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𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻: 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗴𝗹𝗶𝗺𝗽𝘀𝗲 𝘁𝗼 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗙𝗣&𝗔 𝗪𝗲𝗯𝗶𝗻𝗮𝗿 In our recent webinar on ‘𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗘𝗱𝗴𝗲: 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀’, panelists ( Darek Kociemba: Finance Director - Reporting & Analytics, MARS; Antonis Tsimbidis: BI & Reporting Platform Manager, Coca-Cola HBC; Shipra Sooden: Client Partner, Fractal and Prathmesh Thergaonkar - Engagement Manager, Fractal ) discussed how AI, machine learning, and advanced analytics could tackle complex challenges like rising costs, shifting preferences, and digital disruption giving businesses a competitive edge. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘄𝗲𝗯𝗶𝗻𝗮𝗿: 🔸𝗕𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻 𝘀𝗶𝗹𝗼𝘀 & 𝗲𝗺𝗯𝗿𝗮𝗰𝗲 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: Information sharing across departments is key to unlocking insights. 🔸𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗲𝗼𝗽𝗹𝗲: Train your team in AI/ML and foster a culture of innovation. 🔸𝗗𝗮𝘁𝗮 𝗶𝘀 𝗸𝗶𝗻𝗴, 𝗯𝘂𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Integrate internal & external data sources for a holistic view. 🔸𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗿𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Protect your data and embrace a "test & learn" approach. 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗱𝗼𝗽𝘁 𝗔𝗜 𝗲𝗮𝗿𝗹𝘆 𝘄𝗶𝗹𝗹 𝗴𝗮𝗶𝗻 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲. 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/drUpBm-5 Sourabh Joshi, Deepak Sharma, Samit .Chakraborty, Ruchi Sampat(Shah), Amit Agarwala #AI #FinanceAnalytics #Webinar

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Transcript

So in terms of challenges when we're talking about, you know, a big manufacturing and companies. Uh, traditionally we have two elements in the side of the coin to impact. One is the cost to other side is the revenue. This other big bucket you'll see that we're trying to impact. And what I would say as a challenge is that also direct mention what, what the big companies do not like is the uncertainty is the big fluctuations in the market on commodity prices as you mentioned, or any other unexpected turbulence in the market. This is the, the biggest challenges for the, for the big companies, and this is always, for many years now the, uh, the main target for them to reduce. So they want to streamline this, uh, this fluctuation so that we have a more predictive, let's say, outcome that they can plan more easily. So for me this would be this would be the main challenge and especially in the environment that we have today because you know, many of the people that have joined, I suppose that we have that know that we are living in the in the era of scrolling. OK. So we are living in the era unfortunately that people and consumers of our products as well, they are very. Easily bored. They want new changes and they're looking for new experiences, new products, etcetera. And this is creating a very a very fast-paced market out there. So. Traditional companies within manufacturing companies need to adjust very fast in such a reality. And to do that, yes, we will need to go into also technology to to get any benefit out of this. And what I see again as target for our companies is on the one side to try to reduce this turbulence in the market by predictive analytics by. New ways of uh, bringing new ideas to to consumers. Uh, and also by. Uh, it is using the code on the on the one side, so reducing the course with some automations etcetera, but all the new tools are bringing. At the same time, predictive analytics so that we can predict, for example, demand on the market so that we can plan better or that we can predict, let's say the the manufacturing batches that we need in order to have to reduce the cost of changeovers and things like that. And I would put also an element of, uh, you know, uh, user experience, because we know that all the new boutique companies that are coming, coming out there, they have this tendency of focusing and providing practically products or services that are very dedicated, very customized to the, to the customers. And this, I think that what we are looking also with the support of advanced analytics, we are looking for ways that. Who can potentially customize the experience that we're providing to the, to the consumers, to the end users? So these are the main areas that I would say that are both challenges, but also opportunities for their new company, for the for our companies. To to use a bundle list this and you know other technologies in order again to to boost either revenue or general approximation for that. I see those challenges through at 4 lens approach and there is a reason that I will also mention those four different lenses or optics in a particular sequence and that's people process data technology, OK. And for reason technology is last, I mean we all excited. Technology, but, uh, you can have the state-of-the-art technology if the, if you don't deal with the people, process data first, you have nothing. And, and we start with people. And again, now I'll come back to the story we had with your problem. We're building customer PNL and not once, not twice, we have been having sessions with our friends from sales team and it's like, here's your profit on this customer. No, no, no, no, no, I, I've done calculation on the piece of paper, the different, whatever you do. My dear friend from sales in on your piece of paper, it's not a true profit. So breaking those silences, it's first step, OK, breaking the segmentation of within the organization that we have brought together. And I think this is where analytics tools are, are those platforms that can bring people together now. But it's not only about this, it's also about the minds and then culture and and you will have I mean we have had it and we still have it and we will have it. Again, in the future where technology, tools, data will tell us particular story and you will face people saying, no, what are you telling me? I've been doing this for 1020 years. I know better. And then through a number of iterations, you have you win the users, you win them with you is like, yeah. And it's not about finger pointing. It's about working together and saying, hey, but you know, look, hey, it's getting better. Yeah. So it's a journey. So making this shift is is change. Management is crucial in this and again, I don't think you can do it with a big bank and say tomorrow we all wake up and we you live, we live, you live in a data organization, especially big corporations struggle with that and I've had similar conversations with my friends from other FMCG. I know I'm not alone. OK. Now another piece I want to mention is the buy in from the leadership. I keep joking in the organization. It's like, who can be my Elon Musk with on Mars? OK, I know Ellen wants to go to Mars. I need Elon Musk in Mars right now, so people want to want to look at him as an aspirational leader. OK, I want to be like Ellen. I'm still looking for one. Which also shows you that this buy in is a challenge. OK, uh, something else. So just going back to my process. So people, let's talk about process right now. Process things like how do you register data? OK. And and what I see and again I come from finance and we have very often and built in conflict within the organization when you will have like financial operations team who want to be super efficient and they want to go fast and record limited numbers of data. And they say hey, I can post trade expenditure with one posting debit and credit job done. I move on. And then we come from PNA and say no, no, no, no, no. Why don't you post it across customers and products and all of a sudden you have a posting that is 20,000 records. Ohh, that's too much work. How do you change that? How do we? All together think about what is needed, not. What is needed for my team? I need to be efficient. I need to move fast. Yeah. So how do you change your processes that can support this and the goal of being data-driven organization? Then you come to data and when we talk about data, it's like how is your master data? I have not met anyone in the big FMCG's that I will say ohh my cost centers are clean, my product data attributes are clean because if you don't have it clean. Guess what? What I thought your tools are going to produce, you will be also not the best quality. And again I will admit we products, some products that we make like in pet care we have brand pedigree and yes, it's as pedigree and a pack. But in the world product master data it says whiskers, we had those errors and when you have a portfolio of half a million products, half a million SKU, you will have those errors. So how do you clean them And also once you clean them, how do you maintain them relevant going forward? So it's how you record and how you maintain the data. And then you come to technology. There is such a big. You can so many different options you can use and you could probably spend ages trying to find the best one. And I don't think there is the best one. You just have to start somewhere, you know, pick something that you think is good for you and then gradually work with the developers, with the providers, suppliers of the technology so you can make it better. I'm not going to mention the name, but we're working with one of the. Companies that uses SAP data and we actually help them to build a better front and to the users. So collectively we make their products better as well. So many different paths.

Sourabh Joshi

3mo

My personal favorite-Invest in your people!

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