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Detroit, once a symbol of the Rust Belt's decline, is now rewriting its story as a hub for innovation and mobility. Home to the Michigan Central innovation district and Newlab, the city has transformed into a growth engine for startups tackling the future of transportation. With its rich history as the birthplace of automotive mobility and its current momentum as a technological disruptor, Detroit is attracting mobility entrepreneurs eager to reshape mobility for the 21st century.So, what makes Detroit the ideal launchpad for next-gen mobility startups? How do spaces like Newlab support the innovation ecosystem?In this episode of DisruptED, host Ron J. Stefanski sits down with Ben James, the Co-founder and CEO of Tubular Network, a cutting-edge company revolutionizing the last mile of delivery with robotic tube systems. Together, they explore why Detroit's dynamic ecosystem, grit, and historical relevance make it the perfect home for mobility entrepreneurs and companies like Tubular Network.Key takeaways from the episode:The Detroit Comeback Story: Once seen as a symbol of urban decline, Detroit is now a magnet for mobility entrepreneurs, with Newlab serving as a vibrant incubator for innovation.The Tubular Network Journey: Ben James explains why Tubular Network relocated from Austin to Detroit and how the city's mobility-focused infrastructure has catalyzed their growth.Why Ecosystems Matter: Newlab provides the ideal environment for startups to collaborate, innovate, and showcase their technologies in spaces designed to inspire and disrupt traditional mobility.Ben James is an accomplished entrepreneur and product leader with extensive experience in augmented reality, electric transportation, and education. As the Co-founder and CEO of Tubular Network, he leverages his engineering expertise and hyperloop technology background to pioneer innovative logistics solutions. With a career spanning leadership roles at Magic Leap, CoStar Group, and National Instruments, Ben excels in driving product strategy, leading prototyping teams, and integrating cutting-edge technologies to address multidisciplinary challenges.
Welcome to another episode of The Chief Strategy Officer Podcast! Today, we're diving into a pivotal topic for strategy leaders: how to approach mergers and acquisitions (M&A) to accelerate your company's strategic goals. Joining us are two Outthinker CSOs with extensive Strategy and M&A experience. Kevin Ilcisin, has a fascinating career in high tech, most recently as SVP of Strategy and Corporate Development for NI, formerly known as National Instruments, which was recently acquired by Emerson Electric in 2023. Kevin brings a scientific approach and holds a phD in Astrophysical Sciences with a focus on Plasma Physics. Alok Agrawal is the Chief Strategy Officer at Celestica, a $7B multinational leader in the design, manufacturing, and supply chain solutions industries. Alok started his career as a management consultant with Kearney serving industrial clients before taking strategy and M&A executive leadership roles for industry leaders like Johnson Controls, Meritor, and Tenneco. In this episode, Kevin and Alok share their insights and practical experience in the art and science of successful deal-making and integration. In this episode, we explore: The critical distinction between strategy and M&A. (hint- M&A isn't a strategy- it's a tool to realize your strategy) —What people miss about this and why that matters. How to identify the right acquisition targets and develop relationships with key players like investment banks. Practical tips for building and managing a robust M&A pipeline Insights on navigating cultural integration and realizing post-merger synergies effectively. The unique role a Chief Strategy Officer plays in orchestrating and guiding the M&A process- and advice they have for CSOs and M&A leaders early in their career about what they wish they knew early on and mistakes to avoid Whether you're new to M&A or looking to refine your approach, this episode is packed with practical advice and lessons learned from two seasoned leaders. Tune in for actionable takeaways to become masterful at using M&A to accelerate your strategy and execution. Learn more about Outthinker's community of chief strategy officers - https://outthinkernetwork.com/ Follow us on LinkedIn - https://www.linkedin.com/company/outthinker-networks
Scott MacKenzie hosts the Industrial Talk podcast, celebrating its success and encouraging listeners to share their stories and technology. He introduces Preston Johnson from Cutsforth, who discusses their asset management platform, Insight CM. Insight CM integrates various condition monitoring technologies, such as vibration, lubrication, and thermography, to provide a comprehensive view of equipment health. Preston emphasizes the importance of understanding asset failure modes and leveraging data to predict and plan maintenance. He highlights the benefits of workforce optimization, increased uptime, and reduced maintenance costs. Roadblocks include resource constraints, IT integration, and leadership buy-in. Preston provides contact information for further inquiries. Action Items [ ] Understand the client's critical assets and failure modes. [ ] Identify the appropriate sensors to detect developing defects. [ ] Integrate the Insight CM platform with the client's existing data sources and systems. [ ] Develop prognostic capabilities to predict remaining useful life of equipment. [ ] Provide a business case and justification for implementing the Insight CM solution. Outline Introduction and Overview of Industrial Talk Podcast Scott MacKenzie introduces the Industrial Talk podcast, emphasizing its focus on industry innovations and trends. Scott thanks listeners for their support, highlighting the podcast's success and its mission to celebrate leaders in the industrial sector. Scott discusses the exciting developments expected in 2025, including innovation, technology, and solutions in various industries. The podcast aims to promote companies passionate about success and to provide a platform for sharing insights and solutions. Introduction of Preston Johnson and Cutsforth Scott introduces Preston Johnson from Cutsforth, focusing on asset management and the importance of managing assets effectively. Scott reflects on the success of the previous year, mentioning the numerous interviews with industry leaders and the global broadcasts. Scott encourages listeners to create and share their stories, emphasizing the importance of amplifying messages through the Industrial Talk platform. The podcast aims to support listeners in their success by providing a platform for sharing technology and solutions. Preston Johnson's Background and Cutsforth's History Preston Johnson shares his extensive experience in the industrial instrumentation business, including his time at National Instruments. Preston discusses his journey in condition monitoring and predictive maintenance, starting in the early 2000s. Cutsforth's acquisition of the Insight CM software in 2022 is highlighted, expanding their business from generator support to a predictive maintenance platform. Preston provides a brief history of Cutsforth and their involvement in various industries, including power generation and generator support. Insight CM Software and Its Unique Features Preston explains the vision behind Insight CM, focusing on leveraging multiple condition monitoring technologies. The software was developed in response to a large power generation company's need for fleet-wide condition monitoring across various technologies. Insight CM's ability to digitize and compare trends from different technologies, such as vibration, lubrication, and temperature, is emphasized. The software aims to identify defects in equipment by collaborating and comparing data from different monitoring technologies. Implementation of Insight CM and Typical Steps ...
In this Electropages podcast, host Robin Mitchell is joined by Kevin Kleine, Senior Director of Applied AI at NI, now part of Emerson's Test & Measurement division. Kevin discusses how AI is transforming the test and measurement industry, enabling intelligent systems and optimising efficiency. From leveraging AI in data acquisition to predictive maintenance, Kevin offers insights into how engineers can utilise emerging technologies for improved results.
"No one really knows what happens in 70% of Earth's surface” This stark reality drove Gaurav Seth and Vinit Bansal to create PierSight, a spacetech startup using SAR and AIS-equipped satellites to pierce through this maritime blindspot and bring 24/7 visibility to the world's oceans. When the former ISRO scientist Gaurav and National Instruments engineer Vinit first collaborated on a prototype that normally takes months to build, they completed it in just six weeks. This "aha moment" sparked a journey to revolutionize maritime surveillance through a constellation of all-weather imaging satellites. In this illuminating Day One conversation with Manish Advani (Vice President, Elevation Capital), Gaurav and Vinit discuss their journey of building PierSight, why they chose SAR vs optical imaging, their unique approach to team building, going after the right applications, what it takes to win defence contracts, and early market validation while navigating the challenges of building a deep-tech startup in India.
In this captivating interview, David Fuller, Co-founder and CEO of Artificial, takes us on a remarkable journey through his life, tracing his passion for technology from the early days of playing computer games on CPM to his groundbreaking work in revolutionizing life sciences. With a wealth of experience spanning National Instruments, Lego Mindstorms, Kuka Robotics, and now Artificial, Fuller shares his insights on the power of abstraction, the impact of AI and quantum computing, and his mission to digitize and standardize scientific experimentation. Get ready to be inspired by his unwavering pursuit of purpose and his vision for transforming drug discovery, cell and gene therapies, and beyond. 00:08- About David Fuller David is the CEO of Artificial. He has a 25+ year professional background in business and technology spanning Measurement, Automation, and Robotic Systems. He has worked as a developer, Head of Engineering, CTO, Managing Director, and Board Member for global multi-billion dollar tech companies. --- Support this podcast: https://podcasters.spotify.com/pod/show/tbcy/support
People Who Are Good at What They Do Being Good at What They Do
We take a whirlwind tour through managers and mentors from Travis's 17 year heritage at NI (formerly National Instruments, currently Emerson). Ever wondered how a spreadsheet can take over your whole life, learn more... Then, we take a stab at the formula for a high quality, moderate work brisket. Maximize the quality of the brisket without having to output 100% of the effort. Learn about the Texas Crutch.
In this episode of Startup Anthology, the podcast, I'll speak with Paul Austin. Paul is originally from Cleveland Heights, OH, and holds a Bachelor of Arts in Computer Science from The University of Texas in Austin, TX. Join me on a journey through Paul's 30-year tech career. We will reflect on his time as a former employee of National Instruments and explore various aspects of his career, such as developing testing and data acquisition software, partnering with Lego, and creating educational robotic systems. Along the way, we will discover how Paul emphasizes the importance of learning, taking risks, and nurturing a supportive company culture. I hope you enjoy the episode.
Technovation with Peter High (CIO, CTO, CDO, CXO Interviews)
802: Thomas Benjamin; EVP, CTO, and Head of Platform & Analytics R&D at National Instruments (NI); discusses the innovation he is leading at the company focused on what he calls ‘Horizon 3' technologies and the preparation needed to take advantage of these technologies down the road. He shares how his team is organized across the diverse segments that are a part of the broader business and the ways in which these segments interact and collaborate with the IT organization. Thomas also gives an in-depth overview of the innovation labs he leads, the potential value that generative AI can bring to NI, and the skills that will be necessary to leverage these emerging technologies. Finally, he reflects on the lessons he has learned across his diversified career path, explains the benefit of being a hands-on leader, and looks ahead at the trends in technology that he believes will change the game.
Technovation with Peter High (CIO, CTO, CDO, CXO Interviews)
802: Thomas Benjamin; EVP, CTO, and Head of Platform & Analytics R&D at National Instruments (NI); discusses the innovation he is leading at the company focused on what he calls ‘Horizon 3' technologies and the preparation needed to take advantage of these technologies down the road. He shares how his team is organized across the diverse segments that are a part of the broader business and the ways in which these segments interact and collaborate with the IT organization. Thomas also gives an in-depth overview of the innovation labs he leads, the potential value that generative AI can bring to NI, and the skills that will be necessary to leverage these emerging technologies. Finally, he reflects on the lessons he has learned across his diversified career path, explains the benefit of being a hands-on leader, and looks ahead at the trends in technology that he believes will change the game.
Today's guests are TELO Trucks founders Forrest North and Jason Marks. The founders offer expert insights into the current (and future) EV landscape and share how TELO's compact electric trucks maximize efficiency with a revolutionary design.Jason, TELO's CEO, is a former collegiate pole vaulter who spent his early career at National Instruments developing systems tests for U.S. automakers as Chief Business Development Manager. Forrest, the company's CTO, worked on the early Tesla team before founding electric motor startup Mission Motors. He later founded and sold Plugshare, the number one EV charging app, before launching TELO with Jason in 2022. Listen now on your favorite podcast platform. After all, we're talking about mini electric pickup trucks!! Episode Timestamps:00:32 Intro on TELO, Guests || 02:22 Forrest's Background, Bio || 08:57 Jason's Background, Bio || 14:41 Maurice Olley's Influence || 15:47 Origin Story of TELO Trucks || 19:47 What Makes TELO Unique || 26:52 The Electric Vehicle Industry || 32:01 Doing Good and Sustainability || 36:28 The Future, Conclusion || 37:05 End Credits || Episode Resources / Links:More on TELO Trucks → https://telotrucks.com/ Jason on LinkedIn → https://www.linkedin.com/in/jasonmurraymarks/ Forrest on LinkedIn → https://www.linkedin.com/in/forrestnorth/ Conor on LinkedIn → https://www.linkedin.com/in/ckgone/ Hosted on Acast. See acast.com/privacy for more information.
In this episode of The Next CMO podcast, I speak to returning guest, Ana Villegas, the CMO of AffiniPay, the market leader in professional services payments serving legal, accounting, architectural, engineering and construction firms.Ana joined AffiniPay from National Instruments, where she also served as the CMO.AffiniPay has been recognized as one of Inc. 5000's fastest growing companies for 10 years in a row. Each of its brands leads the market it serves with solutions purpose-built by industry including LawPay, ClientPay, CPACharge, and AffiniPay for Associations. AffiniPay's solutions are trusted by more than 60,000 firms with more than 150 strategic partnerships and endorsements, including the American Bar Association and the American Institute of Certified Public Accountants.Learn more about Ana VillegasLearn more about AffiniPayFollow Peter Mahoney on Twitter and LinkedInLearn more about Peter's company, AcceleratusLearn more about Planful for MarketingJoin The Next CMO CommunityRecommend a guest for The Next CMO podcastProduced by PodForte
In our latest episode, we're thrilled to feature Dan Balcauski, Founder of Product Tranquility, as we navigate the world of SaaS pricing models.About Dan Balcauski Started his career in product management at National Instruments, based in Austin, Texas. Ascended to the role of Product Strategy Principal at SolarWinds, a SaaS company serving DevOps and IT professionals. Made a significant shift to B2C, leading product at LawnStarter Lawn Care. Boasted a successful freelance career as a product manager, earning a place in the top 3% of PM professionals worldwide on Toptal. Imparts his industry knowledge as a program leader at Northwestern University, where he teaches product strategy. In 2019, Balcauski launched Product Tranquility, a venture dedicated to assisting B2B SaaS CEOs in defining pricing and packaging for their products.A Personal AdventureWhat sets Balcauski apart is his remarkable spirit of adventure. Before starting Product Tranquility, he embarked on a personal voyage as an independent travel consultant, planning and undertaking a global expedition through 21 countries. This extraordinary journey demonstrated his fervor for continuous learning, during which he acquired new skills ranging from digital marketing and Spanish proficiency to kiteboarding and Argentine Tango.Join us as we dive deep into the insights and stories Balcauski brings to the table.Value-Based PricingIn our engaging chat, Dan Balcauski brought up some crucial insights regarding the struggles businesses often face while setting up pricing in the SaaS industry. There's often a lack of structure, leading to heated debates rather than an organized approach. To combat this, Balcauski introduces the 'Services' model.Key Challenges in Pricing: An unclear target customer profile: Companies often struggle to understand exactly who they are serving. Poor understanding of how they create customer value: Businesses might be unclear on the unique value they deliver to their customers. Unclear product differentiation: Companies often grapple with distinguishing their products from others in the market. Underappreciation for the depth of decisions in pricing and packaging: Many overlook the vast array of factors impacting pricing, focusing only on surface-level elements. The 'Services' Model:The 'Services' model stands for Segments, Value, Competition, and Strategy, and was designed to address these challenges. Segments: Understand the specific context and constraints of your customer segments, as they dictate what they value most. Value: Recognize how each segment perceives value and rank orders value drivers, influencing how they value your product. Competition: Be aware of the competitive alternatives each segment has available. What would they use if your product didn't exist? Strategy: This comes in the Michael Porter sense of the word. Strategy involves trade-offs; you can't be everything to everyone. Decide who you're going to target, how you position yourselves in their minds, and how you'll balance the different elements of SaaS packaging. This includes price metrics, price models, offer configurations, etc. The combination of these four components informs the price level you set, helping your business achieve its objectives. The 'Services' model ensures a more thoughtful, strategic approach to pricing, moving away from arbitrary decisions.What is value based pricing? Dan Balcauski clarified the concept of value-based pricing and distinguished it from other terms like value metrics and price metrics.Value-Based PricingValue-based pricing, at its core, concerns how value is divided between buyer and seller in a transaction. This notion dates back to Adam Smith and the concept of trade, where specialization and trading lead to overall improvements for everyone involved.“...goes all the way back to Adam Smith with trade, right, you've got the butcher, the baker, and the candlestick maker, they don't all try to, you know, bake their own bread and cut their own meat, etc. Because it's better if we all specialize, we're all better off if we specialize in trade, right.” - Dan Balcauski Value Metric vs. Price Metric Value Metric: Using a 'Jobs to be Done' framework, the value metric is how customers measure the effectiveness of your product in achieving their specific outcomes. These outcomes could be economic (saving time, decreasing costs, increasing revenue), emotional (reducing anxiety, boosting status), or social (contributing to causes like climate change, equal rights, education, health care). Price Metric: While value metrics focus on the customer, price metrics focus on the product. The price metric is the unit of value for which the customer is charged concerning the product (e.g., number of users, API transactions, gigabytes of data transferred, etc.). Ideally, the value metric and price metric should be correlated, meaning that the way customers derive value from your product should inform the units by which you charge. Outcome-Based PricingThe question of charging based on actual value delivered, like a CRM charging based on deals closed every month instead of the number of users, led to the discussion of outcome-based pricing. This model aligns the vendor with the customer's success, creating a 'pure' form of value-based pricing.While this approach is theoretically appealing, Balcauski explains it doesn't always work in practice. Exceptions include companies like Stripe, which directly participates in the payment flow and therefore aligns its success with its clients' success.Outcome-based pricing may not work well for companies outside the flow of the success metric. It can lead to complications in reporting and potential conflicts, given that what is considered 'success' may not be clearly defined or could be interpreted differently by different parties. Therefore, while enticing, outcome-based pricing requires careful implementation to avoid straining customer relationships.Bundling and Unbundling in Pricing Models**Bundling, Unbundling, and Usage-Based Pricing**Bundling and unbundling, while seemingly contrary, are not in tension with usage-based pricing. These concepts represent different dimensions of product packaging that can evolve independently. According to the Silicon Valley CEO Jim Clark, the only two ways to make money in business are bundling and unbundling.The history of the PC industry illustrates this with the evolution from monolithic providers like IBM to the unbundling of the operating system from the CPU architecture (as seen with the Wintel monopoly), and then back to bundling via Apple's integration of software and hardware. Dan highlights that such industry transformations often occur cyclically and are influenced by broader market trends rather than by single companies.The Nuances of Pricing MetricsPricing metrics, while essential for defining a product's price, can either aid or hinder a company's competitive positioning. The choice of pricing metric depends significantly on the market context and should ideally align with the customer's business needs and the perceived value of the product. Innovative pricing strategies, like Rolls Royce's "power by the hour" for jet engines, demonstrate how such metrics can mirror customer value, thereby streamlining the buying process.However, such innovative strategies may require substantial resources to educate the market about the change and may be more successful if driven by industry leaders or highly innovative products. Finally, Dan advises caution when attempting to be distinctive with pricing metrics, as this can result in increased effort to justify the difference to potential customers.AI and Pricing for Solo TravelersThe final part of the interview revolved around a hypothetical AI application designed to assist solo travelers, with features like tracking reservations, making dynamic dinner reservations, and offering real-time travel updates. The proposed monetization strategy is a freemium model, with added features for premium users.In response to this idea, Dan expresses concern about the target audience of solo personal travelers due to their potential limited spending power. He urges the developers to consider different customer segments thoroughly, understanding their specific needs and the context in which they'll be using the app.The importance of understanding customers' contexts is emphasized, using the example of airlines, who vary ticket prices based on the nature and timing of travel. Understanding these distinct customer segments and their unique value drivers can guide pricing decisions effectively.In addition, Dan encourages the consideration of competitive alternatives from a 'Jobs to be Done' perspective. Instead of focusing on similar apps or startups, the developers should consider what the target user is currently using to solve their problem if the proposed app didn't exist. By understanding these competitive alternatives and their inherent limitations, developers can better define their product's differentiated value and devise a pricing strategy that accurately captures this value.Episode Recap In this intriguing episode, Dan Balcauski offers his deep expertise and unique perspectives on the world of SaaS pricing models. We delve into various aspects, ranging from the 'Services' model to value-based pricing, outcome-based pricing, bundling and unbundling, as well as the exciting realm of AI in pricing strategies. Each topic comes with a host of insights and stories from Dan's vast experience, illustrating the depth of his knowledge and his ability to communicate complex ideas with clarity and impact.Balcauski's unique background, blending his passion for product strategy and global travel, sets the stage for an engaging, insightful conversation that leaves listeners with a wealth of valuable takeaways. Whether you're an established SaaS CEO or a budding entrepreneur, the wisdom shared by Dan Balcauski is sure to elevate your understanding of pricing and packaging in the SaaS industry. Listen to the full episode now. And don't forget to follow Dan: Product Tranquility Dan's LinkedIn
In this episode, host Sarah Morgan is interviewing Recruitment Marketing expert and LGBTQIA+ advocate, Kat Kibben. Kat Kibben [they/them] is a keynote speaker, writing expert, and LGBTQIA+ advocate who teaches hiring teams how to write inclusive, unbiased job postings that will get the right person to apply faster. Before founding Three Ears Media, Kat was a CMO, Technical Copywriter, and Managing Editor for leading companies like Monster, Care.com, and Randstad Worldwide. With 15+ years of recruitment marketing and training experience, Kat knows how to turn talented recruiting teams into talented writers who write for people, not about work. Today, Kat is frequently featured as an HR and recruiting expert in publications like The New York Times, Chicago Tribune, and Forbes. They've been named to numerous lists, including LinkedIn's Top Voices in Job Search & Careers, and have worked with companies like Twitter, Survey Monkey, and National Instruments. When not speaking, writing, or training, you'll find Kat traveling the country in their van or spending some much-needed downtime with the dogs that inspired the name Three Ears Media. In this episode, Kat shares: The 3 essential elements of a successful job posting The history of institutional bias baked into our job posting tactics The reason why The Great Resignation is more of a marketing problem than a recruiting problem Connect with Kat through their website, Three Ears Media, where you will find links to their social media, their free downloads, and more info on the work they are doing.
http://www.Gabelli.com Invest with Us 1-800-GABELLI (800-422-3554) Justin Bergner, Research Analyst and Portfolio Manger at Gabelli Funds discusses automation leader Emerson Electric and their recent announcement that it had prevailed in an auction process to acquire National Instruments for $8.6B or $60 per share, a 60% premium to National Instrument's equity value prior to Emerson announcing in January that it had acquired a 2% stake and was pushing for the company to sell itself.
Carl Quintanilla, Jim Cramer and David Faber led off the show with market reaction to key inflation data: The March Consumer Price Index cooled from a year ago to levels not seen in nearly two years. The anchors also discussed Warren Buffett's comments from Tokyo in a wide-ranging CNBC interview, including his take that even though "we're not through with bank failures," there's no reason for depositors to panic. Also in focus: Emerson to buy National Instruments in an $8.2 billion cash deal, what's fueling McDonald's shares' jump to fresh record highs, UConn men's basketball head coach Dan Hurley and Connecticut Governor Ned Lamont joined the anchors at Post 9 after ringing the NYSE opening bell, in celebration of the Huskies' 2023 championship. Squawk on the Street Disclaimer
Alyce Andres, US interest rates and FX reporter with Bloomberg News, joins the program to discuss the latest CPI reading and outlook for rate hikes and cuts. Jody Lurie, Credit Analyst with Bloomberg Intelligence, joins to talk about Carnival CFO's moves to pay down debt and outlook for cruising. Karen Ubelhart, Industrials Analyst with Bloomberg Intelligence, joins the program to talk about Emerson Electric's deal to National Instruments and other updates on industrial companies. Johan Grahn, VP and Head of ETF Strategy at AllianzIM, joins the program to discuss investing strategies and ETF flows. Brad Case, Chief Economist at Middleburg Communities, joins the program to discuss real estate pressures from inflation and the recent CPI reading. Morgan Paxhia, co-founder of Poseidon Investment Management, joins the program from the Benzinga conference to discuss cannabis investing and how the SVB collapse impacted cannabis, the possibility of Federal reform, and M&A in the space. Hosted by Paul Sweeney and Matt Miller.See omnystudio.com/listener for privacy information.
Dan Purvis says his passion for working for himself started in junior high when he began selling pieces of gum to classmates. Six companies later, it is safe to say Dan is a certified serial entrepreneur. Dan graduated with honors in electrical engineering and an undergraduate fellowship from Texas A&M and was a Jones Scholar at Rice Business, where he earned an EMBA in 2005. His career began at National Instruments in the upper Midwest. After returning to his hometown Houston, he began building a division for his new employer. After his third successful sale, he co-founded Velentium in 2012, which took over as his full-time job. Velentium is a professional engineering firm specializing in end-to-end support for designing and producing therapeutic and diagnostic active medical devices, intelligent products, and automated test systems for the medical, energy, and manufacturing industries. And in 2020, Velentium faced an unprecedented ask: partner with a small medical device company and a very large vehicle manufacturer to increase emergency ventilator production from hundreds per month to thousands per week—in just 28 days.Dan shares with host Scott Gale ‘19 the risks he took early in his career to get him to the level of success he is at now, the importance of “work/life fit” and the incredible story of how Velentium became a major player in ventilator production in the early days of the COVID-19 pandemic.Read more about Rice Business' #1 entrepreneurship ranking four years in a row in Princeton Review and Entrepreneur Magazine.Episode Quotes:At the end of the day we are all just humans30:11 - In competitive environments, somebody wins the bid, and somebody loses the bid. But then, when we get back to our humanity, we are all equal. We share this ball we live on. Right? And so, remembering to be empathetic, remembering to celebrate life events, remembering to look people in the eyes and remember things about them, remembering to be human, and not allowing even the busyness of a pandemic response, the ventilator project, to get in the way of that, I believe is really important.Getting people involved in volunteering03:34 - If you're thinking about going to Rice or just on your way towards graduation, you're going to have many moments in your career where you have the opportunity to step in, raise your hand, volunteer, and say, "That's me."The origin story of Velentium16:22 - The origin story of Velentium was not about running from something; it was about pursuing something. I had a dream of something greater, just something great. And so the idea that I could be at the helm of a culture-forward, family-first, unbelievably fast, highly technical organization. That was my dream.On having valuable connections at Rice09:33 - There's just an esprit de corps. There's a level of excellence. There's a level of connection within this town to the extent that you want to be a Houston business person. If you have a Rice MBA, you are connected to a very elite group of people that you can call colleagues and classmates. Certainly, fellow alum. And so, there's just a camaraderie at Rice that I enjoyed. But, deeper than that, there was a connection with the people of Rice that was really valuable for me.Show Links:TranscriptGuest Profile: 28 Days to Save the World: Crafting Your Culture to Be Ready for Anything Velentium Dan Purvis on LinkedIn
Dan Balcauski is the founder and Principal Consultant at Product Tranquility, a consulting firm that helps SaaS business leaders accelerate their product growth and increase customer loyalty. He's an expert in digital marketing and e-commerce with a specialization in pricing and product strategy. Dan is also the Program Leader for Kellogg's Executive Education Product Strategy course. In addition to his role at Product Tranquility, Dan works as a freelance product manager and is a member of Veritux. Some of the companies he's worked for include SolarWinds, LawnStarter, and NI (formerly National Instruments). Dan's good news on the podcast today is that there's a golden opportunity that every SaaS company has right now to build a pricing model that their customers (and their investors) will love that won't distract you with a mirage of “free growth.” Tune in and get ready to take some notes. Key Takeaways What does pricing entirely encapsulate and why do many businesses struggle with it?Finding out who in the company should initially deal with the pricing and packaging aspect of thingsProblems and pitfalls that Dan has encountered with all the companies he's helped, and what indicators are there to guide companies in the right directionMetrics that companies can use to verify if they are meeting their goals and whether or not their pricing is playing a role in meeting those goals Pricing model variations between long-term businesses versus exit-driven businessesThe pros and cons of the various discounting strategies in use by different companies i.e free trials, freemium, free tools, etc.The importance of free tools to act as a starting point for the user and fend off buyers remorse without added effort to convert the prospects to customersThe dangers that businesses might run into with a low price; especially with new non-existing products in their category Connect with Dan Website - https://www.producttranquility.com/ LinkedIn - https://www.linkedin.com/in/balcauski/ Twitter - https://twitter.com/dan_balcauski
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati (https://www.iaicenter.com/). In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when. If you liked this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation (https://www.augmentedpodcast.co/81). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati. In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Jay, it's a pleasure to have you here. How are you today? JAY: Good. Thank you for inviting me to have a good discussion about industrial AI. TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing. You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia. The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today. JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system. So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later. But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want. What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them. So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working. But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages. So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work. Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs. So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education. So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation. So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera. So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time. So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera. So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good. So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture. TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new. It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious. And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective. JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember? TROND: Yes, yes. JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000. So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back. Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name. But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs] So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment. So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI. But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today -- TROND: So they have achieved zero downtime after that project, essentially. JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year. TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that. Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You have very, very specific system elements. And they kind of have to work together in some architectural way before you're willing to call it an industrial AI because it may be a machine tool here, and a machine tool there, and some data here. But for you, unless it's put in place in a working architecture, you're not willing to call it, I mean, it may be an AI, but it is not an industrial AI. So how did this thinking then evolve for you? And what are the elements that you think are crucial for something that you even can start to call an industrial AI? Which you now have a book on, so you're the authority on the subject. JAY: Well, I think the real motivation was after you apply all the machine learning toolkits so long...and a company like National Instruments, NI, in Austin, Texas, they licensed our machine learning toolkits in 2015. And eventually, in 2017, they started using the embedding into LabVIEW version. So we started realizing, actually, the toolkit is very important, not just from the laboratory point of view but also from the production and practitioners' point of view from industry. Of course, researchers use it all the time for homework; I mean, that's fine. So eventually, I said...the question came to me about 2016 in one of our industry advisory board meeting. You have so many successes, but the successes that happen can you repeat? Can you repeat? Can you repeatably have the same success in many, many other sites? Repeatable, scalable, sustainable, that's the key three keywords. You cannot just have a one-time success and then just congratulate yourself and forget it, no. So eventually, we said, oh, to make that repeat sustainable, repeatable, you have a systematic discipline. TROND: I'm so glad you say this because I have taken part in a bunch of best practice schemes and sometimes very optimistically by either an industry association or even a government entity. And they say, "Oh yeah, let's just all go on a bunch of factory visits." Or if it's just an IT system, "Let's just all write down what we did, and then share it with other people." But in fact, it doesn't seem to me like it is that easy. It's not like if I just explain what I think I have learned; that's not something others can learn from. Can you explain to me what it really takes to make something replicable? Because you have done that or helped Foxconn do that, for example. And now you're obviously writing up case studies that are now shared in the World Economic Forum across companies. But there's something really granular but also something very systemic and structured about the way things have to be explained in order to actually make it repeatable. What is the sustainability factor that actually is possible to not just blue copy but turn it into something in your own factory? JAY: Well, I think that there are basically several things. The data is one thing. We call it the data technology, DT, and which means data quality evaluation. How do you understand what to use, what not to use? How do you know which data is useful? And how do you know where the data is usable? It doesn't mean useful data is usable, just like you have a blood donation donor, but the blood may not be usable if the donor has HIV. I like to use an analogy like food. You got a fish in your hand; wow, great. But you have to ask where the fish comes from. [chuckles] If it comes from polluted water, it's not edible, right? So great fish but not edible. TROND: So there's a data layer which has to be usable, and it has to be put somewhere and put to use. It actually then has to be used. It can't just be theoretically usable. JAY: So we have a lot of useful data people collect. The problem is people never realized lots of them are not usable because of a lack of a label. They have no background, and they're not normalized. So eventually, that is a problem. And even if you have a lot of data, it doesn't mean it is usable. TROND: So then I guess that's how you get to your second layer, which I guess most people just call machine learning, but for you, it's an algorithmic layer, which is where some of the structuring gets done and some of the machines that put an analysis on this, put in place automatic procedures. JAY: And machine learning to me it's like cooking ware like a kitchen. You got a pan fry; you got a steamer; you got the grill. Those are tools to cook the food, the data. Food is like data. Cooking ware is like AI. But it depends on purpose. For example, you want fish. What do you want to eat first? I want soup. There's a difference. Do you want to grill? Do you want to just deep fry? So depending on how you want to eat it, the cooking ware will be selected differently. TROND: Well, and that's super interesting because it's so easy to say, well, all these algorithms and stuff they're out there, and all you have to do is pick up some algorithms. But you're saying, especially in a factory, you can't just pick any tool. You have to really know what the effect would be if you start to...for example, on downtime, right? Because I'm imagining there are very many advanced techniques that could be super advanced, but they are perhaps not the right tool for the job, for the workers that are there. So how does that come into play? Are these sequential steps, by the way? So once you figure out what the data is then, you start to fiddle with your tools. JAY: Well, there are two perspectives; one perspective is predict and prevent. So you predict something is going to happen. You prevent it from happening, number one. Number two, understand the root causes and potential root causes. So that comes down to the visible and invisible perspective. So from the visible world, we know what to measure. For example, if you have high blood pressure, you measure blood pressure every day, but that may not be the reason for high blood pressure. It may be because of your DNA, maybe because of the food you eat, because of lack of exercise, because of many other things, right? TROND: Right. JAY: So if you keep measuring your blood pressure doesn't mean you have no heart attack. Okay, so if you don't understand the reason, measuring blood pressure is not a problem. So I'm saying that you know what you don't know. So we need to find out what you don't know. So the correlation of invisible, I call, visible-invisible. So I will predict, but you also want to know the invisible reason relationship so you can prevent that relationship from happening. So that is really called deep mining those invisibles. So we position ourselves very clearly between visible-invisible. A lot of people just say, "Oh, we know what the problem is." The problem is not a purpose. For example, the factory manufacturing there are several very strong purposes, number one quality, right? Worry-free quality. Number two, your efficiency, how much you produce per dollar. If you say that you have great quality, but I spent $10,000 to make it, it is very expensive. But if you spend $2 to make it, wow, that's great. How did you do it? So quality per dollar is a very different way of judging how good you are. You got A; I spent five days studying. I got A; I spent two hours studying. Now you show the capability difference. TROND: I agree. And then the third factor in your framework seems to be platform. And that's when I think a lot of companies go wrong as well because platform is...at least historically in manufacturing, you pick someone else's platform. You say I'm going to implement something. What's available on the market, and what can I afford, obviously? Or ideally, what's the state of the art? And I'll just do that because everyone seems to be doing that. What does platform mean to you, and what goes into this choice? If you're going to create this platform for industrial AI, what kind of a decision is that? JAY: So DT is data, AT is algorithm, and PT is platform, PT platform. Platform means some common things are used in a shared community. For example, kitchen is a platform. You can cook. I can cook. I can cook Chinese food. I can cook Italian food. I can cook Indian food. Same kitchen but different recipe, different seasoning, but same cooking ware. TROND: Correct. Well, because you have a good kitchen, right? JAY: Yes. TROND: So that's -- JAY: [laughs] TROND: Right? JAY: On the platform, you have the most frequently used tool, not everything. You don't need 100 cooking ware in your kitchen. You probably have ten or even five most daily used. TROND: Regardless of how many different cuisines you try to cook. JAY: Exactly. That's called the AI machine toolkit. So we often work with companies and say, "You don't need a lot of tools, come on. You don't need deep learning. You need a good logistic regression and support-vector machine, and you're done." TROND: Got it. JAY: Yeah, you don't need a big chainsaw to cut small bushes. You don't need it. TROND: Right. And that's a very different perspective from the IT world, where many times you want the biggest tool possible because you want to churn a lot of data fast, and you don't really know what you're looking for sometimes. So I guess the industrial context here really constrains you. It's a constraint-based environment. JAY: Yes. So industry, like I said, the industry we talked about three Ps like I said: problems, purposes, and processes. So normally, problem comes from...the main thing is logistic problems, machine, and factory problems, workforce problems, the quality problems, energy problem, ignition problem, safety problems. So the problem happens every day. That's why in factory world, we call it firefighting. Typically, you firefight every day. TROND: And is that your metaphor for the last part of your framework, which is actually operation? So operation sounds really nice and structured, right? JAY: [chuckles] Yes. TROND: As if that was like, yeah, that's the real thing, process. We got this. But in reality, it feels sometimes, to many who are operating a factory; it's a firefight. JAY: Sometimes the reason lean theme work, Six Sigma, you turn a problem into a process, five Ss process, okay? And fishbone diagram, Pareto chart, and Kaizen before and after. So all the process, SOP, so doesn't matter which year workforce comes in, they just repeat, repeat, repeat, repeat, repeat. So in Toyota, the term used to be called manufacturing is just about the discipline. It's what they said. The Japanese industry manufacturing is about discipline, how you follow a discipline to everyday standard way, sustainable way, consistent way, and then you make good products. This is how the old Toyota was talking about, old one. But today, they don't talk that anymore. Training discipline is only one thing; you need to understand the value of customers. TROND: Right. So there are some new things that have to be added to the lean practices, right? JAY: Yes. TROND: As time goes by. So talk to me then more about the digital element because industrial AI to you, clearly, there's a very clear digital element, but there's so many, many other things there. So I'm trying to summarize your framework. You have these four factors: data, algorithms, platforms, and operations. These four aspects of a system that is the challenge you are dealing with in any factory environment. And some of them have to do with digital these days, and others, I guess, really have to do more with people. So when that all comes together, do you have some examples? I don't know, we talked about Toyota, but I know you've worked with Foxconn and Komatsu or Siemens. Can you give me an example of how this framework of yours now becomes applied in a context? Where do people pick up these different elements, and how do they use them? JAY: There's a matrix thinking. So horizontal thinking is a common thing; you need to have good digital thread including DT, data technology, AT, algorithms or analytics, PT, platform, edge cloud, and the things, and OT operation like scheduling, optimizations, stuff like that. Now, you got verticals, quality vertical, cost vertical, efficiency verticals, safety verticals, emission verticals. So you cannot just talk about general. You got to have focus on verticals. For example, let me give you one example: quality verticals. Quality is I'm the factory manager. I care about quality. Yes, the customer will even care more, so they care. But you have a customer come to your shop once a month to check. You ask them, "Why you come?" "Oh, I need to see how good your production." "How about you don't have to come? You can see my entire quality." "Wow, how do I do that?" So eventually, we develop a stream of quality code, SOQ, Stream Of Quality. So it's not just about the product is good. I can go back to connect all the processes of the quality segment of each station. Connect them together. Just like you got a fish, oh, okay, the fish is great. But I wonder, when the fish came out of water, when the fish was in the truck, how long was it on the road? And how long was it before reaching my physical distribution center and to my home? So if I have a sensor, I can tell you all the temperature history inside the box. So when you get your fish, you take a look; oh, from the moment the fish came out of the boat until it reached my home, the temperature remained almost constant. Wow. Now you are worry-free. It's just one thing. So you connect together. So that's why we call SOQ, Stream Of Quality, like a river connected. So by the time a customer gets a quality product, they can trace back and say, "Wow, good. How about if I let you see it before you come? How about you don't come?" I say, "Oh, you know what? I like it." That's what this type of manufacturing is about. It just doesn't make you happy. You have to make the customer happy, worry-free. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com and pick up the book in a bookstore near you. TROND: So, Jay, you took the words out of my mouth because I wanted to talk about the future. I'm imagining when you say worry-free, I mean, you're talking about a soon-to-be state of manufacturing. Or are you literally saying there are some factories, some of the excellence factories where you've won awards in the World Economic Forum or other places that are working towards this worry-free manufacturing, and to some extent, they have achieved it? Well, elaborate for me a little bit about the future outlook of manufacturing and especially this people issue because you know that I'm engaged...The podcast is called Augmented Podcast. I'm engaged in this debate about automation. Well, is there a discrepancy between automation and augmentation? And to what extent is this about people running the system? Or is it the machines that we should optimize to run all the system? For you, it's all about worry-free. First of all, just answer this question, is worry-free a future ideal, or is it actually here today if you just do the right things? JAY: Well, first of all, worry-free is our mindset where the level of satisfaction should be, right? TROND: Yep. JAY: So to make manufacturing happen is not about how to make good quality, how to make people physically have less worry, how to make customers less worry is what is. But the reason we have a problem with workforce today, I mean, we have a hard time to hire not just highly skilled workers but even regular workforce. Because for some reason, not just U.S., it seems everywhere right now has similar problems. People have more options these days to select other living means. They could be an Uber driver. [laughs] They could be...I don't know. So there are many options. You don't have to just go to the factory to make earnings. They can have a car and drive around Uber and Lyft or whatever. They can deliver the food and whatever. So they can do many other things. And so today, you want to make workforce work environment more attractive. You have to make sure that they understand, oh, this is something they can learn; they can grow. They are fulfilled because the environment gives them a lot of empowerment. The vibe, the environment gives them a wow, especially young people; when you attract them from college, they'd like a wow kind of environment, not just ooh, okay. [laughs] TROND: Yeah. Well, it's interesting you're saying this. I mean, we actually have a lack of workers. So it's not just we want to make factories full of machines; it's actually the machines are actually needed just because there are no workers to fill these jobs. But you're looking into a future where you do think that manufacturing is and will be an attractive place going forward. That seems to be that you have a positive vision of the future we're going into. You think this is attractive. It's interesting for workers. JAY: Yeah. See, I often say that there are some common horizontal we have to use all the day. Vertical is the purpose, quality. I talked about vertical quality first, quality. But what are the horizontal common? I go A, B, C, D, E, F. What's A? AI. B is big data. C is cyber and cloud. D is digital or digital twin, whatever. E is environment ecosystem and emission reduction. What's F? Very important, fun. [laughs] If you miss that piece, who wants to work for a place there's no fun? You tell me would you work for...you and I, we're talking now because it's fun. You talk to people and different perspectives. I talk to you, and I say, wow, you've built some humongous network here in the physical...the future of digital, not just professional space but also social space but also the physical space. So, again, the fun things inspire people, right? TROND: They do. So talking about inspiring people then, Jay, if you were to paint a picture of this future, I guess, we have talked just now about workers and how if you do it right, it's going to be really attractive workplaces in manufacturing. How about for, I guess, one type of worker, these knowledge workers more generally? Or, in fact, is there a possibility that you see that not just is it going to be a fun place to be for great, many workers, but it's actually going to be an exciting knowledge workplace again? Which arguably, industrialization has gone through many stages. And being in a factory wasn't always all that rosy, but it was certainly financially rewarding for many. And it has had an enormous career progression for others who are able to find ways to exploit this system to their benefit. How do you see that going forward? Is there a scope, is there a world in which factory work can or perhaps in an even new way become truly knowledge work where all of these industrial AI factors, the A to the Fs, produce fun, but they produce lasting progression, and career satisfaction, empowerment, all these buzzwords that everybody in the workplace wants and perhaps deserves? JAY: That's how we look at the future workforce is not just about the work but also the knowledge force. So basically, the difference is that people come in, and they become seasoned engineers, experienced engineers. And they retire, and the wisdom carries with them. Sometimes you have documentation, Excel sheet, PPT in the server, but nobody even looks at it. That's what today's worry is. So now what you want is living knowledge, living intelligence. The ownership is very important. For example, I'm a worker. I develop AI, not just the computer software to help the machine but also help me. I can augment the intelligence. I will augment it. When I make the product happen, the inspection station they check and just tell me pass or no pass. They also tell me the quality, 98, 97, but you pass. And then you get your score. You got a 70, 80, 90, but you got an A. 99, you got an A, 91, you got an A, 92. So what exactly does A mean? So, therefore, I give you a reason, oh, this is something. Then I learn. Okay, I can contribute. I can use voice. I can use my opinion to augment that no, labeled. So next time people work, oh, I got 97. And so the reason is the features need to be maintained, to be changed, and the system needs to be whatever. So eventually, you have a human contribute. The whole process could be consisting of 5 experts, 7, 10, 20, eventually owned by 20 people. That legacy continues. And you, as a worker, you feel like you're part of the team, leave a legacy for the next generation. So eventually, it's augmented intelligence. The third level will be actual implementation. So AI is not about artificial intelligence; it is about actual implementation. So people physically can implement things in a way they can make data to decisions. So their decision mean I want to make an adjustment. I want to find out how much I should adjust. Physically, I can see the gap. I can input the adjustment level. The system will tell me physically how could I improve 5%. Wow, that's good. I made a 5% improvement. Your boss also knows. And your paycheck got the $150 increase this month. Why? Because my contribution to the process quality improved, so I got the bonus. That's real-world feedback. TROND: Let me ask you one last question about how this is going to play out; I mean, in terms of how the skilling of workers is going to allow this kind of process. A lot of people are telling me about the ambitions that I'm describing...and some of the guests on the podcasts and also the Tulip software platform, the owner of this podcast, that it is sometimes optimistic to think that a lot of the training can just be embedded in the work process. That is obviously an ideal. But in America, for example, there is this idea that, well, you are either a trained worker or an educated worker, or you are an uneducated worker. And then yes, you can learn some things on the job. But there are limits to how much you can learn directly on the job. You have to be pulled out, and you have to do training and get competencies. As you're looking into the future, are there these two tracks? So you either get yourself a short or long college degree, and then you move in, and then you move faster. Or you are in the factory, and then if you then start to want to learn things, you have to pull yourself out and take courses, courses, courses and then go in? Or is it possible through these AI-enabled training systems to get so much real-time feedback that a reasonably intelligent person actually never has to be pulled out of work and actually they can learn on the job truly advanced things? So because there are two really, really different futures here, one, you have to scale up an educational system. And, two, you have to scale up more of a real-time learning system. And it seems to me that they're actually discrepant paths. JAY: Sure. To me, I have a framework in my book. I call it the four P structure, four P. First P is principle-based. For example, in Six Sigma, in lean manufacturing, there's some basic stuff you have to study, basic stuff like very simple fishbone diagram. You have to understand those things. You can learn by yourself what that is. You can take a very basic introduction course. So we can learn and give you a module. You can learn yourself or by a group, principle-based. The second thing is practice-based. Basically, we will prepare data for you. We will teach you how to use a tool, and you will do it together as a team or as individual, and you present results by using data I give to you, the tool I give to you. And it's all, yeah, my team A presented. Oh, they look interesting. And group B presented, so we are learning from each other. Then after the group learning is finished, you go back to your team in the real world. You create a project called project-based learning. You take a tool you learn. You take the knowledge you learn and to find a project like a Six Sigma project you do by yourself. You formulate. And then you come back to the class maybe a few weeks later, present with a real-world project based on the boss' approval. So after that, you've got maybe a black belt but with the last piece professional. Then you start teaching other people to repeat the first 3ps. You become master black belt. So we're not reinventing a new term. It really is about a similar concept like lean but more digital space. Lean is about personal experience, and digital is about the data experience is what's the big difference. TROND: But either way, it is a big difference whether you have to rely on technological experts, or you can do a lot of these things through training and can get to a level of aptitude that you can read the signals at least from the system and implement small changes, perhaps not the big changes but you can at least read the system. And whether they're low-code or no-code, you can at least then through learning frameworks, you can advance, and you can improve in not just your own work day, but you can probably in groups, and feedbacks, and stuff you can bring the whole team and the factory forward perhaps without relying only on these external types of expertise that are actually so costly because they take you away. So per definition, you run into this; I mean, certainly isn't worry-free because there is an interruption in the process. Well, look, this is fascinating. Any last thoughts? It seems to me that there are so many more ways we can dig deeper on your experience in any of these industrial contexts or even going deeper in each of the frameworks. Is there a short way to encapsulate industrial AI that you can leave us with just so people can really understand? JAY: Sure. TROND: It's such a fundamental thing, AI, and people have different ideas about that, and industry people have something in their head. And now you have combined them in a unique way. Just give us one sentence: what is industrial AI? What should people leave this podcast with? JAY: AI is a cognitive science, but industrial AI is a systematic discipline is one sentence. So that means people have domain knowledge. Now we have to create data to represent our domain then have the discipline to solve the domain problems. Usually, with domain knowledge, we try with our experience, and you and I know; that's it. But we have no data coming out. But if I have domain become data and data become discipline, then other people can repeat our success even our mistake; they understand why. So eventually, domain, data, discipline, 3 Ds together, you can make a good decision, sustainable and long-lasting. TROND: Jay, this has been so instructive. I thank you for spending this time with me. And it's a little bit of a never-ending process. JAY: [laughs] TROND: Industry is not something that you can learn it and then...because also the domain changes and what you're doing and what you're producing changes as well. So it's a lifelong -- JAY: It's rewarding. TROND: Rewarding but lifelong quest. JAY: Yeah. Well, thank you for the opportunity to share, to discuss. Thank you. TROND: It's a great pleasure. You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Industrial AI. And our guest was Professor Jay Lee from University of Cincinnati. In this conversation, we talked about how AI in industry needs to work every time and what that means. My takeaway is that industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and is empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Jay Lee.
Let's talk about Electronics reliability with the QA guru Cheryl Tulkoff. In this episode Cheryl and I will talk about risk assessment, planning for not only success but also a failure, and understanding the difference between quality and reliability. This discussion is going to be very informative for every PCB designer who wants to get ahead of their game. Watch through the end, and make sure to check the additional resources below. Watch this episode here Show Highlights: Cheryl shares her rewarding career experience in the electronics industry She worked at IBM where she was immersed in electronic manufacturing from beginning to end She also worked at DfR Solutions and National Instruments where she learned all the skills and knowledge in electronics manufacturability, quality & reliability consulting To produce a successful electronic product it is important to have the awareness to resolve every problem, from the chip level, board level, system level, and the environment level Cheryl explains why unique or non-aligned standards exist in the industry – no one size fits all A great piece of advice for all PCB designers is to know what you are designing and who you are designing it for, look at the risks, and then manage them appropriately Planning for success may also include celebrating failures. Budget for failure analysis is often disregarded due to the “success-driven roadmap” mentality Failure should be part of design management Cheryl and Zach talk about the “Startup Culture” Software reliability and hardware reliability go hand in hand What rate of failure is tolerable? Defining what is quality and reliability separately for the product you are designing Manufacturers can not ensure reliability for you Cheryl shares her experience being involved in litigation as an expert witness Redundancy practices in the industry, is it typical? What can designers do to mitigate failures? Understanding what you are designing and who you are designing it for Collect as much feedback as possible – from users, industry experts, and professional organizations Links and Resources: Connect with Cheryl Tulkoff on LinkedIn Read Cheryl Tulkoff articles on Research Gate Checkout Cheryl Tulkoff book Design for Excellence in Electronics Manufacturing Connect with Zach on LinkedIn Full OnTrack Podcast Library Altium Website Get Your First Month of Altium Designer® for FREE
In this episode, we speak to Ana Villegas, the CMO of National Instruments. We discuss how Ana uses corporate responsibility to drive brand value and how she streamlined her plans from 40 major campaigns to 7 to drive more coherence and efficiency in her marketing efforts.Ana grew up in Peru and emigrated to the United States 20 years ago to pursue a career in marketing. After a successful career at leading companies like Dell, she moved to National Instruments in 2019 and was elevated to the CMO role in 2021.For more than 40 years, NI has developed automated test and automated measurement systems that help engineers solve the world's toughest challenges. Let's work together to find creative solutions to help your organization succeed today, tomorrow, and for the next 100 years.Learn more about Ana VillegasLearn more about National InstrumentsFollow Peter Mahoney on Twitter and LinkedInLearn more about PlannuhJoin The Next CMO CommunityRecommend a guest for The Next CMO podcastProduced by PodForte
We don't often think about marketing as something that changes the world, but it is. Whether it's through amazing products, or by creating an inclusive environment for minorities to have their voices heard, marketing is a powerful place to be. We are helping to shape the world view. Today, the CMO of National Instruments, Ana Villegas joins us to talk about how she was able to come to a new country and climb the ranks of amazing brands like Dell and National Instruments by using her superpower: belief in herself.Tune in to learn:How Ana responds to the Lighting Round (00:00)Why you should believe you're already an expert (22:19)How to pivot when faced with the unexpected (25:41)What a rebrand looks like for an established company (29:41)Why shifting to a consumer-based perspective is best (36:55)Marketing Trends is brought to you by Salesforce Marketing Cloud. For more great marketing insights, sign up for The Marketing Moments newsletter. You'll get ideas to help you build better customer relationships, invites to upcoming events, and access to the latest industry research. Subscribe at https://sforce.co/MarketingMoments
Hall T. Martin is a man who lives in the startup world and has founded several angel groups. He is the Founder and CEO of TEN Capital and Host of the Investor Connect podcast program. He launched the firm as the Texas Entrepreneur Networks in 2009. Today, TEN Capital has over 15,000 investors in its network and has helped startups raise over $900M. After a career as the 93rd member of a company called National Instruments, supplying measurement and automation firms, he started making his way into the angel investment niche. Investment is a numbers game that is not so different from sales. Hall has experienced every side of the game - both good and bad! He's a man who has mastered the market and grown from his mistakes. If you're looking for a way to get your numbers up, a way to get the deal ready, a way to get the deal out there, Hall is YOUR MAN. Who Are Angel Investors? Angel investors are accredited investors who want to invest in a startup for returns. They provide capital for startup businesses, usually in exchange for ownership equity. Angel investors provide support to these businesses during the early stages – usually when risks of failure are relatively high. Still, we all know no one wants to get into anything with a loser mindset. YOU, as a start-up, need to make your brand attractive to angel investors. We'll get to that in a bit, but for now, let's talk about the common mistakes angel investors make. Common Mistakes Made By Angel Investors Spending too much time focusing on current market or products - This is one of the top mistakes that investors make – markets change; products come and go. The team is what will really carry it all the way through, that's what you should focus on. Yes! You want to look at the products, check out the competition, hang out with your team, and see what they are doing right and wrong. Looking at the latest hot thing - Yes! That new product looks promising, but you don't want to put all your focus on it. You can check it out, but you shouldn't put all your energy into that one product – you're obviously missing something. Remember that the product won't be hot forever. How To Attract Angel Investors Before getting into any investment, angel investors want to see evidence of traction. They want to see you go into the market with proof that you can sell your product or service. As a company looking for funding, you need to put some things in place to attract angel investors. They include: Using product and market validation as a criterion - When you start putting systems in place to prove and show you can have some repeatability and predictability around your startup, it attracts angel investors because it shows structure within the business. To achieve a better business structure, you need to test your model. You have to show investors it's a profit-making model and can grow even bigger! As a startup, what you want to find is “what does the model really look like?” Testing is key. It shows you've figured out the lowest cost channels and how far those channels can go before moving to the higher ones. That's always very impressive to investors. Make sure you give a name to everything you have - every product, technology, platform, and data set should have a name. If you don't have a brand name, investors can't give you credit for it - they don't know or realize it's there. You can also add an AI algorithm on top of it to make it more attractive! Everything is going online – you have to look at how everything will be connected or represented online. You need documentation, a pitch deck, diligence box, 3-5yrs financial projection, and key documents in the box – All these can only be achieved when you work with experts that help put these things in place. They help make your deck look good, more professional, and fill in the missing pieces. They also help maintain the structure of the business. They see the market every day, so they are the best to give a read. They can coach you on how to break the steps and the actual market valuation. Always remember that valuation is not a formula; it's a negotiation. Approach it that way. Are You An Aspiring Angel Investor? Here's A Little Tip On How To Learn The investment methodology, the timesheet, and terminology; pre-money, post-money can be very new and different. The great way to learn is with other people – You need to be around other investors, watch how they do their thing, share the deal flow and diligence that goes with it. Different people bring different skills and strings to the table as well - that could be very helpful Podcast – A podcast is usually a useful resource! Listening to someone tell the story straight up is also a good way to learn about this investment niche. And when you do start angel investment, Do not find a product and want it on day one – It's going to take you a lifetime to get to the full vision. You have to go into the market with what you can deliver. Then you can start building your visions from there. Understand the rule of software development – It takes 6 months to build and 6 months to sell. If you can't build it in 6month, you're scoping it too broad; and if you can't sell in 6 months, you built the wrong thing. To learn more about Hall, head here: https://tencapital.group/team/ To connect with Corey for more: Website: https://www.coreykupfer.com LinkedIn: https://www.linkedin.com/in/coreykupfer Facebook: https://www.facebook.com/CoreyKupfer Twitter: https://twitter.com/coreykupfer
National Instruments develops software and hardware for engineering in a wide variety of domains, from aerospace to government technology to application testing. The interface between hardware and software presents a variety of difficult engineering challenges. Luke Shreier is a senior vice president at National Instruments and joins the show to discuss the engineering and management The post National Instruments with Luke Shreier appeared first on Software Engineering Daily.
National Instruments develops software and hardware for engineering in a wide variety of domains, from aerospace to government technology to application testing. The interface between hardware and software presents a variety of difficult engineering challenges. Luke Schreier is a Senior Vice President at National Instruments and joins the show to discuss the engineering and management The post National Instruments with Luke Schreier appeared first on Software Engineering Daily.
National Instruments develops software and hardware for engineering in a wide variety of domains, from aerospace to government technology to application testing. The interface between hardware and software presents a variety of difficult engineering challenges. Luke Schreier is a Senior Vice President at National Instruments and joins the show to discuss the engineering and management The post National Instruments with Luke Schreier appeared first on Software Engineering Daily.
In this era of start-ups in India, invention, innovation, and disruption have become a reality. Founder Thesis presents you the journey of one such duo of techies who are on a mission to enable robots with human-like vision, which could unlock endless possibilities. In a candid conversation with Akshay Datt, Nikhil Ramaswamy and Gokul NA, Founders, CynLR, have taken us through their journeys. Nikhil was an urban boy inspired by tech revolutions while Gokul saw automation challenges at a farm & studied the impact of code on real life. After gaining experience at National Instruments, they started CynLR in 2019 and has solved 70+ industrial machine vision problems with a 100% success rate, putting it on an exponential growth trajectory. Tune in to this episode to hear Nikhil and Gokul explain cybernetics, what the future will be with robots and how it will alter the business landscape. What you must not miss! The challenges to improving Machine Vision. The Paradigm Shift in Robot systems Design. Scalability & Expansion of the CynLR Platform.
The opportunity to work from home may be taken for granted a bit more within the last year and a half, but for years Rev.com has been providing opportunities for tens of thousands to work from home. Adri Nowell the VP of Marketing at Rev, came to our studios in Austin, Texas to talk about what it means to her to see so many people able to work from home with Rev. Adri's experience as a marketer and a leader gives her a unique ability to serve both the Rev customer, as well as the tens of thousands of transcriptionists that Rev employs in a massive remote workforce. “We work with about 70,000 professionals who, some of which don't have great options for how to make money [because] they have an elderly parent or they're a primary caregiver for a child. When I connect with the Rev-ers in our community, it brings me so much joy. I've talked to mothers who have sick children in the hospital who are transcribing at the foot of a hospital bed. Being able to put your child first and be able to provide that type of love and compassion and care for your child while also being able to make a living. Those moments make me so proud.” Learning how Adri runs an ABM campaign, what skills she uses as a leader, and how she thinks about scaling her team will give you great insight into your own exciting growth and leadership. It was so great to speak with Adri in person about her experience in marketing and how they're growing at Rev. Get inspired with Adri, up next here on Marketing Trends. Main TakeawaysThe transition from Doer to Leader: When you're in the trenches doing the actual work, your actual day-to-day responsibilities are different from those of the leadership of your marketing team. Transitioning to leadership isn't for everyone; some really enjoy the work of making the campaigns happen. When you're the leader you have to rely on the savvy of the marketers on your team and give them the tools that you know work and watch them make it happen! Account-Based Marketing Challenges: One of the biggest challenges of running a successful Account-Based Marketing or ABM campaign is getting the structure of the accounts right. Define what a segment is, define who your tier one in the funnel is; define what an account is. If you go through this legwork and really take the time to build a good foundation, you'll have set yourself up for a great campaign. Working with Speed and Excellence as You Scale: When your company is experiencing massive growth it's tempting to just start moving really fast and being okay with things breaking. If you can take a little extra time to make sure that you don't go too fast and make needless mistakes, that is way more profitable in the long run. You need to quickly automate whatever you can when you're in a high-growth environment so that you can leave that task with confidence as you go to solve the next big problem. Key Quotes“Now that we're going after [more] market segments the marketing responsibilities are going to shift around. We generally test everything that we can; learn quickly; fail quickly; fail cheaply, and for the things that work, invest in them. When you have that type of mindset, you get scrappy marketers that are willing to tackle new challenges, and test new channels or test new tactics.“People get really nervous [about transitioning to leadership]. It's an emotional thing. It's a natural, emotional reaction. And Molly Graham actually describes this really well. And she talks about this concept, this emotional rollercoaster that people go through during these transition periods as she uses the metaphor of building a LEGO tower and then giving away your LEGO tower, which is so relevant. You have all these smart marketers that can jump in and they can tackle a challenge. And they built up their Lego tower and made it successful and then they have to hand their LEGO to the next person coming in. It can be really nerve-wracking. ‘What if someone breaks the LEGO tower? What if they build it back up in the wrong way, or maybe they don't expand upon it in the right way?' And I've found her description of this to be really relevant and taken her advice to talk about it." “Marketing is never settled. You're never done in marketing. Consumer behaviors are always changing. You always want to go back and retest or test different variations. We measure [our success] by getting people to respond. ‘Are we getting them to the next action?' Whether that's actually converting into a paying customer or taking the next step with us in their journey… and when new channels work, we expand them; when they don't, we abandon them. [We're] constantly just exploring new outlets.”“We work with about 70,000 professionals who, some of which don't have great options for how to make money [because] they have an elderly parent or they're a primary caregiver for a child. When I connect with the Rev-ers in our community, it brings me so much joy. I've talked to mothers who have sick children in the hospital who are transcribing at the foot of a hospital bed. Being able to put your child first and be able to provide that type of love and compassion and care for your child while also being able to make a living. Those moments make me so proud.” “With any launch, you start all the way at the timeframe of ‘What's the problem that you're trying to solve?' My philosophy is to listen to the market. You should be talking to your customers; you should be talking to your prospects. You should be talking to people that want to do business with you should also be talking to people who don't want to do business with you.”“The most important thing with account-based marketing is in how you structure the accounts that you want to go after. How do you define what a segment is? What is an account? Who are the customers? Who do you want to reach? What are the contexts within each of those accounts? Who goes into your tier one bucket? And then who's kind of your catch-all for what you want your one-to-one for your tier one accounts. You want your tier one accounts to receive more of a personalized experience, but you don't want to overdo it. If you're going so extreme that it feels forced, people are going to reject the marketing material. There's definitely a place for it, but it's really about finding the right balance.”“Speed is tough and the thing that I've found the most difficult is balancing the speed at which you accelerate growth and operational excellence is it's not hard to go fast. It's hard to go fast and not break things. And so that is where we've found probably the biggest challenge is how can we continue to accelerate growth, but at the same time, establish a foundation that is going to scale. And so with marketing, that's incredibly important because you need the right operational pieces. It is acceptable for some period of time to do things manually, but you can't stay there. You have to put operational pieces in place so that you can scale. Finding the right balance is very challenging.”BioAdri Nowell is VP of Marketing at Rev.com. In this role, she serves as the executive leader accountable for the strategy and execution of marketing programs across all segments - individual users (B2C), Enterprise/Mid-market (B2B), and developers. She provides leadership and management oversight across Product Marketing, Performance Marketing, Email Marketing, Demand Marketing, Content Marketing, Web, Brand, and Creative for the company.Before joining Rev, Adri served as the Senior Director of Product Marketing at Bazaarvoice and before that as Director of Marketing at Volusion. Prior to that, Adri held a variety of roles at engineering technology provider National Instruments including Product Marketing Manager and Support Engineer. Adri began her career at the University of Oklahoma as a Software Developer in the Robotics Institute of Machine Learning. Adri holds a Bachelor of Science in Computer Science from The University of Oklahoma, in Norman, OK.---Marketing Trends podcast is brought to you by Salesforce. Discover marketing built on the world's number one CRM: Salesforce. Put your customer at the center of every interaction. Automate engagement with each customer. And build your marketing strategy around the entire customer journey. Salesforce. We bring marketing and engagement together. Learn more at salesforce.com/marketing.
The auto industry is learning how to slash the time it takes to develop new cars and components with a technology called Digital Twins. By simulating a product or a process, automakers and suppliers can perfect the design before it's actually used in the real world. Digital Twins are revolutionizing the product development process. Prith Banerjee from Ansys and Ashish Naik from NI discuss the impact it's having.
The auto industry is learning how to slash the time it takes to develop new cars and components with a technology called Digital Twins. By simulating a product or a process, automakers and suppliers can perfect the design before it's actually used in the real world. Digital Twins are revolutionizing the product development process. Prith Banerjee from Ansys and Ashish Naik from NI discuss the impact it's having.
When working on mission-critical assets, quality and reliability are imperative, but how do you leverage a client's experience and respect the integrity of the system while also introducing valuable new technology and ideas? In today's episode of Women and Manufacturing, Rosemary Coates speaks to Cheryl Texin, an award-winning Principle Systems R&D Engineer for the Aerospace, Defense, and Government Business Unit at National Instruments and the Principle of the Austin Section for the Society of Women Engineers (SWE). Cheryl shares her perspective on being a catalyst for change, not only creating value by implementing new technology within government systems but also by maintaining a strong voice as a woman in a male-populated industry like engineering, particularly in the military space. We touch on the benefit of building a network of support, how the SWE drives change in the engineering industry through awards and speaking opportunities, and how Cheryl is thinking about leadership and development in the future. Plus, she shares her advice for other women engineers: don't be afraid to communicate what you want! Tune in today to learn more! Learn more about your ad choices. Visit megaphone.fm/adchoices
In our latest Electronic Specifier Insights podcast, we spoke to Robert Morton, Vice President, Sales, EMEA at NI about the company's strategy and how it will benefit customers and distributors and progress so far.
Dan Balcauski is the Principal Consultant at Product Tranquility, a consulting firm that helps SaaS business leaders accelerate their product growth and increase customer loyalty. Dan is an expert in digital marketing and e-commerce with a specialization in pricing and product strategy. He is also the Program Leader for Kellogg's Executive Education Product Strategy course. In addition to his role at Product Tranquility, Dan works as a freelance product manager and is a member of Veritux. Some of the companies he's worked for include SolarWinds, LawnStarter, and NI (formerly National Instruments). In this episode… So you just finished creating your SaaS product. You've researched the market and developed a solution for your target audience's pain points, but there's one important question remaining: How much do you sell it for? Properly pricing your software is difficult. Not only are the numbers changing everyday, but there are also multiple avenues for charging your clients, such as premium costs and subscription models. It can be difficult to know what will work best for your company. However, there are niche specialists who have vast experience in product pricing. Dan Balcauski has done intensive research on the topic and now helps major SaaS brands determine the best approach to take when pricing their software. Want to know his pricing fundamentals? In this episode of the Revenue Engine Podcast, Alex Gluz sits down with Dan Balcauski, the Principal Consultant at Product Tranquility, to talk about how to price your SaaS product the right way. They discuss the three main approaches to pricing and how they work for different companies. They also talk about who should have control over pricing in an organization, how to effectively test your prices, and the steps that new businesses should take when developing a product. Stay tuned!
About Ted Miracco Ted is co-founder and CEO of Cylynt. His high-technology experience spans 30 years in electronic design automation (EDA), semiconductors, defense electronics, RF/microwave circuit design, and cybersecurity. Prior to Cylynt, Ted was a co-founder of the EDA company AWR Corporation, which was acquired by National Instruments in 2011 and became part of Cadence Design Systems in 2020. In addition, he has worked with several Fortune 500 software companies, including Cadence Design Systems and start-up company EEsof Inc., which was acquired by Hewlett Packard in 1994 and is now Keysight Technologies. Ted holds a B.S.E.E. from Carnegie Mellon University. The Cylynt platform is trusted by some of the world's leading software companies for enhanced business intelligence and globally is protecting around $50 billion of software assets. Cylynt Cylynt solutions, which evolved from anti-piracy and license compliance roots, are an integral part of the ongoing battle to safeguard intellectual property against increasingly sophisticated evasion techniques and result in significant revenue recovery and brand protection. Detailed usage analytics provide unparalleled understanding into how users interact with a software vendor's product and deliver valuable insight into customer experience, product development, lead generation, and sales processes. Across these elements, clients are currently realizing a gross ROI from Cylynt solutions of up to nine times their investment. To find out more: Cylynt.com
About Abhishek Nag:My next guest on The One Percent Project is Abhishek Nag. Abhishek is the Director of Business Development at Netflix, previously with Facebook, Uber, Hike and National Instruments. He is also an Angel Investor in 40+ startups. He is a graduate of RV College of Engineering and Indian School of Business, ISB.In this conversation he discusses:The future of the internet in the next 50 years.How the product is the key driver of Market Entry and Growth?His learning from working at Facebook and how Facebook landed to be a social media giant?What did Scared Games do for Netflix India?Adoption Vs Retention- Which is more important?Experience investing in 40+ seed and pre-seed stage start-ups.Rapid Fire:The hardest thing about your job? It's the fact that I'm basically now doing these two jobs. And I'm trying to be good at both of them and better and better every passing day. But there are only 24 hours in a day. So, the hardest thing is prioritizing by constantly wanting to do both Netflix and investing really, really well. One book or a blog that has influenced you personally and professionally?Built to Last, I can think of many built to last, Principles by Ray Dalio, Crossing the Chasm.Your most favourite superhero?I probably don't have a favourite superhero. But if I had to pick one, it would be Batman.
After enduring a couple of bouts of writer's block, author and journalist Blake Snow has learned not to sit down with the intention of writing a whole book. Instead, he breaks a book into chunks, accomplishing one chapter at a time. Talking to Published Author Podcast host Josh Steimle, Blake says: “For any of your listeners out there wanting to write a first book, it is a big challenge. “But one thing that did help me, despite my writer's block, was this whole concept of don't try to write a book, write one chapter, and then the next chapter,” he explains. “Try to break it up into bite-sized things you can accomplish. That was way easier for me to do with the second book than the first. But that advice, I think stands regardless, and for anyone that's interested in publishing a book, you have to break it up.” Top Takeaway: Your Book Must Engage And Interest The Reader Blake says that if your book isn't engaging and interesting to a reader, it simply won't sell. He says the hallmark of an amature writer is to think that a business book needs to be stuffy and formal. No, he says, explaining that entrepreneur-authors should write the way they talk, in a way that's interesting for people to engage, listen to, and interact with. Blake has written as a journalist for half of the top 20 U.S. media outlets, including CNN, Wired, and USA Today. He also advises Fortune 500 companies on their content strategy. He's a blogger and author of two books: Log Off: How to Stay Connected After Disconnecting, and Measuring History: How One Unsung Company Quietly Changed The World, which is the story of National Instruments, a company you probably haven't heard of, but which has had a global impact on lives big and small, and is explained in the episode. Despite being a professional writer, Blake has experienced a couple of debilitating episodes of writer's block, the first one when he was working on Log Off. The struggle lasted for about eight months until it finally dawned on Blake that the successful approach to writing was breaking a book into bite-sized pieces. “Instead of writing a book, it was: ‘Let's write 1000 words today'. So anyone can do that, and you can break things up,” says Blake. “What I found with writing two books—as with most all things in life—it really is about momentum. “If you can keep that momentum, you won't stall out as long as I did, or as hard as I did in my first book. So it's all about that momentum, creating bite sized chunks, just sticking to it and not being afraid to fail.” As an experienced writer, Blake says: “First and foremost, I write for myself. If I know that I don't like it, I'm pretty sure no one else is going to like it. And so that I use that as a litmus test of like, How good is my current writing, the page right in front of me?” This isn't guidance for a new writer, who should instead work with an editor to determine the quality of their writing. However, aftertime, everyone who writes a lot can learn to quickly determine what's good and what their audience wants to read. Finally, Blake encourages all entrepreneur-writers (or ghostwriters) to read a good range of classics, from Mark Twain and Herman Melville through to Alexander Dumas and Victor Hugo, as well as Charles Dickens or Thomas Hardy. Novels such as theirs, says Blake, will simply make you a much better writer. Links BlakeSnow.com SUBSCRIBE TO PUBLISHED AUTHOR PODCAST If you enjoyed this episode, don't forget to subscribe. And if you want to spread the word, please give us a five-star rating review and tell your friends to subscribe, too. We're available on Apple podcasts, Spotify, and everywhere else you listen to podcasts. And if you're an entrepreneur interested in writing and publishing a nonfiction book to grow your business or make an impact, visit PublishedAuthor.com for show notes for this podcast and other free resources. Twitter Youtube Facebook.com Linkedin.com Instagram.com Josh Steimle Josh Steimle - LinkedIn Josh Steimle's book: Chief Marketing Officers At Work
Theodore Miracco - Chief Executive Officer Cylynt Ted is co-founder and CEO of Cylynt. His high-technology experience spans 30 years in defense electronics, RF/microwave circuit design, semiconductors, electronic design automation (EDA), and cybersecurity. Prior to Cylynt, Ted was a co-founder of the EDA company AWR Corporation, which was acquired by National Instruments in 2011 and became part of Cadence Design Systems in 2020. In addition, he has worked with several Fortune 500 software companies, including Cadence Design Systems and startup company EEsof Inc., which was acquired by Hewlett Packard in 1994 and is now Keysight Technologies. Ted holds a B.S.E.E. from Carnegie Mellon University.
Aditi Chauhan is Director of Marketing at NI (formerly, National Instruments), where she leads a global team of marketers, overseeing field, campaign and channel-marketing efforts for the NI brand. In this episode, Aditi gives us a breakdown of the company's latest rebranding, launched bang in the middle of a pandemic, and walks us through what she learnt in leading GTM efforts across diverse markets in Asia and Europe.
Support the Show. Get the NEW AudioBook! AudioBook: Audible| Kobo| Authors Direct | Google Play | Apple SummaryHey everyone. I wanted to jump in quickly let you know about the release of the audio version of my book, The Entrepreneur Ethos, narrated by David A. Conatser. If you want to support the show, you can buy it wherever audiobooks are sold. Links are also in the show notes. Now on to my guest for today. Nate D'Anna, founder of Dumpling. When Nate D'Anna was thinking of his next steps after working in corporate acquisitions, he realized that the companies with lasting success endured because of the grit and passion of their founders. He's found that passion through the business he co-founded, Dumpling, which is based around the question of helping those who are often invisible to the typical Silicon Valley entrepreneur. First Nate tried crowdsourcing data from workers to help companies improve but found that the market for that data was not ultimately helping the people he wanted to help. After crossing the country and talking to an array of people working in blue-collar jobs and trying out gigs like Instacart shopping, Nate and his co-founders decided to build tools that would help gig workers take charge of their own businesses. Dumpling seeks to be the solution to the recent rise of underpaid, exploited gig worker and to help aspiring entrepreneurs be the decision-makers about the services they provide. Nate has drawn on his experiences working in technical customer support, product management, and corporate acquisitions in National Instruments and Cisco to develop and build Dumpling. Nate clearly is motivated by more than just wanting to build a successful business: he is driven by his passion to help others take charge of their own success. Now let's get better together. Actions to Try or Advice to Take Like many entrepreneurs, D'Anna and his co-founder went out of their comfort zone by trying out gig work himself and going out and talking to many blue-collar workers and then developed a process to help them. Getting out of your comfort zone and looking for unexplored areas might just be the key to your unique selling proposition. D'Anna stresses the importance of passion and grit for long-term success. Are you passionate about your business? If not, how might you bring passion into your business? What can you get so passionate about that you can see doing it for the next 10-20 years? Dumpling is based on the idea that delivering a service is more than just an action: it's the building of a relationship. Are you focused on the sale, or on building relationships? The person providing the service is part of the relationship, too. Links to Explore Further Dumpling.us Dumpling's Facebook page Nate D'Anna on LinkedIn Keep In TouchBook or Blog or Twitter or LinkedIn or JSYPR Learn more about your ad choices. Visit podcastchoices.com/adchoices
NI is a B2B company in the engineering world formerly known as National Instruments, it makes automated test and measurement systems for a range of industries, from auto to wireless, energy to manufacturing equipment. In June NI launched its rebranding campaign. NI's CMO Carla Piñeyro Sublett tells MediaPost's Brand Insider this week how the company handled that rebranding.
In this throwback episode, Kaitlin interviews Kristen Novak, Account-Based Marketing Manager for Strategic Accounts at National Instruments. ------------ Join me for weekly special LinkedInLive sessions where I interview your favorite guests like Pat Lencioni, Seth Godin, Whitney Johnson, and Kim Scott — LIVE. Here's the one-click invite: https://evt.mx/mSGV4Ka8
Electronic warfare and radar solutions are being driven by signal processing innovations such as the Xilinx RFSoC FPGA and OpenVPX computing solutions. These solutions are also enabling artificial intelligence (AI) capabilities for electronic warfare, radar, and other military applications. In this podcast, Haydn Nelson, principal marketing manager for wireless prototyping deployment at National Instruments, discusses with me how AI can benefit the warfighter through embedded signal processing applications as well as the impact open architecture initiatives such as the Sensor Open Systems Architecture (SOSA) can have on military technology development. He also talks about how test and measurement solutions fit into this ecosystem. This podcast is sponsored by: Aerospace Tech Week, which will now take place March 24-26 2021 in Toulouse, FRANCE after being postponed due to the COVID-19 pandemic. The show encompasses six different events — Avionics Expo, Connected Aircraft Europe, Aerospace Testing Europe, MRO IT, Flight OPS IT and FACE. To learn more about Aerospace Tech Week 2021, visit www.aerospacetechweek.com.
Thermal management of electronics in military systems is a continuous challenge for designers as the aircraft, ground systems, and naval platforms become more complex. In this podcast with thermal management expert Gerry Janicki, Vice President, Meggitt Defense Systems, he discusses challenges, requirements, and design trends trends in military-electronics thermal management. He also details how thermal management needs to be thought of from the ground up on new designs and in tandem with power management; how thermal management costs factor into a platform's life cycle cost, and much more. This podcast is sponsored by National Instruments, who's mission critical test assets demand a proven test strategy. For more than 40 years, National Instruments has developed high-performance automated test and automated measurement systems to help you solve your engineering challenges now and into the future. The demands of building test systems to support long-life programs and identifying test assets that can keep up with commercial aviation, vehicle, and weapons system design require an experienced business partner. Learn how you can create test systems that can scale to support evolving test requirements at ni.com/aerospace.
In this episode Kaitlin interviews Kristen Novak, Account-Based Marketing Manager for Strategic Accounts at National Instruments.
Cognitive radio and spectrum management continue to drive innovation among military communication system designers to enable more capability and faster decision making for warfighters. Efficient spectrum management is becoming especially important as the spectrum gets more crowded and as the U.S.Department of Defense is considering naming the spectrum as another warfare domain like cyber. In this podcast sponsored by Pentek with Manuel Uhm, Director of Marketing, Ettus Research, a National Instruments company and Chief Marketing Officer of the the Wireless Innovation Forum (formerly the SDR Forum), he discusses the challenges in developing cognitive radio and efficient spectrum management, potential solutions and the efforts the Wireless Innovation Forum is making to see these technologies become reality for warfighters on the battlefield and everyday consumers.
Red Wing’s Oil and Gas HSE Podcast was at the third annual Internet of Things (IoT) in Oil and Gas Conference in Houston, Texas. The Internet of Things in Oil and Gas has grown each year since its inception and this year alone hosted more than 400 oil and gas professionals interested in the latest technology moving our industry forward. During the conference, Patrick met with Janie Pascoe of Deloitte Digital to talk about their augmented reality technology and how it’s changing the way maintenance is planned and carried out. Deloitte Digital has combined technology from companies like Thing Worx, National Instruments and HP Edgeline. The Internet of Things has enabled Deloitte to partner with Texmark to create the Refinery of the Future. While still in the early stages of implementation, the Refinery of the Future is using the most advanced technology to set the standard in the industry. Click Play to Hear the Oil and Gas HSE Podcast Episode 52 – Deloitte Digital We Have A Winner!!! Congratulations John Kennedy, Owner of Watson Farm and Timber; you are this week’s winner of the Red Wing Offshore Bag! To get your hands on one of these awesome offshore bags, all you have to do is enter! Follow the link below and select Oil and Gas HSE and enter your information. We pick one lucky winner each week. Click Here to Enter More Information The Internet of Things (IoT) is the next step in the advancement of the oil and gas industry. To find out what Deloitte is doing with the Refinery of the Future, Augmented Reality, Predictive Maintenance and beyond check them out To find out more about how Deloitte is advancing the Internet of Things (IoT) for the oil and gas industry you can find them at their website and through their social channels. Find Deloitte on their website by clicking here. Connect with Deloitte on LinkedIn. To connect with Janie, you can find her on LinkedIn by clicking here. Leave a Review Help your oil and gas peers find the Oil and Gas HSE Podcast by leaving us a review in iTunes. The more, and better our reviews, the easier we are to find in iTunes, so help the industry out by leaving us a short review. Leave us a review by clicking here. If you would like some help leaving a review in iTunes the folk at HubSpot put together some easy to follow instructions that you can check out by
I am happy to share my conversation with Mike Yakubovsky. Mike is the STEM Coordinator and lead engineering teacher at Coppell High School, a 1:1 iPad Apple Distinguished School. He has been with CISD since 2003 and started the CHS School of Engineering in 2006. The School of Engineering is a 4-year pre-college engineering program focusing on design in which learners work on projects that prepare them for college STEM disciplines. Activities expose learners to design, applications of math and science, electronics, kinematics, and coding. Learners have begun Swift Playgrounds and Swift as a core component of their coding instruction. In addition to teaching, Mike is the department Instructional Coach. Mike was honored as an Apple Distinguished Educator in the spring of 2017. In 2015, the Metroplex Technology Business Council named Mike the Tech Titan of the Future for High Schools. Prior to that, Mike was presented with the Excellence in Engineering Education award from National Instruments and was named a runner up for the Discover Educator Award. Mike is the advisor for several organizations related to the Engineering program: Society of Women Engineers, Society of Minority Engineers, and the Coppell Solar Racing Team. I would like to thank Mike for taking time out of his busy teaching and engineering coaching schedule. Coppell School of Engineering - http://www.coppellisd.com/engineering Mike Yakubovsky - http://www.coppellisd.com/Domain/377 Coppell Engineering on Facebook - http://www.coppellisd.com/engineering Mike - Twitter - https://twitter.com/myakSTEM @CoppellSTEM - https://twitter.com/CoppellSTEM @CoppellSolar - https://twitter.com/CoppellSolar @Coppell_SWE - https://twitter.com/Coppell_SWE Show links Apple Distinguished Educators - http://www.apple.com/education/apple-distinguished-educator/ C programming language - https://en.wikipedia.org/wiki/The_C_Programming_Language Lego Mindstorms - https://en.wikipedia.org/wiki/Lego_Mindstorms Lab View - http://www.ni.com/en-us/shop/labview.html Arduino - https://www.arduino.cc Swift language - https://swift.org Larry Reiff - https://twitter.com/Mrreiff Dr. Chris Penny - https://twitter.com/chrispenny Douglas Kiang - https://twitter.com/dkiang Swift Playgrounds - https://appsto.re/us/eHUj2.i Intro to App Development with Swift curriculum: Teacher - https://itunes.apple.com/us/book/app-development-with-swift/id1118577558?mt=11 Student - https://itunes.apple.com/us/book/app-development-with-swift/id1118575552?mt=11 Accidental Tech: Episode 205 - People Don't Use the Weird Parts - http://atp.fm/episodes/205 Everyone Can Code K - 5 with Tynker - https://www.tynker.com/everyone-can-code/ Favorite Podcasts Wired Educator Podcast - https://itunes.apple.com/us/podcast/the-wired-educator-podcast/id974270220?mt=2 Swift Unwrapped - https://itunes.apple.com/us/podcast/swift-unwrapped/id1209817203?mt=2 Runtime - https://itunes.apple.com/us/podcast/runtime/id1122203945?mt=2 Swift Teacher podcast Facebook page - https://www.facebook.com/SwiftTeachers/ Join the Swift Teachers Slack Group - mailto:brian@swiftteacher.org You can find also find the show notes and other information on my blog: Swift Teacher Blog - http://www.swiftteacher.org/podcast
Jennifer Marsman is a Principal Developer Evangelist in Microsoft's Developer and Platform Evangelism group, where she educates developers on Microsoft's new technologies. In this role, Jennifer is a frequent speaker at software development conferences around the world. In 2016, Jennifer was recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. In 2009, Jennifer was chosen as "Techie whose innovation will have the biggest impact" by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity & Inclusion Award, Gold Club, and Platinum Club. Prior to becoming a Developer Evangelist, Jennifer was a software developer in Microsoft's Natural Interactive Services division. In this role, she earned two patents for her work in search and data mining algorithms. Jennifer has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Jennifer holds a Bachelor's Degree in Computer Engineering and Master's Degree in Computer Science and Engineering from the University of Michigan in Ann Arbor. Her graduate work specialized in artificial intelligence and computational theory. Jennifer blogs at http://blogs.msdn.com/jennifer and tweets at http://twitter.com/jennifermarsman.
THE STEM ED COALITION : THE ROLE OF INDUSTRY James Brown of The Stem Ed Coalition is joined by Ray Hsu, Academic Section Manager of National Instruments Presented by Carolina Biological
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