Jeff Winter

According to ISO - International Organization for Standardization, we are at a stage within Industry 4.0 where some of the new disruptive technologies are mature enough to be leveraged by the industry (referred to as “𝐄𝐧𝐚𝐛𝐥𝐞𝐫𝐬”). As part of this journey, there are also a set of design principles that are currently under development and of high relevance for archiving successful implementation of smart manufacturing (referred to as “𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐫𝐬”)

𝐓𝐡𝐫𝐞𝐞 𝐤𝐞𝐲 𝐜𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐥𝐨𝐰𝐞𝐝 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 “𝐄𝐧𝐚𝐛𝐥𝐞𝐫𝐬”:
▪️ Cheap and abundant data storage
▪️ Fast and responsive computers and calculation power
▪️ Ubiquitous and place-independent connectivity

Together these Enablers serve as the basis for today’s accelerated digital transformation.

When the “Enablers” are fully utilized along with the “Enhancers” there will bound to be new disruptive scenarios as we collectively transition into a “Smart Industry”. The future effects will lead to:

▪️ 𝐂𝐢𝐫𝐜𝐮𝐥𝐚𝐫 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: the possibility to make products and production more sustainable and take advantage of retired products and their collected lifecycle information, filter out the essence, and use feedback for improving the manufacturing processes as well as the product itself.
▪️ 𝐌𝐨𝐝𝐞𝐥-𝐛𝐚𝐬𝐞𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐚𝐧𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: the ability to utilize models at every point in the product lifecycle for the optimum use of the product.
▪️ 𝐅𝐮𝐥𝐥𝐲 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐞𝐬: the ability to use new technologies to enable new configurations of production automated to a very high degree.
▪️ 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: the possibility to make mass-produced products more personalized
▪️ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: the possibility to act prior to an unfavorable event (e.g. failure) and use in-process monitoring to enable the identification of the optimum time to replace parts, and hence maximize the service life.
▪️ 𝐄𝐝𝐠𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: the possibility to have all automation, including real-time control, in the edge of the cloud.
▪️ 𝐒𝐞𝐫𝐯𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: the possibility to augment traditional physical products with related services. Hence, the product becomes a by-product of the service.
▪️ 𝐃𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥𝐬: business models that are developed and customized based on data from the business. The more data, the better the business model.

For more information, I encourage you to read the full white paper here: 
https://lnkd.in/epYbS9re

#industry40 #smartmanufacturing
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AI without data is like a car without fuel: powerful but useless.

After analyzing the World Economic Forum’s Future of Jobs Report in 2023 and then their 2025 report, one thing is clear—a lot can change in just a few years. Let’s break it down:
• In 2023, businesses thought they were embracing data—𝟕𝟓% planned to adopt AI, big data, and cloud computing. Cute. By 2025? 𝟗𝟖% of companies now say AI, big data, and digital transformation are mission-critical. Turns out, data isn’t just useful—it’s everything.
• AI & Machine Learning Specialists, Data Analysts, and Data Engineers went from being nice-to-haves in 2023 to business lifelines in 2025.
• Automation expectations doubled—companies thought 𝟒𝟐% of tasks would be automated by 2027 (2023 report). Now, they predict 𝟖𝟐% by 2030.
Machines are rising, but they still need data-savvy humans.

Bottom line? If you're not prioritizing data talent, you’re not just behind—you’re irrelevant.

In my opinion, these “Data” based roles can be broadened into 3 main buckets: Analysts, Scientists, and Engineers. The skills of these roles can easily overlap, but what makes them distinctly different is their focus.

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭: A person who specializes in making sense out of past and current numerical data to find answers to business questions and help business leaders make better decisions. (𝐴𝑙𝑠𝑜 𝑘𝑛𝑜𝑤𝑛 𝑎𝑠 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝐴𝑛𝑎𝑙𝑦𝑠𝑡 𝑤ℎ𝑒𝑛 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠)
• 𝐅𝐨𝐜𝐮𝐬: Storytelling, trend analysis, presenting business simulations, understanding business requirements, creating visualizations

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭: A person who specializes in building analytic and predictive models (with data received from data engineers) to interpret complex data.
• 𝐅𝐨𝐜𝐮𝐬: Applying statistical/machine learning tools to classify patterns, determining strength of patterns and relationships, quantifying cause-and-effect, training and optimizing machine learning models

𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫: A person who specializes in building, testing, optimizing, and maintaining the data ecosystems that allow data scientists and analysts to perform their work.
• 𝐅𝐨𝐜𝐮𝐬: Designing the big data infrastructure and preparing it to be analyzed, building complex queries to create “pipelines”, cleaning data sets, and arranging problems (typically given by data scientists) in the programmed system

These are the best definitions I settled on after reading about a dozen articles.  Tell me your thoughts to make Rev 2.0 better! 😎

𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/enUy7DSJ

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• Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends
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Once upon a time, in the land of business, there were two neighboring cities: 𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐯𝐢𝐥𝐥𝐞 and 𝐑𝐞𝐚𝐥𝐢𝐭𝐲𝐛𝐮𝐫𝐠. Each embarked on a journey of digital transformation, but their paths and perceptions varied greatly...

𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐯𝐢𝐥𝐥𝐞 𝐕𝐬. 𝐑𝐞𝐚𝐥𝐢𝐭𝐲𝐛𝐮𝐫𝐠:
In Expectationville, citizens believed digital transformation was a magical express train. Hop on, and arrive instantly at Success Central. High-tech gadgets lined the streets, and everyone expected overnight miracles. But soon, they realized their train was on a loop, going nowhere fast.
Meanwhile, in Realityburg, the approach was different. They saw digital transformation as a river steadily flowing, sometimes calm and sometimes challenging. They understood that navigating this river required patience, strategy, and a crew that worked together harmoniously.

𝐓𝐡𝐞 𝐌𝐨𝐫𝐚𝐥 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐭𝐨𝐫𝐲:
Our tale teaches us that digital transformation is neither an instant solution nor a one-size-fits-all journey. It's an ongoing process that requires a mix of technology, strategy, and most importantly, people.

𝐓𝐡𝐫𝐞𝐞 𝐏𝐢𝐞𝐜𝐞𝐬 𝐨𝐟 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐘𝐨𝐮𝐫 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐕𝐨𝐲𝐚𝐠𝐞:
• 𝐂𝐡𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐎𝐰𝐧 𝐂𝐨𝐮𝐫𝐬𝐞: Understand that your organization's journey is unique. Don't rely on cookie-cutter solutions. Tailor your strategy to fit your specific needs, culture, and goals.
• 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐊𝐞𝐲: Digital transformation isn't just an IT voyage; it involves the entire company. Encourage collaboration and open communication across all departments. Everyone should feel like a valuable part of the journey.
• 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐋𝐨𝐧𝐠 𝐇𝐚𝐮𝐥: Brace yourself for a marathon, not a sprint. Be ready to adapt and evolve. Celebrate small victories along the way and learn from the challenges.

In the end, the citizens of Realityburg thrived, while those in Expectationville learned the hard way that shortcuts rarely lead to sustainable success. Remember, true digital transformation is a journey, not a destination.

*******************************************
• Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends
• Ring the 🔔 for notifications!

According to ISO - International Organization for Standardization, we are at a stage within Industry 4.0 where some of the new disruptive technologies are mature enough to be leveraged by the industry (referred to as “𝐄𝐧𝐚𝐛𝐥𝐞𝐫𝐬”). As part of this journey, there are also a set of design principles that are currently under development and of high relevance for archiving successful implementation of smart manufacturing (referred to as “𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐫𝐬”)

𝐓𝐡𝐫𝐞𝐞 𝐤𝐞𝐲 𝐜𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐬𝐭𝐢𝐜𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐥𝐨𝐰𝐞𝐝 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 “𝐄𝐧𝐚𝐛𝐥𝐞𝐫𝐬”:
• Cheap and abundant data storage
• Fast and responsive computers and calculation power
• Ubiquitous and place-independent connectivity

Together these Enablers serve as the basis for today’s accelerated digital transformation.

When the “Enablers” are fully utilized along with the “Enhancers” there will bound to be new disruptive scenarios as we collectively transition into a “Smart Industry”. The future effects will lead to:

• 𝐂𝐢𝐫𝐜𝐮𝐥𝐚𝐫 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: the possibility to make products and production more sustainable and take advantage of retired products and their collected lifecycle information, filter out the essence, and use feedback for improving the manufacturing processes as well as the product itself.
• 𝐌𝐨𝐝𝐞𝐥-𝐛𝐚𝐬𝐞𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐚𝐧𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: the ability to utilize models at every point in the product lifecycle for the optimum use of the product.
• 𝐅𝐮𝐥𝐥𝐲 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐞𝐬: the ability to use new technologies to enable new configurations of production automated to a very high degree.
• 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: the possibility to make mass-produced products more personalized
• 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: the possibility to act prior to an unfavorable event (e.g. failure) and use in-process monitoring to enable the identification of the optimum time to replace parts, and hence maximize the service life.
• 𝐄𝐝𝐠𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: the possibility to have all automation, including real-time control, in the edge of the cloud.
• 𝐒𝐞𝐫𝐯𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: the possibility to augment traditional physical products with related services. Hence, the product becomes a by-product of the service.
• 𝐃𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥𝐬: business models that are developed and customized based on data from the business. The more data, the better the business model.

For more information, I encourage you to read the full white paper here: 
https://lnkd.in/epYbS9re

If you want a high-resolution version, let me know in the comments! 👇

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• Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends
• Ring the 🔔 for notifications!