It's almost axiomatic - business growth requires a clear vision or goal and strategies to support it. However, a lack of clear direction continues to be one of the most common complaints I hear from the development groups of industrial business to business companies. Of course a lack of direction also means wasted innovation resources, so let me share a story about a company dealing with that issue.

I was meeting with a group of top managers from a mid-size lighting company struggling with new product programs that were severely delayed. They were being constantly interrupted with smaller opportunities and just couldn't seem to make progress on what the team felt were the really big opportunities and threats they were facing in the market. As a result, growth had stagnated and an increasing percentage of their sales were becoming commodity driven.

Of course, that prompted me to ask about their strategy. If these new areas had been identified as important elements for success, what was the strategic level plan to address the gaps? One of the managers replied "What strategy—we don’t have one!" Another replied, "No that’s not fair, there’s a strategic plan and it’s managed by the CFO.” That's when I knew what a big part of their problem was.

Nothing against CFOs in general, but in this case what they were calling a strategic plan was actually just a top-down, multi-year budgeting exercise. It didn't really address key market facing initiatives and cascade them down to the focus areas and activities within each part of the company—not all that uncommon in mid-size and smaller firms.

When I asked how they knew what areas they should be focusing on, the VP of R&D looked across the table at me and just shook his head, "We know what the important drivers are in the market and try to run projects against them, but sometimes I feel like our strategy is to just work on projects for whichever customer is the biggest or screams the loudest. For once, I’d like to be sharing new products that we’ve already developed in anticipation of their needs."

The company was at a point where they needed to make a transition from customer driven to market driven, but without a clear delineation of their new product strategy and its importance to their growth, they were struggling with the transition.

So here are some of the key questions the company needed to answer in order to create a clear market driven new product strategy around each potential market driver or unmet need:

What's changing in your customers' world making it harder for them to do business?
What kinds of challenges are those changes creating for your customers in terms of new sales throughput, working capital and operating expenditures (Delta T, I, and OE for those familiar with Theory of Constraints)?
What solutions (products, services, or a combination) could you potentially offer to help them address the change?
What value would your solution create for your customer and for downstream users as compared to competitive alternatives?
How would you share in that value through either value-based pricing strategies or through new business models?
Would that share of the value be a good return on your development investment?

If a cross-functional team can answer these questions to provide a compelling argument for investment, then it’s time to put dedicated resources against a plan to build that new market segment. From there, execution still requires competent project and resource management, but recovering this hidden innovation capacity all starts with a clear strategic focus on unmet market needs.

Looking to uncover hidden capacity and accelerate your new product innovation? Mike Dalton's Guided Innovation Group has helped companies double new product throughput without adding resources. Download their Growth Equation Diagnostic to identify your best opportunities for improvement.

PTC

Taking Digital Twins for a Test Drive with Tesla, Apple

How tech leaders are using the technology in product development

Jesse Coors-Blankenship

Apr 29, 2020

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.