In today’s manufacturing world, there are as many uses of data as there are products. So how do companies determine what to focus on to build out their strategy? That’s what we’re talking to Maggie Slowik, Global Industry Director for Manufacturing at IFS about in today's bonus episode.
According to Maggie, data is the new currency. We dive into what that means and how, as an organization, the ownership of data can give you more power than you ever imagined. We also address concerns that some in food and beverage have about data, including how exactly they’re going to embark on their digital transformation as well as how they’re going to find – and fill – the talent pipeline when it comes to data analysis.
Erin Hallstrom: Maggie, welcome back to another bonus episode of the Food For Thought podcast. Let's jump in with a really broad question: What's the deal with data?
Maggie Slowik: Hey Erin. Thank you so much for having me back on your podcast.
Data. That really big topic for any manufacturer I would say. I think it's worth sort of starting on this high level because data, in a way, reminds me a bit about of digital transformation or sustainability. And by that, what I mean is that the companies generally acknowledge that data is important, but the extent to which they're willing to do anything about it really varies.
How important data is to our organizations, given all the disruption as of late—COVID, supply chain bottlenecks, inflation all these other things that are disrupting companies—many organizations must now make very difficult decisions on how to thrive and even survive. And for that, it's so important to have accurate and up-to-date data, and even more importantly, methods of organizing and utilizing this data to make even more informed decision. I think there is something to be said about using data in a way that it translates or monetizes into business value because there's a lot of evidence out there that speaks to the point of how data backed decisions are creating a measurable, positive impact on organizations.
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I've seen research around companies that are excelling at integrating data into their strategy, operations, and culture and these companies are largely outpacing their peers in revenue growth and profitability. I’ve seen similar data around investments in digital transformation, and how digital transformation goes hand-in-hand with the data piece. There's a very strong link to business impact there if you do data right. This suggests that companies that have invested in—and continue to invest in—maturing the management of their existing data and usage of data analytics will continue to thrive even more in times of economic uncertainty. We also know that disruption is happening at an accelerated pace and the beauty with data is that it can be used by different people for very different reasons.
You could even argue that data is also becoming more useful the more it is being used because it will have a different meaning and purpose for different people as it is being repurposed and reused. I guess the bottom line here is that we are in data economy or data culture, where those companies that make data-led decisions will de deliver business value and especially so in, in the food and beverage industry, which is and will come to that in a second, which is really surrounded by, by a plethora of data.
Erin: We started out macro with the big broad question and are going to get progressively micro as we go on with our conversation today. I’m curious, what are the challenges companies are facing right now when it comes to data?
Maggie: We as an industry have been talking about this and it's not necessarily tied to manufacturing; certainly not only tied to food and beverage either. I think all industries are facing that issue, but it comes to a host of different issues. It’s also a widespread reality.
One of these issues is around information within food and beverage companies. They tend to operate quite large supply chain, especially when you consider their ecosystem with their suppliers and their customers. These large supply chains make data very complex and difficult to interpret due to the different phases of each process.
The risk with incomplete information is that that it makes it very difficult to make informed or precise decisions. In other words, decisions are made without a vision of the complete scenario of the supply chain. Imagine one person facing this incomplete data set and having to make the ultimate decision, then you’re having to reverse it because of human error. That could be detrimental if you think about a company's quality issues. Another issue that companies are facing is data is often siloed and it comes from multiple sources.
We have just recently done a survey at IFS showing that companies actually do struggle with different data being stored in different systems within the organization. Especially if we're talking about a large organization that might have gone through multiple acquisitions. This is leading to incomplete or inaccurate analysis down the line. And let alone of the fact that it's difficult to retrieve all that data and pulling it into one efficient system. Combining data manually is not only time consuming, but also prone to error and other inefficiencies. Companies need to start thinking about how they can get all of data pulled into one system no matter where this data might be sitting across the organization. Another issue is the quality of the data and, and having incorrect data can be just as, or even more harmful than not having any data at all.
Nothing, in my opinion, could be more harmful to data analysis than inaccurate data. If the idea is to use data science and machine learning practices, for example then you should not be doing that with low quality data because without a good input, the output will be absolutely unreliable. And your investment, your effort will be a dead one if I may call it in such a dramatic way.
A fourth and final problem that companies are facing is the lack of talent when it comes to data skills. This is a trend that we at IFS are seeing across manufacturing as a whole. The industry is suffering from a lack of talent in the area of data analysis, science and AI. It's really difficult to find these people. And if we do not have these types of skills within the organization, then it's really difficult to also progress on our digital transformation journeys and make the most of our data. In other words, even if the manufacturing organization does have consistent data, the execution of making the data actionable is a large challenge.
Organizations need to think about how to get these skills into the pipeline and aligning all of that with that data strategy that they're building not just for today, but also for the future.
Erin: Can you talk to me about the impact digital transformation has had on data?
Maggie: Digital transformation has led to literally an explosion of data. If you think about the fact that every interaction in the digital world creates data, that is sort of a blessing. And the reason I say it’s a blessing is down to the fact that companies need to develop the ability to manage this data to their advantage. They need to have a proper data strategy in place. That is not a given, believe it or not. And it's not just about making better decisions with data, but data in itself has also become some sort of currency in a certain respect. We are now even in an era where we talk about the ownership of data, giving you a lot of power as an organization, because you might be in a position where you can sell this data to another organization that can then repurpose it, or build another product or revenue stream based on this data and in the food and beverage industry.
For example, especially with the pandemic, a lot of sales have shifted to mobile apps online, ordering and delivery. And that means that a lot of other players know more about your customers potentially more than you. You have to manage data in a way that you establish where the, the lines of ownership are. And at the same time find ways of monetizing the data, because I already talked about the fact that it’s the new currency and in certain ways it has become potentially more important than the product itself.
Erin: How can you use data to your advantage as a food manufacturer and perhaps more importantly, how do you prioritize what matters more?
Maggie: I think the exciting part of the story when it comes to data in food and beverage particularly is that there's quite a few use cases. We won't have time to talk about all of them in this podcast episode today, but one of my favorite examples is demand forecasting and that is something that at IFS we're actually doing quite a bit with. Why does data matter in the context of demand forecasting in food and beverage? We all know that margins in the food industry are razor thin and consumer demands are changing very quickly. To maximize the revenue of the organization, it's really crucial to get demand forecasting for production planning, as accurate as possible.
At IFS, we are using an AI-assisted weather forecast that correlates weather patterns against previous demand patterns to essentially improve forecasting accuracy. For example, if I was a beer producer, I might want to take that weather forecast, apply an AI algorithm and see what happened last time the weather was nice. Did I sell more? Did I sell less? With that sort of knowledge, you can improve your forecasting accuracy. In our case, we actually do see customers reporting an accuracy improvement of up to 78% compared to existing demand planning models. That is a huge impact on the business because it helps you to improve your inventory levels and cut your waste, and essentially maximize your revenue as an organization in this very competitive field.
Another example of this could be something like customer sentiment analysis, where you have an idea of how customers might be talking about a certain product category. You can pull that information into your demand planning data models.
Another great use case is the supply chain management area. Here, data science helps to build transparency within supply chains and really enables food and beverage companies to be more accurate and more proactive with the customers—customers being retailers and distributors, but also consumers—they expect to know more about how the food is being produced and which kind of raw materials are being used and how the product is being stored.
Being able to collect and deliver this information to your customers and end-consumers has become incredibly powerful. And this relates back to one of my earlier points where I said that data has become a currency of its own and in today's consumers are buying into that concept. It could be even more valuable to them to have access to this data than the product itself. Just imagine how powerful data would be in the consumer's perception.
And another use case is around quality and, and, and traceability and being able to anticipate things going wrong before that actually happens. Data is also frequently used for driving efficiency in food and beverage manufacturing processes. And one of the key technologies here is IoT. That of course, is not a new buzzword anymore; IoT has been around for a while. The good news is that with sensors, they have become way more affordable. Companies can invest in them, and they can easily use this IIoT data to get information about the impact that, for example, temperature, humidity, nutrients in the soil, etcetera can have on crop production. They can get a lot of insight based on just using these sensors and drive efficiencies around that.
Another area that we won't have much time to talk about in this episode is the use of data for better understanding of customer as well as end-consumer behavior. I think there's a lot of opportunity to explore there, especially as food and beverage companies are trying to not bypass their distribution channel, but more so build a stronger relationship with their end customers to understand how their demands are changing, how to anticipate these new demands, and form a closer relationship with these with these end consumers going forward.
Erin: Do you have any recommendations for how to engage with people that may not be as intimate with data, say R&D plant managers, planners, and other stakeholders to get their buy-in?
Maggie: I think this one comes back to change management. Data can help you confidently make and justify decisions for your business, because it provides a holistic picture of your operations, and it can help you perform with more accuracy. This will boost confidence in stakeholder decision-making, right? This picture that I just painted makes a lot of sense for someone in the C-suite, but you also need to think about those people who are on your manufacturing shop floor. For example, why should an operator trust the data that they're seeing on a display when they have years full of experience knowing when something is going wrong with a production line or machine? They can trust that they're familiar with the sounds on the shop floor.
To influence stakeholders, you really must sell your vision strategy as early as possible. You need to make people part of the journey. The journey starts early and in these early stages, and you need to involve your stakeholders. You need to ask them for the feedback along the way, because their input will be so invaluable in making your data management a success down the line. But I think most importantly, you need to make data also relevant to your stakeholders, right? What's the point of convincing somebody on the shop floor to use the data, if it's not relevant to their given role? At IFS, we have what we call industry lobbies and they can give any person within your manufacturing operations, a view of data that relates as, as it relates to the specific role. So for instance, if I'm a planner, I can easily access the data points that matter to me today. And I can see all of the data all in one screen. So it's almost customized based on my role and the data that I need in order to do my job every day. So it's also about the, the relevancy at the end of the day, to make it stick with people and to sort of guarantee that user adoption down the line.
Erin: Can you talk to me more about technologies that help organizations get the most of their data? And I'm curious, how can you unlock the value of data to make business decisions?
Maggie: I think when we look at manufacturers as a whole, we still see a lot of archaic technology in place. It's not that companies have the luxury to sort of rip and replace shop floor technologies. That is an evolution in itself. But I think there is an argument to be made to invest in modern and agile technology, wherever it makes sense. Certainly, to invest in a technology, for instance a modern ERP solution that can help you bring all of your data into one place and be able to manage it end-to-end.
There's something to be said about the volume of data. If you have enough of data, then it could make sense for you to start applying AI algorithms. The great thing about AI is that it can help you make decisions that can provide a plausible likelihood in achieving a specific goal. The beauty of AI is that it really helps to improve your decision-making.
I think the unlocking of data often comes into play by combining it with other new technologies and sort of really bringing that benefit out. At the end of the day, you need to have that visualization piece, that the ability to present the data to a to an end-user in a way that they can identify what matters most and how can they actually act on the data and prioritize.
Erin: Any recommendations on how a company can improve its data management?
Maggie: It comes back to the concept of being on a journey. I know that sounds a little bit cheesy, but data needs continuous management, and it grows as much as an organization grows. You might be investing in new systems, or you might be revising your data strategy, or your recruitment and skill strategy. Whatever you do, you need to find ways to democratize data and, and make it accessible to stakeholders within your organization. You need to put tools in place to contextualize data so that It makes sense and adds the right value, no matter the context or the person who is utilizing the data. At the end of the day, we need to make it easy to share the information, the data across the organization as well.
It's also key to add analytical skills to the hiring process. Companies also need to invest in the constant training of the team, too. It's a dynamic process and we need to keep revising our strategy and making sure that we're still getting the most out of our data and that it's not just sitting there in silos.
Erin: Are there any other opportunities to use data that the food and beverage industry maybe hasn't thought of yet?
Maggie: I personally think that we will see more innovation and channels of commerce opening up via the much disputed Metaverse. You might be saying Maggie, you are absolutely crazy, but let’s think about this for a moment. The Metaverse is a shared virtual world that attempts to replicate reality, using things like virtual reality and augmented reality devices. In this digital world, people meet, and they socialize that they go out, they pretend to be shopping, eating, and playing. Of course, we could be saying that eating involves sensory experiences and that cannot be recreated in the digital world. I think that food and beverage and brands will have a massive opportunity to promote these new experiences in the Metaverse and create digital spaces to connect consumers there as well.
It's a new platform for these consumers to trial new things. I recently read about this example of a famous restaurant chain that basically opened a virtual presence in the Metaverse and then was giving out promotional codes to the actual physical restaurant. Just think about how clever that is because people are then getting into touch with the brand in the Metaverse. I personally think that the Metaverse will enable consumers to learn real world skills, such as cooking and creating recipes, and learning about new food trends. And for food processors, that means product development ideas. Food and beverage companies have an opportunity to be part of that Metaverse evolution. I think the potential is just mind-blowing.
Erin: I'm looking forward to seeing how food and the Metaverse play together. That's an exciting thing to look forward to hopefully we'll see it in our lifetimes.