How To Get Manufacturing Back to America

I was reading the transcript of Tim Cook’s interview with Charlie Rose and was struck by this particular exchange

Tim Cook: Yeah, let me-- let me-- let me clear, China put an enormous focus on manufacturing. In what we would call, you and I would call vocational kind of skills. The U.S., over time, began to stop having as many vocational kind of skills. I mean, you can take every tool and die maker in the United States and probably put them in a room that we're currently sitting in. In China, you would have to have multiple football fields.
Charlie Rose: Because they've taught those skills in their schools?
Tim Cook: It's because it was a focus of them-- it's a focus of their educational system. And so that is the reality.

I am not involved in the front lines of manufacturing any more but it seems like Tim Cook is lamenting the fact that there is real shortage of people in the US that know manufacturing and industrial engineering well. This should have been expected since most of the manufacturing moved to Mexico or China. Toyota Production System amply demonstrates that majority of the manufacturing innovation and improvement happens on the plant floor (Gemba). Perhaps we should teach Toyota Production System and its principles in all Community Colleges and in the University System as well as the following topics

  • Tool and Die Making
  • Source Inspection and Poka-Yoke
  • Kaizen
  • Single Minute Exchange of Die (SMED)

The Fallacy of BCG’s Perspective on Industry 4.0

This morning I got up and found an email from BCG in which I discovered that this article on Industry 4.0 is one of the most read. The article and its facetious nature ticked me off a little bit and prompted me to write this. The article might as well have been written to see how many buzzwords can be stuffed into a high sounding paper. Worse would be to find that a large number of people are impressed by this paper.

The misconception starts right in the first paragraph.

The steam engine powered factories in the nineteenth century, electrification led to mass production in the early part of the twentieth century, and industry became automated in the 1970s. In the decades that followed, however, industrial technological advancements were only incremental, especially compared with the breakthroughs that transformed IT, mobile communications, and e-commerce.

To the authors: Toyota Production System, one of the most important innovations in manufacturing in recent decades, was developed, fine-tuned and broadly adopted in the 1970s and beyond.

The next paragraph is even more egregious in its use of bloviation and exaggeration.

Now let’s look at the nine foundational technologies selected by BCG that make up Industry 4.0 transformation.

Big Data and Analytics

Statistics arrived on the scene a good hundred years before “Big Data and Analytics”. Manufacturing companies have been using statistics with and without big data for several decades now. I do believe that advanced techniques such as Machine Learning have an increasingly role to play in manufacturing. But Big Data and Analytics is not as ground shaking as the article suggests as far as manufacturing is concerned. These are many fancy use cases around Predictive Quality using Big Data (analyzing all data that is), but more often than not Source Inspection and  Poka-yoke strategies are better.

Besides and in many cases a simpler approach is often faster and cheaper than the big data approach as can seen in this post.

 Autonomous Robots

This is Elon Musk’s biggest fear. Imagine autonomous robots attain consciousness, communicate and go on strike.



I have no idea what the authors are talking about here. CAD/CAM/CAE and discrete event simulation have been around for decades and have been used extensively in manufacturing domains. What about SMED?

That said simulation of complex systems (integrated mechanical, electronics and communications systems) continues to be devilishly difficult and advances in this area can help design, development and manufacture of intelligent products.

Horizontal and Vertical Integration

Companies have been attempting cross-functional engineering for decades now. It’s been hard to accomplish because of human nature and tools provided by vendors suck.

Vertical integration and its practice is dependent on company’s core competencies, market dynamics and how much control a company wants to have in the value chain.

Industrial Internet of Things

Less said the better. Because in a couple of years IoT and IIOT will cease to exist. Most people will instead talk in terms of connected products, experiences and intelligent machines/manufacturing systems


Security is a defensive measure. How does that transform manufacturing and make it better and more productive?


I am not sure why this is even listed.

Additive Manufacturing

Finally. This is a valid technology that has the potential to transform certain segments of manufacturing. Some of the most exciting work is being done with next generation materials (nano-materials) that are ideally suited for additive manufacturing.

Augmented Reality (AR)

AR is certainly an exciting and transformational technology for games and you know what. The authors also correctly note that AR can be transformational in product development, training and service. But before that happens it has to be cheap, good and easy to adopt. It’s not there yet.

Overall, the Industry 4.0 paper lacks intellectual rigor that is expected of a BCG paper. It has taken an unwarranted and excessive technology slant and failed to take into consideration other essential aspects, including new materials, new manufacturing processes, transportation trends, regional economic and human resource trends and geopolitics.

How Ready is Your Supply Chain for Delivering Personalized Products

Zero lead time, zero inventory and zero waste are aspirations of Toyota Production System.  Of course, they are physically impossible to achieve but those aspirations shape the questions asked, decisions made and how production activities are organized in the Toyota Production System. Toyota in fact managed to reduce lead time between a custom order and production to 5 days back in 1999. It is an amazing accomplishment even by today’s standards.

The idea of making and delivering personalized products to meet individual tastes has been an alluring one for product companies. Companies like Harley-Davidson, Dell and Motorola recently have offered some level of personalization for products to meet individual tastes. Latest example of Coke allowing names on personalized bottles also points to a future where customers can even personalize small and ephemeral purchases. Time will tell if it catches on and if this level of personalization will be a competitive advantage to companies that can support it.

Nevertheless, it is important that product companies be prepared to support some level of customization and personalization, especially in consumer markets. Companies such as HP and Apple use product design and manufacturing strategies such as postponement to support customization. Customization and personalization does not come easy and has a significant impact on supply chain and manufacturing strategy and performance. Supply chain and manufacturing infrastructure needs to be designed and implemented such that customization is possible and order-to-delivery lead times are reasonable. In addition, the story of Dell offers a cautionary tale for going over-zealous on personalization and configuration. Dell essentially lost marketshare as PCs turned into commodities, customers de-valued configurability and more variety was widely available in electronics stores with zero lead time. In consumer markets companies have to be especially vigilant of changing customer preferences and balance must be struck between lead times, availability and personalization.

Since most companies are not Toyota as far as Supply Chain Planning and Production Scheduling is concerned a combination of forecast based and order based production is most appropriate. Many companies in consumer products sector (food, electronics, etc.) have to necessarily use forecast based production planning and manufacturing execution systems because customer purchases are based on availability on the shelf.

On the other hand companies that manufacture and deliver customized products with short lead times can use order based production planning and manufacturing execution processes. This should be the case when order-to-delivery cycle time is greater than production cycle time. The Toyota Production System strategies that include single piece flow and mixed production are also ideally suited for these companies.

Serverless Computing on Cloud Platforms – The Game Changer

Amazon’s Lambda and Google Cloud Functions are ushering in the next generation computational model, which in my opinion will have a profound impact on how applications will be developed, operated and maintained. As the capabilities of Lambda and Cloud Functions improve there will be less incentive for developing applications based on virtual machines and containers. We are closing in on the iOS application model on Cloud platforms.

IoT Platform Comparison – Amazon Web Services vs. Google Cloud Platform vs. Microsoft Azure

This post is a follow on to the “What You Need In An IoT Platform” post and examines IoT (Internet of Things) platform offerings from the 3 leading Cloud Computing platforms.

As highlighted in the original post an end-to-end Internet of Things (IoT) platform needs to provide more than just data collection and data analysis capabilities. The following outlines the list of capabilities desired in an IoT platform. Continue reading IoT Platform Comparison – Amazon Web Services vs. Google Cloud Platform vs. Microsoft Azure

What You Need In An IoT Platform

Despite some overzealous efforts related Internet of Things (IoT) the trend has legs and real business value. Internet of Things is one of the foundational elements of Digital Transformation. Advances in IoT are transforming products into connected experiences and services.

An End-to-End Internet of Things (IoT) platform, however, needs to provide more than just data collection and data analysis capabilities. Based on my experience with a real world (cost constrained, patchy connectivity, low power) IoT scenario I found the need for following capabilities in an end-to-end IoT (software only) platform. Hardware aspects are omitted in this post because of the diversity of use cases and design considerations that cannot easily be generalized. Continue reading What You Need In An IoT Platform

Architecting An Enterprise Document Management System With Google Drive And Google Object Storage

There is no doubt that Google Drive is a great document management and collaboration system for individuals and teams. Google Drive does start to show its limitations when you begin to use it as a document management system for the whole organization.

Organizations use an enterprise document management (like Documentum, Alfresco…) to support key business processes in marketing, sales, product development, manufacturing, quality management, customer service, etc. In addition, proper document management is necessary to meet regulatory requirements in many industries. Some of the expected capabilities of an enterprise document management include the following

  • Storage capability for terabytes of data and millions of documents
  • Sophisticated access control
  • Audit trail
  • Workflow
  • Archiving
  • Collaboration (internal and external)
  • Revision control
  • Search, Full text indexing, etc.

While Drive has some of the above capabilities, some of the limitations can make it difficult to use it as the enterprise document management system. This is where Google Cloud Storage comes into the picture. You can learn more about Google Cloud Storage here.

The figure below shows the high-level architecture of how to leverage Drive and Google Cloud Storage to create your enterprise document storage management system.


In the example above, users (Homer Simpson and Lisa Simpson) use Google Drive for local storage and collaboration and Google Cloud Storage as the central enterprise wide data storage.

However, certain amount of integration and application code needs to be written between Google Drive and Google Cloud Storage to complete the enterprise document management system. We will cover the integration and application code in a later post.

In summary, the combination of Google Drive and Google Cloud Storage creates a compelling enterprise document management solution for organizations of all sizes that users will love to use and CIOs approve.

Amazon’s NICE move

Nice move, Amazon. High Performance Computing (Simulation, Computer Aided Engineering (CAE), Graphics rendering, etc.) has been one of the earliest use cases and for Cloud Computing. This was true even before the advent of AWS and the likes. The computers and infrastructure that were traditionally used for these jobs were Super Computers from Department of Energy, Super Computing Centers and/or clusters of computers. Move over, DoE. We have a new super computing cluster in the town.