Data in Depth

Join us for an AI Deep Dive as we explore how manufacturers can unlock untapped potential with an aftermarket sales strategy. This episode delves into the benefits of integrating Configure, Price, Quote (CPQ) tools and customer portals to streamline re-orders, replacement parts, and services, transforming aftermarket sales into a seamless, profitable revenue stream. Discover how Revenue Lifecycle Management (RLM) extends beyond the first sale, maximizing customer lifetime value through proactive support. Learn how AI-driven predictive analytics enhance customer experience by anticipating needs, offering tailored recommendations, and boosting loyalty. Don't miss these insights on turning aftermarket sales into a growth engine.

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In this AI Deep Dive, we delve into the obstacles ETO manufacturers face, such as complex CPQ workflows, manual processes, and data silos, and reveal how Mountain Point’s expertise helps tackle these challenges head-on. Learn about their comprehensive solutions that not only improve efficiency and accuracy but also pave the way for advanced AI integration and intelligent automation. With Mountain Point, ETO manufacturers can streamline operations, boost customer satisfaction, and stay competitive. Tune in to discover a smarter way to navigate the future of ETO manufacturing!

In this episode, we sat down with Carl Osipov with CounterFactual.AI and the author of Serverless Machine Learning In Action. Carl shared some real-world use cases for serverless machine learning and identified strategies to get the most from a machine learning investment. 

“One of the things that happens at the beginning of a machine learning project — and this is a well-known problem for data scientists and machine learning practitioners — is spending way too much time cleaning up their data sets and focusing on things like data quality instead of actually building out machine learning solutions. I think, as practitioners, machine learning developers and engineers have created a set of techniques over the past few years to help formalize and accelerate this process. But it’s still a concern, especially if you think about scenarios that are common to manufacturing where different data silos have to come together for a machine learning system. This also happens in the scenarios where manufacturers acquire companies and then integrate data and use that data for machine learning systems. What happens is that if companies don’t actually have a rigorous approach for transitioning their machine learning systems code into operations, they find themselves in a situation where data scientists and machine learning engineers actually end up doing a lot of operations involved in putting machine learning systems into production. So what I’m describing here is what I call an ML ops trap. This machine learning operations trap, where these highly compensated practitioners are essentially spending their time working on something that’s not their core competency.” 

Connect with Carl on LinkedIn. 

In this episode, we talk with Ed Kuzemchak from Software Design Solutions. Ed digs into the ways companies can use the Internet of Things (IoT) to increase efficiency. He shares advice on how to identify areas of opportunity to implement IoT and strategies to make the most of an IoT investment. 

“I think the most important part for a company is to look at systems they have today and say “what part of these systems that we have, can we make more efficient or more cost effective or higher performing if we had better information?’ Cause that's really all that IOT is all about. It's about gaining data where you didn't used to have data or you couldn't get good or up-to-date data. You know, if you had to wait until the reports came back from the field, from your field sales tech or your field service techs on machine failures, you might have a two week lag on machine failures. And the data that you're looking at is always two weeks old. Well, what if it was only five seconds old?”

Connect with Ed Kuzemchak on LinkedIn.  

In this episode, we sat down with Kyley Darby from Mountain Point and Skye Reymond with Terbium Labs. Kyley and Skye explore how manufacturers can leverage descriptive, predictive, and prescriptive data to optimize business outcomes. They also dig into the ways Salesforce’s Einstein Analytics can help companies better plan for the future. 

“‘To move forward and look beyond the “what has happened,” manufacturers need to start pulling data together in a centralized manner — to switch from seeing what has happened to “what could happen, what could we change?” I think having data all over the place is something that holds them back.” - Kyley Darby

“I’ll add to that, Kyley. In the past, a lot of these methods have been really technical and if you don’t have access to the technical talent that’s necessary, you can find yourself following a predictive model that’s incorrect. This can cause the business to lose a lot of money, time, and effort. That technical talent that can utilize predictive and prescriptive analytics has historically been hard to find. But, fortunately, with things like Einstein, Salesforce is making this skill more accessible to everybody. So I think in the future, you’re going to see more of that, where you don’t need an entire data science team, but a good understanding of Einstein, if you’re a Salesforce user, and what those results are going to mean for your business” - Skye Reymond

Connect with Kyley and Skye. 

In this episode, we talk with Bastiane Huang with OSARO. Bastiane digs into the practical uses of deep learning and machine learning. She explores beyond the academic applications of machine learning and details some real-world scenarios, including the ability to expand the use of robots in less structured environments. 

“We use machine learning to allow robots to react to changes in the environment, learn to handle a wide range of different items, and have a range of different tasks. And more importantly, to learn, “Oh! This task [required] minimum human supervision.” So this way, you can really save a lot on human costs and on a lot of the surrounding systems,. These kinds of surrounding systems are usually more than four to five times the robot costs, so it's really significant. And lastly, it also enables robots to be used in new use cases. For example, you don't really see robot arms being used in warehouses right now. Because in a typical warehouse that has millions of different products it’s not feasible to program a robot. You're able to deal with a million different products in a million different ways. So now, because of machine learning, robots can be used in this kind of less structured environment. ”

Connect with Bastiane on LinkedIn and Medium. 

In this episode, we talk with Alex Reneman with Mountain Leverage. Alex explores the importance of innovations like voice-directed solutions in the midst of a global pandemic when the supply chain may be disrupted. 

“There's a lot of tribal knowledge that sits in some workers that you can put into a system. So we take some of that data and put it into a voice system to voice-enable the process. So, for example, if you have a flex worker who is fully trained at one station and maybe they're only partially trained on another station, they really wouldn't be able to fill in there. But if you add a display with voice that walks them through the process, maybe they’re less efficient than if they were fully trained on the station, but they’re able to get through. So that's something that's pretty impressive normally. But then put the COVID-19 lens over it, and now that individual can be effective while maybe their partner is out with COVID-19 or unavailable based on distancing or different things. That's where we’re finding some of these solutions really interesting at these times.”

Connect with Mountain Leverage on LinkedIn. 

In this episode, we talk with Mendy Ezagui with Nucleus Technologies and Rapid Logistics Couriers. Mendy digs into strategies manufacturers can employ to customize automation and streamline internal processes to increase productivity.

“When you review a [new] software, put it into the hands of some of your users and say: ‘Hey, check this out. Is it simple? Are you getting your job done much better than the way you're doing it right now? Is this efficient? Can you walk through your steps, far more easily than you have up to this point and could you put in the information that's important?’ Obviously in the back end of that, as an admin, as an operations leader, as a manager, as an executive, you want to review that information almost immediately and see if it's accurate. But it shouldn't be underestimated. [There’s an] incredible importance [in having ] a user experience that's intuitive to the person who's using it because that is really where much of the bad data comes from.

Connect with Mendy on LinkedIn.

In this episode, we talk with Lisa Arthur, marketing expert and advisor for Scoutbee. Lisa digs into strategies manufacturers can use right now to create or expand on a data-driven marketing strategy. That strategy starts with not marketing to customers, and instead, using data to create informed buyers.

“The first step around getting strategic, and building that foundation for data-driven marketing, is really deeply understanding those buyers and prospects. Understanding what they find of value. And then building the vision and the strategy around how your products and services can actually meet and exceed those needs. So, that's where you can use that virtual internal force, to pull together some of those insights and touchpoints.”

Connect with Lisa on LinkedIn. 

In this episode, we talk with Michael Cromheecke from SteamChain to discuss blockchain and machine-as-a-service. Michael breaks down how this model improves machine productivity and performance, with reduced risk and little up-front capital. 

“We use the same data management mechanism, the blockchain technology that enables cryptocurrencies to exist, but we apply it to machine-as-a-service. And what that creates for us is a record that can be shared between organizations, between corporations, between businesses in a way that's transparent to all parties. That's objective, the data that goes in, you know, it's gonna be the same data three years, five years, 10 years from now. It's resilient over time. And the big important thing is it doesn't allow one party to restrict access from the other party. So both parties truly have shared ownership of that data. No one party can change it to the disadvantage of the other. No one party can turn off access to the disadvantage of the other.”

Connect with SteamChain on LinkedIn.