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11/22/2021

Driving Consumer Loyalty & Brand Trust Through Data Analytics Across the Supply Chain

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In today’s volatile environment, top CPG priorities include both gaining and retaining consumer trust. When such quick-changing factors as food safety and waste are incorporated into the mix, it’s no surprise some companies are struggling to keep up.

Explore the methods today’s future-forward companies are leveraging to increase food safety and freshness, minimize waste, enhance their brand’s reputation, and grow profits by leveraging data analytics across the supply chain. As part of this, learn how one company was able to crunch the time spent managing data in a consumer safety issue from 24 hours down to just a few seconds.

Lisa Johnston: Hello everyone, and welcome to “Close the Innovation Loop with Traceability and Lifecycle Intelligence.” My name is Lisa Johnston. I'm senior editor at CGT, and I'm excited to welcome you here today.

Today's topic is going to explore the impact that food safety and waste can have on both a company's bottom line and also consumer perception of a brand. CPGs need to keep these issues top of mind from the beginning of product design — and all the way through the supply chain to the shelf, pulling invaluable consumer insights along the way.

And so I'm excited to welcome a pair of industry experts who are going to highlight why it's important for CPGs to keep these issues at the forefront of the conversation, as early in product development as it's designed. We're going to explore why it must be carried through the supply chain into the shelf, and as part of this, we're going to learn how CPGs can increase food safety and freshness, minimize waste, enhance the brand's reputation, and grow profits by leveraging data analytics across the supply chain.

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Joining us today are Nina Verstandig and Andrea Sassetti. Nina is part of the thought leadership digital enterprise team at Siemens. As industry manager for consumer products, she works with customers across the globe to leverage PLM MLM and automation technologies effectively. Working primarily within one of the Siemens Digital Enterprise Experience Centers where visitors can explore the solutions in a mock manufacturing environment.

We also have Andrea Sassetti. Andrea has been with Siemens Digital Enterprise since early 2017, initially as an innovator, and now as an Innovation Manager within the Siemens Innovation Lab. His background is in the manufacturing executions space, but his focus has grown to cover PLM, MLM, and automation. He has deep knowledge of all the technologies used in digitalization, excuse me, and the value of applying them across industries.

With that, I'd like to get things started, Nina, the floor is yours.

Nina Verstandig: Thank you very much. Hello everyone, I'm looking forward to speaking about this exciting topic of traceability and a unique solution that we've come up with, which started from a proof-of-concept type notion, and now is taking off in this industry specifically, but also across different industries as well. The topic of traceability and trust across the entire supply chain is highly relevant today, and it will continue to be more and more important for the years to come. Before going into the details of what trusted traceability is about and the different topics we'll cover today, I want to show you a couple facts that are a little bit alarming and are the reason why we really believe this is important.

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In the food and beverage industry specifically, one in 10 people fall ill every year from contamination in food and beverage, 400,000 of people die from contamination, and 56% of food and beverage companies suffer at least one recall every year. When we think about a recall, the direct cost of a recall can be really high — an estimated $11.5 million — but that's just the direct cost.

Think about the impact on brand, sales, and reputation — those costs can skyrocket to an estimated $60-70 million. That's huge because we think about the safety of the consumers and ultimately reputation. We need to have transparency to prevent these issues, to prevent the recalls, or to react faster to those recalls. Then, 15% of food and beverage sold are actually counterfeit and could be dangerous. This is something that we all care about, as a consumer.

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As a consumer myself, I want my brands to be trusted. I want to trust my brands and I want to trust that their supply chain is secure, safety is taken seriously, and I want transparency.

That's exactly what we'll talk about today. We’re going to discuss the supply chain, which is long and complex. We're all part of a supply chain — manufacturers, suppliers, distributors, consumers — every supply chain is different and is growing more complex.

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Think about how long it is, how complex it is. There are many issues that can arise from the lack of transparency, including issues with manufacturers, in different parts of the supply chain, and distribution systems. I’ve named a couple already, such as slow recalls, lack of transparency, not the ability to actually transparently calculate, for example, the energy footprint. That's becoming more and more important. Sustainability is huge, and we need to have that transparency in order to have an accurate representation of the carbon footprint of every product. Ultimately, that is the goal.

Counterfeiting: If we don't have visibility of the supply chain, how can we fight counterfeiting? Optimizing quality across the entire supply chain, not just within the manufacturer's four walls, but end-to-end from raw materials to the consumer. Being able to have a lifetime history, what we call a birth certificate, a transaction that tells you what all the transactions are. What has happened to this product? That's what we'll show you with an example today.

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Ultimately, providing transparency to consumers because as consumers, we want transparency from our brands. We expect it because we see that brands are all telling us products are safe, sustainable, vegan, eco-friendly, etc. Now we’re no longer brand loyal as consumers, but data loyal. We may switch brands if they provide more transparency.

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Consumers are data loyal in the fact that they want to trust brands, but also want data to back the claims. That all stems from having transparency and control over the data that goes to the consumer, but having a two-way street between the brand and the consumers as well.

That's huge to be able to capture data from the consumers and feed it back in the innovation cycle, as well.

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When we think about how to achieve this level of transparency, how to provide that transparency to consumers, think of it like a three-layered approach. The first layer is the typical IoT layer — IoT being any sort of information coming from suppliers, trucks, systems in certain fields, machines being operating in the different suppliers of fields, or distribution systems.

They all have that transparency within their four walls and in the equipment themselves — the equipment that is making the products all the way to the final product to the consumer.

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That’s the IoT layer. The IoT layer allows you to take the data, feed it to the cloud, analyze it, and aggregate it. The second layer is the cloud layer. We have different ways, different mechanisms of capturing that data. Not all data needs to go into the cloud, but we can combine edge computing and cloud technology to aggregate and analyze that data.

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The next question is: How is all of this data going to be consolidated for the consumer or for a brand? A quality engineer or supply chain manager might want to ensure they have a single view of all this information. They don't want to go through different systems, which is truly the issue today.

One way would be to have a central authority, capturing all of this information, but the issue with that is that there could be trust issues, can that central authority handle all of that data and secure the data. Of course, thinking about cost and delays. If there's a middle man all the time, that's not ideal. That centralized authority is not the approach that we recommend because consumers don't necessarily trust a central authority.

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This is where we've included blockchain. A lot of you have heard of blockchain, and probably heard it in different contexts, although not typically this context. It's unique in that we can leverage blockchain technology to capture different transactions along the supply chain. Andrea will do a bit of a live demonstration on our cold brew coffee scenario.

Essentially, blockchain is there for two reasons. First, it's a distributed ledger containing all the information relevant to a certain serialized product. In the video you will see that these are a chain of events, and that's unique to the blockchain to be able to create this chain. You can navigate forward or backward, and associate data too. We can then associate IoT data or any sort of data to these chains of events, which is the distributed ledger.

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The other part of it is trust. If you know anything about blockchain, it's trusted and immutable. The idea that trusted transparency and trusted traceability is enabled by the fact that the data, or at least the transactions referring to that data are stored in the blockchain, are immutable. No one can go back on their claims or modify the information. If there's an issue, there is data to back it up or solve, and ultimately, get to the root cause.

Before we get into the cold brew coffee demo, I’d like to explain the concept. Oftentimes people don’t want a conceptual perspective, but a real example. Siemens doesn’t make cold brew coffee, but this demonstration of the entire digital enterprise portfolio brings to light the solutions.

We start with a consumer. In this case, Michael Jones is looking at his phone, in a grocery store, scanning a QR code on a cold brew coffee. There is different information he can get about this cold brew coffee: sustainability rating, certifications, the actual history of the coffee — everything from the coffee beans, where they were picked, to the transportation of every raw material, sugar, milk, the different roasting processes, blending and mixing, and all of the things that have occurred.

This would not necessarily be all the information that's provided to a consumer, but that's the type of transparency we're expecting to understand how this product is made, and be able to trace all the raw materials you see here.

What we’re sharing is an international coffee with sources from all over the world, which is able to bring that information to the consumer at their fingertips, using what we call Mendix. Mendix is a low-code app development platform that provides feedback: great rating on the flavor, good consistency, great packaging, I love what you've done, it's a carton package, it's sustainable, whatever it might be.

This feedback that we're providing, via the app, is then generating another transaction. This is associated with the blockchain and is now part of the journey of this coffee, part of itsbirth certificate, so to speak. If there is negative feedback, we can also associate it with the entire history of that coffee. If it's positive, we can understand what about the supply chain, route, or journey was positive. This is the tie to the consumer.

Let me take a step back and share how we created this scenario and how the coffee even got to Michael Jones in this case. The system itself doesn't care what it's getting the data from. When we talk about a digital twin, that could be many things, including a single machine, multiple machines, or an entire plant. In this case, it’s an entire supply chain the system, the blockchain and the trusted traceability system. It doesn't matter if the information is coming through a simulation, a physical system, or physical IoT sensors and equipment.

The digital supply chain involves everything from the coffee cherries, capturing things like humidity, temperature, or whatever is critical to the quality of the product. In this case, we can generate virtual sensors, capture the information. This virtual supply chain is actually being fed to the blockchain in events that are captured and that Andrea will walk you through.

Here, we go through the milk producer, the distribution system, through trucks, through boats, and different mechanisms. The system doesn't know that it's virtual, it's just data. If we ever were to go into a physical system, we could talk about virtual commissioning — we have virtually commissioned the system to work in the virtual world. When we get into the physical world, it's just plug and play, it's the same mechanism in the same system. Getting into the plant is a critical part of the supply chain.

“Typically, they would have to recall more than they need in order to play it safe because they didn't have that accurate representation. Now, they're looking at mapping additional parts of the supply chain. What’s interesting is they were able to start small with a specific issue or concern in mind, and then grow to map more and add additional systems in as well.”
Andrea Sassetti, Siemens

I’d like to pause there because it's important to note that when we talk about transparency and traceability, it can't just be about the end-to-end supply chain and treating the manufacturing floor as a black box. It must include MES, which is the Manufacturing Execution System, as well as Warehouse Management System information. That information must be included seamlessly otherwise a lot of people say, I understand what comes in, then something happens inside of my plant, and I know what comes out.”

Of course, what comes in can lead to many errors or a black hole in your transparency. We must be able to have that information. In addition to having stimulation, what we have is a Manufacturing Execution System which is taking care of the production of our cold brew. The production involves taking in the raw materials, the lots of the raw materials, and automatically linking those different lots to the equipment that's actually handling the material to the transformations that occur.

In this case, we're bringing all the material into a mixing tank, which is then transferring into two roasters. All of those different transformations within the manufacturing process, the recipe is captured with our MES. That's contextualized with the equipment so that we have traceability. If we ever want to go back to understand, what was the history of our product? What equipment was it on? What was any sort of IoT data associated with these equipment? We now have all of the pieces of the puzzle.

Now that I've talked a bit about the supply chain end-to-end, and how we're capturing and understanding the traceability of the manufacturing process inside of the plant. We've generated all of this data, but what does it look like?

What does it look like for a process engineer, for example, of our manufacturer, if they want to see the full transparency of cold brew coffee. To go into a bit more detail and to show you a little bit of what we call our dashboard. Now, Andrea is going to walk you through and show you a bit in real-time what the blockchain looks like. You'll have a visual representation of all of this information. With that, Andrea, I'll pass it over to you.

Andrea Sassetti:Thank you. Let me start by showing you a bit more in-depth how trusted traceability works, behind the scenes from the visualization point-of-view. We are scanning one of the serial numbers on a cold brew coffee, and this is the landing page where you can see all the data from the trusted traceability from the blockchain, visualize it, and provide different data. The first one is most important, it is the list of ingredients for the product. We can start to provide insights based on how it looks, the genealogy in terms of location of all the different products, as well. We can start to provide insights regarding the possible counterfeiting. This is one of the widgets that is capable of analyzing if the product is a counterfeit, for example.

We are supposed to sell the product in Germany, based on the map. While scanning, you can see that I'm out of the range and the system provides me an alert to say, “Be aware that someone is taking the product from Germany and selling it to you.” This is one of the first ways that we are leveraging using the blockchain technology. Because the data is stored inside in an immutable way inside the ledger, it means that there's no way to later counterfeit all the information.

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The second part that’s relevant for the trusted traceability, is the full chain. As you can see now, I'm able to visualize, step-by-step, all the different transactions happening inside the supply chain. For each one, I'm able to visualize all the basic information in terms of asset, when it's happening, my transaction, and correlation with the IoT data. One of the points we integrated data that are coming from not only from the Siemens system, but also as well to integrate with different third-party systems like IoT, different storage as well, also ERP and so on.

This is one of the very interesting things, because one of the values that you're able to correlate in one single source of truth, all the different data that are coming from all the parts from the supply chain. These area few different couples. For example, this specific one is for during the shipment, I'm able to connect the IoT sensor that is inside the package during the shipment as well, the GPS location regarding the trucks that are performing the shipment.

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This one instead is inside specific for my production environment. I'm able to take the SCADA data that is usually coming directly from the production environment and integrate directly inside the blockchain as well, with the MES system in terms of providing the information regarding the genealogy. Once the data is inside, we can perform different queries and one of the most interesting is the material core. Let's think about that one of our suppliers told us that, "Hey, we have contamination inside one of our raw materials."

We are able toscan the serial number of one of the possible raw materials, and perform the query inside the full-trusted traceability, and provide all the information regarding where it is currently, the final product, and recall the product as quickly as possible. This is one of the big values because behind the scenes, we have a query-able ledger that allows us to perform all the different queries, to extract all the different insights, regarding the product. In this case, recall as quickly as possible the product from the market. Now, I will hand it over again, back to Nina for the final conclusion.

Verstandig: What Andrea just mentioned — the traceability forwards and backwards, being able to take a lot and trace it all the way through with visibility of exactly where the raw material is in every part of the supply chain (in-process, in finished goods, or backwards) — from a final product and search backwards, that's something that's interesting. It's a unique feature, we have patents on how to leverage the blockchain to do this. It's the use case the main customer that piloted with us, has leveraged.

Before I get into that, a lot of people will say that not everyone needs to have all of that data. Of course, the view that Andrea provided would be from within the brand or manufacturer because they have full transparency. However, there are other actors within the supply chain who may need to interact as well.

We discussed the consumer at the very beginning, and being able to grab data from the blockchain via the serialized item to provide feedback. Positive feedback, negative feedback, whatever it might be, that becomes part of the chain. Then, we need to be able to capture information along other parts of the supply chain. This is where our Mendix platform comes in, allowing you to create applications in a low code environment. Some of these applications we're showing — the consumer one, as well as this one — were created in just a few days. You don't need a huge, heavy coding background to do it, it's essentially creating a UI that works for that user.

In this case, it's a Logistics Manager who would be a part of the supply chain and shipping — let's say the coffee beans for point A to point B, a simple example — what they need to do is create a label and create all the things that need to occur for that shipment to take place and to start and to finish.

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What we've done is create a quick and easy way, in the same way that he's creating his label, for someone to create a transaction in the blockchain. Once created, he can interact with the blockchain from his phone to say, “OK, shipment has started,” the blockchain now has an event or transaction that says the shipment started.

Then, the shipment occurs and this shipment could have a truck with temperature, sensors, or vibration sensors humidity, whatever is determined by R&D to ensure the quality standards, be able to capture the IoT data that occurs in that shipment, and associate it with the specific shipment that was started via shipment manager. When the shipment ends, we're able to say, “OK, we've captured the shipment. We are changing the ownership from whoever is the shipment company, or the owner of the shipment to wherever the destination is.”

In the blockchain, that is how we can also capture information beyond the shop floor. It’s an easy way, without someone having to log into a desktop and do all of this work seamlessly into typical activities, if you will.

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I’d also like to mention a bit about the customer success aspect. There are a few proof of concepts and activities that we're doing now that I wanted to highlight. The first one being a global food and beverage company. We're not able to name who that is for privacy reasons, but the company came to us with an issue that the glass in the drink bottles had a risk of breaking if a supplier provided faulty glass.

You can imagine, the supplier tells the brand, “Some of your glass may be faulty; it may or may not break.” For the brand, that's huge. The safety of the consumer is at risk, and you don’t want them cutting their hand or face while consuming this drink. The brand needed to be able to know, based on a lot of a raw material, exactly what products had that raw material.

Previously, they would have gone through many, many different systems with many people in a room. It was more than 24 hours of searching through data, trying to correlate things and link back and forth with Excel. This was complicated.

What they did was focus on the part of the supply chain that has to do with this glass and mapped that in the solution. Using this solution, in just a few seconds — literally in just a few clicks — they can upload the information about the raw material and know exactly what final products or products in process have this glass.

That's huge. That's not only accurate data, but an accurate way to handle this recall, prevent any issues, and prevent unwanted cost. Typically, they would have to recall more than they need in order to play it safe because they didn't have that accurate representation. Now, they're looking at mapping additional parts of the supply chain. What’s interesting is they were able to start small with a specific issue or concern in mind, and then grow to map more and add additional systems in as well.

It's worth mentioning that they did have most of that information already. It wasn't about convincing other people in the supply chain to provide data. They already had a lot of that information, but it was not contextualized. It was all random data in different systems that had no correlation to the event or the supply chain journey of their products. That was a big win for them.

Another one to mention is an agricultural company in Singapore. For them, they wanted to prove the authenticity and traceability of not just the fish in the farm, but the fish journey through the farm, all the way to the consumers, but also including the feed for the fish. The ability to go back and if there's an issue with a fish or an issue with the feed, to be able to know exactly what fish was fed, which feed. This is a hard thing to be able to identify what feed was fed to each fish. Having that traceability and transparency is key because authenticity is important in the fish industry.

There is a need nowadays to prove that, “Hey, I'm buying salmon, is it really salmon?” As consumers, we care about authenticity as well. The Mendix platform is a low-code app development platform, which has had huge success in many different industries, across many different customers being able to connect information from different systems and provide UI based on the user.

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The group is much larger than this, but this is the main team: the innovation, the idea, the concept initially came from Andrea, and the rest of his innovation colleagues on our team, who are the brains behind the operations. It's now been productized into a service provided by Siemens where we can work with you to understand how to start small, think big, and what systems you have to do that. It will help you identify what data is missing and things like that because it has to be a conversation.

You can’t simply install a blockchain and boom, suddenly you have all the information you need. More than likely, you already have a lot of that data. It's a matter of knowing what to do with it, how to contextualize it. That's the biggest hurdle we see and can help you get over.

Johnston: Thank you, Nina and Andrea, for all that information. You did a great job of taking a fairly complex technology and making it, and not just easier to digest for our audience, but to see its potential. Blockchain can also have a reputation of being difficult to wrap your head around. I'd like to get your take on what some of the biggest misconceptions are when it comes to using blockchain to improve transparency.

Verstandig: One thing to reemphasize is that Siemens does not sell blockchain technology; however, we can leverage any sort of blockchain. In this case, we were using Hyperledger for the blockchain technology. In terms of misconceptions, a lot of people think it's used only in the financial market, or generally not in this space. This is a unique way to use the blockchain. Another misconception about this solution overall is that you need to map your entire supply chain at once and need every bit of data in order to be productive. That's not the case — start small and also keep privacy in mind.

Think about the different players along the supply chain. Everyone can decide what stays in house and what gets exposed to the brand. It has to be a relationship where not every piece of information is going to be divulged to everyone. As consumers, we're not going to get to see every piece of information, but everyone — whether it's the brand, suppliers, or distributors — can decide what information is published on the blockchain publicly. You're exposing your books and all that information, sometimes people get worried when they see this. That's one of the misconceptions that I hear. Andrea, I don't know if you have any in mind that you can think of.

Sassetti: You’ve highlighted the right items. Most of the people are scared regarding privacy because sometimes the customers don't want to publish the recipe directly inside the blockchain, this is why we are creating a separation layer to keep some data private and other data public, for all the differences for the audience.

Johnston: With that in mind, what kind of advice can you offer about obtaining corporate buy-in for investing in increased transparency and traceability?

Verstandig: Go back to an issue, problem, or a business case. At the end of the day, you want to prove the value, you don't want to install technology for the sake of technology. For example, with the use case of the global beverage manufacturer, they had an issue with glass. The company knew that was something that would cost them a lot of money, a lot of time, and desperately needed a solution. They came to us with a use case and now they're expanding to different use cases.

“Gain transparency in your plant, then expand to the whole supply chain. It's not just a one-size, go big or go home thing.”
Nina Verstandig, Siemens

It helps if there's certain use cases to start with. Let’s first identify the use cases that make the most sense from a business perspective, and then grow. Trying to get corporate buy-in by saying, “Let me set up this system not the entire supply chain, it'll take X amount of years.” That doesn't make any sense. It’s best to start small, think big things based on use cases and challenges, and start with data that is in-house. Start by getting transparency in your own manufacturing facilities. That alone is something that a lot of food and beverage manufacturers don't have today. Gain transparency in your plant, then expand to the whole supply chain. It's not just a one-size, go big or go home thing.

Sassetti: My suggestion is to leverage as much as possible, the digital twin. Using a simulation environment, you can make sure that there is enough data to provide the traceability, and how to correlate the data across all the different stakeholders during the supply chain. The digital twin in this case, for our simulation tool, is a great tool because you are able to mix and evaluate all the possible options and start to make it happen, then push into the trusted traceability.

Verstandig: That’s a great point about the digital twin. That goes back to the simulation I shared — that was an example of a digital twin. The more you can validate virtually, and that goes for everything we do, the better you are because then you can learn virtually and the system can virtually commission, validate, and catch issues.

Sassetti: This is what we did every time with our customer because during the initial discussion to evaluate which data was available, we leveraged the digital twin that allows us to figure out — in an easy way — the right pattern to grab the data and maintain it.

Johnston: You've touched upon this already, but maybe you can expand on it. How widely is the system currently being used?

Verstandig: It is a global opportunity or solution. We have different customers around the globe using it, starting to use it, or in discussions to use it. We're unable to divulge the names of specific users or customers because people want to keep that private, however, we could definitely go into a list. I just shared a couple examples, but there are quite a few in parallel — we're a team.

I don't have visibility into all of the different activities around the globe that involve this solution, but it's growing and evolving. We're continuously improving the solution as we have more customers who are piloting it, tweaking it, and giving feedback. For my use case, we needed to tweak this and that. It's a very dynamic solution, but we'd be happy to go into a deeper discussion about different use cases that are being used — there's quite a few.

Johnston: What best practices can you offer consumer good companies that just want to get started on improving the traceability or those who still have a lot of heavy lifting to do.

Sassetti: Initially, it's leveraging the digital twin to better understand the customer directly — what is the right number of data, the right pattern. Start to aggregate all the data in trusted traceability, as well as start to analyze a few use cases using the app. The app is the most relevant to provide and see what the data looks like and try to find possible anomalies. This is one of the best practices we start with for our customers. This is the typical approach that we now follow with a couple of partners.

Verstandig: I second that. In general, just have use cases and a specific focus, maybe little proof of concepts within the company to start small, think about what systems already exist. If you already have an MES, does it have traceability functionalities, do you have transparency in your plant, if you don't how can you start? Don't just think about the big picture and attack the big picture all at once. The best practice would be to think about the existing system and existing infrastructure, find a specific use case, tackle just that use case, and prove it out, then get buy-in. The best practice is to be able to prove value early. Then you have the buy-in to continue the efforts. Start small, start based on the systems you already have, and we have best practices.

Something we didn't mention today that’s important —  the core of our entire strategy and the way our portfolio is set up —  is what we call digital threads. Digital threads leverage the digital twin, and the physical world as well, to go beyond a simulation. The whole design aspect — the manufacturing, shop floor, automation, all the way to the consumer — has digital threads defined that can help serve as best practices. They’re essentially best practice processes to help guide your journey. There's many best practices out there, so you don't have to start from scratch.

Johnston: Those are some great words to leave us with. I'd like to thank our speakers for giving us their time and subject matter expertise. I'd also like to thank Siemens for sponsoring today's webinar. Finally, I'd like to thank the attendees for giving us your time today; we hope you found it worthwhile. Have a great rest of your day.