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Manufacturing, the Smile Curve and Digital Transformation

The Internet of Things (IoT) is the perfect prescription for revitalizing the political poster child for the Manufacturing industry. I mean, what could be more exciting than to couple IoT (e.g., sensors, beacons, geographical positioning) with artificial intelligence to create manufacturing capabilities with lower costs, higher quality and more agile, than human-bound, manufacturing practices.

The chart in Figure 1 validates that point with the Manufacturing industry seen as one of the biggest beneficiaries of IOT.

Figure 1: BCG Perspective: Winning in IoT


However, businesses are learning that the value in a manufactured product is less in the actual manufacturing, and more in the product’s research and development, design, branding, marketing and services. The differentiating value in manufacturing is in helping distributors and customers to increase the utilization and utility of the products.

Stan Shih, the founder of Taiwan’s Acer Inc., illuminated this phenomenon in the early 1990s when he used the “smile curve” to emphasize where the value is generated in the manufacturing industry. The middle of the smile—the lowest point of value creation—is where the actual manufacturing takes place. The highest points of value creation are found at the corners—with R&D at the beginning and customer service and services at the end[1] (see Figure 2).

Figure 2: The Smile Curve


BusinessWeek recently confirmed these manufacturing value creation points in the “Factories Won’t Bring Back the American Dream” article. The article discusses the topic of manufacturing with respect to the Apple iPhone.

“In a 2010 study, the Asian Development Bank Institute pulled apart an iPhone and figured that the process of assembling it in China accounted for 3.6 percent of its production cost. The remaining 96.4 percent was paid to the parts suppliers, and Apple, as the creator, claimed the big profits. Net income at Apple, which does almost no manufacturing, was an impressive 21 percent of revenue in its last fiscal year, and its shares trade at 18 times earnings. Meanwhile, Taiwan’s Hon Hai Precision Industry Co., one of the companies to which Apple outsources its manufacturing, recorded net profit of 3.5 percent of sales.”

Apple captures 91 percent of global smartphone profits with a steady increase in the associated profit margins as compared to the actual manufacturer of the iPhones (see Figure 3).

Figure 3: Apple Captures 91 Percent of Global Smartphone Profits

One does not need to be a data scientist to understand upon which of these two lines one would want to base their business model.

So if the value to the Manufacturing industry is not in the actual manufacturing of the products, where then can IoT, data science and big data really help to power the manufacturing industry’s business model?

Where Big Data and IoT Can Impact Manufacturing Industry

The actual process of manufacturing may not be the most fruitful area upon which to focus a manufacturing company’s IoT and Big Data initiatives. Let’s review each of the key functions that comprise the manufacturing process (smile curve) to contemplate where and how IoT and Big Data can drive business value.

Additionally, we can create new manufacturing optimization opportunities by:

  • Embedding IOT sensors into every aspect of the product design, development, manufacturing, support and services in order to gain further insights into how to deliver greater customer value, how to reduce costs, how to extend market reach, and how to capitalize on new monetization opportunities.
  • Create a collaborative value creation “hub” that captures all the product performance and customer usage data and corresponding analytics so that you can re-apply these analytics across the manufacturing value-chain process.

Figure 4: Collaborative Value Creation Hub

Manufacturing Digital Transformation

In the same way that a caterpillar goes through 4 stages (i.e., egg, larva, pupa, adult) to reach butterfly metamorphosis, manufacturing companies don’t become digital overnight.  Manufacturing companies must also go through the digital transformation stages in order to metamorphosize into a digital company (see the 5 stages of the Big Data Business Model Maturity Index in Figure 5).

Figure 5: Big Data Business Model Maturity Index


Manufacturing companies must avoid the assumption that they key to leveraging IoT for manufacturing success starts with applying IoT to the manufacturing processes.  Instead, look at the larger value chain and identify opportunities (use cases) where the combination of IoT (data about machine performance and behaviors) and Big Data (data about human performance and behaviors) can impact the high-value activities of a manufacturing company as a whole.

As we learned about the smile curve: “the middle of the smile—the lowest point of value creation—is where the actual manufacturing takes place.  The highest points of value creation are found at the corners—the R&D at the beginning and the customer service at the end.”


Figure 1:  BCG Perspective: Winning in IoT

Figure 3:  Apple Captures 91 Percent of Global Smartphone Profits

[1] The Smile Curve

The post Manufacturing, the Smile Curve and Digital Transformation appeared first on InFocus Blog | Dell EMC Services.

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More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide. Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata. Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications. Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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