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Manufacturing CIOs: Don't Ignore SMAC for Innovation
CIOs at manufacturing companies have a reputation as no-frills, no-nonsense types with a knee-jerk aversion to technology fads and the “The Next Big Thing.” That’s for the dot com guys, right? Whether this attitude reflects the nature of the industry, an inherent personality type or the fact that IT budgets in the space are notoriously lean is open to debate. Regardless, manufacturing CIOs who dismiss emerging technologies as hype are missing an opportunity to drive significant benefit.
Social, Mobile, Cloud and Analytics (SMAC) technologies are redefining the pace and focus of product innovation, enabling almost real-time insight into market trends and customer preferences. This insight can help manufacturers enhance and innovate existing product lines, as well as develop new products that are more directly targeted to customer needs.
By its nature, manufacturing is often considered as being less responsive to customer preferences than sectors such as retail or consumer packaged goods. Given the time required to develop and manufacture a product, quickly responding and changing products to suit evolving customer demands presents a challenge. Nonetheless, customer satisfaction, loyalty and product innovation have always been just as important to manufacturers as to any other industry. And innovation has always been essential to maintaining margins and fueling growth.
Social, Mobile, Cloud and Analytics (SMAC) technologies are redefining the pace and focus of product innovation, enabling almost real-time insight into market trends and customer preferences.
—Ralph Billington
Traditionally, many manufacturers have relied on market research and insights from top sales people, distributors and dealers to define product innovation priorities. The problem is that the process of researching, soliciting input, gathering and analyzing information and then applying the analysis to develop new and better products is time-consuming and unwieldy. Indeed, under this approach, the latest model is influenced by a cycle that typically started three years earlier.
SMAC speeds up the innovation process by enabling connectivity to the user, the product and market demands. Rather than ask market researchers, sales people, distributors or dealers what customers want, manufacturers can use SMAC to directly ask their entire customer base what they want. The “ask” is done not in the form of an explicit question, but rather by automatically scanning, acquiring and analyzing a wide range of data from multiple sources, such as website visits, mobile apps, purchases and demographic records.
Third-party service bureaus and in-house teams then aggregate, segregate and analyze all of that data on an ongoing basis. The result is a continual real-time feedback loop that identifies ergonomic, economic and functional nuances that can be linked to customer needs and innovation strategies. For example, a phone app in the cab of an excavator can collect data that identifies a need for a more comfortable seat and a more efficient heating and cooling system. It could also potentially monitor the operator’s heart rate when the excavator reaches maximum incline and contribute to a more stable and safer design.
At a high level, SMAC tools can gather intelligence regarding a product launch in a new geography. For a manufacturer considering the Russian market for the first time, for example, SMAC can be immensely valuable in understanding customer requirements and expectations, cultural factors and the competitive climate.
At a more granular level, SMAC is being applied to address key questions regarding specific customer preferences and requirements, as well as the cost-effectiveness and competitive viability of innovative strategies. Consider dishwashers and how functionality and features can align to variances in customer requirements and preferences: for Saudi consumers, efficient use of water is paramount. Meanwhile there’s no shortage of water in Ireland or Antwerp, but houses and apartments are much smaller than in Saudi Arabia, so space is at a premium.
For the CIO, enabling SMAC capabilities requires close collaboration with the marketing function, aimed at understanding and articulating how the combination of social media and data analytics can deliver insight into customer requirements and drive product innovation. By defining desired outcomes and the questions that need answering, enterprise buyers can be more effective in finding the right solution.
Service providers offering SMAC solutions must go beyond discussions of generic technology capabilities and focus on business results. Buyers – particularly skeptical CIOs in the manufacturing sector – don’t want to hear about the wonders of mobile and social platforms, or about the latest cloud or ERP capabilities. What will genuinely resonate is demonstrated success at delivering solutions that generate insight into consumer interests that translates into increased demand. Providers who can show how they’ve used technology to improve product lifecycle management and new market development will stand out in an increasingly crowded competitive landscape.
Another imperative is to stay focused with a manageable project and discrete data set that moves the needle by solving a specific problem or delivering a specific benefit. Providers should resist the temptation to aim too high, as an overly ambitious approach risks becoming mired in oceans of data, resulting in analysis that leads to too many questions and tangents and, ultimately, a lack of actionable results.
In their eagerness to tout their expertise and stand out, many providers oversell their innovation capabilities, or imply that technology change equals innovation. We often see providers stretch their definition of what constitutes innovation, resulting in overdeveloped expectations and underwhelming reality.
Social media and analytics offer enterprises a critical opportunity to enhance product innovation and, specifically, IT’s contribution to innovation. CIOs of manufacturing organizations would be well-served to set aside any remaining skepticism regarding SMAC as a technology fad. The reality is that these tools and technologies are delivering results and will remain a critical driver of success for manufacturers.
Ralph Billington is a managing director with Alsbridge, a global sourcing advisory and consulting firm.

If you’re reading this on an iPhone, your phone is likely capturing data and sending it to Apple for analysis. With every tap and swipe across the billions of iPhones in the world, Apple has a better understanding of which software components are working smoothly and which aren’t. Every few weeks, Apple applies the insights it gains from this data to update the software across every device in use.
As you know, Apple isn’t the only one doing this—the practice is common throughout the tech world. Extensive data capture and analysis are part of the reason Apple and others have been able to release incrementally better products every year for over a decade.
Now, imagine if the practice of continuously capturing performance data and applying it to product development was applied to every product. This is the ultimate goal of the Internet of Things (IoT) and digital twins.
Digital twins are detailed, data-driven digital representations of products in the field. As more products come equipped with sensors, manufacturers gain the ability to collect data in the digital twin, enabling real-time analysis of product performance and conditions. By aggregating data from the digital twins of all their products in the field, manufacturers can gain performance insights at a scale not unlike that of iPhones and other consumer electronics.
Forward-thinking manufacturers are already doing this. Case in point, Tesla creates a digital twin of every vehicle it sells. Sensors from thousands of cars continuously stream data into each car’s simulation in the factory, where Artificial Intelligence (AI) interprets the data and determines whether a car is working as intended or if it needs maintenance. For many maintenance issues, Tesla’s software integrations are so thorough that problems can be fixed with software updates—for instance, adjusting the hydraulics to compensate for a rattling door. By merging AI and IoT, Tesla is able to constantly learn from the real world and optimize each of its cars individually, in real time.
Tesla is a great model for what is possible when a digital-twin approach is baked into product development and manufacturing. But what if digital twins could go beyond predictive maintenance and software updates? What if, on top of optimizing products in the field, digital twins could be used to optimize the design of future products? This could soon be possible with generative design.
Generative design is an evolving Computer Aided Design (CAD) technology where AI is applied to optimize mechanical designs for the goals specified by the designer. As it currently works, product designers manually specify the material properties, boundary conditions and forces expected to act on the part at its points of contact. AI then optimizes the design for the parameters set by the designer. When combined with digital twins, generative design can optimize designs based on the real-world conditions of the product in the field.
Aggregating the live data from thousands of products in the field, digital twins can simulate the performance and conditions faced by the average product over its entire lifetime, with real-world accuracy. Armed with this data, generative design AI could then serially tweak the product design and simulate its lifetime performance under real-world conditions until it arrives at the best solution that satisfies the designer’s goals.
With high-performance cloud computing, a wide range of goals can be optimized for with generative design. Depending on the product, part, or use case, the designer could use AI to optimize their part to extend lifetime, maintain strength, reduce weight, stay within a heat transfer threshold, or limit drag. Designs can also be optimized for different manufacturing techniques, enabling manufacturers to create designs that can be manufactured with the equipment they already have, whether it be casting, milling, or extrusion.
But generatively optimizing parts for manufacturing is perhaps most valuable when optimizing for additive manufacturing. The most efficient generative designs are often made up of complex organic geometries that are difficult to manufacture without additive manufacturing. Additive manufacturing also offers extra flexibility, allowing on-demand printing without significant retooling.
One can imagine a scenario where a motorcycle manufacturer follows Tesla’s model and equips their motorcycles with dozens of sensors connected to digital twins. When a part breaks on a bike, the driver takes it into the shop to get it fixed, where the performance data in the bike’s digital twin can inform a generatively designed replacement part, tailor-made for that bike. Instead of ordering a replacement part and waiting for it to arrive, the shop can 3-D-print the generative replacement part that day.
The examples this article cites may have been limited to vehicles, but generative design and digital twins can work in harmony to improve industrial designs across industries — from wind turbines all the way to entire buildings. In an architectural setting, the structural components of a skyscraper could be equipped with sensors to measure the impact following an earthquake. With generative design, this data can be used to build stronger skyscrapers in the future.
Product development has always been driven in part by analysis of existing products. As IoT sensors become more widespread, digital twins and generative design will empower manufacturers to understand the performance of their products at an unprecedented scale and automatically apply these insights to build better products, accelerating the pace of innovation.
Jesse Coors-Blankenship is the senior vice president of technology at PTC.




