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<h2>Strategies and tools should conform to three rules for intergenerational success.</h2>With some of America’s legacy operators being in business for over a hundred years, they have acquired a long backlog of designs – often going back to the 1920s or earlier. Of course, schematics for these designs are not kept on their original centuries-old paper. Today, many legacy manufacturers are uploading their oldest documents into online databases for safekeeping.The image of a one-hundred-plus-year-old design being uploaded to the cloud is emblematic of how manufacturing has — and must continue – to evolve in the face of technological advancement. Whenever any business needs to advance technologically, a core challenge is the successful implementation and adoption of new tools.The manufacturing industry also faces unique challenges due to the age structure of its workforce. In 2023, 51 percent of all manufacturing workers were over the age of 45 and this figure will only grow. With an increased demand for skilled workers and a vacuum of necessary skills amongst younger workers, getting the most out of industry veterans will become increasingly critical for manufacturers.For that reason, when industry leaders implement AI and IoT technologies, they need to make sure the strategies they develop and tools they purchase conform to three rules for intergenerational success.<h3>1. Design a Tool with Functionality in Mind</h3>New technologies need to be easy to use. In any company, implementing new systems poses adoption challenges. The first hurdle is whether or not employees even bother to use the technology. This struggle is especially acute in intergenerational firms.While young professionals may be easily convinced to invest the time and effort needed to understand a complex system, experienced employees can be disinterested. Having learned how to do tasks ‘the old-fashioned way,’ adding additional layers of complexity can be a tough sell. The most critical factor in determining an AI or IoT product’s ease of use is the quality and simplicity of its user interface (UI). A good general rule is that a tool should be as easy to understand and start using as Google Drive.<h3>2. Ensure Widespread Usage Across Departments</h3>The second rule is related to the previous one, but touches on a separate issue facing manufacturers: the need for tools to be used across departments. Although departments like engineering, procurement, and fabrication operate under the same roof, too often they are divided by different technology systems.Take engineering, for instance. Engineers and designers are taught to think about particular problems in a specific way. They are also taught how to use specific software to design solutions. Autodesk, the software company that develops AutoCAD, is not successful because its products appeal to the largest market possible. Their success is a result of building software specifically for engineers.Although Autodesk has captured one group of manufacturing workers, its popularity hinders communication between departments. This is because Autodesk does not design for non-engineers, making cross-departmental work difficult. Working with many different software platforms also decreases intergenerational productivity because it increases the number of tools a hesitant worker has to learn. Finding a solution that serves all departments is a more effective fix.<h3>3. Leverage Specialized AI for Niche Industries</h3>Finally, manufacturers need to avoid giving in to the AI hype. That’s not to say AI isn’t important or that it doesn’t present enormous opportunities for the manufacturing industry. It does, but not in the same ways it does for other industries. For that reason, industry leaders need to learn to look past what’s popular and identify practical solutions specifically tailored to manufacturing problems.Companies like OpenAI or Google might seem like they’re on the edge of AI development. The reality is that they are only at the forefront of general-purpose, consumer-facing algorithms. AI experts make a distinction between horizontal and vertical AI. Horizontal AI describes programs that have a wide range of applications but with no specialization. Vertical AI refers to systems that are designed to tackle a specific problem.When you have an exact, industry-specific problem you want to solve, using a vertical AI trained on data related to that problem yields far better results. Specialization also helps experienced workers master the system faster. Being designed for purpose means that these systems need less prompting to get the correct result, making it easier for workers to master.ChatGPT would be an example of horizontal AI, while a program designed specifically for one or more problems faced by the manufacturing industry would be a vertical AI. When implementing AI, manufacturers should look beyond popular horizontal AI systems and find tools that help them deal with the problems they’re facing.By following these three principles, you can find the most effective technologies to help your intergenerational workforce stay ahead of emerging trends. Doing so will set your company up to survive and thrive over the next one hundred years.<a href="https://www.mbtmag.com/artificial-intelligence/blog/22888357/how-to-implement-iot-and-ai-tools-that-accommodate-a-multigenerational-workforce" rel="noopener noreferrer" target="_blank">See the original article here.</a>

Implementing IoT and AI Tools for a Multi-Generational Workforce

<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Fa44624f271da47d6be3084f3eb09f777">Are you an Amazon addict? The sort of person who always has stacks of brown boxes piling up outside their door? If so, you are far from alone. As of 2023, 67% of all American households have an Amazon Prime subscription, and Amazon owns 38% of the e-commerce market in the US.It’s easy to see why the online marketplace is so popular. Simply by searching for a pair of shoes within an intuitive user interface, users are given a seemingly limitless amount of choices each with prices, photos, measurements, and any other metric a shopper could desire.Would Amazon be successful without giving users an abundance of data? Imagine trying to find shoes in an unintuitive Amazon. There’d be no photos, no prices, nothing except a terse description.All you can do is try to decipher perplexing descriptions to try to visualize or remember product details like image and price, take a leap of faith by ordering one, and discover a pair of oversized, overpriced slippers dropped on your doorstep a few days later.As preposterous as this thought experiment seems, it is the reality of procurement departments in manufacturing firms across the United States. Worse yet, their responsibilities are far more important than purchasing a pair of sporty sneakers.<img src="https://cdn.builder.io/o/assets%2F209ee507cedf4c298e100edae4bba2df%2Ff0e06ebd2adc4398bfa9ea538c7db253?alt=media&amp;token=b274de4a-15a8-46c4-8fe8-18f77013e66f&amp;apiKey=209ee507cedf4c298e100edae4bba2df" alt="Screenshot of an Amazon search for shoes showing brand, price, and size filters on the left and product listings with color swatches, prices absent"><h2>The Procurement Predicament</h2>Procurement professionals need to secure thousands of parts for many different products at the best price possible for their company. Getting overcharged or ordering the wrong part can be the difference between profit and loss.Worse yet, as long-time professionals retire, getting the right part at the right price will become even more challenging. This difficulty stems not merely from a lack of memory, but from a lack of familiarity with parts never encountered before.&nbsp;Without centralized methods of storing data, when workers leave manufacturing companies, key insights and manufacturing depart with them. This leaves new employees in a bind, struggling to ensure a steady influx of parts.To manufacturing professionals, this may seem natural, even inevitable. But manufacturing can improve the procurement process, along with company-wide efficiency, by ‘Amazon-ifying’ their internal records of drawings.<h2>Bringing the Amazon Online User Experience to Procurement</h2>What might an Amazon-ified system look like? And how would it help firms strengthen their bottom line? Instead of drawings, sales data, and various records being scattered across the organization, they would all be centralized in one software system. Anyone could search through the directory and find a page containing drawings, prices, and other information about a specific part.Within <a href="https://supplychaingamechanger.com/featuring-our-12-best-procurement-articles/" rel="noopener noreferrer" target="_blank">procurement</a> departments, the current model outlined above would melt away in favor of an intuitive and easy-to-search interface. This would transform procurement professionals’ work and improve efficiency drastically.Instead of calling up suppliers, procurement officials would have immediate access to the prices of competing products. This would enable them to find bargains that would have otherwise been invisible.Additionally, suppliers will have a more holistic understanding of the value of what they’re paying for. Two I-beams may be priced the same but having access to drawings, photos, and other additional information could reveal significant pricing inconsistencies that can be fixed with replacing a supplier or changing the design.For example, one type of steel material might have been cheaper at one point due to raw material price fluctuations and a new material might yield significant cost reduction.<h2>It’s Not Only Procurement That Benefits From Being “Amazon-ified”</h2>Successfully implementing such a system would reverberate beyond procurement. Engineering and fabrication would stand to benefit from having a centralized, easy-to-navigate record of all the pieces and parts a given company has ordered or developed.In many engineering departments across major manufacturers, schematics and critical knowledge are scattered across filing cabinets, various services, or, worst of all, employees’ minds.Engineers can spend hours trying to recover lost files. Sometimes, this process is so time-consuming that it is simpler to start again and make a new drawing from scratch. When critical engineers leave, the problem grows even more acute. These workers can know critical information about products. So, when they walk out the door, a part of the company’s product leaves with them.Keeping drawings inside the same Amazon-ified interference would prevent these problems from occurring. Engineers could easily search and find old schematics. By keeping all the details about a product in a centralized location, there is no risk of losing knowledge to attrition.Using this system would also solve an age-old problem in manufacturing organizations: communication between engineering and fabrication departments.Engineers are used to the logic and language of designing a product while fabricators are concerned with turning these ideas into reality. These gaps in understanding cause friction when developing and manufacturing a new product.Leveraging an Amazon-ified database as a tool for sharing designs between each department would improve communication. Instead of using the clunky UIs found in most engineering software, fabrication officials could explore new designs through an intuitive interface.This way, collaboration would improve between both groups, leading to greater innovation across the organization.Managing procurement may never be as pleasurable as binge shopping on Amazon. However, through Amazon-ifying their procurement process, manufacturers can cut costs and grow their bottom line. And isn’t that more satisfying?

“Amazon-ifying” Procurement and How it Can Reverberate Across Manufacturing!

<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Fca031340692d410ea5f4d367cabc4467" alt="CADDi Drawer title graphic reading &#x27;IoT and AI Tools Implementing&#x27; over a blue sky with a network of connected nodes"><h2>Strategies and tools should conform to three rules for intergenerational success.</h2>With some of America’s legacy operators being in business for over a hundred years, they have acquired a long backlog of designs – often going back to the 1920s or earlier. Of course, schematics for these designs are not kept on their original centuries-old paper. Today, many legacy manufacturers are uploading their oldest documents into online databases for safekeeping.The image of a one-hundred-plus-year-old design being uploaded to the cloud is emblematic of how manufacturing has — and must continue – to evolve in the face of technological advancement. Whenever any business needs to advance technologically, a core challenge is the successful implementation and adoption of new tools.The manufacturing industry also faces unique challenges due to the age structure of its workforce. In 2023, 51 percent of all manufacturing workers were over the age of 45 and this figure will only grow. With an increased demand for skilled workers and a vacuum of necessary skills amongst younger workers, getting the most out of industry veterans will become increasingly critical for manufacturers.For that reason, when industry leaders implement AI and IoT technologies, they need to make sure the strategies they develop and tools they purchase conform to three rules for intergenerational success.<h3>1. Design a Tool with Functionality in Mind</h3>New technologies need to be easy to use. In any company, implementing new systems poses adoption challenges. The first hurdle is whether or not employees even bother to use the technology. This struggle is especially acute in intergenerational firms.While young professionals may be easily convinced to invest the time and effort needed to understand a complex system, experienced employees can be disinterested. Having learned how to do tasks ‘the old-fashioned way,’ adding additional layers of complexity can be a tough sell. The most critical factor in determining an AI or IoT product’s ease of use is the quality and simplicity of its user interface (UI). A good general rule is that a tool should be as easy to understand and start using as Google Drive.<h3>2. Ensure Widespread Usage Across Departments</h3>The second rule is related to the previous one, but touches on a separate issue facing manufacturers: the need for tools to be used across departments. Although departments like engineering, procurement, and fabrication operate under the same roof, too often they are divided by different technology systems.Take engineering, for instance. Engineers and designers are taught to think about particular problems in a specific way. They are also taught how to use specific software to design solutions. Autodesk, the software company that develops AutoCAD, is not successful because its products appeal to the largest market possible. Their success is a result of building software specifically for engineers.Although Autodesk has captured one group of manufacturing workers, its popularity hinders communication between departments. This is because Autodesk does not design for non-engineers, making cross-departmental work difficult. Working with many different software platforms also decreases intergenerational productivity because it increases the number of tools a hesitant worker has to learn. Finding a solution that serves all departments is a more effective fix.<h3>3. Leverage Specialized AI for Niche Industries</h3>Finally, manufacturers need to avoid giving in to the AI hype. That’s not to say AI isn’t important or that it doesn’t present enormous opportunities for the manufacturing industry. It does, but not in the same ways it does for other industries. For that reason, industry leaders need to learn to look past what’s popular and identify practical solutions specifically tailored to manufacturing problems.Companies like OpenAI or Google might seem like they’re on the edge of AI development. The reality is that they are only at the forefront of general-purpose, consumer-facing algorithms. AI experts make a distinction between horizontal and vertical AI. Horizontal AI describes programs that have a wide range of applications but with no specialization. Vertical AI refers to systems that are designed to tackle a specific problem.When you have an exact, industry-specific problem you want to solve, using a vertical AI trained on data related to that problem yields far better results. Specialization also helps experienced workers master the system faster. Being designed for purpose means that these systems need less prompting to get the correct result, making it easier for workers to master.ChatGPT would be an example of horizontal AI, while a program designed specifically for one or more problems faced by the manufacturing industry would be a vertical AI. When implementing AI, manufacturers should look beyond popular horizontal AI systems and find tools that help them deal with the problems they’re facing.By following these three principles, you can find the most effective technologies to help your intergenerational workforce stay ahead of emerging trends. Doing so will set your company up to survive and thrive over the next one hundred years.

CADDi leverages AI to digitize drawings and procurement data, making manufacturing insights searchable by shape, text, dimensions and part names.

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Implementing IoT and AI Tools for a Multi-Generational Workforce

“Amazon-ifying” Procurement and How it Can Reverberate Across Manufacturing!

Implementing IoT and AI Tools for a Multi-Generational Workforce