Last week on Let's Talk Supply Chain, I was joined by Trace Haggard of TSG Fleets.Now Trace is a really interesting entrepreneur with a diverse portfolio spanning multiple industries.
So it was fascinating to hear his founder story and the ethos behind his business success.
And of course, we also talked about TSG and what they do, the current landscape of trucking and the challenges and opportunities, plus the incredible benefits that TSG is bringing to customers by providing a range of much-needed solutions all under one roof.
We're talking more and more about the fractured nature of supply chain and the need to consolidate and unify So, it was a pleasure to hear how TSG are simplifying things for their customers.
So, I hope you enjoyed the show, but if you missed it, you can catch up over on LetsTalkSupplyChain.com, on our YouTube channel, or anywhere else that you subscribe to the show.It was episode 431.
Let's Talk Supply Chain is not your average supply chain podcast.We feature not just the top of the industry, but also diverse voices from within the community.New innovations and the disruptors making waves in the industry.
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Welcome back to Let's Talk Supply Chain.So let's start with a question to set the scene for the show.Today's guest helps clients leverage AI to solve their biggest supply chain challenges.
And the AI market within supply chain keeps growing and growing.But by how much?Well, let me know your guesses over on social and keep listening because I'll let you know at the end of the show.
So today I'm joined by a brand that is passionate about building technology that moves life and commerce forward.This is their second time on the show in as many weeks, but who are they?Well, I'll reveal it all after the question of the week.
So we ask you a question every single Wednesday morning on the Let's Talk supply chain, LinkedIn and Instagram. And this week's question, we had almost 100 votes and the question was, which logistics look are you rocking this spooky season?
63% of you said the psychic forecaster, which does not surprise me because I think everybody wants to know what's coming.21% of you said the invisible stock.15% of you said the super supplier.
Now, Jessica came in for the win and said, Haunted Warehouse, Ghost Orders, Just in Tomb Delivery, and Deadstock.Well, we had a lot of fun with this one, and thanks so much to everybody who weighed in on this question.
Make sure to come back every Wednesday morning and be part of that conversation.So now back to today's episode, and which supply chain software brand is joining me today?Well, it's Manhattan Associates.
Now, Manhattan provides more than just supply chain solutions.
Through advanced cloud native technology and deep collaboration, their team of experienced industry experts are dedicated to equipping their partners with the tools needed to navigate the supply chain commerce world effectively.
And it's Jeff Beadle, Senior Director of Science Management at Manhattan, that is joining me for the show.
A seasoned computational and algorithmic data scientist, Jeff brings nearly 25 years of experience in pioneering innovative solutions to complex optimization and analytical challenges in the supply chain industry.
So I cannot wait to hear what he has to say today.
Today, Jeff and I will be talking all about Manhattan's unified forecast method, how a hybrid forecasting approach is different to the typical models and the benefits it brings to supply chain planning, the power of optimized inventory and leveraging AI to empower supply chain teams.
So welcome to the show, Jeff.
Thank you.Thank you for having me.
I am so excited to have you here.I mean, it's not, you know, every day that I get Senior Director of Science Management on the show.
And I had Bryant on the show only a couple of weeks ago for a real deep dive into some of the biggest industry topics right now.We talked about resilience, visibility, why technology shouldn't be so hard.
It was a really great way to kick off our series with Manhattan.And that was episode 430, I believe, for anybody who wants to go back and check that out.And I'm really looking forward to taking on a brand new topic today and hearing from you.
So let's get started.Can you introduce yourself?Talk to us about what you do, maybe your background and what you do at Manhattan.
Sure, so I'm a senior director within R&D.I head up the science team within R&D of Manhattan.
So Manhattan being a supply chain software vendor, R&D is the team that builds all of our products and the science team provides all of the mathematical modeling, optimization, data science, AI and ML that goes into our products.And I work
Both, we have two teams on the optimization side, as well as on the data science and ML side.
In particular, as a scientist myself, I focus on the optimization or aspects of supply chain planning, as well as I principally head up all of the data science and machine learning initiatives and use cases and models and research that we have here across our products.
As a scientist, I have to ask you, have you always been in the supply chain?Because I have to tell you, this is super exciting to know that there are scientists.
I mean, we've talked about data scientists, but like scientists that are coming into the supply chain and changing how we do things.I mean, that's so awesome.But have you always been in the supply chain or were you in other industries?
No, I actually started in physics.
Okay.You probably didn't even know supply chain was a thing.I didn't, no.
But I did know predictive modeling, um, machine learning, uh, back then it was more like statistical learning and things like that.Cause I was, I worked, um, uh, in the physics department in a lab called the applied chaos lab.
And we focused on research and work around non-linear and chaotic dynamics, especially in biological systems.
And so I did a lot of modeling and especially in control systems, you're doing a lot of prediction, at least near-term, short-term prediction type work in very high dimensional spaces, complex spaces.
And from that, that's where kind of my, the foundation of my predictive modeling and kind of career started.
I love that.And then one day you ended up in supply chain, grabbed onto and you're like, I'm not going anywhere else.
Very natural jump.Yeah.That's, that's another story in itself, but I love it.I love it.I've been in Manhattan 26 years, all doing this the whole time.
And, uh, you know, as a data scientist, I mean, there's not a better sandbox than a supply chain and especially Manhattan, given the breadth of advanced solutions that we have across that space.So.
See, supply chain is fun and exciting, people.All right, so today we're talking about your unified forecast method, otherwise known as UFM.Not UFO, UFM.And Brian and I also talked about supply chain unification.
Unification really is key to Manhattan's approach to helping their clients and like improving the industry.And the more we talk about it, the more I can see just how much it actually works.
So can you talk about the importance of unification when it comes to forecasting?I mean, forecasting and supply chain right now is all over the place with all of the disruptions and how do you forecast, et cetera, et cetera.
But talk to us about unification, how that helps.
Sure, well, I think it to kind of back up and usually I like to start with the context as probably most people do, but everything starts with a plan, right?A plan that's typically heavily built from some forecast.
And then eventually supply chain execution systems and processes are often running in their best attempts to execute against that plan as efficiently and optimally as they can.
But if that plan is off or it drifts, or there's some misalignment in that plan between the different operational units executed on that plan, or it's not readily adaptable or brittle or static, then execution systems, of course, do what they do so well, they'll adjust and continue executing.
However, there's a big miss there, right?Clearly it's not ideal, it's inefficient, it's suboptimal, and certainly costly across a number of aspects.
So as we'll see when it comes to forecasting, unification is important and beneficial, and actually quite similar to those benefits as with just general supply chain unification.
And by unifying demand forecasting methods into a single composite model,
It elevates the capability and robustness, adaptability and accuracy, and therefore all of the optimization of supply chain processes and applications that are consuming that unified models output or its forecast, such as demand planning, replenishment, allocation, all within kind of the supply chain planning space, which then in turn
through its integration with other supply chain solutions, especially here at Manhattan, given our native unification integration with other solutions, it leads to an overall performance boost to customers for Manhattan, for the active ecosystem, if you will, along with even their integrated applications that are outside of the Manhattan fold, but that Manhattan's integrated with.
It elevates everything overall.
Yeah, and one of the things that I've been hearing from a lot of retailers is that they have been tasked to take the capital costs and reduce inventory.
And so I can only imagine how much unification and the forecasting can really help with that because they're tasked with taking out millions. from their inventory and the capital costs that are staying within their four walls.
You know, and so how do you do that when you all, with all of these disruptions and how do you plan and how do you forecast?So what you're talking about, I think is exactly what the community is needing at the moment.
So talk to us about or give us an overview of UFM and talk about how its hybrid forecasting approach kind of differs from more traditional statistical models and standalone machine learning models.
Sure, so hybrid forecasting, what do we mean by that? So first I'll explain that, and then I'll get into UFM.
So simply put, hybrid forecasting or hybrid AI models combine statistical time series models with machine learning algorithms, offering a uniquely powerful and balanced approach to demand forecasting. But why combine them?
Why not just stick with one solution or algorithm family versus the other?Well, first, we'll start with traditional time series models.
And these methods can often work quite well, especially for demand planning approaches, which mostly rely on internal sales history.These methods, though, however, are
heavily dependent on relatively stable linear historical demand patterns and often struggle to incorporate external demand signals or external factors influencing or correlated with demand if they can at all.
Often they can't even incorporate that, which ends up with the forecast being less responsive to dynamic external changes within the environment.
Another challenge too is with time series models is they're typically tailored or designed, if you will, for specific forecasting problems, which can lead to a number of multiple different methods needed or models needed to cover the variety of demand patterns that are needed to be addressed across a customer's portfolio.
And then in turn, managing and optimizing all these models becomes complex.
And then even if you have a model that's working really well, if the demand dynamics or the distribution of the demand changes or drifts, new models are often needed, complicating the process further, especially again, for very large portfolios.
The automation can help here, but they often struggle with changing demand patterns and leading to the accumulation of errors over time before they are switching models.
and so that's the yeah did you no no go ahead okay okay so the other uh so that's the kind of the traditional time series approach the other um uh approach in the camp here is machine learning, right?
And it brings a number of advantages to the table for demand forecasting.You know, it thrives in environments where demand is volatile and nonlinear and influenced by a number of external factors.
That's what everybody wants to hear. Because we live in that volatile supply chain environment at the moment.And so you're saying that machine learning is going to help us with that.All right, keep going.
Yes, yes.It's very good at picking up those intricate nonlinear patterns and relationships in the data that traditionally, you know, time series models
miss and often they just can't process it very well at all because typically they're very linear based.And I'll explain here in a second what I mean by non-linear.
But the other thing that I want to mention too is, which I touched on, is the external factors.They can integrate seamlessly, for the most part, a wide variety of external data sources.
And the combination of these two things together leads to more accurate adaptive demand forecasting. And so by nonlinear, I mean things like pricing, weather, customer, consumer preferences, economic indicators, things like that.
It could even be promotions, local events, all kinds of things that are influencing either from a causation perspective or even correlated to demand patterns.
Interesting, because even geopolitical, I would imagine as well.Oh, yeah.Right.Depending on what's going on on the routes and how you're going to bring the cargo in.Weather is a big factor.I mean, we've just seen that.So.Yeah, all really.
And the different external data sets. um, have relevancy based on the, the kind of the context of the breadth, the horizon of how you're forecasting as well.
So some, some models that are maybe forecasting for a shorter time horizon, maybe some external data isn't as relevant, or maybe it's there, but it's very subtle versus the longer.
You know, uh, longer forecast horizon type, uh, initiatives, or maybe for short life cycle, like new or new items, you know, introducing within these kinds of dynamic environments. having that external factor capability to model against is critical.
Well, and especially with seasons and seasonality, depending on where you're sitting in the world as well.So let's talk about the benefits that this hybrid approach brings to supply chain planning.
Let's talk about, let's first start talking about market changes because I think this is something most businesses are still struggling with.They don't necessarily have enough
accurate real-time data to plan, but historical data is filled with anomalies, largely because of the pandemic.And meanwhile, the market keeps changing, which is what we were talking about, right?
Supply chain is really in this volatile spot, and there's so many moving parts to this.And I think, you know, this hybrid approach that you're talking about can help organizations be more adaptable which I think is the key here, right?
Because at the end of the day, we're all on that journey.There's not really a destination.And I don't think there's the right, there's a perfect choice.There's really like, you've got different choices, and you've got different
pathways to take, and they all have risk, and they all have technology that's needed, and all sorts of different things, and nothing's ever going to be perfect.
But you can then, with the help of technology, decide on the right path for you, knowing the risks that are going to happen.So talk to us about that.
Yeah, so the hybrid AI model really combines the best of both worlds, the best aspects that I mentioned. out of statistical modeling and machine learning modeling.
It leverages the strength of both by integrating the statistical modeling and stability of time series methods with the extensible adaptive pattern recognition aspects of machine learning.
So then it enables a hybrid model that can then handle a very wide variety of domain environments while addressing the shortcomings of each.Because I didn't touch on, there are shortcomings to ML.
And productizing ML, many people may have experience with that.And it's been a challenge for many people.There are still high failure rates, quoted by Gartner and others, which is still fascinating to me after all these years.
But I think it can be difficult to successfully implement a useful ML model in production.
But the other thing, that aside, just from the infrastructure aspects, ML can struggle when demand related to a particular variable in the model or related to some aspect that the model is trying to learn, where that information is limited or sparse or just bad, incomplete or partial.
So the model will learn that.So in ML, there's no preconceived notion.There's no preconceived knowledge like on the statistical modeling, where it has decades and decades of theory and academic research and proven results in time series modeling.
For ML, it's not.It builds all of that knowledge, if you will, strictly from the data it observes.So if it has
an incomplete aspect of the data model it's looking at, maybe overall for some categories of products it's performing well or for some locations, but others, if that information is sparse or questionable, it can lead to misleading results.
So ML alone, even though it's great, it still has some challenges and you still have to have this a team of specialists monitoring and adjusting and doing all that.
And there's systems that help with that, but in an automated sense, an automated manner, it's difficult to do.
Yeah, well, and what you're also talking about is everybody making sure they have their data house in order, which I feel like we've been talking about for a very long time.
You need to make sure you have the right data to be able to drive the ML side of the option.But you're right, having that hybrid, and we talk about this a lot, right?
Having that knowledge, that tribal knowledge that comes from a previous experience and being in the industry and doing things manually, As well as the technology, how do we bring both of those together in an environment?
And that's really what you're talking about with this hybrid approach.
That's exactly right. they bring their strengths to it.
So with the ML aspect and the model, the hybrid model picks up on this, it senses this kind of this inability of the ML aspect to kind of fill in the picture where a statistical model can do that.
So you get just a better overall robust, resilient forecasting capability.And even if it's something that a product that is very stable over time or
having the ML infusion with the statistical modeling aspect still strengthens that further, even if there's not any kind of external influence, which generally that's kind of hard to say that's true, but in some, some categories, maybe that's true, but it's still strengthens the overall, because ML learns from the collective, you know, uh, overall product, uh, catalog, if you will.
and all of the historical information, all of the external variables and so forth that are potentially available.Whereas most statistical models only learn on the given item or the given skew or whatever that you're forecasting for.
So you're going to get this collective wisdom that can help strengthen forecasting for products that may even have good stable historical patterns.
Well, and you were talking about the shortfalls and you were like, I don't know how Gartner and all of them are saying that it's still not particularly working.And I read something recently that it's really only 20% technology.
The rest is all change management.The 80% failure rate is actually in the change management piece. So it's not really the technology, it's really in how it's being implemented.
It's not the ML, the science, if you will, and even a lot of the technology that's used to enable.There's a lot of tools out there that help with it.But yeah, you're totally right.
Yeah, so something to keep in mind, right?Because we're talking about the technology and you're like, I don't know how this happens.And it's because 80% of a successful digital transformation is in that change management piece.
So let's talk about the results. that organizations can really optimize their inventories, right?They can get stock to the right place at the right time.
Talk to us about that and the impact that has for businesses because being able to make those decisions is really going to have a knock-on benefit to cost, customer service, sustainability, competitive advantage, like there are benefits across the board.
So talk to us about that.
Yeah, absolutely.The UFM, or we call it UFM.AI, you can call it either one.
If you hear me say that, please forgive me, but UFM, that combined hybrid approach takes on an inside-out, outside-in demand planning mechanism approach to modeling forecast, to building forecast models.
So the inside out is leveraging internal data, obviously your sales history, plus any kind of extended internal information that you can bring into the model, along with all the outside and all the external data, right?
So these two things working in conjunction. It provides a very reactive, very adaptive forecasting mechanism that approaches the forecasting demand for retailers and wholesalers will observe more responsiveness, more accurate.
I think I've said it a few times. But ultimately, those accuracies obviously translate into improved inventory accuracy, inventory service, and overall customer service, and just general inventory optimization and execution.
Again, it's not just the inventory side of it and the buying and replenishment and so forth.That impacts all of the cross-functional solutions.I say solutions because I'm a product guy.
The processes, operations and so forth, it impacts those as well.So you strengthen that inside out, outside in mechanism within demand planning and supply chain planning.
But then, you know, like we do here in Manhattan on the active platform, we have this unification capability, very native, low level unification capability across a number of our solutions.
Then the impact coming from this hybrid AI forecasting mechanism strengthens all of those, you know, planning and predictive modeling things going into those other systems.
Yeah, so what you're really doing is sort of building the building blocks to that fortress of like everybody working together and being able to share in the same data.
And like we said before, the knock-on effects of the decisions that you're making within forecasting supply chain planning.
what that means for other departments and how you can successfully work collaboratively daily rather than, you know, it taking so long to be able to work together.
Data and like uploading and downloading and all sorts of different things that everybody's doing.
And what version are we on? Yeah.Yeah.There's that too.Yeah.It's not just the, the business application to us, all the technical aspects to underneath it.
Absolutely.So how does it work?Can you talk to us about how UFM continuously learns and updates its forecast in real time?Because I think like you and I talked about, you know, that it has to be good data.
How do you know that the data that's continuously coming in is actually the data that you need to be able to feed the UFM and make sure that it's picking up on the right data and utilizing that to be able to help us?
Yeah, definitely.So UFM is highly dynamic and adaptable in that there are a number of mechanisms.One within, I'll call it the predictive or data science that's into the model, as well as the underlying infrastructure that's managing our models too.
But for now, I'll just focus on the UFM model itself.While, again, traditional time series models often require calibration or model switching when demand patterns shift, or sometimes even ML models, like I mentioned earlier, you have to have
specialists monitoring MLOps specialist data scientists and so forth monitoring models and potentially maybe having to, you know, consider new models or, you know, a new in feature engineering and so forth.How, how do you
adapt for those changes typically requires people for both traditional or ML.
UFM, with its unified statistical model, senses and adjust model parameters continuously across all aspects, whether it's, you know, seasonal, intermittent, seasonally lumpy, there's, you know, it's transient trends happening within there, if it's drifting between
you know, being regular moving, intermittent, volatility aspects, all the while including, you know, any kind of events happening on top of all of that.
it adjusts to those parameters because it's a unification, it's a refactor combination across multiple different statistical modeling approaches into one unified statistical model that is then integrated with ML in a very kind of novel manner.
And it's that ML component that allows it to continuously learn and adapt from new data, adjusting predictions in real time as basically market conditions evolve.
And so does that mean that they're managing by exception at that point then, because it's really working, it's doing everything on its own.
And then we are giving time back to the user because at the end of the day, they're then managing by exception and not managing sort of by reaction.
That's right.Yep.Preemptively.
I love that.I love being proactive.I mean, who doesn't like to be proactive and get some time back in our day to do creative things and become more strategic and innovative and organizations thrive from their teams being able to do that.
So what advantages does this provide compared to more traditional static forecasting models?I imagine there's, you know, a huge advantage there for a lot of people, right?
That continuous autonomous learning must be freeing up teams to focus on more strategic stuff, which is what I just spoke about, right?Not only strategic,
being innovative, bringing new ideas to the table, maybe even, you know, some technology that we haven't thought about or being able to look at the system and saying, we can do all of this with the system and we're not actually even tapping into that now.
So talk to us about that.
Yeah, absolutely.So yeah, it's very autonomous, self-governing, self-tuning.In other words, it's very hands-free.It doesn't require special staffing or oversight.And this comes about through a couple of mechanisms.
One, the modeling mechanism itself, which we've touched on a few times already, but this unique combination of this unified statistical model with the machine learning aspects
of UM that eliminates this need for model switching and simplifying the overall forecasting process, and then, you know, reducing errors and so forth, and the overall operational burden by the teams having to make adjustments.
This makes UFM particularly valuable, right, for companies with very large
portfolios and instead of allowing them, you know, often they have to focus on the top 20% and, you know, and sacrifice the lower 80% or focus on my A's and B's and C's and D's and so forth.But however, those contribute, right?
And even within the A's and B's, sometimes they can be very large across different categories or different regions that they're trying to, you know, the same product across different regions, either here within the U.S.
or even globally, the patterns can vary wildly and change unpredictably.So the model itself has a lot of these mechanisms to continuously adapt and tune and adjust itself.That's one aspect.The other
important aspect is like the call the other the other magic, if you will, of UFM AI is the within the underlying model management system. that's used and we call this, it's on the active platform, we call ActiveML.
The special thing this provides, it's an autonomous model ops system that essentially emulates the actions and operations of that ML ops team, model ops or data scientists and so forth.
A system that can act intelligently and scientifically and preemptively on behalf of such a team. This active ML system can autonomously self-govern, self-heal, self-tune both the underlying software system because it's on the active platform.
as well as the UFM models it's building and curating and serving, you know, executing, reliably executing, managing all aspects of the model life cycle in an autonomous, you know, governed manner.So it's, it's a combination of both of those.
It really provides the kind of distinction of value for UFM.Hmm.
I love that.So one last question for you, who should be making the most out of your unified forecast model?Is there like an ideal client for this type of solution?
Ah, everyone. I don't know, I mean, or at least anyone who wants to get the most out of their demand planning solution.Kidding aside, it's not unique to any given industry.
It wasn't tailor built or specially built for just short life cycle or long life cycle.It adapts to the patterns very well.So it's a very good, robust, generalized model, but you're not sacrificing.
It's preserving the fidelity of solution quality and performance, predictive performance. Sometimes when you overgeneralize methods to try and solve everything, you end up diminishing predictive performance.This doesn't do that.
Again, this kind of infusion of ML with statistical modeling, they bolster each other and provide robustness across both near-term and long-term forecasting horizons.
So it's nice to hear that that solution, like you said, is kind of for everybody.
But also for people who are very focused on what that looks like as far as, you know, inventory, bringing some of those inventory costs down as far as how much inventory you're keeping maybe in one location that needs to be in
a few locations or planning that inventory coming in based on a multitude of factors rather than just, you know, traditionally what you've always done in the past, which is not what we want to do with technology.
Yeah, exactly.Yeah.The better plans we have, the better forecasts we have, the less we have to react through execution systems to accommodate, right?I mean, that's just suboptimal.
So you want a better plan, a better projection, the more accurate, the tighter that is, the better the overall downstream impact is.So that's what we're trying to achieve here.
Everybody's going to love to hear that.So in today's complex market, advanced analytics and machine learning models are key to understanding and managing demand, optimizing inventory and servicing clients at the highest level.
So Manhattan's unified forecasting method helps you to react swiftly to market changes, improve inventory positioning and overall service levels while freeing up teams to work on the big stuff. So did you have a guess at today's big question?
Well, at the top of the show, I asked you, the AI market within supply chain has grown significantly, but by how much?Well, by 45.5% from 2017 to 2025.
That's a nearly 50% increase in only eight years, which is clear evidence that AI in supply chain is here to stay. Now, if you'd like to hear more from us, we have plenty more content for you featuring the best and brightest in the industry.
Head over to letstalksupplychain.com to check out the latest.And if you want to find out more, you can check out Manhattan at manh.com.
A massive thanks to Jeff for joining me, but remember, you can continue this conversation by being sure to visit and meet with Manhattan team at NRF in January.
If you are going to be at NRF in January, you definitely want to go and talk to, stop by their booth, talk to Manhattan about their unified forecasting. method.For more information, you can check that out over on the Manhattan's website as well.
So Jeff, thank you so much for joining me here today.
Yeah, thanks for having me.It was fun.
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If you would like to hear more from us, we have plenty more content for you featuring the best and the brightest in the industry.Head over to letstalksupplychain.com to check out the latest.
And if you have a supply chain challenge, we've most likely had the solution on the show and you can put your keyword into the search bar and all of that content will come up. And remember to come back next week.
I'm going to be joined by Annie for this month's episode of our Woman in Supply Chain series.Now, Annie is Vice President of Enhanced Business Solutions at Texas, where she has worked for over 26 years.
So I'll be asking her all about how she's become a driving force in innovation at Texas. the journey that led her to this point and how the industry has changed over the course of that journey.
We'll also be talking about her passion for balancing professional and social responsibility, being a mentor for women in the industry, and the exciting news that she recently received the Trailblazer Award in the Woman in Supply Chain Awards.
I'm really looking forward to celebrating with her and shining a light on her incredible story.So make sure that you tune in. Now, if you enjoy the show, there's a few ways to support us.You can follow us on LinkedIn, Instagram, Facebook.
We're also over on TikTok.Subscribe to our YouTube channel, Let's Talk Supply Chain.We also have a few newsletters that you can subscribe to over on letstalksupplychain.com.
I have my own LinkedIn newsletter called The Monthly Hustle where I share different stories and examples of my journey, but I also talk a lot about self-worth.
And then we also have a Let's Talk Supply Chain LinkedIn newsletter as well that you can subscribe to and stay on top of all the latest news.
If you have a supply chain or in your life and you're looking for some really cool merch, head over to our shop at letstalksupplychain.com.We have hoodies, water bottles, tote bags, you name it, we have it with some pretty fun sayings on there.
And if you're looking to join a community of like-minded professionals so that you can either network virtually or maybe you want to learn from some of the experts the best practices
Or maybe you're a woman in supply chain and you're looking for some regular meetups or even a marketing professional in supply chain where you're looking to learn and collaborate with peers.Well, we have the space for you.
It's called the Secret Society of Supply Chain.
you can head over to SecretSocietyofSupplyChain.com and check out which membership is right for you because we have one for everybody and I cannot wait to see you in there because we share only some exclusive content with those folks in the Secret Society of Supply Chain.
And next, if you'd like to be featured on an upcoming episode, head over to Apple Podcasts and rate and review the show. Have a great week everyone, thanks for listening, and remember, SHIP HAPPENS!
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