This is the second article in a two-part series on adapting the Stage-Gate product innovation methodology to Industry 4.0

Manufacturers today need agile, lean innovation methodologies to reach their business objectives. And while product innovation processes like Stage-Gate work for many industries, they require adaptation to stay relevant for developing connected systems.

As discussed in this earlier blog post, companies can mitigate risk and keep their teams agile by using digital simulation to create virtual prototypes of the connected product’s use cases.  

But adapting Stage-Gate to the needs of Industry 4.0 also means addressing the primary business objectives that lead companies to Industrial IoT – including reduced downtime and the opportunity to create new revenue streams by turning products into services.

Linear vs. Cyclic Stage-Gate

Here’s a model of the Stage-Gate innovation methodology in its common form:

Stage-Gate IoT innovation process

This recognizable process must be modified for Industrial IoT. Analyzing products post-launch (i.e., in production) is one of the most important steps in product innovation and should result in product insight that can be applied to the first stage of the next release of the product.

The problem: The traditional Stage-Gate methodology often relies on anecdotal customer feedback at best, and gut feel at worst – and that’s not enough.

In a cyclic IoT innovation process, behavior analytics supplied from the in-market product feed directly back to the beginning of product design. Product teams apply data from an IoT product – product performance, customer usage, and even issues in the connected system –  back to the IoT product blueprint or Model.

This direct feedback empowers teams to improve the product in subsequent releases – systematically improving product adoption and lowering product costs.

The StageGate IoT innovation process made cyclic

Take user experience, for example. If analytics from an IoT system show that certain features aren’t being utilized by customers, the product team can review the features on the model level and consider adapting or removing them entirely to save on costs.

In the same scenario, without objective data, product teams would have to interview customer services, talk with key customers, and spend hours figuring out why customers aren’t using the feature. Even then, their conclusions would be drawn from subjective research, which is significantly less reliable than behavior analytics.

Alternatively, think about parts failure. Imagine that a behavior or ‘instruction’ between two parts of the system isn’t being triggered properly – for example, an IoT system is built to display an alert on a web dashboard if a product part is about to break down, but the alert isn’t ever triggered.

If it’s mechanical failure, no problem – but if it’s a problem inherent in the system, that information needs to be applied back to the design process to improve the product. And only objective data will supply that information.

The success or failure of product innovation for Industrial IoT depends on a product or design team’s capacity to adapt the traditional stage-gate process.

The ability to apply data-driven behavior analytics to product design, thereby creating a cyclic loop innovation process, is what creates a truly lean, efficient new product development process for Industry 4.0 systems.