Incorta’s Brian Keare on Using Data to Improve Supply Chains

hi i'm james mcguire and on today's e-speaks we're talking about cloud-based analytics and how companies are using data analytics successfully and unsuccessfully to navigate the current shock to global supply chains to discuss that i'm joined by a very special guest with me is brian care cio of encorta brian very good to have you with us today hey thanks for having me really nice to be here you know i mean i think that the question of data analytics is is a tough one these days and that uh a lot of companies most all companies realize hey we've got to get on board with us and most companies are making an effort to get on board but i think plenty of companies are still struggling uh to get the most from their analytics platform and why why do you think that's something what's the core challenge there well look i mean it depends when you say core platform uh core platform could be xl right we all have xl and that's probably the most popular data analytics bi tool in the world so it is everyone's got xl and um you know i think that the way the tools that we've had at our disposal over the last couple decades are both a blessing and a curse they're a blessing because we have them all they're a curse because it's really hard to do data analytics well if you end up resorting to monstrous excel worksheets in order to do your work um so i think that uh you know there's a couple of big challenges that the pandemic and supply chain issues over the holiday period have brought into full focus for companies that are trying to manage data analytics and i think that you know i think the answer will get into this but i think the answer is that there are some companies that are doing it really well and some companies that are really flailing or that are stuck in technology and processes uh a couple decades old what is it about those companies that are not doing it so well i mean what where are they going wrong do you think well i think that they're probably going wrong because their mindset is that they want to optimize a system that they see is relatively fixed and so they will have data coming from their erp and i need to take that data and i need to put it into a data warehouse and i need to get it set up for you know these processes so that people can access it in a in in a way that they want and then the problem is that you've got a new piece of data that's coming from a new data source or i need real-time data i don't need last month's data i don't need last quarter's data because something just happened that is upended um my view of the world uh netherlands just shut down or europe's about to shut down how do i redo my demand forecast given what's going on in uh europe right now and around the world right now and if i'm stuck in data that's uh half a year old it's really hard it's really hard to answer those questions and so i think that companies that rely on a more static model that requires a lot of heavy lifting are the ones that are finding it much harder to be nimble and to do real-time decision making whereas companies that are leveraging more modern cloud-based tools and modern data analytics are able to be much more nimble and to have real-time answers at their fingertips to adjust to things like a stuck global supply chain or new uh news that comes out about the pandemic that i need to adjust to you know you make a really interesting point and that you know we use the term data analytics and i i use that term often myself but i guess there is obviously there's a thing called streaming data which so it's real time or very near real time and then there's also what i think of is the dashboard which can be very helpful sometimes you know that dashboard appliance or application does look back at data that is six weeks or three months old or you know who knows how old it is but i guess you know maybe maybe we need to stop using this term data analytics and start using the word the phrase streaming data because the data's got to be really real time is that what you're saying i i'm saying that um a lot of the decisions that we need to make need data that's much closer to real time than many companies are used to and so i think i think that what you say is right and i think that different companies define data analytics in a different way some companies might say well my data analytics uh gets put on powerpoint slides the first week of a month and it analyzes what happened in my company uh last month or last quarter other companies saying no data analytics is that real-time dashboard it's the first thing that a ceo or a cfo consults when they wake up and it is not powerpoint slides that they're looking at it's something they can interact with i saw that something really funny happened in europe yesterday on my yesterday sales what is that drill down country by country or product line by product line or customer by customer and i've got these hypotheses in my head about what might have happened yesterday or last week let me see if i can answer those by interacting with the data asking questions of data and being able to explore answers in real time and i think that that that wrestling with the question and what does the data say what are the answers that the data says is how companies get to much better solutions than if they're just um looking at uh powerpoint slides of rows and columns of kpis that uh happened a long time ago yeah interesting right so well i think you've talked about this but i guess if i would if i if you would give advice to a company some company comes to you and says hey you know brian we're really struggling uh you know chris they may or may not identify their problem but the real question is what what advice would you give to companies i mean regardless of whatever their issue is what should they be doing to optimize their their analytics well i mean i think there's probably two or three things that i would say first is that uh you shouldn't be settling for the status quo and what we have done over the last couple of decades doesn't cut it anymore it certainly doesn't cut it for your competitors your competitors uh the successful ones are much more nimble and they're able to uh take real-time data from disparate sources and to immediately make sense of it um without having to have a war room of analysts uh do that um so that's the first thing don't don't settle for the status quo the second thing probably is that the tool itself isn't the answer um it's really how your people and your processes inside of your company uh use that tool and use it to drive data driven decisions in a nimble way in your in your company that matters and then the third thing probably is that uh gone should be the era of big projects that take over a year and are multi-million dollar projects if you can't um go from zero and make a measurable impact on a critical use case at your company within one quarter within 30 60 or 90 days tops then you're probably not doing it right and you need to re-engineer your people your processes and look for new tools in order to really make an impact and i think that we are seeing that from leading retailers such as best buy uh leading technology companies we are seeing that the best most nimble companies are demanding uh really quick execution of projects and uh results uh really quickly in the data analytics right so the the projects are smaller and they're more pinpointed so they can get finished faster that's really the point that's right so the idea is not how am i going to transform data across the entire organization and have that be a 18-month project it's i have a real problem a tangible problem in my finance and sales organization my sales people aren't getting the correct invoices attributed to them so that i can make my correct commission payments to my sales reps how can i use data and processes and tools in order to fix that or i have a collections problem how can i enlist uh collections uh in order to uh have better um working capital position in my company or it might be um you know the up the upended supply chain means that my position my inventory position and my demand planning is upside down how can i quickly give my company a couple of uh uh tangible options for how to re-optimize my supply chain and how to rethink about my supply chain in the face of what we've just seen in the holiday season so you pick one use case and see if you can make a tangible result rather than saying oh what i really need to do before i can do any of that is to is to revamp my architecture of my data in my company which is going to take 18 months and people put your stuff on hold for 18 months while i do that i think that needs to go away right so i mean let's talk about the global supply chain and it's it's it's become a very fashionable topic who knew we would be talking about you know global supply chains this much this much uh so so obviously pre-backlogged how can analog analytics help with this well so let's take my example before i was cio at incorda i i ran uh it and data analytics at a company called nortec which is a global manufacturer uh nor tech did what many companies did we had tens of thousands of products super successful company um and we optimized our global supply chain primarily around china we invested heavily in china we had um uh we had a really robust manufacturing capability there and we did like many other companies we produced a low or reasonable cost high quality good with a just-in-time supply chain uh that would serve our company and our customers really well uh the first thing that upended that was the china tariff trade war a couple years ago and that put into question the whole stability of the whole supply chain that we did we optimized it around uh a few inputs that would allow us to produce 10 000 products and do it in a just-in-time way all of a sudden it wasn't a low cost it wasn't a low-cost solution anymore and we needed to figure out what the alternatives are i think that this last quarter has taught us that companies have still relied they put too many eggs in one basket in saying i am still going to optimize my supply chain around one set of suppliers and what did we see we saw that that supply chain coming to the ports got stuck couldn't get through the ports we had problems getting it onto the store shelves and they didn't have eggs in other baskets that they could turn to they were stuck so i think what data can do is to help a company understand what the different options are and how you can place different bets in different alternative supply chain possibilities that you can then flip the switch on at any point in time the pandemic is teaching us that we need to do that right now right as comp as countries close down and restrict the free movement of goods and services as they try to fight the pandemic we need to be nimble we need to have an option closer to home and we need to not put all of our eggs in one basket and so if you're nimble with your data and you have real-time data you know how to divert the flows of the supply chain to plan b c d and e and you're not just optimizing on plan a which is what too many companies do too much of the time well of course i guess that presumes you're going to have a number of variables or number of possibilities for supply chains it seems like it'd be expensive to have option b c and d you know set up would it not i i it is but i think gone are the days when you have the luxury of putting all your eggs in the low-cost uh plan a basket i think that that is what the last couple of years have taught us and and you need to be more nimble you need to actually make sure that you've got multiple different vendors and options that you can turn to and yes if you need to spend a little bit more money to make sure that you can light up those manufacturing lines on your plan b c and d those companies are in fact the ones that will have been okay at the end of the day once we see the q4 results from 2021 and the ones that put all their eggs in the basket above plan a are going to be the ones that sometimes got left hold in the back or didn't have shelves that didn't have shelves or inventories that were able to meet demand interesting so all right let's take a look at at the the future of cloud-based analytics which i think correctly if i'm wrong really is the future of analytics because i think there will be no difference between analytics and cloud-based analytics if that's true please enlighten me on that but what what do you see if you look a few years ahead in terms of the future of cloud-based data analytics well i think cloud is a ideal place for a lot of this to come together right if i've got um disparate data um that is in systems that are all over the place you know take the supply chain example i'm going to have data from my erp which probably is in the cloud i've got data coming in from vendors i got data coming in from shipping companies ups and the like and i need a place to store all of that and um getting aggregating all your data bringing it together in the cloud is an ideal place to do that it has to be secure of course um but the cloud is a great way to do that you know i think secondly the cloud is a really great place analytics requires some horsepower and in order to do uh in order to do it well and to spin up resources and allocate resources to doing data analytics when you want to and then spinning it back down when you don't need it the cloud makes it very flexible place to do it so you've got all your data in one place and then you can analyze it as you need to uh when um you know when the time crunch is on and then when the time crunch is off then i don't need to pay extra money for um you know for big server farms or anything like that so i think that the cloud is a really good place to do that and then finally in an era of uh remote workers where people are dispersed around the globe and working from home cloud is a great place uh for people to come together and and do that and so um cloud architected right and when when it's secure is the ideal place for all of this to happen for sure i i think you must be right about that last one uh brian thank you very much for sharing your expertise today was great i definitely learned quite a bit uh thank you very much cheers thanks

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