Most organizations tailor their NPD framework to their context and product development maturity, building flexibility levels into their processes, governance, decision-making, and overall modus operandi with customers and suppliers.
New product development (NPD) gates are not just simple checkpoints but critical go / no-go decision points towards the next strategic step. Responding to market complexity, continuously changing requirements, and more innovative, dynamic project approaches requires more flexibility and more agility than ever.
In this context, Cooper (2017) highlights a “product definition [which] is ultimately reachedthrough a series of cycles, with each cycle producing a more refined product design, spiralling toward a complete product.” Furthermore, to illustrate the point, he expands on the fact that “adaptivity is accomplished through the incorporation of iterative development cycles designed to get something in front of potential users early and often.”
This converges through new governance models and collaborative platforms for teams and organizations to learn as they go. Such approaches are equally relevant for start-ups and established OEMs seeking to re-invent their operations and adapt to new market conditions.
In this article, I discuss business value from agility, adaptivity, and scalability — building better NPD governance, driving better data integration with portfolio management, incorporating continuous improvement, towards open innovation, and reinforced cross-functional accountability.
Agile product delivery is by design a customer- and user-centric approach which counterbalances traditional inside-out organizational perspectives. Design thinking approaches can positively contribute to ensuring desirable, economically viable, technically feasible, resilient, and sustainable solutioning through co-creation and regular feedback loops.
Loh et al (2019) claim that, with agile product-lifecycle management, “automotive OEMs can improve their ability to spot new, untapped growth opportunities. They can improve profit per vehicle by 5% to 10% and slash both engineering and capital costs per vehicle by as much as 30%. Perhaps most critically, OEMs can shorten development cycles by as much as 40% and respond rapidly to customer demands by providing over-the-air software updates.”
“By taking a demand-centric view, OEMs can gain insight into customers’ priorities and reduce the guesswork behind product design and engineering”—Loh et al (2019).
Additionally, they highlight that OEMs can improve operational efficiency and minimize costs across engineering and manufacturing by adopting a scalable modular vehicle architecture which fosters reuse and learning.
Building a learning organization
A learning organization is typically characterized by its ability to effectively manage knowledge creation, acquisition, transfer, and how it converts knowledge into actionable insight. It is often said that feedback loops provide the dynamic process of presenting and disseminating information, leveraging existing and new knowledge, in a collaborative context.
For NPD activities to be effective, value creation, collaboration, and feedback loops must be integral to the product development process.
For instance, this includes:
- Reducing or removing non-value-added activities and streamlining essential administrative tasks to improve performance management and remove gateway delivery latency.
- Driving progressive maturity development and applying value stream principles to yield learner stage-gate management.
- Onboarding high talent density to drive empowerment, continuous alignment and skill “recycling” across teams and functions (positive attrition).
- Building a culture of trust, autonomy, innovation and empowerment to foster effective decision-making, supported by clear roles and responsibilities.
- Leveraging direct customer-supplier interactions, maintaining a healthy sustainable pace of change and delivery (ref. the Agile Manifesto).
Iterating quickly, failing fast
Cooper (2017) highlights the benefits from a hybrid state-gate process which combines Scrum and Agile sprints, “producing a rhythm, with a pattern of activities that defines the heartbeat of the project.” Furthermore, he notes that such hybrid approach is very resource intensive when fostering rapid problem solving, leveraging “instant within-team communication and to build a strong sense of team identity, which helps drive engagement and sustain the intense effort of the sprint.”
A culture of intense feedback requires a culture of transparency and constant waste elimination to ensure successful course-correction. Interestingly, the Agile Academy refers to fail fast as a “process of starting work on a project, immediately gathering feedback, and then determining whether to continue working on that task or take a different approach—that is, adapt. If a project is not working, it is best to determine that early on in the process rather than waiting until too much money and time has been spent.”
Creating a data-driven culture
Ongoing health-checks are essential to capture early symptoms, assess root causes, and build long-term corrective actions where possible. Enterprise and product data often drives such health-checks as they inform on both the maturity of the product and the maturity of the organization.
In context of the agile product-lifecycle approach, Loh et al (2019) highlight the following core questions that (automotive) OEMs must consider — though these also apply to other industries:
- How refined is our understanding of the customer for each segment of the demand map?
- Does the voice of the customer inform decisions related to the portfolio, product design, and complexity?
- Have we established a set of scalable architectures?
- What percentage of each vehicle can be assembled from modular parts?
- Is a modular vehicle architecture truly embedded in the operating system of the company?
- Are cross-functional product teams in place and supported by agile ways of working?
To which I would add the following complementary questions:
- How does data flow and integrate across the enterprise?
- Is the enterprise architecture supporting and effectively enabling the required business capabilities?
- Are data analytics aligned to the decision-making process?
- Are business, IT and OT functions aligned behind a common joint master data management strategy and implementation roadmap?
- Which stakeholder groups will benefit from which business capability implementation and improvement, and how will it drive product innovation and value creation?
The above agile considerations rely on making the required data available to the people who need it, at the time they need it, and in the relevant format need it. For example, right view reporting fosters data analytics to quickly identify issue areas, looping back to master authoring data to solve problems or address delivery requirements in the next sprint. The ability to swiftly implement changes to enterprise digital tools is equally important to build a data-driven culture of efficiency and effectiveness.
What are your thoughts?
Loh A, Heller K, Quinn M, Brahmandam J, Miles N (2019). Activating Agile Product-Lifecycle Management in Automotive. BCG.
Cooper RG (2017). Idea-to-Launch Gating Systems: Better, Faster, and More Agile. Research-Technology Management, 60: 48-52.