Sandeep DamodaranProduction Engineer| Metito Overseas LimitedDubai, DU, United Arab Emirates
I'm curious to hear real-world experiences from the community. In process-driven environments like manufacturing, water treatment, or supply chain, integrating AI or automation tools (like predictive maintenance systems, chatbots for procurement, or scheduling optimizers) sounds promising—but the journey isn’t always smooth.
In one of our water treatment projects, we trialed an AI-based demand forecasting tool. While it improved planning accuracy, it also highlighted gaps in data hygiene and team readiness for tech adoption.
👉 What worked well for you?
👉 What challenges did you face—technological or cultural?
👉 How did you manage stakeholder expectations?
Looking forward to your insights—especially from those in hybrid or traditional sectors!
Yes, there are definitely roadblocks and challenges. In my experience, I’ve used AI primarily to improve scheduling methods, rather than for predictive analytics.
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1 reply by Sandeep Damodaran
May 09, 2025 3:10 PM
Sandeep Damodaran
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Thanks, Abolfazl—that’s a great practical application. Scheduling improvements through AI can make a big impact, especially in environments with variable demand or constrained resources. Did you find any resistance from planners or operations teams when introducing the AI-based methods? And were there any learnings in terms of data prep or tool selection that helped the transition? Would love to hear more about your experience—appreciate you sharing!
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Sandeep DamodaranProduction Engineer| Metito Overseas LimitedDubai, DU, United Arab Emirates
May 09, 2025 9:50 AM
Replying to Abolfazl Yousefi Darestani
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Yes, there are definitely roadblocks and challenges. In my experience, I’ve used AI primarily to improve scheduling methods, rather than for predictive analytics.
Thanks, Abolfazl—that’s a great practical application. Scheduling improvements through AI can make a big impact, especially in environments with variable demand or constrained resources. Did you find any resistance from planners or operations teams when introducing the AI-based methods? And were there any learnings in terms of data prep or tool selection that helped the transition? Would love to hear more about your experience—appreciate you sharing!
Sandeep, I have worked on some of the enabling technology for automation such as wireless measurement systems. I was specifically looking for remote angle measurement systems to replace mobile test equipment using long, expensive, frequently damaged cables. In the process I found a lot of existing applications the available tools. In civil engineering sensors measure bridges for vibration and steep slopes for movement. In automated factories, inventory levels can be monitored by how much the storage space shelf flexes due to the weight of the current product level. That technical data can be a gold mine for data analytics.
The biggest challenge I faced was that many people dismissed the feasibility of wireless transmission systems because when they investigated it 5-10 years ago, the technology wasn't ready. IoT technology has improved immensely since then. Not only is the technology ready now but it is cheap, simple, reliable, and off-the-shelf.
The other problem is that having the data is one thing, but you still need smart people to interpret the data. In much of my work with data analytics, I could point to some very important root causes, but you must still understand why the data points to the cause, and then explain how the math proves that to non-technical stakeholders. Keith
Keith
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1 reply by Sandeep Damodaran
May 13, 2025 3:35 AM
Sandeep Damodaran
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Hi Keith, Thank you for sharing your experience—it's a powerful reminder of how quickly enabling technologies, like wireless measurement systems, have matured from experimental to mainstream and affordable solutions.
Your point on the legacy bias toward outdated perceptions of technology feasibility really resonates. I’ve observed similar resistance, especially in operational environments where past failures tend to harden skepticism. It often takes small proof-of-concept wins or peer success stories to gradually shift that mindset.
I also appreciate your emphasis that data alone isn't enough without skilled interpretation and storytelling. In some of my recent projects integrating AI-driven dashboards for production performance, the hardest part wasn’t the data extraction—it was helping teams connect the analytical outputs to operational realities and secure buy-in from leadership who may not be data-native.
Best regards,
Sandeep
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Sandeep DamodaranProduction Engineer| Metito Overseas LimitedDubai, DU, United Arab Emirates
May 11, 2025 3:33 PM
Replying to Keith Novak
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Sandeep, I have worked on some of the enabling technology for automation such as wireless measurement systems. I was specifically looking for remote angle measurement systems to replace mobile test equipment using long, expensive, frequently damaged cables. In the process I found a lot of existing applications the available tools. In civil engineering sensors measure bridges for vibration and steep slopes for movement. In automated factories, inventory levels can be monitored by how much the storage space shelf flexes due to the weight of the current product level. That technical data can be a gold mine for data analytics.
The biggest challenge I faced was that many people dismissed the feasibility of wireless transmission systems because when they investigated it 5-10 years ago, the technology wasn't ready. IoT technology has improved immensely since then. Not only is the technology ready now but it is cheap, simple, reliable, and off-the-shelf.
The other problem is that having the data is one thing, but you still need smart people to interpret the data. In much of my work with data analytics, I could point to some very important root causes, but you must still understand why the data points to the cause, and then explain how the math proves that to non-technical stakeholders. Keith
Keith
Hi Keith, Thank you for sharing your experience—it's a powerful reminder of how quickly enabling technologies, like wireless measurement systems, have matured from experimental to mainstream and affordable solutions.
Your point on the legacy bias toward outdated perceptions of technology feasibility really resonates. I’ve observed similar resistance, especially in operational environments where past failures tend to harden skepticism. It often takes small proof-of-concept wins or peer success stories to gradually shift that mindset.
I also appreciate your emphasis that data alone isn't enough without skilled interpretation and storytelling. In some of my recent projects integrating AI-driven dashboards for production performance, the hardest part wasn’t the data extraction—it was helping teams connect the analytical outputs to operational realities and secure buy-in from leadership who may not be data-native.
