How Close Can We Get to a Self-Driving Supply Chain?

Lisa Johnston: Hello, everyone. Welcome to “Redefining Consumer Goods Demand Planning.” My name is Lisa Johnston. I'm the managing editor at CGT and I'm excited to welcome you here today.

Joining us today are Simon Ellis, program VP at IDC. He currently leads the global supply chain strategies practices at IDC Manufacturing Insights, specializing in advising clients on supply chain digital transformation, network and ecosystem design, supply chain planning, global sourcing, transportation and logistics. He has almost 35 years of experience in manufacturing working across all major areas of the supply chain. He previously was also the supply chain strategy director for Unilever, North America.

Next, we have Ben Hosseinzadeh, VP supply chain at Caulipower. Ben's responsible for the company's strategic supply chain planning and execution, and he and his team manage all aspects of the supply chain from demand planning to delivery of goods to customers.

Finally, we have Shastri Mahadeo. Shastri's a serial entrepreneur who's been building companies in the food and beverage industry since the age of 18. He's now co-founder and CEO of Unioncrate, an AI-powered, integrated business planning platform built specifically for the consumer goods industry. Prior to founding Unioncrate, Shastri was the founder and CEO of a specialty beverage brand and he founded Unioncrate based on the struggles he faced operating that business.

Thank you, everyone, for joining us. Now this webinar is an extension of “Not Normal Just Yet: Demand Planning in 2021,” which is the recently published special report by CGT. The truth is, and this likely comes as no surprise to anyone here, that while optimism is certainly growing as we all look forward to putting the pandemic behind us, the consumer goods supply chain will have to deal with its ramifications for quite some time. One executive we spoke with while preparing the report compared the disruption with 9/11, noting that inventory levels in that case took about 18 months to reset.

As part of the report, we received insight from Lora Cecere of Supply Chain Insights. She provided details from a study she conducted last fall with supply chain leaders, which revealed that just 13% of companies strongly agreed their supply chain was working well during the pandemic. This is down 18% before the pandemic.

During the pandemic, many companies were unable to use their demand planning solutions and many of them simply just turned them off and kind of white-knuckled their way through it. Now the issue with this strategy of course, is that order and shipment data was out-of-step with the market.
The models weren't flexible enough to use consumption data to understand the changing market conditions. Companies are now rethinking their demand solutions in order to be more nimble in the use of channel data and flexibility of models. As a result, Lora tells us to expect the step change in the building of outside in processes and improvements in modeling.

A recent article in Harvard Business Review meanwhile, noted that while many consumer-facing companies responded to the degrading forecast accuracy by trimming their production and marketing, the ones that pursued new data sets, simulations and model developments actually had initial success in better predicting demand. As a result, these companies are expected to be in a stronger position once the market stabilizes and other companies are being strongly encouraged to follow suit.

Now Simon was also a contributor to this report and I'm really excited to have him here to share his insight. He's going to share some additional insight today that's not in the report. Simon, welcome.

Ellis: Thank you, Lisa, pleasure to be here today. I work for IDC. We always joke that data is our middle name because actually, it literally is International Data Corporation.

I think we saw 2020 was a crazy year for everybody for many, many different reasons. I always used to say that when country leaders, prime ministers, presidents, whomever talk about the supply chain, that's not a good thing. It's not because the supply chain is really well, it's because we have issues. At the same time, I actually think in many ways that the supply chain performed quite well.
Let's drill into the data a little bit. I think there's a tendency — and I fell into this a little bit last year as well — to think of COVID as sort of a disruption. But it wasn't actually a disruption. It was a whole bunch of individual disruptions. Factories in some parts of the world closed, where in other parts of the world, it didn't.

When those factories reopened, perhaps factories in other parts of the world then closed. Or social distancing, staying at home, closing of businesses meant we saw demand shift in different timings. We saw impact and demand earlier in Asia, a little later in Europe and North America. So it was this sort of weird year where it wasn't just one disruption. It was a whole bunch of disruptions.

Given that I love data, and I had an opportunity last year to do a couple of surveys — one in March and one in December — what were some of the things that we heard? Clearly demand has declined. That actually was not in all industries because at the bottom you see demand has increased.

If you happened to be, for example, a pet food manufacturer, your business probably did really well. If you were in the novelty or impulse business (candy, gum, movie theaters were closed, airports), you dramatically reduced foot traffic, so people aren't walking by the shops in the airport and impulse buying the things that they normally would. So we saw this interesting demand decline certainly, but also demand increased in other cases.

We also saw some hoarding behaviors. Anyone who tried to get paper products in March and April, they were hard to come by. Not because people were necessarily using more, but there was certainly some hoarding behavior going on.

We saw transportation delivery delays and that impacted demand. It impacted the ability to deliver orders, to deliver products. Suppliers were unreliable or unpredictable because suppliers have their suppliers, and their suppliers have their suppliers. So we saw this knock-down effect of supply. We still see a little bit of that, not in the consumer goods industries necessarily, but the automotive manufacturers are struggling because there's a silicon chip shortage.

So that will have to play out over the course of 2021, as well. But again, a mixed impact over the year. Certainly demand's a big part of it. It's a bit of a pet peeve of mine that when folks talked about the supply chain, the focus was always on the supply side. I get it because of “supplies” and the term “supply chain,” but in many ways, the demand impact and demand implications were broader and deeper, and I think will be longer lasting.

Lots of things that could help with demand planning, certainly we're seeing improved demand focused on algorithms. We're seeing companies use data better. The ability to use predictive tools for both today and tomorrow helps a lot. Visibility — we've been chasing visibility in the supply chain for a long time.

Some companies have done better than others, but we're finally to the point where there are now visibility tools that can give companies a much better view to do inventory certainly because inventory was a real challenge. A lot of companies found that as we've historically had, we've had the wrong things sent to wrong places invariably.

So visibility into better inventory or smarter inventory and data is really important. Then it's easy, maybe even a bit lazy of us to say, well, technology will solve that problem — sometimes it can, sometimes it cannot — but I know many companies that are starting to use artificial intelligence, cognitive computing, whatever term you'd like to use, to start thinking about how to better manage forecasting.

How to balance responsiveness and forecasting. It doesn't always have to be a forecast; it may be that I have very short-term real-time data and I simply react to it. Artificial intelligence, cognitive computing can play a big role there. I've touched on some of this, but we see a lot of focus around this notion of an automated supply chain.

Somebody the other day mentioned the term self-driving supply chain, which I thought was quite good. This ability to leverage people where people excel, and leverage technology where technology excels. A lot of the technology that we're starting to see can have a massive impact on, or substantial impact on, forecasts and forecasting improvements. From a demand challenges perspective if you focus solely on the demand side and set aside supply, set aside transportation, set aside inventory.

A lot of companies saw demand increasing. Much of it was consumer hoarding behaviors, but not all. If you were in the barbecuing business or pressure treated lumber, it was real demand increases. Other companies say demand has declined significantly.

Again, there was an element of dumb luck. If you happened to be in any services, true. If you happened to be a restaurant in the city, your business got crushed through no fault of your own. Again, some companies saw an actual increase in demand, and then almost 20% say, "Actually, we haven't had any demand planning or forecasting issues."

That number will always be considered in this sort of 25-30% range. Now it's down to 18, so clearly, the broader notion of demand forecasting declined and deteriorated in 2020. Be clear about what happened in 2020: What went right, what went wrong. Where were the issues? How do you start to think about managing demand volatility better?

There's one company that I work a lot with who said, “The only thing true about any forecast is it's going to be wrong.” It doesn't mean you shouldn't forecast or try to improve the forecast, but I do think you have to recognize there will always be times when the forecast proves to be wrong.

Then you have to start thinking about the agility of your supply chain. How can I quickly respond? Maybe demand went up, maybe demand went down, maybe it went up in the very short term, or maybe it went down in the very short term.

How do I start thinking about agility? Is it about being more flexible in terms of how I think of my inventory? Is it better to pull levers in factories and do line changeovers in a more nimble way? Whatever it is, in some ways, it's the flip side of the forecasting coin. If demanding forecasting is heads, then agility and responsiveness is tails.

Here are a couple of slides that I was going to touch on, that actually weren't in the report.

We did a survey in March and in December. By December, I think the significant negativity that we saw back in March had been tempered a little bit. For many companies, the impact on the supply chain wasn't quite as dramatic as they thought so people who said there would be a significant negative impact declined dramatically in December. But people who said that there would be somewhat of an impact increased. We saw it maybe not quite as bad, but still had an impact.

I've been talking a lot over the years about supply chain resiliency. It was both rewarding and frustrating to me that resiliency became the overused term in supply chains last year because I think it's not a jump on the bandwagon thing. It's really important.

Here you can see when we asked companies about what were, for the future of their supply chain, the gaps that if they didn't fix, if they continued to have that gap, that would be most problematic. The two were: supply chain resiliency and the digital competencies in the supply chain to pivot the business model if necessary. Those that could reposition themselves as local markets and you have to have the supply chain capabilities to be able to pivot, to do that. I won't linger on this other than to suggest that we saw, in a way, three major impacts on the supply chain. We're talking mostly here today about demand.

Certainly, there were supply issues, right Factories closed, tier one supplier problems. Maybe even below tier one. It's very common in the supply chain to discover to your horror that all of your tier one suppliers are dependent on a single tier four. If that tier four goes down, all of your tier one suppliers have a problem. A lot of supply impact, logistics as well. Massive increases last year in e-commerce and direct-to-consumer.

Probably much of that will stick, but that means shortage of capacity and shortage of capacity in the classic Keynesian economy, the very means, the rates will climb. So we saw issues around supply logistics, certainly. I've focused mostly on the demand part because that's the one that's the hardest to deal with.

Johnston: Thank you, Simon, very much for sharing all those details. I also like the idea of the self-driving supply chain. I think that's a great one. Now we're going to receive the brand view when it comes to demand planning. I'm excited to talk with Ben Hosseinzadeh from Caulipower. Can you get us started with just some quick background on Caulipower?

Hosseinzadeh: Sure, Caulipower is a growing CPG brand known for gluten-free pizzas. Really, our mission is to use the power of veggies to bring about a healthier and more convenient version of the foods you crave, that actually taste like the foods you crave. So you can find us down the freezer aisle in every major retailer and I'm very excited to be part of this group.

Johnston: We're very excited to talk with you today. When it comes to demand planning, tell us a little bit about some of the pain points you were experiencing. I know this leads you to bring on demand planning technology, in your case, an integrated business planning platform. So we'd like to get some background on that.

Hosseinzadeh: We are a high-growth company and as we started to continuously grow rapidly, we found several pain points in connecting the dots between making sure that we had the right real-time information from the shelf, down to the supply side and demand generation. Bringing about accurate forecasts and timely forecasts to be able to make the right decisions.

As the company continues to grow, we needed a tool that allowed us to have a little bit more consistency and create a baseline where we can build both long-term and short-term planning from.

That's the main reason why we brought on a technology that allowed us to be able to do that. It supports the functions between operation, supply chain, sales, and finance to be able to come together with a set of data that is consistent in allowing us to be able to make the decisions we need to make to be successful.

By implementing a tool that allows us to do this, we have decisions that will be made that allow us to have a higher-than-standard fill rate. Both in general, but even during the pandemic, giving us the foresight and the insight to be able to build inventory levels. Not to carry too much or too little, and making sure to get the product to the consumer on the shelf during the highs and the lows, both during panic-buying and when it's stabilized. Then up and down again. The benefit was seen immediately.

Johnston: I want to dig into the tech a little bit because I know that's a key part of helping deal with those highs and lows. Shastri, can you give us a quick intro to Unioncrate?

Mahadeo: Unioncrate is an integrated business planning platform that unites artificial intelligence with human intelligence so that brands can plan and execute independently of their supply chain strategies by clicking buttons on the platform.
Johnston: You've talked about the industry needing a new benchmark for accuracy, what does that look like in today's consumer goods landscape?

Mahadeo: The benchmark for accuracy now, depending on the size of the company, is around maybe 40-50%. The reason why that benchmark needs to be reevaluated is because that benchmark completes calculated debates on a brand forecasting at the brand level, or forecasting at the customer level. Not being able to go down to the granularity of where they're shipping to because instead of just forecasting to Walmart, we want to forecast to every individual Walmart's distribution center.

When most brands calculate that forecast accuracy, they're not able to go down the granular level. We need to reevaluate how we're calculating accuracy and what level that accuracy is being calculated, because if you're not calculating it based on the individual SKU and tool level, there's a lot of money being left on the table when it comes to logistical costs and so on.

What needs to be done a bit more is understanding how the accuracy affects the entire business. Most of the time, accuracy is just talked about from the sales perspective: How am I forecasting my sales? How am I forecasting the inventory? But how that actually affects the fill rates, the shelving cost, finance, marketing ﹘ understanding how accuracy affects the entire business needs to be reevaluated.

Johnston: I'm actually going to welcome the whole group so we can talk more about how it affects the whole business. Ben, I know everyone has hope that 2021 is going to be better than 2020, but I think we're realizing that might not necessarily be the case for everyone. What is something that you learned from 2020 that you think you're going to bring into 2021 in order to keep thriving?

Hosseinzadeh: More communication, more collaboration, and more often. What I mean by that is typically, we would have a handoff between when a forecast was complete, it would go from marketing to sales to supply chain, and then we would communicate back to our manufacturers to be able to get the process going.

What we've learned is that having the collaboration often and early between the cross functional teams allows us to be more nimble, more agile. It gives us the opportunity to make shifts where we need to.

Now layer into it the use of the technology that gives us a fantastic baseline to build off of. That interaction, providing the inputs that are required to fill any gaps that the platform has, gives us that success factor. I don't think that's ever going to change moving forward — certainly not for us.

Johnston: Shastri, can you talk more about the tech perspective? What do you think brands need to keep doing in order to survive and thrive for 2021?

Mahadeo: I think Ben's point and Simon as well, brands need to think about demand planning and supply chain planning as a company-wide activity and a holistic activity. The days of forecasting and silos of sales doing their own forecasts, and marketing doing their own, and the supply teams doing their own has to be gone. Everyone's remote, there's too many different factors at play for everyone to be able to forecast individually.

As Simon mentioned, the ability to be agile. In order for a company to be agile, you can't have a forecast of good sales and then wait for a meeting with supply, then wait for a meeting with the manufacturers. You need to be able to have all this in real-time.

In order for a company to be successful moving forward, they need to think about supply chain planning as a holistic approach, and realize every department affects the one goal of the company, not forecasting in silos.

Johnston: I'd like to hear the perspective from all three of you. If you had to list the No. 1 thing brands should start to do if they aren't already, to compete in the new normal in terms of demand planning, what would that one thing be?

Hosseinzadeh: Good question. I think you should spend the time that's required to meet often, even if you can't afford to. It'll save you in droves later on by dedicating that time to be able to collaborate.

Ellis: I don't know that my view today is necessarily all that different than it was before 2020. I think that forecasting is one tool for the supply chain. As I mentioned before, the only thing that is universally true about any forecast is that it's going to be wrong. Hopefully it's not really wrong, but it's going to be wrong. So as a business, as a supply chain, if you calibrate too rigidly to a single forecast, when it starts to divert or diverge from that number, that creates problems. For me, more so now than ever before because we are seeing forecast accuracy challenges across many industries, not just brands. This view that forecast is just one part of the whole, delivering again, it's your supply chain obligations.

You also need to be able to be responsive. It's a supply chain planning problem, too, but build the capabilities into the supply chain so that when the forecast proves to be wrong — I thought somebody was going to order 1,000 and they ordered 5,000 — okay, that's a good problem to have, but it's still a problem. You still have to be able to find that additional 4,000 units. Do what you can to forecast better, use technology where technology makes sense, but don't only rely on forecast. Focus also on the ability to react when the forecast proves to be less accurate than you might've ideally hoped.

Mahadeo: I was going to give one answer, but as Simon spoke, I changed it a little bit. The forecast, even if you're 80% accurate, you're still 20% wrong — you're going to be wrong at some level. One thing that brands need to focus on, or they should, is understanding why it's wrong.

Right now, it's very difficult to understand why the forecast is wrong. You may go back and say again, we have good problems like distribution, or we had a manufacturer that ran out of oranges and we couldn't ship out the product on time. But if you understand why the forecast was wrong, why there was a 20% error or a 50% error in the forecast, then you can make changes to fix that.
Leveraging technology is yes, a solution.

It doesn't have to be something like ours, it could be something as simple as just the AI tool, to help you to see the data holistically. I know you mentioned a self-driving supply chain, I have a little disagreement with it because the self-driving supply chain will say that technology takes over everything, but you can't ever replace human intelligence. You can't replace that completely.

You can leverage technology. If you've ever seen Iron Man, you have Jarvis, right? Jarvis assists Iron Man, but he's not taking over everything. It's probably a good analogy that people understand.
Leveraging technology to then understand why the forecast was wrong so you can make changes to it, is probably the No. 1 thing I would focus on.

Johnston: Let's talk about AI or Jarvis. I want to hear your perspective on AI. Of course, it's important, but what value is it bringing for demand planning in the supply chain?

Mahadeo: AI, in a very general sense — how it's been treated in the past couple years, is a very general view of AI. It's very rare that a company will take AI and make it specifically applied to an industry, which is what we tried to do. We've tried to take AI and train it specifically on consumer good trends. That allows us to be a little bit more accurate when we're forecasting for some of these companies.

What AI can do is automate all of the tedious tasks across the department. So Ben mentioned being able to communicate between departments, if sales makes a change, it says here's a new distribution. That should automatically trickle down and change the financial forecast, it should automatically change the supply forecast.

If supply says, "Hey, we can't get you oranges," then that should automatically change everything going backwards. It takes away the manual work that causes a company to not be agile. It saves them time in between. AI, when it's applied to specific industries, can remove a lot of the time it takes to understand why something's going wrong or why something's going well.

AI alone can't solve all of our problems because no one, again, taking COVID as an example, COVID is unpredictable. We would have never anticipated COVID to happen, but we can actually see signs of it. We can see signs of its impact along the way and AI can say, like Jarvis would, "Hey, you're flying into a place and you're going to run out of fuel."

But the human can say, "Well, I think I can make it this much further with this level of my foresight." Collaborating with an AI to automate a lot of the tasks that we have will help us to use technology and AI at its full potential.

Johnston: Again, I'd like to hear from all three of you. What's your best estimate on a return to more normal forecasting accuracy?