can be applied across the value chain and this is the theme bridge from marketing to supply chain I am solid on one car I am the CEO of Pluto seven more importantly I also lead a I and ml professional services team and I have the good fortune of having some of my customers sitting along with me will join me shortly on this panel so without further ado being in the business for such a long time and I've worked as both an employee of a large corporation and a management consultant and now I'm working as helping companies come up and do DRI breakthrough innovation using the latest technologies the key thing that has stood out for me is that typically lot of enterprises end up in having focus on the front what I call as the front end of the enterprise value chain i defined that more as marketing the front end of the whole business the customer facing side of a business in the value chain marketing sales and then you have the operations and the supply chain which for our reference I will define as the back end typically there's been a lot of focus on how do you make the profitability of the enterprise or the customer experience that an enterprise can provide its customers seamless and we've seen time and time again that this has pretty much been a challenge even though there have been advances in technology just to illustrate that I just I thought this was a very apt quote from Mark Anderson pretty much he captured this very well saying that well there were a lot of technical breakthrough technology possibilities really the business there was a challenge in how business could achieve the results in despite those technological innovations and that it was not only the fact that it was the business who was willing to drive but there were technology challenges that still stopped the business from attaining those goals so that falls very perfectly with my opening statement which is essentially the key to all this driving the business benefit starts by making sure that you are able to take the process the process dia is basically people process and technology and have it work seamlessly from the front to the back it starts with the customer and ends with the customer but for you guys as enterprises there's always the front end or the demand generation the marketing side of the processes and then you have your fulfillment processes which are mostly operational that deals with supply chain so the key thing is AR and ml proposes some possibilities to have a responsive back-end that can go hand-in-hand with the front end and today's teams as you will see in the breakout I mean in the panel discussion is to give you some ideas on how we can see that happening or in fact what are the challenges that still exist that need to be overcome in order to make it even better and how does AI and ML fall into the strategic aspect as well as the tactical aspect of enabling this so lot of companies have already started using AI on the marketing side on the to some extent even on the customer relationship management and sales ai has been seen to have given very good results in this area the key is whether we can follow that up and leverage AI and m/l also on the operations and supply chain and that piece is still evolving so just to encapsulate this pretty much some of the key things here are you can come up with a very good efficient and targeted marketing leading to competitive advantage that can be achieved through AI and ml it has been achieved through traditional analytics to some extent and you will see my panelists talk about it and then the key here is on the operation side is doing exception based demand planning time and time again when I provided solutions to large customers and even small customers even though they have been good solutions and some of you are aware of that them like sa P Oracle value chain planning sa P's advanced supply chain planning every time we implemented the customer I still come back and ask why can't I do exception based planning meaning I need to only know where the demand where do I have to pay attention to my demand and where do I have to pay attention to my supply so these are typical outcomes that where people have started using nml we have seen and again will go as the panel is talked about it hopefully this comes out it really helps for example in coming up with the 360 view of the customer and that leads to increasing for example your marketing campaign efficiencies and helps drive revenue on the front end on the back end it really helps if you do this right by coming up with the goods customer experience especially in retail where retail has to be very responsive especially with omni-channel etc and you it allows you to do this while maintaining productivity and profitability and to really come to this piece where you can have the true benefits you have to see some sort of implementation across the enterprise that's the goal so how can ml drive your transformation of your business these are the three key elements in that whole value chain and then there's the marketing ml there's a demand ml and there's a supply Mel I'm just simplifying this you heard yesterday and today a lot of AI and ml making things simpler so to keep it simple and easy those are the branding that we have come up with and we'll expand into this in the panel so without further ado I would like to invite my panelists up here to join me and we have a great set of panelists today they are our customers and we will talk with them on each of their experiences that deal with the front end and the back end of the process what are their thoughts what are their what do they see as strategic elements the tactical elements and their experiences in trying to leverage AI and ml in accomplishing what I just said so before we start the panel discussion I would like each one of you to introduce yourself my name is Vikram I work as a senior manager for a REM L group in Cisco I'm su docker I run the enterprise applications at synaptics just brief overview of the company we make electing that you interact with an electronic device Solutions behind those interactions like we touch orbit we fingerprint solution or you might be ordering pizza or something on your Alex our device so we make solutions for wise finger print and touch so it would be easy for you to get that context when I speak about how we have utilized ml hi I'm Adam spun burg I lead the global tech exploration part in supply for a be in Bev so that's anheuser-busch for those of you don't know so beer production on a global level across all the continents hi I'm Deepak my ultra chief adventurer at California design then we make something very simple Google wants you to be on cloud we want you to sleep on cloud we make bedding products and from a typical design manufacturing white-label goods for retailers in North America a couple of his years back we turn to the b2c side and now I like a retailer so we'll talk more thank you so I don't know how many of you attended adam's presentation yesterday he went into more detail on some of the things around what they have done preventive maintenance you mention some of it again here today but that was an excellent presentation you can go back and review it as you know those are still available to be later on Thank You Adam so let's start vikram with you and you may want to keep the mic closer sure so we talked about AI and ml and I presented the front end and the back end of the processes and then we talked about lost opportunity right and there's Marc Anderson's quote which sort of talks a little bit about the same thing what's your perspective at a very high level I agree with this code but at the same time technology the enabler of the vision that we have to put in in the front if we don't have the vision then technology is not going to help us in the long run there are a lot of examples like Google Amazon all these companies where technology was not there when they started it so if the vision was not there and if the willingness of taking that vision to reality was not there technology would not help us now there are a lot of use cases also where technology came in and as a human being when we learned somebody else doing something we try to we try to see how we can take benefit of the same concept and evolve to the next level so there are a lot of examples in that as well so so it is a philosophical debate but it depends on how you want to take it I think ideas and the willingness to take those ideas to execution and more important than technology agree so the problem of the demand-supply matching and time-to-market were always there so we had to look into solutions that could help us to bridge the gap as to optimize our inventory so recently for the last couple of years we have realized that there is ml and there are technological improvements wherein we could leverage those and that technology to bridge those gaps so now we think we are at that point where and we could leverage ml significantly to to achieve our goals any comments sure I think you know one thing interesting about that quote it focuses on the 90s as if where we stand today we solved all the problems that prevented those businesses from succeeding then but I think you know this is a universal construct that will probably continue to happen and people with great vision today may have ideas that aren't possible now but might be more more available in 10 to 15 years so I'd go so far as to say that I agree with this quote and actually think it's kind of prophetic in some ways about how technology works but as we advanced maybe with such exponential growth that gap will diminish and we'll be closer and closer between vision and technology I think what Adams head is so perfect which is you know exponential growth and bridging the gap I don't think any of any one of us in the room can imagine a world where only large companies with deep pockets are going to rule so the machines are here whether they going to take over the world or not that's a philosophy cultivate we can have but you know as a as a business that that is not big let's let's pick up a fight with Amazon for now which happens to be our number one channel but also happens to be our competition the question is what do we do that we are in the race we are not behind it we can hopefully then compete and how would we scale how would we optimize how would we reduce wastages so all those questions for any business around it's very fairly simple I mean it's it's not going to be easy but if you're not in the race you will be out of it so with that in mind things are only going to get very interesting so following that your comments earlier how do you what is it about that perspective that you would like to relate in the work that for example you are doing in your area or even from your thought processes as it relates to like marketing and sales and absolutely there are a lot of scenarios that we are currently working on for example when we started this journey two years ago it was always about customer success mission how once you achieve once you bought something how do we drive the value of that product as soon as possible to the customer and the key concept over there was how can we understand what you are looking for from our software and where are the issues what are the challenges that you are facing and how do we provide that information using the resister channels or within the product as well now we sell you guys all know Cisco's has so many product and so many software's it's just not possible humanly to do all of that so we had to we had to apply them we had to use the machine to start with we had to go with business rules but later on we went into AI and all those things there is from a technology point of view I still feel that there is a lot of scope in technology improvement for example when we look at a ai ai today is very specialized ai it can do certain things but when you look at generic AI is still not there yet and I don't think the technology is even there - but we cannot just think about technology as a roadblock but think about what can we do what exists today and what is it that we want to drive so when we started this journey it was more about can we know our customers better like customer segmentation customer journey how did they use the product what are the features that they use what they want to achieve what should we tell them to use them more and through all digital channels how we can communicate that information back to the customer and then see what works what doesn't work so this thing's took a lot of effort but this is where we are heading towards and we are working tirelessly to to meet these goals very good you mentioned a very good thing or you mentioned journey so sue doctor if I may ask you he gave the perspective from the digital marketing what's your perspective on this a IML journey as it relates to like supply planning my operations so the space where synaptics is and it's in a high-tech world fast-moving products and cell phones and tablets and laptops and at the market that we are on or the the alex is in the world so the our time to market and we have to make sure that our inventory is optimized right at the same time you don't want to leave any money on the table so our use case was hey if you have to optimize the inventory then how do we go about then we have Wow absolutely we wanted to focus on the demand so though our goal was inventory optimization so we thought why not start looking into demand there's a lot of fluctuation there's a lot of inputs coming from different parts of the world and things are changing as you can see I think the cell phone that the life of a cell phone is pretty and small when compared to some of the other products so we had to look into areas where we could look into demand right how would we try to look into demand predictive analytics kind of things so that's when we we thought that why not start looking into ml and so our journey has been B thought and let's start looking at if we can have ml predict our forecast for the stable mature product so obviously the goal was to do the inventory optimization so it's been a great journey we did get to a point where in for certain mature products we we do have getting some confidence that maybe we can't start leveraging ml to start predicting our forecast as you mentioned before so we can focus on the exceptions so standard products everything that's matured maybe ml will drive the demand and the rest of it be managed by exception so it's been a great journey so far taking from that and I'm a Adam if I may ask you to add to what sue doctor just said but you would you did a phenomenal your company did a phenomenal work in applying AI and ml working with us to really come up I mean do a change that was sounds simple right which you presented in great detail yesterday what are your thoughts that you would like to share with this audience in allowing the AI and ml journey through a for innovation and breakthrough business benefits taking that as an example so that they can understand like for somebody who wants to embark on that journey what are the things that one should pay attention to sure and I think you know a B and bedstand stands out a little bit from the other companies on this panel because the work that we're doing is not so much software based you know we're a manufacturing company more or less we're creating beer and so you probably wonder where - where does machine learning and artificial intelligence play in there well actually in very many places and I think we're just scratching the surface so to go into the specific example where Pluto 7 is working with us is we found that we could predict a much more accurate filtration process by using an artificial intelligence it's pretty remarkable how you can study certain variables by going through the data and having enough data to do that and be able to predict with much keener accuracy when you should replace a filter in that fermentation process so it's pretty extraordinary thing and you know there's so much that goes into making beer for example that you know this is just one example that we're starting with to see how this how effective AI can be and you know we're very optimistic about those results so I think you know spawning off on that any part of maintenance could be predictive maintenance it could be something in our packaging lines it could be something with maybe more along what you were suggesting predicting inventory or customer demand there's really the sky's the limit on this and I think you know for all of you out there they're involved in companies are considering this this really does seem the most cutting-edge way to make that next a huge bump in advancement toward the future thank you and debug if I may ask you you are a fast growing retailer if a lot of business growth and scaling is something that is always at the top of your mind so with that in mind and related to what a demands so that I just discussed what's your perspective for us at California designed in our focus has also been trying to tie the two ends which is demand forecasting and supply chain the whole question of optimization of supply chain being like a start-up again with limited resources and you know growth that's surprising and clearing anything that we had ever forecasted it became very important for us to ensure that the decisions are near real-time and again you know managing by exception now I mean imagine I mean how many of you really think that a bed sheet you know that's what one of the key products we make needs a retail shelf space on the seventh floor at Macy's I mean really do we really need a huge floor of selling bed sheets how many of you would go there and be able to actually open one of those bags and feel it probably not so we see products like ours which are fairly commoditized completely being online and therefore the growth is phenomenal and we are competing with our channels whether it's Walmart comma or amazon.com so it becomes very important for us to be very nimble be fast and in the journey towards you know from from from basically ensuring our all our data is in one place everything is flowing into one place having a very distributed supply chain running from here to factories in India and China we have been taking on this journey and saying you know very good results of course those results are are still very basic whether it's inventory reduction or its you know speed in making decisions for changing what is in production line but how we are looking at ML and then AI is that as we scale up and we you know we face this hyper competition we would be ready for whether its demand forecasting supply chain planning price optimization and what-have-you thank you so Vikram coming back to you and marketing right on a similar theme now that you've considered you've looked at I think you always drove the envelope when it came to technology because you have to do that in digital marketing what are the consideration especially around going on about your AI and m/l journey that you want to point out right that would be of interest to this audience so one of the things that I would say we have been using a lot of prediction based modeling earlier we used to call it as forecasting for like late nineties in Cisco from a supply chain point of view but this motion of using the data science not from forecasting perform prediction point of view is pretty new and from strategical angle there are multiple things which comes top of the mind there is a big hype around AI nml a lot of companies today are investing because they feel is a magic and it's one silver bullet which will solve all of their problems but I would just caution it is not a magic it's not a it's not one silver bullet which will solve your problem it it's a big step forward and it requires a lot of things thought process change in the company itself your business objective should be very very clear a second thing is the way you use it from a technology and from data point of view data science and AI can only work on the data itself if you don't have the data in a way that could be useful machine it doesn't mean that you need to have Amazon or Google like exabytes or petabytes kind of data but you should have enough diversify that you could use it from machine or algorithm and the third thing is as I said it's not a silver bullet it requires a lot of work sharing the data in and there should be a willingness to take that insights from the ml and put them into the process sometimes we get a lot of insights but when we're trying to drive the experience with the customers like cisco.com for example when we want to say this customer wants to only learn about this tell them that message it goes into that Channel oh we know more how can should this be the message or no we this page cannot be customized for an example so sometimes it's in the front end kind of work it becomes a little difficult when you have the insight you know what customer wants but when you want to show that message to customer your tools platform doesn't support so there are a lot of constraints I will just caution that first from a statistical point of view that AI is not not which can be implemented tomorrow it takes it takes some it takes time especially in a big company like Cisco the second thing from tactical point of view is normal activities like what is it till the time if your business objectives are clear till the time you get there what is how can you keep on adding the value in the process because everybody thinks about AI they always think about either optimization whether it's a cost or process or you say how can I get the innovation or competitive advantage and how can I use it to extend my business to the next level but all of these things when you go to the tactical level these things will take time and technically you cannot wait for a couple of years to produce the results so you have to understand what those business challenges are where the things could immediate here at the value so for example we were lucky enough that we were part of the sales organization and there are a lot of companies which has done a lot of things for the sales already so we had to take those learnings and see adjust to our own culture so for example customer churn model customer risk analysis life cycle advantage product recommendation all those things but what is the right contact on this company what is the timing that you should call that guy what that prescriptive insight when you talk to that customer all of these things came came in handy to drive that immediate value that we had to show step by step before we get to that bigger goal the second thing from a customer success point of view was all about how do we provide the right relevant message to the customer and change that experience so so tactically it's more around how can you keep on adding the value but only when you're strategical objectives are very clear very good points there I heard data folk keeping focus on data which we heard even during some of the keynotes I heard Shawn mentioned that yesterday in and then also people always when you talk about a and M L it's all about data and then understanding where you want to go so sue doctor similarly any insights you want to talk about in the first MLA I like a proof of concept that you did that we would like to point out I think it has to be tightly integrated with business planning so we just it's not just about having data and trying to come up with a magic formula that it's going to predict especially for our use case it was demand plan come up with an accurate demand plan so you have to be tightly integrated with business planning as to what are the elements that go into a business plan one has to work with the given amount of data that is available to put those things along with the the algorithms that the ml can come up with so it's not just throwing in data and then it's going to spit out an end result and that's going to be an accurate forecast that's not happening so we the the business plan anything that's subjective objective or anything that you're getting from the field data when one is creating that demand plan and the business plan any element that goes into their thought process we have to make sure that the the ml can we could try to incorporate as much as you can only then maybe it starts understanding that there's not one thing that you look into and your entry and so if you are the person that's responsible for next year maybe yeah whatever how many folks are going to attend this conference so it's not it could not be one factor or two for you to accurately predict that similarly we had to get into that it has to be very tightly integrated with the overall business plan and it mean like he said it's not something that comes out automatically so you have to then look into what what products can you really apply that and that's going to be more helpful so we're still in a very early stage but we do see some positive results of course some places there are disappointments so we just have to continue to tweak it and fine-tune it and get to a point so it's it's extremely important to put those all the elements that are there in a business plan into it so it's been a great journey as to some we had some successes and some obviously you still have to learn from deeper right it's not magic it requires work so Tucker mentioned that hey there has to be focus and plan behind in your example one of the things that I heard and I want to relate it to your experience right that was presented in the keynote today by Rajan says it's all about simple AI and I think in your case what we achieved or if what you achieved was something that at the end became very simple right so any thoughts around that well you know one thing I think is interesting about what we're all trying to do here and you know I'll use our case with a B and F as an example is we're talking so much about machine learning but what are we really trying to relate to that's humans so it's it's kind of an irony in a way that we're using a machine to better understand ourselves so you know and then until we enter a world where maybe robots are running everything and then they'll be marketing to themselves who knows but for now it's all about that human aspect and that actually doesn't just go for consumers that's also with the the organization itself and that's you know a battle that you know I've had to fight and I don't say that as a negative thing I think it's about educating and getting more and more people aligned especially a big company like ours ABN Bev has 250,000 employees or somesuch so this you're not going to have 250,000 people on board with AI from day one and certainly not key stakeholders who aren't familiar with the technologies yet and haven't had a chance to really understand the value that it can bring so I think you know you say simple AI but what I would kind of taking a play on those words I think a real key for us if we want to lead this AI revolution is to find a way to simplify the message how can we show this value to everybody top up and down that they can see what we can achieve and that it's not a threat if anything it's an enhancement thank you deepak any comments I think as far as ml nei is concerned you know it's it's more like a journey right so as you said it's not a magic bullet and the biggest advantage that that we feel at California designed in trying to ensure that we set the vision that this is what is going to drive competitive advantage when product differentiation may not be a differentiating factor this kind of drives us as a company to set everything in place so it's not that we have to set up some new system that's only going to do ml base demand forecasting right so whether it is basic analytics to advanced analytics or you know what have you it's it's like a journey right so but once you have a vision that this is where you need to be and many of those visions are still visionary visions this does bring in a lot of efficiencies it kind of becomes a rallying point between the key stakeholders you there's the you know there's an added advantage where you'll have to relook at all your processes and what have you because as Adam said you know not everyone on board right away so you know you kind of internally debate figure out what you do you know kind of break old moles make new ones so we see a IML not just in what we expect it to produce after few years but also as a driving force towards being you know on your feet and you know just pushing hard and you know being being in the front so between Vikram you and sadaqa you sort of represent the high tech industry right I would like to hear Vikram first to you and then so that were following him your thoughts on now that you have embarked on this AI and ml and started using it at least getting to understand how to use it in your respective spaces I would like I think you articulated in some places that okay it's a journey what you see right in the high tech space where do you see AI lab being leveraged more and where do you want to give this audience okay here you might be tempted to go into AI but you may want to go with a little bit of caution right because I think one of the things that what I'm trying to understand is in your experience that I'm sure that what the audience would like to understand is as you are going through this journeys there are learnings like okay because AI everybody wants to get into the ml bandwagon but not every use case may be meant for AI and ml so any perspective taking that into consideration so when we talk about a high your ml there are always risk and benefits to that as well when we talk about any model or any recommendation there is always a percentage of accuracy and and one of the things that I was talking earlier was when we look at the ml today these are specialized models specialized model learning from the data about previous behavior with this set of behaviors this was outcomes at car driven and that drives your prediction about what shall you do tomorrow because there's the same pattern which will repeat the same thing as zooming that so when we took put the models in we get one thing is we cannot just blindly trust the model there has to be some way off of understanding what those models or outputs are talking about put the human intelligence around it and then say does the sense does this cases even make sense or not so when we were working on a project customer segmentation which we are still working on to evolve it to a next level we realized that we were able to divide our customer in around for that one particular product the customer set into like 16 different segments but when we looked at with the business owners trying to understand what does it really represent and how shall we drive the experience of each of these segments or each of this set of customers half of them did not even make sense to them because it was more around you know what this seems pretty much similar to that their their messaging will be little different so why don't we merge it together this sense that this one does not even make sense because these things is like if everyone is using perfectly then what we will do with them so when we look at the machine and these kind of risk we cannot blindly trust the machine yet it's a specialized AI which does the specialized things even better than human in certain aspect aspect but human supervision in putting that to the motion is still very much required that intuition of looking let's say this patterns meant customer will order but those patterns does not mean much in today's environment how can we how can we understand from it how can we apply our human knowledge and how can we keep it always tuned in into the realistic business scenario becomes the most important work in AI it's not just getting the data getting meaningful data driving the insight and putting the insight to work but it's all about taking that model making an intelligent decision around it and then putting it to work which which makes it a recursive cycle so so human interaction is is very important in my opinion to take this AI to the market maybe when the technology was in in certain time and when we have general-purpose AI and then it's a different story but but for now it's a specialized AI and MLS have specialized model based on the data it should not be taken on the face value there are certain use cases like vision detection and all those things where a is already improved beyond human capability but when we talk about business sense of what makes sense and what does not I think putting it blind you blind trust in that and and taking the action on that without supervision can produce more data can be can produce more risk than advantage as well in certain scenarios okay thank you anything the summary what you said is a any AI solution requires technology and domain and business expertise and the second thing is there are models that can be leveraged as is winning like what they call as the pre-built models right vision API you talked about but there are certain situations and some of the most of the business situations where you have to have a custom model thank we we took it a little bit of a simplistic approach wherein obviously like a dimension first thing was ml is going to predict my demand no way so you can ml really predict what kind of dinner are you gonna have no way so obviously there was that that kind of thinking within the company then we took much more simplistic approach as to okay in all the different product lines or different customers or segments let's pick something that's where you think there could be a little bit of more predictability within the human thinking so within your own thinking there could be certain things that you are more more consistent in your thinking and there could be some where you are you are trying to gather additional data to digest and come up with a solution so or an answer so we then we had to dig through those questions as to okay what are the areas where you could be more consistent the customer could be more consistent a market could be more consistent so we can drive our inventory optimization by understanding how the demand is being generated so we picked up more of those mature products or markets or and then so obviously the acceptance has to come into picture that hey what the machine is predicting is pretty much close to what can I rely on it so you have to take the use case wherein one starts getting confidence and so obviously you want to pick up the the products and the markets and there are a little bit more consistent obviously the value is in the unpredictability where it can predict but you can't start with that so we took a simplistic approach let's take baby steps so we are at that stage for we came up with the first use case now we'll take it to the next level can I really apply to the NPI new product introduction oh we don't know we will I think that's that's to be seen thank you I think the summary there is crawl walk and then run I think from a beam birth perspective you presented a very great region in your discussion yesterday I think as a representative of the manufacturing industry because I look at a be in bath I mean as one of the large largest breweries and you can say it's representing the manufacturing industry what would your takeaway be from that perspective around usage of ein ml sure and I I do have to say you keep saying we did this great presentation but honestly give yourself some credit you know Pluto seven is the company that really did the machine learning in AI we just accepted it and put it into our system so all credit to you guys but you know I'd say this you have to find use cases that make sense and I think this is you know very in line with with what was being said before is that the fact is AI is not going to be the right solution for a lot of things I think there will be more and more use cases as technology advances and you know foundational layers of technology so say in our breweries the more advanced our data collection is for example the more opportunities will emerge where we can do things with that data but it's it's essential that you really do have the right situations and I think that's where someone could run after this a little irresponsible and guess gets so excited about the AI machine-learning idea that say oh we can do ml everywhere and that's that's really not true I mean if you were gonna go swimming in a children's pool you wouldn't say let's go buy scuba gear you know to do that you know there's certain things you have to find the right setting and I think that's a combination of in our case working together with experts who really do know the right situation in the case of Pluto 7 with us you know we actually held a hackathon where we explored a number of areas where we thought a I could be valuable and we look to them to come up with a model that worked and from those different options it became apparent that we had found something here so I think you know to pursue something in this space for all of you I would just make sure that it's a prudent decision definitely I'm not trying to stop anybody's excitement be as passionate as possible but you know save yourself the trouble of overdoing it for the wrong situations Thank You Adam and then before we open it up to the audience debug final thoughts from you as a representative of the retail industry and high growth as far as representing retail that mr.<br><br>Bezos would be better off but you know I'm thinking like ml AI is like a little baby right so you know you start putting in data you still have to you know kind of feed and change the diapers and that's where we are right now we are hoping that in a few years this baby is gonna grow up and she's gonna run and she's going to do pole vault which I never did and you know she's gonna win Olympic medals which I couldn't even dream of so this is this is pretty much how we are looking at ml nai right so it's with us we have to pick you know with this we are driven to ensure that we think future we set up systems dataflow you know rethink processes and then like for example the planning in a box that we did with the Pluto seven you know if you were able to see some good results as far as demand forecasts and supply chain optimizing is concerned so we press a button you know we see what's going on we quickly change what's in the supply chain I mean it's still basic there's a speed now so 500 1000 2000 SKUs that we have to manage we are able to do it quick and really fast now when will this baby grow up and just take all charge well we'll see when thank you I think with that I'd like to open this up to the audience we have about five minutes left it's more for you I guess we didn't use any package I think I will leave it I live it to the park yes people the question is is there any packaged application out there that can help jumpstart their business well it'll sound like a sponsored ad but you know we used planning in a box that Pluto 7 has which uses of course Google Cloud and ml nai things within the Google ecosystem that did help us a lot as far as our inventory controls are concerned so with you know much very fast demand very decent demand forecasting and a quick connection to supply chain we were able to you know see very visible inventory reductions so right I think more with the fast movers because again you got you know we as a company are slow movers are really slow so you know one of the disadvantages as we were talking earlier you need to have data to run some of these morals and see real results so our slow movers are really slow mover so you know we didn't expect actually some of the results to be benefitting the slow moving SKUs and what have you so we kept our safety stock levels in that case and worried more about you know what was really selling and where we had growth any other question okay if not I think that's pretty much it feel free to drop by our booth at Pluto 7:00 I have to put it up there we can show you more demos on what's possible with the A&M<p><img src='https://images.unsplash.com/photo-1518081461904-9d8f136351c2?ixlib=rb-1.2.1&q=85&fm=jpg&crop=entropy&cs=srgb&ixid=eyJhcHBfaWQiOjQ1NzMwfQ'><br/></p> <br /><center><hr/><em>Posted from my blog with <a 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