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. 

Data in Depth

Demystifying Serverless Machine Learning

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&apos;s really all that IOT is all about. It&apos;s about gaining data where you didn&apos;t used to have data or you couldn&apos;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&apos;re looking at is always two weeks old. Well, what if it was only five seconds old?”Connect with Ed Kuzemchak on LinkedIn.  

Using IoT to Maximize Efficiency

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 ReymondConnect with Kyley and Skye. 

How Salesforce’s Einstein Analytics Tool Helps Manufacturers See the Big Picture

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&apos;s really significant. And lastly, it also enables robots to be used in new use cases. For example, you don&apos;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&apos;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. 

Beyond Academics: The Practical Applications of Machine Learning

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&apos;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&apos;re only partially trained on another station, they really wouldn&apos;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&apos;s something that&apos;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&apos;s where we’re finding some of these solutions really interesting at these times.”Connect with Mountain Leverage on LinkedIn. 

How Manufacturers Can Decrease Downtimes With Voice-Directed Solutions

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&apos;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&apos;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&apos;s accurate. But it shouldn&apos;t be underestimated. [There’s an] incredible importance [in having ] a user experience that&apos;s intuitive to the person who&apos;s using it because that is really where much of the bad data comes from.Connect with Mendy on LinkedIn.

How Aligning People, Processes, Technology, and Data Helps Manufacturers Do More

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&apos;s where you can use that virtual internal force, to pull together some of those insights and touchpoints.”Connect with Lisa on LinkedIn. 

Using Data to Create Informed Buyers

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&apos;s transparent to all parties. That&apos;s objective, the data that goes in, you know, it&apos;s gonna be the same data three years, five years, 10 years from now. It&apos;s resilient over time. And the big important thing is it doesn&apos;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. 

Reduce Risk, Increase Productivity with Machine-As-A-Service

In this episode, we talk with Zach Boyd from Hirebotics a company that allows manufacturers to hire a robot for hourly work like they would an employee. Zach explores the ways that data and the Internet of Things (IoT) can be harnessed to provide proactive and preventative remote support and maintenance to their customers. “We Cloud Connect our robot so that we can gain real-time insights into how that robot&apos;s performing, what problems it might have. And with that, we&apos;re able to 98% of the time, come to a resolution, identify that root cause and provide a fix from a mobile application that we&apos;ve developed versus sending somebody on a plane to go help that customer.”Connect with Zach on LinkedIn. 

Robots for Hire: The Future of IoT

In this episode, we talk with Tyson Higginbotham from Supply Source Solutions. Tyson shares tips for manufacturers to increase the ease of doing business (EODB) for their end-users and effectively implement an eCommerce model. “We went from a manufacturing age to a digital consumer age over the course of the last 15 years...I think the big change that really happened — the hardest part for both the wholesaler as well as the manufacturer to understand — is the end-user consumer is no longer calling that wholesale distributor to have a salesperson come visit them at their office or location and bring a catalog with different sell sheets to sell them on the value proposition of that manufactured good. They are now able to access all of that information at the point of their buying journey when they are at the point of awareness and then consideration and evaluation. They do that on the manufacturer&apos;s website because they wanna go straight to the source to understand truly the value in features and benefits of what that product is.”Connect with Tyson on LinkedIn. 

How Manufacturers Can Effectively Navigate the Digital Consumer Age

In this episode, we talk with Nick Humpries from Zeiss. Nick shares strategies to head off waste and ensure quality by integrating datasets to monitor production in real-time.“The time that they used to spend wearing out a path between their workstation and the quality lab, is now all spent at their workstation and they are able to make adjustments in a much quicker fashion. And then the data integrity increased because they were able to mask those first few parts that they ended up scrapping. So we saved them a lot of time, for one, and then a lot of money in scrap as well.”.Connect with Nick on LinkedIn. 

Proactive not Reactive: How Manufacturers Can Prevent Waste and Ensure Quality

In this episode, we talk with Clark Richey from FactGem. Clark offers insights into how manufacturers can take small — but effective — steps to break down data silos for a more complete view of the company."Information is now being connected, and that&apos;s what we&apos;ve come to expect. And successful businesses are doing that really well. Whether it&apos;s in the manufacturing space, supply chain, retail, they&apos;re connecting all the dots. And that&apos;s where those silos lead to problems. We&apos;re separating those dots, and we have hard walls between them. So we need to figure a way to reconnect those so we can create that larger context to allow the business to make decisions between those silos. So to understand, for example, how does my inventory affect my ability to deliver and manage my supply chain and not to see them as separate pieces of the puzzle?"Connect with Clark on LinkedIn. ---

Connecting the Dots — How Manufacturers Can Find Context and Meaning in their Data

In this episode, we talk with Caroline Hilla from Cisco&apos;s Global Manufacturing team. Caroline offers practical and foundational tips for capturing, integrating — and effectively using — IoT data. "There&apos;s a perception right now that if it&apos;s not broke, don&apos;t fix it. And while that may have worked for 15, 20 years now, what we&apos;re really seeing is that you&apos;re not really able to tap into the full potential of the data that you can be collecting from your different devices and machines using the internet of things until you&apos;ve really been able to use it and actually unite those data islands... And when it comes to business productivity, I mean, this is huge. This is what allows you to operate as efficiently as possible. This is what allows you to reduce downtime. And ultimately, this allows you to get your products out the door faster and tap into new markets that your competitors have not been able to reach yet." - Caroline Hilla, CiscoConnect with Caroline on LinkedIn.Listen in to the Cisco Manufacturing Leaders Podcast

How Manufacturers Can Use IoT Data to Drive Bottom-Line Results w/ Caroline Hilla from Cisco

"Too many companies I&apos;ve seen initially get into this thinking they&apos;re gonna have some 12, 18-month ROI... they&apos;re gonna put a bunch of data in and all of a sudden they&apos;re going to have some super whiz-bang thing happening. And it&apos;s definitely not that. It&apos;s a long-term journey."In this episode, we talk with John Clevenger, Managing Director of Accession Consulting. John shares the many opportunities the Internet of Things (IoT) brings to the manufacturing industry. He offers practical tips for getting started and outlines what NOT to do or expect when implementing an IoT strategy. Connect with John on LinkedIn.Check out Accession Consulting.---This season, we&apos;re giving away a pair of Bose QuietComfort 35 wireless headphones!   How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details > 

The Manufacturer's IoT Playbook: Where to Start and What NOT to Do - with John Clevenger

"Since we&apos;ve started this, we&apos;re literally adding tens of thousands of dollars of extra revenue when our product ships." In this episode, we talk with Chris Muto, Sales Operations Manager for Pro-Tech, a manufacturer of industrial snow plows. Chris shares how Pro-Tech is using data to improve customers&apos; experience, drive additional sales, and do more with less.Connect with Chris on LinkedIn.Check out Pro-Tech.---This season, we&apos;re giving away a pair of Bose QuietComfort 35 wireless headphones!   How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.Subscribe on ANY of the following:Apple PodcastsStitcherGoogle PlaySpotifyAlexa/Tune InReview us on Apple Podcasts or Stitcher;Follow us on Twitter @DataInDepth; andTweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth. Full details > 

How Manufacturers Can Use Data to Improve Customers' Experience, Drive Additional Sales, and Do More with Less; featuring Chris Muto from Pro-Tech

Data in Depth explores the world of advanced analytics, business intelligence, and machine learning within the context of the manufacturing industry. In each episode, we talk with industry leaders and analytics experts to help manufacturers gain a 360-degree view of the shop floor, their business processes, and their customers. We dig into the concepts of descriptive, prescriptive, and predictive analytics to help solve modern manufacturing problems. From MRP to quality control, from field service to customer experience, our conversations are designed to spur innovative, data-driven thinking for those working to build the factories of the future.

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Data in Depth

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