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Matthew Halliday: Alright Hello everyone started in a few moments we're just letting some more people join the webinar so sit tight and we'll be with you momentarily.
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Matthew Halliday: Those of you just joining we're going to be starting in probably 30 seconds just waiting a little bit for some more attendees to come in, thank you for joining and we'll be underway very soon.
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Matthew Halliday: Alright we're going to go ahead and get started Thank you everyone for joining us today.
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Matthew Halliday: we're excited for our session, which is the third in a four part series that we're doing around Oracle EBS and analytics.
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Matthew Halliday: and specifically today we're focusing on in quarter for supply chain.
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Matthew Halliday: We started off with an overview with them, we did financials we do supply chain, and we have another one left in this series which we'll talk about in a moment.
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Matthew Halliday: Just a little bit of housekeeping here before we jump into everything.
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Matthew Halliday: i'm sure most people are familiar with zoom and two webinars certainly avail yourself of the chat or the Q amp a function, we will be taking live questions any questions you might have.
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Matthew Halliday: put them in the chat when you have them, we will get to them address them at the end, or maybe even address them as we go, so please.
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Matthew Halliday: Give comments and feedback anything you want to just kind of comment on as well put that in there, we love to hear your feedback any insight or perspective, you might have please do that as well.
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Matthew Halliday: And so, with that the recordings will be sent to you and will be available, so if you have co workers colleagues of wanting to be here, but weren't able to you can share it afterwards.
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Matthew Halliday: Or you can watch it again in the event that you have to leave during the webinar for some reason.
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Matthew Halliday: And so today we're going to be going through supply chain and talking a little bit about some of the things that we have going on and what we see how we seen quarter playing in that space.
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Matthew Halliday: And so, as part of these sessions i've been able to bring in some really gifted smart people to help in the conversation and to bring a unique and interesting perspective.
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Matthew Halliday: And so today my two speakers are introducing a little more detail in a moment, but we're going to be focusing on supply chain, and we have ashwin who's going to speak to that.
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Matthew Halliday: And then also we have Patrick who's going to be offering an overview of the Platform, as well as a DEMO and then we'll have a Q amp a session at the end any questions, please put them in the chat and the Q amp a and we'll get to those.
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Matthew Halliday: So i'd like to thank my speakers for being on joining us today so just going to introduce ashwin first actually Maria is the senior director of product management, adding quarter.
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Matthew Halliday: has been here a number of years, sounds for years now, but prior to that he'd spent over eight years at Oracle but has a wealth of experience in this, and specifically around the area of supply chain so welcome ashwin it's great to have you.
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Ashwin Warrier: Thanks Matthew thanks for having me.
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Matthew Halliday: And then another one another heavy hitter that we have here is Patrick rafferty director of sales engineering and quarter, but prior to in quarter.
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Matthew Halliday: Patrick it's been a number of years working in an analytic space leading a lot of exciting projects and doing a lot in terms of taking software and making it actually perform magic at customers, and so I actually co founded a.
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Matthew Halliday: Consulting company that did very well and excited to have Patrick join us today so welcome as well, Patrick.
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Patrick Rafferty: Thank you Matthew happy to be here.
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Matthew Halliday: So one of the cool things that we've kind of been discussing and talking about it just getting to know these.
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Matthew Halliday: You as individuals as well, and your backgrounds.
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Matthew Halliday: Is we've been making this comparison about our technology and a lot of the approach that people fundamentally relying on, even though they might be using new quote innovative products is really founded on an approach that kind of dates back to the 80s.
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Matthew Halliday: And when we're just thinking about the ETS, some of us have dread, and some of us have fond memories, and so one of the questions we've been asking just quickly to kick it off.
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Matthew Halliday: We, like our 80s, music, but not so much our software, but ashwin for you, what was the music from the hades that spoke to you.
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Ashwin Warrier: In flied.
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Ashwin Warrier: dire straits.
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Ashwin Warrier: It would that really stand out but yeah we need these was was it was a great time for.
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Ashwin Warrier: Iraq and best way to relive, it is now with with peloton you get on the peloton bike you you've got all the music back on it's been fun.
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Matthew Halliday: I love that I love that i'm hoping that the peloton bikes and not having the same supply chain shortage there we're seeing you know, there is with some of the tech so.
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Matthew Halliday: that's cool love to see the your your leaderboards there in terms of your power outage and how it, how it kicks up when brothers and arms kicks off.
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Matthew Halliday: Patrick how about yourself what are these music, are you into.
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Patrick Rafferty: yeah so I tend to frame it, you know, not a million miles away from peloton but I didn't to frame it in terms of karaoke karaoke fan so i'm going to go with the hall and oates with a special sort of Honorable mention for don't you want me by the human league.
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Patrick Rafferty: I think there's a good.
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Patrick Rafferty: A good set of karaoke songs for your library you're out there, trying to build out some tunes.
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Matthew Halliday: So I know for me i've learned to new things already and we're only a couple of minutes into the webinar so I didn't know ashwin was a huge peloton.
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Matthew Halliday: Right now, listening to his 80s, music and I had no idea that Patrick listened to karaoke so that's pretty awesome.
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Matthew Halliday: Alright, so we're going to just kind of dive in here and have a kind of conversation about supply chain, so we really want to focus on.
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Matthew Halliday: You know what are we seeing with supply chain, and what I thought we should do is kind of just start ashwin you know you worked at Oracle policy article has a supply chain system.
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Matthew Halliday: US specifically working around supply chain, maybe tell us a little bit about what were the problems you're trying to solve at Oracle what was the big thing that you were hoping to get done and how are you what was the approach that you are utilizing to try to get to that end result.
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Ashwin Warrier: yeah good question Matthew I think for supply chain.
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Ashwin Warrier: As as a business, I mean.
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Ashwin Warrier: There are two problems that really stand out one is the and you talk about supply chain, the analytic needs for supply chain.
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Ashwin Warrier: it's the breadth of coverage that you need so when you when you talk of supply chain It all starts with your order capture sales orders, so you have bookings that come in.
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Ashwin Warrier: you've obviously got a full food there.
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Ashwin Warrier: There are multiple ways, you can fulfill them right if you're if you're Amazon and you've got suppliers, you get procurement from the suppliers you've got inventory, which is your your buffer on which you, you base your.
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Ashwin Warrier: Meeting meeting the demand and then, if you're a manufacturing company you've got manufacturing and subcontract manufacturing.
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Ashwin Warrier: And all of a sudden you've now got a fairly wide expanse right and you've got transportation, to look at you've got warehouse management in your different facilities.
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Ashwin Warrier: And all of a sudden supply chain is this huge piece that you have to manage in terms of enterprise applications and the analytics that you need on top of it to really have like the full visibility is is quite large so it's bread, but not just Brett I mean if you think about.
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Ashwin Warrier: Your end of period close and you've got your inventory adjustments and you're going through.
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Ashwin Warrier: Multiple adjustments to make sure, everything is aligned, you want to look at your transactions coming in, you want to look at your inventory numbers.
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Ashwin Warrier: And you want to do this, not just at the category level Yukos sub category.
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Ashwin Warrier: inventory locator lots of lots cereals, so now all of a sudden you've got depth as well and depth for a company large company.
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Ashwin Warrier: means scale and performance issues when you're trying to look at all this data right, so this That was the big challenge was how do you solve a breadth of coverage and how do you solve for depth and provide analytics for On top of this.
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Matthew Halliday: So, so what what's the way that you tried to solve for that when you were an article like what what was what was the way that they were you know what was the approach.
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Ashwin Warrier: yeah, and I mean data warehousing was a very common construct back then didn't have to go back to the 80s, even as late as late 2000s.
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Ashwin Warrier: Oracle had a fairly mature data warehousing platform and applications built on top of it called the Oracle bi applications and one of the.
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Ashwin Warrier: offerings that that I work very closely on was supply chain and manufacturing.
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Ashwin Warrier: And so there was a breadth of coverage in terms of pulling from Oracle E business suite as a source or Oracle jd edwards is a source and building this ETF pipeline right massive pipelines that were built so that.
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Ashwin Warrier: If if you as a as an oracle customer had supply chain modules it became very easy for you to.
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Ashwin Warrier: take these modules off the shelf will start to deploy them.
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Ashwin Warrier: and start to get at least close to your end state we run as much as possible.
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Matthew Halliday: yeah I remember looking at some of those and have to DEMO some of those in my time as well, and one of the things that always struck me is just.
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Matthew Halliday: How fragmented those areas where it was always like you'd have insight potentially in one area and then, if you needed to go and find something else about that item to see where it wasn't a different part of the business.
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Matthew Halliday: or different part of the flow you'd have to go to a different dashboard and then try and you know copy and paste the Item number and kind of.
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Matthew Halliday: You know kind of stitch everything together manually yourself, and that was always a kind of stressful experiences kind of DEMO, that is to show it as a unified flow of how someone would actually use this in the real world.
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Ashwin Warrier: yeah and it was great for for predefined dimension models right So if you are within the confines of a dimension model.
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Ashwin Warrier: It was great all the data was there, you were able to get to it a lot faster than doing the three and a half based approach, because you could never, never run.
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Ashwin Warrier: Large queries that scale against your third normal form data, unless you kept using your database and keep kept building very specific indexed queries against your source systems right, so it was great for.
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Matthew Halliday: You just dropped a big word that maybe people might not be familiar with certain normalization form or three NF for short, Patrick or or.
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Matthew Halliday: ashwin How would you define that How would you explain that to someone who might be, you know from supply chain is in the business and go, what are you talking about like talk about these dimensional models and talking about three and a half, what does that mean for me.
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Ashwin Warrier: yeah I mean it's it's basically a way for all enterprise applications right whether it's Oracle E business suite it's optimized for for basically providing.
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Ashwin Warrier: updates So if you if you're doing forms and you're putting data in the system, it is optimized to make sure that you enter it once and it lands in the right place and all the relationships etc maintained in a very easy form.
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Ashwin Warrier: it's great for for an enterprise application it's not great for analytics because for analytics now you've got to span across multiple tables and multiple relationships and joints to be able to look at it, so what.
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Ashwin Warrier: analytic tools have historically done knitting back to the Ralph gimbal model in the early to even the late 90s is they've said the relational model is great, the third normal form.
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Ashwin Warrier: SAP Oracle source systems are great for for capturing the data, but if you wanted to analytics you have to take the data out converted to a dimension model or a star schema.
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Ashwin Warrier: And that's the best way for query performance and for scale and.
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Ashwin Warrier: What we're here to talk about today is Matthew what you in the co founders have done an encoder very early in the process is that direct data maverick framework right and that's a big paradigm change where.
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Ashwin Warrier: you no longer have to do the dimension modeling of the flattening to really get the query performance and the scale that that was missing the late 2000s and.
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Ashwin Warrier: Even as far back as two or three years ago.
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Matthew Halliday: So So what does that mean, then, unlocking that so you can actually do analytics on the source data as it is in those applications from someone who has been in this business of helping provide analytical insight to supply chain people.
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Matthew Halliday: Across the board what what does that what does that unlock for you like someone's now looking at that same problem with a different set of tools at your disposal, what did that open up like what got you excited from that perspective.
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Ashwin Warrier: yeah I mean one was just initially I couldn't believe that was possible right, I mean we've we've grown up we've learned about.
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Ashwin Warrier: dimension modeling is the only way forward just because of performance and scale so for you to say that.
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Ashwin Warrier: Without without having to flatten your data keep the data in the same form as your source system, and you can get the same query performance.
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Ashwin Warrier: And scale or, if not better in our case, actually proving out to be much better was was eye opening, but what that opens up for a supply chain.
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Ashwin Warrier: Business user or a supply chain executive is it becomes so so much easier for you to do your analysis and if you've got new sources you're bringing on you, moving from Oracle E business suite to.
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Ashwin Warrier: The fusion on the cloud or you're bringing two or more sources hey there's a new source to open as a great supply chain to.
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Ashwin Warrier: You want to bring in data from these tools now you don't have to spend six months have data engineers cranking up all your ETA maps it's just.
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Ashwin Warrier: connect point create your relationships and you're up and running in six weeks and I think if I can share my screen, one of the things that really stands out for me.
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Ashwin Warrier: Is what I heard about shutterfly and they they were a great example of a success story and coming from.
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Ashwin Warrier: Supply Chain executive, who says that in kota is the greatest thing since sliced bread was.
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Ashwin Warrier: It tells a lot right but to me what stands out is the six weeks to integrate with all of Oracle E business suite.
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Ashwin Warrier: get up and running and even despite having the Oracle bi applications warehouse it used to take customers months.
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Ashwin Warrier: Sometimes, six months, just to get up and running to get to about 70% of usage and then for the remaining 30% to get to nirvana it would take them weeks and months because they've got custom tables they've got to make them all it's almost like trying to fit.
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Ashwin Warrier: A square peg in a round hole and the amount of work that goes in is huge right so compared the six month implementation with six weeks.
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Ashwin Warrier: Compare the fact that, without having to do all the heavy lifting you get the the speed you get scale you get time to insight really fast and your kpis are starting to tell a story by itself right 90% reduction stock outs is huge for supply chain.
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Matthew Halliday: It is indeed yeah it's funny.
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Matthew Halliday: That quote greatest thing since sliced bread probably hasn't aged to well, given that probably a number of people in on this on the webinar today have started baking their own brand and.
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Matthew Halliday: Creating sourdough and all of the country loaves and kind of.
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Matthew Halliday: focus on that, but the interesting thing here is that this story goes even further than that right, this is the ripples and the ramifications of being able to reduce those stock outs, to be able to manage your supply chain.
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Matthew Halliday: With days on hand to know when to exit but I product when to you know you know all of the options and levers, you can pull and push and press.
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Matthew Halliday: Being able to have that insight in a timely fashion, one of the things that was interesting with shutterfly is that.
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Matthew Halliday: They they forecast during that period they make 75% of the revenue within a six week period within a year.
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Matthew Halliday: and being able they're forecasting twice a day and being able to take those forecasts and and then be able to analyze and then there's a buyer adjust and course correct.
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Matthew Halliday: Is the reason you see those huge drops right and it's the time to inside the time to adjust that supercritical, to the point that.
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Matthew Halliday: The companies that they used to go to when they used to get to a point across shutterfly once their customers have a great service and if they can provide it at that time, they would leverage, you know other party vendors, to provide that.
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Matthew Halliday: And one of their largest vendors suit saw a massive drop off because they weren't getting the workloads from shutterfly they were expecting and actually missed their numbers as a result.
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Matthew Halliday: And they actually found out and connected the dots, and the reason why, in that company became an encoder customer as well.
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Matthew Halliday: When they just saw how impactful this their supply chain to shutterfly had been is you know they wanted the same thing for that business so super super exciting use case that definitely thanks for sharing ashwin.
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Matthew Halliday: What what other things do you see now, though, is like how supply chain experts on the phone are on the line listening in.
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Matthew Halliday: How should they be thinking about analytics and the use and what kinds of things they should be trying to solve and where it can you know where they can change with this data coming in, like what what should be the way that they should think about this now in this new paradigm.
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Ashwin Warrier: yeah I think the best way for me to do that is shared example, an example that.
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Ashwin Warrier: Most of you are.
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Ashwin Warrier: With a supply chain background can can relate to, and a very common use case is your booking billing backlog right you start with your booking cycle, if you.
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Ashwin Warrier: You go through your your fulfillment cycle you've got you've got the billing and you manage the backlog both your fulfillment backlog in terms of.
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Ashwin Warrier: Things that still are to be shipped and your your financial backlog, which is things that you've you've got to invoice or if the invoice you've still got to recover cash for.
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Ashwin Warrier: But trying to do that analysis, where you're trying to do cycle times is has historically been very hard.
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Ashwin Warrier: Just quitting directly against your EBS instance and it's required a data warehouse based solution historically right and the data warehouse solution is i'll show you is not easy, as well.
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Ashwin Warrier: But this is the kind of analysis that supply chain executives looking for is give me a quick quick way to to see my kpis to see how it's trending.
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Ashwin Warrier: And if there's a problem or an exception alert me to that right, then, then I can drill down and say Okay, I see there's a problem with order to shift.
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Ashwin Warrier: The times are trending up and it's specifically a problem with electronics, so I can keep rolling and trying to find the root cause, but to do this analysis.
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Ashwin Warrier: In a quarter it's actually it's actually a much easier process.
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Ashwin Warrier: What we do is we keep For those of you who are familiar with Oracle E business suite some of these tables might might make sense.
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Ashwin Warrier: Or you can relate to this, but what we're trying to show you, is it, this seems like a cobweb of tables and relationships, but look at.
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Ashwin Warrier: it's got 1015 tables it's got the data that you need and it's it's exactly a copy of what you'd see in your system, but the direct data map, so we have makes this really effective were based on your query based on the the kpis that you saw.
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Ashwin Warrier: The insights that you saw.
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Ashwin Warrier: It knows that in quarter knows the, these are the tables, you need to go.
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Ashwin Warrier: retrieve the data from.
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Ashwin Warrier: And it gives you a result set lightning fast, and it can do this at scale right two things that we could not do.
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Ashwin Warrier: With the data warehouse model and if I if I were to just move on to the next one.
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Matthew Halliday: Once I can actually if you just go back.
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Matthew Halliday: So this is this is that three and a half model right, this is that.
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Matthew Halliday: normalization representation of the data it reminds me of something that bar Samuelson.
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Matthew Halliday: says from Forrester, and he talks about this whole idea of don't bring data to be I bring bi to data and the whole point is is like bringing your bi is close to the source data as it is.
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Matthew Halliday: And to me that that sounds exactly what you're doing right here, right is you're saying I can bring this as close to your source system as possible and get all of the benefits of things like.
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Matthew Halliday: Flexibility of looking at data but also ingestion rates, being a lot faster the data pipeline is less than invasive and complex and difficult less error prone all of those kinds of benefits.
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Matthew Halliday: So this is definitely the kind of the holy grail of how you want to look at it right that's what everyone starts from this point if you're doing a data warehouse or you're doing a new quarter approach.
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Matthew Halliday: That you start from this this everyone starts with the same set of data from these applications.
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Ashwin Warrier: Exactly and another nice part is.
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Matthew Halliday: I mean, if you think about.
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Ashwin Warrier: every customer who's got Oracle E business suite or most enterprise applications you've got custom extensions right you've got extension tables custom tables and.
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Ashwin Warrier: If you have custom tables, this was the biggest challenge with with Oracle bi applications or any data warehouse model right.
00:22:02.220 --> 00:22:13.050
Ashwin Warrier: If you've got extension tables in our scenario it's it's it's importing the table, creating that relationship couple of steps in Europe and ready and good to go right, you can start to get insights.
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Ashwin Warrier: But if you were to look at the the more traditional data warehousing model just for me to do the analysis that I showed you the order to order to cash or the order to invoice psycho look at all the relationships, I had to build first you had to build.
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Ashwin Warrier: As many as seven star schema seven dimension models are subject areas, and then you also have to each hub and spoke is actually a complex ETF map that you're building right and you're writing sequel code to take the data from.
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Ashwin Warrier: The third normal form shape that you just saw earlier and you're trying to convert it or force fit it into this structure and it served a purpose in the early 2000s, this was the best way to get data out.
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Ashwin Warrier: But it is, it is extremely hard to make extensions add a new data source.
00:23:04.890 --> 00:23:18.180
Ashwin Warrier: The ability to make changes is hard and it only this only gets you so far right, because how how many data engineers, can you keep cranking away at this, so if you have a new problem and it doesn't fit the mold.
00:23:18.630 --> 00:23:30.930
Ashwin Warrier: Then you've got to go back and spend another six weeks trying to to enhance this so that you can get the report that you asked for six weeks ago, which is now too late, because now you've moved on to the next problem so.
00:23:32.100 --> 00:23:36.780
Ashwin Warrier: encoder makes it nimble it's it allows you to do like fast incremental refreshes.
00:23:37.620 --> 00:23:51.810
Ashwin Warrier: I know our customers do half are incremental refreshes was unheard of in the data warehousing well, I mean they talked about micro etfs but you could not do a fully deal cycle more than once a day at times for large customers.
00:23:53.070 --> 00:24:06.360
Matthew Halliday: yeah you know when when I look at this it kind of makes me think about the importance of experimentation and how this model does not allow experimentation it doesn't allow you to bring in things and try and look at new columns because the overhead of getting into so much.
00:24:06.780 --> 00:24:18.780
Matthew Halliday: And then, when you do bring it in it becomes a part of your data pipeline and then, if you ask them now, you can remove that column i'm guessing it doesn't get removed right these things just become technical debt that just reside in them the complexity builds and grows.
00:24:19.530 --> 00:24:20.730
Matthew Halliday: Because it's very hard and if.
00:24:20.970 --> 00:24:27.750
Matthew Halliday: People want to see something, whereas if you have access to every single column that's in your source system and it's just a matter of drag and drop.
00:24:28.590 --> 00:24:40.830
Matthew Halliday: But that means I can create an unlimited number of permutations of experiments and segmentation of data and way that I want to look at my data that maybe i've never done before and they're not part of these very rigid structures that are.
00:24:41.490 --> 00:24:44.040
Matthew Halliday: Pre brittle, especially when you want to look across them right.
00:24:44.340 --> 00:24:47.160
Matthew Halliday: So you're seeing these seven separate data sets.
00:24:47.400 --> 00:24:51.630
Matthew Halliday: Not not joined not connected not giving you the full flow.
00:24:51.990 --> 00:24:57.090
Matthew Halliday: You piece that bit together and hence copy paste look at this go to a different dashboard and then try and.
00:24:57.360 --> 00:24:58.560
Matthew Halliday: carry on where you left off.
00:24:59.550 --> 00:25:10.470
Ashwin Warrier: yeah I know the next next webinar series stops a lot more about the it analytics and some fat, but even if you move to a tool like snowflake or articles.
00:25:11.310 --> 00:25:19.920
Ashwin Warrier: economist data warehouse you're still not solving for the problem that you've got all these hub and spoke right each hub and spoke in the sequel to drive it might be easier you've.
00:25:20.520 --> 00:25:30.060
Ashwin Warrier: moved to a model platform, taking care of costs, but the effort to try and manage this as the sources new sources come on like I said new J E to open.
00:25:30.480 --> 00:25:41.940
Ashwin Warrier: Now you've got all these new sources you've got to go back to the plumbing all over it, so that that's The biggest challenge is to keep this all going in to this at low cost and to give performance and scale.
00:25:43.860 --> 00:25:49.860
Matthew Halliday: But any other examples that you could point to hear the that might be of interest.
00:25:52.440 --> 00:25:57.360
Ashwin Warrier: We talked about the inventory, I mean in metairie turn, is a good example right, I mean.
00:25:58.260 --> 00:26:07.260
Ashwin Warrier: If you've got two definitions that if you've not got this built into the system and, as the new to start to proliferate you start to see people use.
00:26:07.710 --> 00:26:15.360
Ashwin Warrier: Some of the data warehouse model, but they also start to use that lower power bi because now you've got to be nimble and do department to applications.
00:26:15.750 --> 00:26:25.080
Ashwin Warrier: So now you're starting to build these some of these metrics you go look at this and say Okay, I have these but maybe I don't have one off let's say inventory, just as an example.
00:26:25.710 --> 00:26:35.670
Ashwin Warrier: In the in the predefined stars humans, we start to build this out in the department of application and now you've got multiple users multiple executives were sitting there and saying.
00:26:36.210 --> 00:26:45.720
Ashwin Warrier: I calculated inventory turn using this formula and somebody is inventory turn looks great and looks strawberries and cream just because it's.
00:26:46.170 --> 00:26:57.870
Ashwin Warrier: They built it, they have they have the logic gave us a formula that they've used is very different from somebody else and now you've got multiple sources of truth, you don't have a single semantic model, so it becomes really hard when you've got.
00:26:58.980 --> 00:27:05.820
Ashwin Warrier: The span across and you're doing analytics that you've got warehouse management systems you're bringing up so you've got data coming from.
00:27:06.210 --> 00:27:22.740
Ashwin Warrier: Your w Ms that you want to try and tie in or another example, could be a natural extension to inventory is service parts you've got service parts that are coming from a field service application so if you want to look at a holistic view of inventory, you could pull in from from service.
00:27:24.240 --> 00:27:33.570
Ashwin Warrier: data as well, so you have to aggregate all the data from your service system use the same sort of inventory turn calculations and things that you do and apply.
00:27:34.020 --> 00:27:45.150
Ashwin Warrier: there as well, so you need a holistic solution that is based on a platform that is easy to build on easy and can evolve and scale over time.
00:27:48.840 --> 00:28:03.030
Matthew Halliday: Great yeah that's awesome so thank you very much ashwin for joining us definitely you know jump in with any comments or color that you can provide in the section is moving into now, but I want to just kind of move over to Patrick.
00:28:04.590 --> 00:28:17.760
Matthew Halliday: and Patrick Maybe you can can share a little bit more about this this in quarter product that we've been talking about and the impact that it can happen supply chain and maybe show the audience here a little bit about what this actually looks like.
00:28:20.670 --> 00:28:23.220
Patrick Rafferty: or percent thanks Matthew so we're going to show you this.
00:28:24.240 --> 00:28:30.540
Patrick Rafferty: will show you in quarter here in real life, how does this actually impact your business, what does, including give you.
00:28:31.380 --> 00:28:46.110
Patrick Rafferty: Give your give your key decision makers that they don't necessarily have to that they can't do in terms of the the old way of doing things the 80s way of doing things so we've talked about here at the outset and supply chain is very.
00:28:47.310 --> 00:28:52.800
Patrick Rafferty: very hot topic right now i've got a couple slides here, where I talk about you know some of the current challenges that are going on globally.
00:28:53.190 --> 00:29:03.750
Patrick Rafferty: And I put them together a couple days ago, and I feel like they're already there already passe to a certain extent, even just this morning was taking my car and to be serviced and listen to Bloomberg radio and.
00:29:04.620 --> 00:29:12.330
Patrick Rafferty: The Secretary of transportation pete Buddha judges gonna be going on Bloomberg this afternoon to talk about supply chain to let people know that.
00:29:13.020 --> 00:29:22.140
Patrick Rafferty: As you're doing your Christmas shopping this year, please keep in mind that we're in a real supply chain crunch, in terms of capacity right because that's really what supply chain is all about is.
00:29:22.650 --> 00:29:29.940
Patrick Rafferty: i've got orders coming in and how do I, how do we fulfill them what is my capacity to to fulfill them and we talked a little bit about shutterfly and your shutterfly needing.
00:29:30.420 --> 00:29:33.720
Patrick Rafferty: supplies and stock and being able to fulfill orders.
00:29:34.470 --> 00:29:43.860
Patrick Rafferty: We work with shutterfly we work with a lot of different customers a lot of different companies that have this challenge, and so the challenge that i'm going to kind of focus around today is it's really about capacity.
00:29:44.250 --> 00:29:52.200
Patrick Rafferty: And orders, how do I fulfill the orders that my sales people that my customers are actually placing how do I fulfill my my promises.
00:29:52.710 --> 00:29:58.320
Patrick Rafferty: To those customers just kind of what we're gonna be talking about today, but the thing to keep in mind as i'm talking about this is.
00:29:59.310 --> 00:30:06.570
Patrick Rafferty: Yes, this is about inventory in this particular aspect, but some are most interesting customers inventory is only half of the story.
00:30:06.930 --> 00:30:14.190
Patrick Rafferty: And we talked about capacity, we have a number of precision manufacturers on board here in quarter where not only do they need very specific parts.
00:30:14.670 --> 00:30:24.600
Patrick Rafferty: To build the products that they sell to consumers, but they also require a very particular set of skills and a very particular set of machines to build those things together.
00:30:24.900 --> 00:30:35.940
Patrick Rafferty: And we kind of group that into capacity it's not just raw materials and I got to ship an item from point A to Point B, but it's also do I physically have the capability and the the bandwidth to produce and and.
00:30:37.800 --> 00:30:41.010
Patrick Rafferty: And fulfill those orders so kind of going back to the 80s.
00:30:42.720 --> 00:30:48.120
Patrick Rafferty: The 80s reference here, you know a little bit of c&c music factory i'm not that's not one of the ones that I.
00:30:48.690 --> 00:31:02.520
Patrick Rafferty: happened to sing and karaoke, very often, but how do we use all of our available data to make these decisions, so should I just orders and what is my fill rate and what was my full rate last month and how much is salesperson a versus salesperson be selling.
00:31:03.300 --> 00:31:14.820
Patrick Rafferty: How do I use all the information I have available to me machine capacity inventory parts and Labor everything across the board that kind of ashwin showed you in that star schema model, how do I bring all that together.
00:31:15.780 --> 00:31:23.580
Patrick Rafferty: into a single you know into a single view so I can fulfill my commitment to my customers my customer being the kid who's waiting for Christmas present.
00:31:23.910 --> 00:31:30.360
Patrick Rafferty: The customer being someone who is going to go ahead and produce something in their garage and they buy raw materials and parts for me.
00:31:30.570 --> 00:31:38.760
Patrick Rafferty: Maybe they're going ahead and building a robot in their garage something like that, how do I fulfill my commitments to them and then obviously for all our large customers, how do I fulfill our.
00:31:39.990 --> 00:31:46.590
Patrick Rafferty: their commitments to their investors right, and so this is one where i'm probably rework this slide two or three times in the last week or so.
00:31:47.100 --> 00:31:54.030
Patrick Rafferty: You know bed bath and beyond, you know huge dive in their stock price last week I follow this guy on Twitter again and breslin.
00:31:54.570 --> 00:32:00.690
Patrick Rafferty: Who tweets about all kinds of things, but he just happened to talk about what the situation is off the west coast, the United States, right now, in terms of.
00:32:01.350 --> 00:32:06.270
Patrick Rafferty: anchored vessels off a long beach everyone remembers the Suez Canal ship getting stuck but.
00:32:06.870 --> 00:32:14.880
Patrick Rafferty: that's just a very visual and very memorable symptom of a larger problem which is hey there's a lot of issues out here in the supply chain.
00:32:15.300 --> 00:32:23.370
Patrick Rafferty: From people unloading ships at docs to supplies raw materials across the room so, whereas in quarter come in here right, and so this is the.
00:32:23.970 --> 00:32:30.660
Patrick Rafferty: kind of the long way of getting to here's what we're going to kind of show you today, so this is going to be using that same kind of technical.
00:32:31.170 --> 00:32:39.630
Patrick Rafferty: model that ashwin mentioned about raw access to data we've got about 3 billion rows of data in this particular in quarter instance and we're about to show you.
00:32:40.230 --> 00:32:55.530
Patrick Rafferty: And we're going to show you how to do current inventory current set of orders and going from orders to inventory and back again very quickly and very easily within a single pane of glass and and quarter and then kind of following on from that.
00:32:57.210 --> 00:33:02.100
Patrick Rafferty: The real advantage of in court is yes, we can answer all these individual questions along the way.
00:33:03.150 --> 00:33:15.660
Patrick Rafferty: But the questions you're going to ask tomorrow or three weeks from now or six weeks from now are fundamentally going to change if you're operating an active agile nimble supply chain organization and.
00:33:16.260 --> 00:33:26.520
Patrick Rafferty: You need to be able to enable your business users your key decision makers to make changes on the fly and to work with data very rapidly so we're actually gonna go and show you how we build these things a little bit and.
00:33:26.940 --> 00:33:33.570
Patrick Rafferty: I can actually break break down a particular report, a particular dashboard a particular insight and and quarter and add to it very quickly and very.
00:33:36.900 --> 00:33:43.800
Patrick Rafferty: Alright, so with that i'm going to go ahead and pivot over into the quarter plan, so this is an encoder instance that I have.
00:33:44.370 --> 00:33:53.820
Patrick Rafferty: stood up inside of one of our cloud instances, and this is designed to kind of just give you a full understanding of all the capabilities of the quarter platform.
00:33:54.540 --> 00:34:10.470
Patrick Rafferty: I can be someone who comes in here today and wants to look at a snapshot of all my on hand quantity all the amount of materials that I have in my warehouse at any given time over the past year, the past five years the past week, however, I choose to interact with my data.
00:34:11.580 --> 00:34:17.130
Patrick Rafferty: But how do we get to this point, how do I get to the point where i'm producing these great insights I can go ahead and make.
00:34:17.550 --> 00:34:24.510
Patrick Rafferty: Better decisions for my organization so in core platform typically starts by loading data into the quarter platform.
00:34:24.900 --> 00:34:36.870
Patrick Rafferty: And through this screen here, I can bring in data from all kinds of different sources whatever kind of source, you have out there, you have the capability to bring that data entry and quarter netsuite Oracle SAP mongodb.
00:34:37.800 --> 00:34:49.110
Patrick Rafferty: You can upload flat files and excel spreadsheets anything that you might have inside your organization that you use to make decisions in court, I want you to be able to bring that data in and make it available to your end users.
00:34:51.030 --> 00:34:56.970
Patrick Rafferty: When we bring in that data we load this into what in quarter, we will we call a schema essentially an area where.
00:34:57.330 --> 00:35:04.470
Patrick Rafferty: you've got specific sets of data related to specific business functions such as order management within Oracle.
00:35:04.770 --> 00:35:10.980
Patrick Rafferty: or inventory management within Oracle or inventory management within SAP we load that data into the quarter platform.
00:35:11.310 --> 00:35:20.460
Patrick Rafferty: As is so you heard Matthew mentioned and and kind of query ashwin on third normal form we bring that data in as is and we allow the users.
00:35:21.000 --> 00:35:29.160
Patrick Rafferty: to interact with that data, so why is that important right well we kind of went over that a little bit earlier, but what that allows you to do is.
00:35:29.700 --> 00:35:38.340
Patrick Rafferty: every piece of data every row every column everything that's in there, that a user might need to make a decision is made available.
00:35:39.120 --> 00:35:47.130
Patrick Rafferty: To that, and they have the capability to use that without having to pick up the phone and talk to someone and do a whole bunch more work to move data around.
00:35:47.400 --> 00:35:58.440
Patrick Rafferty: And to change things and to build out structures, they have everything they need right there in front of them to be able to be used, another key benefit here is things change.
00:35:59.640 --> 00:36:06.600
Patrick Rafferty: Items leave the warehouse they get scanned out they get scanned in orders get delivered orders get delayed i've got data coming in all over the place.
00:36:07.680 --> 00:36:17.160
Patrick Rafferty: Because in quarter doesn't change the shape of that data before making it available to those end users when something changes that data flows into in quarter.
00:36:17.580 --> 00:36:20.460
Patrick Rafferty: And it's available right there in front of them to make an updated decision.
00:36:20.820 --> 00:36:33.150
Patrick Rafferty: This isn't a decision where they make the decision in the morning and then that's it they don't have any more fresh data until the end of the day, there was a rapidly interact with their data, because in quarter allows you to to go down to that level.
00:36:36.780 --> 00:36:48.180
Patrick Rafferty: Thirdly, we have this concept called a business scheme these systems are incredibly complex, I mean unless you have a telescope you probably can't even see the text that was on actions diagram right where it's very small text very.
00:36:49.200 --> 00:36:57.000
Patrick Rafferty: lots of different tables and objects everything like that quarter brings in that data at that level, but then makes it available to the end users.
00:36:57.570 --> 00:37:09.360
Patrick Rafferty: At a more abstracted view of that data, so they can interact with that data without having to understand that underlying model, they can go ahead and work with those all those columns and all those rows in a way that's more easy to understand.
00:37:10.590 --> 00:37:22.470
Patrick Rafferty: And then, finally, as I showed you in the first my first share my screen, here we have the ability to visualize that data in in quarter, for your end users and so here's where i'm at I kind of come in today and talk a little bit about.
00:37:23.130 --> 00:37:39.840
Patrick Rafferty: orders and inventory so encarta has when it comes to Oracle on the Oracle landscape, a series of pre built assets that we call we kind of grouped into the term blueprints essentially a pre built set of dashboards content semantic views business schemas.
00:37:41.010 --> 00:37:48.480
Patrick Rafferty: To allow customers to get a huge leg up when it comes to starting to see the value of their data inside the quarter platform.
00:37:48.900 --> 00:37:55.500
Patrick Rafferty: So today what i'm going to do is i'm going to focus on starting with one of these dashboards called open sales orders Essentially, this is a dashboard.
00:37:55.830 --> 00:38:02.820
Patrick Rafferty: Where i've loaded all that data 2 billion rows of data, and I want to see everything that I have out there in the system.
00:38:03.570 --> 00:38:09.930
Patrick Rafferty: that's currently pending right, so the order has been booked it's a waiting shipping, or it hasn't actually progressed to that point.
00:38:10.920 --> 00:38:20.280
Patrick Rafferty: And I want to see between now and the end of the year it's great that i've shipped orders sold a lot of product and moved a lot of product over the past nine and a half months or so.
00:38:21.660 --> 00:38:23.010
Patrick Rafferty: But I want to know what's happening right now.
00:38:23.550 --> 00:38:32.220
Patrick Rafferty: From this point to the end of the year, what are the orders that I have to fulfill luckily I don't have anything that's passed through yet, but I can see, these are my top 10 customers.
00:38:32.670 --> 00:38:40.440
Patrick Rafferty: These are the top 50 items and what warehouses i'm planning on shipping them to how much that will those orders total to how much.
00:38:40.980 --> 00:38:56.070
Patrick Rafferty: How much physical units, I have available to me, and again, because in quarter gives you that capability to go all the way down very deep and very wide everything that's in the system, essentially is available to me and i'm not limited.
00:38:57.540 --> 00:39:07.350
Patrick Rafferty: When I interact with this information I interact with the state, I can go ahead and dive as deep as I want to I can come in here and particular and and select a particular customer, so you target or walmart.
00:39:07.740 --> 00:39:17.550
Patrick Rafferty: I can come in here and pick my top item a seven inch Amazon fire tablet in canary yellow click on that and everything i'm going to see on screen here is now going to update.
00:39:18.120 --> 00:39:30.000
Patrick Rafferty: Everything is interactive every single dash I can see i've got $500,629 of that big of that big number from before is tied up in this one particular item, and I can see that.
00:39:31.200 --> 00:39:48.000
Patrick Rafferty: i've got order quantities that I need to fulfill 13,911 1114 at walmart 654 target and again, as I scroll down, I can see all the detail here, so the idea here being that whatever happens in my supply chain, if I need a slice or look at my data from a different angle.
00:39:49.260 --> 00:39:55.140
Patrick Rafferty: Everything is available to me, everything I have at my disposal is made available to me as menus.
00:39:56.280 --> 00:39:57.810
Patrick Rafferty: And that's great Thank you my orders.
00:39:58.140 --> 00:40:05.400
Matthew Halliday: And one things you Patrick is people in nursing right that there's a little bit of a lag it takes a couple seconds, because this is not cached data right, this is.
00:40:05.820 --> 00:40:13.350
Matthew Halliday: Real queries against the 2 billion plus records that you're interacting with and you're actually seeing what is going on, so this is what they can expect.
00:40:14.010 --> 00:40:23.190
Matthew Halliday: And they're seeing that performance without those complex transformations that are taking the data from the sternum form into this star schema approach which takes a long time.
00:40:23.760 --> 00:40:35.160
Matthew Halliday: and brings them a lot closer to the near real time and still get that level of performances like I never seen i've seen dashboards built on star schemas that take longer to render quite frankly than that.
00:40:36.480 --> 00:40:45.150
Patrick Rafferty: yeah exactly right it's it's interactive speed, we want you to be able to explore that your data at the speed of thought you're trying to do, deep work and solve hard problems.
00:40:45.690 --> 00:40:50.880
Patrick Rafferty: You can solve those problems I can speak from experience, you can solve those problems by clicking on something.
00:40:51.480 --> 00:41:05.610
Patrick Rafferty: And then waiting 510 minutes going to get a cup of coffee coming back and trying to remember where you were in quarter really unleashes the the intellect and the brainpower if you're important decision makers, because of that, because of that speed and that processing capability.
00:41:06.720 --> 00:41:14.940
Matthew Halliday: And I think one of the ways that maybe users can think about this is when they look at it is this idea of segmenting data like there's a lot of.
00:41:15.660 --> 00:41:26.970
Matthew Halliday: ways in which you might have a dashboard you say well i'm not gonna change my order amount of my item description, but you might want to look at something very specific, you might want to look at all vendors that used fedex as as a shipping.
00:41:27.510 --> 00:41:32.790
Matthew Halliday: methodology and then look at everything and see delay times and various things and be able to look at that.
00:41:33.810 --> 00:41:37.800
Matthew Halliday: and any any piece of data that might exist in that system.
00:41:38.370 --> 00:41:51.300
Matthew Halliday: Really, you can just filter it so it's not even that you have to build a new page is that as you're clicking and filtering you could just find a new column and say oh i've never looked at it this way, let me segment my data this way and see if that gives a very new interesting perspective.
00:41:52.170 --> 00:41:58.560
Patrick Rafferty: Exactly and you want to be able to bring as many perspectives to bear on a on a single problem, as you possibly can so.
00:41:59.010 --> 00:42:07.890
Patrick Rafferty: I mean, even if you look at what we're looking at right now, where we've got this one particular item that we're down to that we've sold $500,000 worth for me to ship, by the end of the year.
00:42:08.970 --> 00:42:20.340
Patrick Rafferty: Probably the next question is well, what do I have right i've got a I got a ship 13,911 units, what does inventory look like and within quarter, I can go ahead and just click on this item.
00:42:21.690 --> 00:42:30.660
Patrick Rafferty: And now shift my perspective, and now i'm looking at inventory now i'm looking at how much product do I actually.
00:42:31.470 --> 00:42:42.240
Patrick Rafferty: Have and if I scroll down here and I look at where I am at and how much of the of these individual products, I have in my different warehouses in cemetery in portland in nashville and Dallas in Chicago.
00:42:43.320 --> 00:42:52.830
Patrick Rafferty: I can see here that actually i've only have about 1000 on can't see that i'm actually short now even something like that right super powerful, I was able to go and just.
00:42:53.610 --> 00:43:01.230
Patrick Rafferty: Basic just pivot and turn my entire view around from a order focus view to an item focused view focused on inventory.
00:43:01.830 --> 00:43:06.030
Patrick Rafferty: incredibly powerful kind of inversion there right, but even that's a little bit.
00:43:06.540 --> 00:43:09.000
Patrick Rafferty: awkward right and then in terms of like ED remember.
00:43:09.270 --> 00:43:19.320
Patrick Rafferty: hey it was 13 911 there right because that the number and include a desolate it kind of flipping go back right and go and Sarah will take me back to that sales orders page click the little arrow up here.
00:43:19.560 --> 00:43:26.820
Patrick Rafferty: And i'm gonna come back and confirm like I think i'm 5000 short here right and yeah I am i'm 5000 short.
00:43:27.360 --> 00:43:38.760
Patrick Rafferty: What do I do now, now I need to take some action right or I need to tell someone I need to report this up, I need to come up with a plan, maybe I need to make shipments out of lane, you know from warehouse to warehouse to fulfill a certain set of.
00:43:40.080 --> 00:43:46.620
Patrick Rafferty: Sales orders that I have available to me, but wouldn't it be great if I could actually take this dashboard it started as an orders dashboard.
00:43:48.150 --> 00:43:58.650
Patrick Rafferty: and show elders and inventory on the exact same screen and the idea of in quarter here is hey it's great to have visualizations and tabular views of data, but we really want to enable.
00:43:59.310 --> 00:44:04.980
Patrick Rafferty: Everyone, to be able to do this type of work we don't want this to be a technical person, a person with a PhD.
00:44:05.670 --> 00:44:12.210
Patrick Rafferty: Or you know, a person working in you know deep in the IT organization to make these types of changes, so what i've done here is I just clicked into the edit.
00:44:12.930 --> 00:44:20.730
Patrick Rafferty: into the edit button on this on this insight that we're looking at that shows me a breakdown of my orders by the warehouse i'm going to ship them from.
00:44:21.030 --> 00:44:26.610
Patrick Rafferty: The Item number in the item description and the outstanding order amounts in order quantities and what i'd like to do here.
00:44:26.970 --> 00:44:36.900
Patrick Rafferty: As i'd like to add how much I have on hand to this particular dashboard along the left hand side here you'll notice that I have a series of columns and what looks like.
00:44:37.380 --> 00:44:41.850
Patrick Rafferty: objects inside of this tree, this is that business schema that I mentioned earlier, where.
00:44:42.240 --> 00:44:50.340
Patrick Rafferty: we've up leveled the conversation with your data to a set of curated columns that go across all the data, you have available to you.
00:44:50.760 --> 00:45:00.900
Patrick Rafferty: And I can do things in quarter like well, I wonder what I have for on handed here I type the word I start to type the word on hand and I find in here i've got an object, called on hand inventory.
00:45:01.830 --> 00:45:06.600
Patrick Rafferty: And within that I have a column called on hand quantity, I can go ahead and drag that in here.
00:45:07.650 --> 00:45:15.720
Patrick Rafferty: And I want to do some of the on hand quantity quantity by my warehouse my item and my item description and now, when I go back to this page it's going to show me.
00:45:17.430 --> 00:45:24.240
Patrick Rafferty: Okay i've ordered this much or the by customers of order this much now, what do I actually have on hand, what do I actually have to serve.
00:45:25.380 --> 00:45:35.340
Patrick Rafferty: to serve these particular words and now again now we're going even over more data we're going across a billion rows of inventory and 2 billion rows of orders to answer this question, I can see here right away.
00:45:35.940 --> 00:45:51.450
Patrick Rafferty: 1481 on hand in nashville 2491 ordered 1535 have another related skew 2448 I could see that i'm under and now in court, I can also start to build additional visual cues here to say.
00:45:52.620 --> 00:46:07.170
Patrick Rafferty: All right, well, how can I make it more easy for my to illustrate the problem that I have incorporating allows you to come in here and Sarah i'm going to go ahead and add something, just like you'd add an excel where I want to go and say, I want to show me the the on hand quantity.
00:46:11.520 --> 00:46:13.470
Patrick Rafferty: And I want to subtract that from.
00:46:15.450 --> 00:46:20.040
Patrick Rafferty: Your order quantity, so how much Am I short give me the actual numbers show that to me.
00:46:21.060 --> 00:46:34.800
Patrick Rafferty: I could divide it by the on hand quantity if I want to give me a percentage come 20% below and 80% oversubscribed something like that in here click validate and I can also do things like hey I want to format this thing I want to go ahead and.
00:46:36.150 --> 00:46:39.420
Patrick Rafferty: add a conditional format where hey if this thing is less than zero.
00:46:41.490 --> 00:46:42.390
Patrick Rafferty: show it to me and read.
00:46:50.430 --> 00:46:51.420
Patrick Rafferty: make my text.
00:46:53.820 --> 00:46:59.670
Patrick Rafferty: Blue let's say here I click save me to come back into this dashboard and now i've taken.
00:47:00.210 --> 00:47:14.130
Patrick Rafferty: A dashboard that included gives you out of the box on top of EBS to kind of answer these types of questions i've taken that and now i've customized it to actually fit exactly what I need show me them down 1000 in terms of my.
00:47:16.140 --> 00:47:24.480
Patrick Rafferty: In terms of how many Amazon fire tablet I need and again here's the thing i'm drilled into just one particular set of items I can go drill further I can back this out.
00:47:24.990 --> 00:47:34.140
Patrick Rafferty: And look at even more items item categories, everything is available to me here in terms of across the board interaction with my data.
00:47:35.640 --> 00:47:49.800
Patrick Rafferty: So hopefully that DEMO was a was informative for you guys and kind of show you the power and a quick little 15 minute conversation of what you can do with your data and how you can kind of react very quickly and very agile Lee to.
00:47:50.250 --> 00:47:52.950
Patrick Rafferty: whatever kind of comes your way in terms of supply chain challenges.
00:47:53.940 --> 00:48:13.140
Matthew Halliday: That was that was great Patrick as great example, there are just that ability to really understand the connectivity of data and to show the data is valuable in context and so many of the solutions and analytics today strip it of context and make you force you to make.
00:48:13.170 --> 00:48:15.120
Matthew Halliday: context, limiting decisions up front.
00:48:15.510 --> 00:48:22.980
Matthew Halliday: they'll say what are you going to know you have really specific and you don't know what you're going to need to know you don't know how you're going to adjust the ever changing demands in the.
00:48:23.820 --> 00:48:30.810
Matthew Halliday: In the supply chain, specifically, right now, and so having that time to inside that That, in essence, what is, you know what.
00:48:31.860 --> 00:48:37.170
Matthew Halliday: Patrick is just demonstrated a new question a new way of how do we want to look at this data that can help us.
00:48:37.860 --> 00:48:53.850
Matthew Halliday: Be more effective those things being able to be done in five minutes of dramatically different than six weeks and six weeks, quite frankly, is if if your organization is doing it well in the old kind of model but doesn't come even close to what we're seeing here with that so.
00:48:54.990 --> 00:49:00.540
Matthew Halliday: Those are really, really great example there, of course, there's many more examples that you can get into.
00:49:00.960 --> 00:49:03.870
Matthew Halliday: Patrick showed you one specific piece, but hopefully.
00:49:04.230 --> 00:49:14.700
Matthew Halliday: That the penny drops a little bit you begin to think well what would that mean in my other carriers, you might have different questions that are top of mind for you today, and we always ask what are those bring those to us if we kind of.
00:49:15.390 --> 00:49:18.810
Matthew Halliday: go through to the next slide here we'll just talk a little bit about.
00:49:20.040 --> 00:49:22.260
Matthew Halliday: Some of the things that we're offering to our customers.
00:49:23.040 --> 00:49:32.820
Matthew Halliday: or prospects or anyone who just wants to learn more about this and says hey you know i've got i've got some EBS data, I want to better understand it can be supply chain, it can be financials it can be you know anything that's fine.
00:49:33.210 --> 00:49:42.390
Matthew Halliday: But will give you 30 day access to our cloud platform will walk side by side with a technical resource someone like a Patrick who will help you through.
00:49:42.900 --> 00:49:45.540
Matthew Halliday: This process to understand that you can pick the use cases.
00:49:45.870 --> 00:49:57.210
Matthew Halliday: and pick something that really would give you confidence right we don't want you to give something you go oh yeah that's kind of novel and you give something and be like wow that that actually is transformative that that's meaningful to my business that we've done.
00:49:58.350 --> 00:50:03.750
Matthew Halliday: And some great if it's something you kind of been built today right if it's something you've struggled and you say you know.
00:50:04.860 --> 00:50:14.640
Matthew Halliday: Where our wits end we've been trying it's just not feasible like bring those problems to us well you got to lose you don't have any solution right now that doesn't show you how encoding might be able to help you with that.
00:50:15.810 --> 00:50:24.690
Matthew Halliday: And then, if we go on to the next slide here we just talked a little bit about just a thing called a cloud trial, you can go and experience this today it'll have the platform.
00:50:25.470 --> 00:50:35.460
Matthew Halliday: You can go sign up, it takes less than five minutes, the entire process you'll be have access to an environment where you can start to get some hands on experience there's some examples there's some.
00:50:35.730 --> 00:50:39.840
Matthew Halliday: blueprints that Patrick mentioned that you can actually start to leverage and use.
00:50:40.560 --> 00:50:52.320
Matthew Halliday: But also, I would just point out that if you do go try that option definitely avail yourself of the chat that is that those are connected to technical experts like Patrick who's going to lead you through the platform that can help you.
00:50:52.740 --> 00:51:01.980
Matthew Halliday: achieve what you want to do don't just go there and and say i'll i'm not sure what to do, please me, you know we tried to make it as easy as possible for you to to reach out.
00:51:02.220 --> 00:51:09.540
Matthew Halliday: and tell us, and if, and if you have a bad experience to tell us as well right, we want to improve it, so we very much like your feedback.
00:51:10.650 --> 00:51:19.740
Matthew Halliday: The next thing is we do, as I mentioned have another series left or part in the series so last session is next week and that's going to be specifically around it.
00:51:20.190 --> 00:51:25.020
Matthew Halliday: And so, Patrick will be joining me again, if you like, some of the technical stuff that Patrick showed here from a.
00:51:25.710 --> 00:51:35.940
Matthew Halliday: Supply Chain perspective we're going to dig more into just some of the technical capabilities of the Platform, some of the unique challenges that you see from something like Oracle EBS and how we can.
00:51:36.390 --> 00:51:53.850
Matthew Halliday: help transform and change that So if you have anyone in your company that's in the IT space, who is responsible for keeping analytic solutions up and running who's looking for creative better ways to do this work definitely ask them to join the session and to register for next week.
00:51:55.980 --> 00:52:06.330
Matthew Halliday: And then also we have just another thing that we're going to be doing we've been doing this for a couple of weeks now, and this is Twitter spaces if you're on Twitter if you're not you can sign up.
00:52:06.720 --> 00:52:15.570
Matthew Halliday: pretty quickly, and you can join this conversation, this is much more of an interactive conversation, this has been a webinar where you could put things in the chat which is great, but.
00:52:15.990 --> 00:52:23.970
Matthew Halliday: We really want to hear from you, and what here what's your insight, what do you challenge, what are you seeing, and so the topic is going to be condemned to save us from the supply chain crisis.
00:52:24.210 --> 00:52:27.690
Matthew Halliday: That we're seeing and the first question will be kicking off, as you know what is changing faster.
00:52:28.050 --> 00:52:36.240
Matthew Halliday: Is it the analytical technology we're using or the environments that underpin modern SEM are you finding the things are moving so fast the analytics cannot keep up.
00:52:36.630 --> 00:52:42.870
Matthew Halliday: And what are some of the struggles are we have number of questions we'll be hearing from a wide range of people who will be joining us for that so.
00:52:43.470 --> 00:52:52.440
Matthew Halliday: The link is in the chat if you don't want to try and write down that little bit Lee code and so click on that, and you can join and we'd love to see you, there will be starting that right at.
00:52:53.010 --> 00:53:02.490
Matthew Halliday: The top of the hour, so in about eight minutes or so so that's what we'd love to have from there okay so let's see we've got a few minutes left here.
00:53:03.180 --> 00:53:16.950
Matthew Halliday: Again, just another push if you want to ask a question put it in the chat put it in the Q amp a session, and we can go ahead and answer those so while you having those questions we did have one that came in.
00:53:18.720 --> 00:53:32.580
Matthew Halliday: So morality asked is there an ability to write programmatic logic on data points like custom functions ashwin did provide an answer, but maybe actually you can provide the answer, and we have little context of why would someone want to do that, why is that important.
00:53:33.930 --> 00:53:37.200
Ashwin Warrier: yeah certainly, so I think there when we talk about an.
00:53:37.320 --> 00:53:44.040
Ashwin Warrier: enrichment servant transformations like the tea in the atl historically, it was done for two reasons right one was the.
00:53:44.520 --> 00:53:54.720
Ashwin Warrier: The flattening of the data you you flatten it so you create a dimension model but there's also the other component which is your natural and enrichment that you're doing so you could be.
00:53:55.200 --> 00:54:00.240
Ashwin Warrier: Like you mentioned, you have custom functions that you might want or you might have procedural code that you want to.
00:54:00.840 --> 00:54:08.580
Ashwin Warrier: To bring in and and create your data sets right that your business needs to use so for all of those use cases there is there is.
00:54:09.270 --> 00:54:23.700
Ashwin Warrier: Even though we don't do dimension modeling it doesn't mean that we don't do enrichment right we support that through the pie spark framework can make it really easy for you to write your logic, or even put your logic and start to build that out as part of your ITO pipeline and quarter.
00:54:27.570 --> 00:54:28.860
Ashwin Warrier: So hope that helps Marty.
00:54:29.940 --> 00:54:30.930
Matthew Halliday: yeah thanks ashwin.
00:54:32.220 --> 00:54:40.980
Matthew Halliday: Again, if any other questions put them in we did get one, is there a partner that link, you can provide for the EBS offering.
00:54:42.390 --> 00:54:51.690
Matthew Halliday: Yes, we'll see if we can get one to you in the chat for you, Robert so just sit tight otherwise certainly reach out.
00:54:52.230 --> 00:55:05.460
Matthew Halliday: to inquire get on on our website, there is a chat Bot, if you want to engage there you can ask these kinds of questions will connect you to our our partner team who can help with that as well, so we we definitely will have that available so we'll make them.
00:55:06.840 --> 00:55:18.660
Matthew Halliday: See if there's any other comments that came in here, we did have one other comment from Robert to Patrick I do charlie's prides kiss an eagle good morning.
00:55:19.980 --> 00:55:27.420
Matthew Halliday: Maybe that's that's that makes sense to you, Patrick maybe it's one of the songs that Robert does in his karaoke.
00:55:28.590 --> 00:55:30.810
Matthew Halliday: One other question here, maybe.
00:55:31.980 --> 00:55:32.550
Matthew Halliday: ashwin.
00:55:33.570 --> 00:55:35.640
Matthew Halliday: When when you think about.
00:55:38.280 --> 00:55:48.300
Matthew Halliday: Using blueprints and looking at your solutions that you have today what's what's a good way to get started, if a customer already has the stuff you put out there feeling that pain and they're saying.
00:55:48.990 --> 00:55:56.250
Matthew Halliday: Okay So how do I move forward like I Is this a lift and shift and just you know rip the band aid off and I stop.
00:55:56.610 --> 00:56:05.880
Matthew Halliday: You know, doing everything and have to re implement everything into the platform liking quarter what what is the approach for me to modernize this technology that's not serving as well at this point.
00:56:06.870 --> 00:56:18.300
Ashwin Warrier: yeah yeah and it doesn't have to be rip the band aid in one shot right, I mean you could, if you think about this you're skeptical about hey they talk about scale performance.
00:56:19.110 --> 00:56:34.110
Ashwin Warrier: They talk about this being better than the data warehouse model which is prevalent has been prevalent for the longest time, the best way to to to prove this out, is what our customers did right which is user blueprints sign up for a trial.
00:56:35.310 --> 00:56:49.140
Ashwin Warrier: Access a blueprint it's basically giving you a hooks into your system you define your credentials for your EBS instance you pull the data in it light up the dashboards and you start to see how easy it is.
00:56:49.800 --> 00:57:05.670
Ashwin Warrier: With even with very even if you start with your supply chain blueprint, but you focus on just order management in very quickly in a matter of hours, if not less, you will see that you can connect your source, you can light up to dashboards and you start to see.
00:57:06.780 --> 00:57:16.470
Ashwin Warrier: Bringing bringing your largest cable right we'll start to see some of the relationships around it and how easy it is to interact with encoded dashboards and the whole.
00:57:17.190 --> 00:57:26.520
Ashwin Warrier: I mean encoder is is known for its simplicity, the ease of use so very quickly out of the gates you don't have to do a lot of configurations and set it up.
00:57:27.510 --> 00:57:34.200
Ashwin Warrier: As long as you've got your connections you've got data loaded into schemas Patrick talked about the business schemas which are the.
00:57:34.650 --> 00:57:46.200
Ashwin Warrier: The abstract data sets you can start to either use the dashboards that are pre built or build your own ad hoc queries and start to build your dashboards and you can be up and running fairly quickly so start small.
00:57:47.820 --> 00:58:00.750
Ashwin Warrier: Look at the the scale and performance look at the query response times and you start to start to like the experience you'll see how it compares and contrasts with legacy applications with different tool sets that you've explored.
00:58:02.820 --> 00:58:18.090
Matthew Halliday: Great we got another question in here from Raj he's asking so is this only EBS sources or can we do reporting on other systems such as peoplesoft maybe Patrick don't take that one.
00:58:19.680 --> 00:58:23.340
Patrick Rafferty: yeah absolutely people so it's absolutely something you can can do some reporting on.
00:58:24.510 --> 00:58:34.380
Patrick Rafferty: across a number of different modules that people saw pads right so not just you know orders and financials but hcm and things of that nature we've got a lot of.
00:58:35.580 --> 00:58:45.570
Patrick Rafferty: A lot of customers leveraging peoplesoft data today and actually quite a few proof of value type exercises that my team has been focused on over the last.
00:58:46.170 --> 00:58:53.880
Patrick Rafferty: couple weeks of involve data from from peoplesoft and they integrating it with other systems of data that they might have available to them.
00:58:56.670 --> 00:59:04.980
Matthew Halliday: Great Thank you very much, just one final ask if there's any other questions if you do have some questions definitely reach out, we will.
00:59:05.640 --> 00:59:15.060
Matthew Halliday: be glad to answer those I think that's that's pretty much it so for today, just like to again thank Patrick for awesome DEMO.
00:59:15.630 --> 00:59:24.870
Matthew Halliday: Thank you ashwin for joining us and sharing your insight and experience with analytics in the era of supply chain over the last almost two decades now.
00:59:25.830 --> 00:59:33.450
Matthew Halliday: certainly appreciate your insight and an input just a final push again remember this time next week, we have our final session.
00:59:33.900 --> 00:59:40.530
Matthew Halliday: and feel free to join that and share that with other people as well and, as always, the recordings are available.
00:59:41.040 --> 00:59:47.520
Matthew Halliday: So those will be sent out in an email so keep your eyes out for that if you want to look at any of the recordings from this series in the past.
00:59:47.790 --> 00:59:55.620
Matthew Halliday: For those of you who are interested in other system to real might not just have EBS we do have a parallel track that we've been running for out for netsuite.
00:59:55.980 --> 01:00:06.450
Matthew Halliday: And so that that is available on encoded.com slash resources, where you can go in and watch those as well, where they kind of go through similar things but answering it specifically from the vantage point of.
01:00:06.870 --> 01:00:19.380
Matthew Halliday: Of netsuite so if that's of interest you definitely go ahead and check that out like to thank you for joining us today, I hope to see you next week and, once again, thank you very much for your time hope to see you on the Twitter spaces.