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Smart Manufacturing and What It Means to Win the 1% Race

I had the great fortune to sit on an Ericsson-Wired Magazine panel with some outstanding thought leaders to debate the ramifications of the Fourth Industrial Revolution.  We debated whether the era was a revolution or just an evolution (I argued revolution because it will force organizations to think differently about their business and operational models) and whether IoT had failed so far (I said that to date it had “under-performed” from a business value perspective).  Members of the panel included Vishy Gopalakrishnan (AT&T), Mimi Spier (VMware), Rob Tiffany (Ericsson), Tom Wujec (Wujec Group) and Nicholas Thompson (Editor in Chief of Wired Magazine).

Previous Industrial Revolutions – Steam, Electricity and Information – all advanced the quality of life and work, allowing us to do more with less, and raising the fortunes of the entire population.  The expectations for this 4th Industrial Revolution should be no less.

I believe that the defining aspect of the 4th Industrial Revolution is the evolution of smart or programmable environments (things, spaces) that can learn via advanced analytics without human intervention.  Think autonomous vehicles that automatically navigate traffic, smart buildings that optimize energy consumption while providing a comfortable work environment, digital oil wells that maximize the lifetime value of a well while reducing environmental costs, and precision agriculture that optimizes the mix of water, fertilizer, herbicides and pesticides to deliver the highest yielding acreage at minimal cost and environmental risk. All of these Smart environments will possess the ability to self-monitor, self-diagnose and self-heal (see Figure 1).

Figure 1:  3 Stages of Creating Smart

See the blog “3 Stages of Creating Smart” for more details on how to create “smart” or intelligent products that know how to self-monitor, self-diagnose and self-heal.

4th Industrial Revolution = Intelligent or Smart Products

The 4thIndustrial Revolution will exploit new advanced analytic capabilities (Edge Analytics, Machine Learning, Deep Learning, Artificial Intelligence) and the massive amounts of data (transactional, web, social, mobile, IOT/PLC/RTU sensor, GPS location, switch, telemetry) to create analytics-infused intelligent or “smart” products.

But what do we mean by “Smart”? Smart” is the sum of the optimized use cases (decisions) necessary to support an organization’s business or operational objectives (see Figure 2 for a sample list of the use cases for a “Smart” Factory).

Figure 2: Sample List of Use Cases that Comprise a Smart Factory

That’s a lot of use cases and can seem debilitating if one lacks a process to identify, validate, value, and prioritize those use cases in collaboration with the organization’s key business and operational stakeholders. This is not a “show up and throw up” cursory process where the stakeholders are involved at the beginning and the end.  This requires a thorough process that ensures that the voice, objectives, impediments, risks and operational requirements of those stakeholders are validated, tested and reconfirmed throughout the process.  This is a process that leverages the “realm of what’s possible” (envisioning) in order to uncover variables and metrics that might be better predictors of performance while driving organizational alignment.

Taking a use case-by-use case approach enables organizational learning along the process – to reapply the learnings and digital assets that are developed in the earlier use cases to future use cases.   Taking a use case-by-use case approach enables the organization to exploit the “Power of 1%”.

The Power of 1%: Lesson in the Power of Compounding

Thanks for the story, Andrew!  England had no record of success in Olympic cycling, winning only 1 gold medal in the event’s 76-year history.  However, under the leadership of Sir Dave Brailsford, all of that changed and it changed for maybe the simplest of concepts – the power of 1%.  Brailsford’s strategy was to break down the elements of winning an Olympic Gold medal into a series of subsystems (use cases), and then sought to improve each of the elements in these subsystems by 1%[1].

But this concept of compounding is nothing new.  Albert Einstein once said “Compound interest is the eighth wonder of the world. He who understands it, earns it … he who doesn't … pays it.”

The article “10 Reasons why Compounding Interest is the 8th Wonder of the World” highlights some key points that play an important role in applying the concept of Compounding to more than just financial endeavors, including:

  • Compounding utilizes momentum
  • Compounding teaches patience
  • Compounding teaches and rewards discipline

Figure 3 from JamesClear.com highlights the power of compounding 1% improvements.

Figure 3: Source: “How to Build a New Habit: This is Your Strategy Guide

A 1% improvement compounded 365 times yields a 37.78x (times) improvement! Dang, I love math! So, what does all of this have to do with the Fourth Industrial Revolution?  Everything!

Fourth Industrial Revolution:  Call to Action!

In the Fourth Industrial Revolution, there are multiple areas – use cases – where blending data from new sources of data, coupled with advanced analytics, will yield new sources of customer, product and operational insights. However, organizations will struggle not due to lack of opportunities; organizations will struggle because they will have too many.  And delaying starting might pose the biggest threat as compounding rewards those that start immediately.

So, to start today, break your smart or intelligent product into its smaller subsystems and use cases to exploit the power of compounding 1% improvements.  Embrace a process that leverages envisioning (design thinking) to unleash the organization’s innovative thinking in identifying, validating, valuing and prioritizing the organization’s most important use cases.  Then exploit the unique economic characteristics of data and analytics to accelerate the sharing, re-use and refinement process (see Figure 4).

Figure 4:  Exploiting the Economic Value of Digital Assets

See the blog “Why Tomorrow’s Leaders MUST Embrace the Economics of Digital Transf...” for more details on the unique economic characteristics of data and analytics.

Summary

This use case-by-use case sharing, learning and refinement process ultimately accelerates business and operational time-to-value while de-risking project execution.  In digital industries, the economies of learning are more powerful than the economies of scale and in the 21stcentury, all industries will become digital industries.

[1]Source:  The Harvard Business Review “How 1% Performance Improvements Led to Olympic Gold

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Tags: #AI, #BigData, #DataAnalytics, #DataMonetization, #DataScience, #DeepLearning, #DesignThinking, #DigitalTransformation, #DigitalTwins, #Economics, More…#IIoT, #InternetOfThings, #IoT, #MachineLearning, #NeuralNetworks, #OpenSource, #Smart, #SmartCity, #SmartSpaces

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