Lean Digital Thread: Micro-Solutions—Solving One Challenge at a Time

In previous columns, I’ve written about topics related mostly to the shop floor but started swimming upstream and wrote about design for manufacturing and process engineering. In the last column, I focused mostly on the critical phase of moving from design to manufacturing and how to tackle the BOM management:

  1. Design for manufacturing is much easier now
  2. Data collection and the basic questions you can answer
  3. Material management and its impact
  4. Data-driven decisions and micro-solutions in manufacturing
  5. Accelerating global new product introduction (NPI)

In the November column, as promised, I would like to jump back to the manufacturing floor and share my thoughts on the role of a manufacturing execution system (MES), reporting, and analytics. I will describe in more detail the micro-solutions concept and why I think it will make a huge impact on achieving productivity excellence.

I always like a good quote to stream the overall message of my column:

"Great companies will have a strong, lean vision in place with the senior management vision and are working daily at getting on with doing a small number of important things consistently—day in, day out, week after week, month after month, year after year, as part of the middle management action plans. And finally, the results must be visible at the shop floor level. That is what makes for effective lean leadership within companies." —Total Excellence Manufacturing (TXM)

What Is the Most Used App on Your Smartphone?
I am constantly looking for bottlenecks and barriers to improve what I do. I’m not sure if it’s a leftover from my military service or just a part of my personality and education. Last week, I checked the iPhone screen time feature in more detail to answer an important question: Am I really over-using my phone? The answer is yes, for sure. But the answer is also much more complicated. If I’m using my phone to do things that I used to do with my laptop, it’s really over-use. Surprisingly, one of the most used apps was a simple calendar. I guess that a big part of my productivity is impacted by a very simple solution.

During the last decades, manufacturing operations management has been evolving from manufacturing execution to the enabler of smart manufacturing. During the 1990s, the focus was on standalone MES for improved manufacturing execution:

  • Focus on vertical integration
  • Rich set of industry-specific OOTB functionalities
  • Real-time data acquisition

During the 2000s, the manufacturing scope shifted from execution to broader coverage of manufacturing disciplines from MES to monolithic MOM to digitalized manufacturing execution, quality management, planning, and scheduling:

  • Scalable solutions, extensibility, and codeless configuration
  • Data synchronization and contextualization

In fact, the MES systems evolved in a similar way to an operating system; it’s the Windows/Android/iOS of the factory. Many functionalities were added along the way, such as the web browser, emails, and so on. But there’s still a need for small and focused solutions. In manufacturing, we call them micro-solutions.

Imagine that your factory runs the MES, and you can go to the app store to choose an app to solve a specific problem. There are no strings attached—just run it, use it, and decide if it brings you value or not. This offers agile deployment, a clear ROI, and a simple but sophisticated solution.

sagi_fig1_1120.jpg
Figure 1: Choose a simple solution from the manufacturing app store.

Ask the Right Questions About Value
To better understand what the right solutions are for us, we need to ask the right questions: Where do I really waste time and energy? In my first startup, we developed and sold a retinal imaging camera. For some reason, very few users were able to get good outcomes with the device. For a very long time, we assumed that the main reason was related to our analysis algorithms.

Guess what? It turned out that the biggest challenge was to take a good picture with the device, and it took a very long time for the users to learn how to do that. You need to ask for many different opinions. Sometimes, the problem is different than what I assumed and maybe even easier to solve.

Let’s start with a simple question you can ask yourself: If the same error pops a few times in a row, how long does it take for the operators to go to the machine and check what happened—five minutes or 15 minutes? What if you can simply shorten the time it takes him to know about the error? We should also discuss throughput. How can you increase it? Can you increase it? Is it really related to the machines, or is it something about the process? Can you better predict the test results and optimize them?

Focusing on the Product With Predictive Analytics

I would also like to share two use cases with you:

  1. X-ray is an expensive test in resources, time, and cost. What if you could better predict what to test and what not to test?
  2. In a rapidly evolving environment, time to market is critical. What if you would ramp up the production, predict which units are likely to fail, and send them to repair as early as possible?

Conclusion
To summarize, you can always improve, and it does not require a huge investment and commitment. You can try the micro-solutions out there and keep evolving in your digital manufacturing journey.

If you plan to achieve production excellence, please PM me on LinkedIn.

Sagi Reuven is a business development manager for the electronics industry at Siemens Digital Industries Software. Download your free copy of the book The Printed Circuit Assembler's Guide to… Advanced Manufacturing in the Digital Age and visit I-007eBooks.com for other free, educational titles. You can also view Siemens’ free, 12-part, on-demand webinar series “Implementing Digital Twin Best Practices From Design Through Manufacturing.

Back

2020

Lean Digital Thread: Micro-Solutions—Solving One Challenge at a Time

11-24-2020

As promised, Sagi Reuven jumps back to the manufacturing floor and shares his thoughts on the role of a manufacturing execution system (MES), reporting, and analytics. Reuven describes in more detail the micro-solutions concept and why he thinks it will make a huge impact on achieving productivity excellence.

View Story

Lean Digital Thread: Accelerating Global New Product Introduction

10-21-2020

James Dyson once said, “Manufacturing is more than just putting parts together. It’s coming up with ideas, testing principles, and perfecting the engineering, as well as the final assembly.” In this column, Sagi Reuven describes the importance of process engineering or new product introduction (NPI) and how process engineers can make a big difference.

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Lean Digital Thread: DFM Is Now as Easy as Spellcheck

09-30-2020

In past columns, Sagi Reuven has written about topics mostly related to the shop floor. In this column, he talks about design for manufacturing (DFM). It is clear to everyone in the Industry 4.0 era that the holy grail is to close the loop between design and manufacturing.

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Lean Digital Thread: Data-Driven Decisions and Micro-Solutions in Manufacturing

08-26-2020

In past columns, Sagi Reuven has written about two pillars: (1) data collection and the basic questions you can answer, and (2) material management and its impact. In this column, he discusses the next level—changing the mindset from reporting to analytics and focusing on making small improvements.

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Lean Digital Thread: Driving Productivity Excellence—Lean PCB Manufacturing

07-22-2020

During the last few months, thanks to COVID-19, Sagi Reuven felt the supply chain impact on our hardware production for the first time and hence dedicates this column to effective material management.

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Lean Digital Thread: Realizing ROI Starts With Making Smart Data

06-16-2020

Sometimes, you think too much about the bits and bytes, and you forget why you started this journey; we’re all caught up in a tornado of marketing buzzwords. In Sagi Reuven's debut column, he shares how initialization has to start by turning data into smart data.

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