Complex product engineering requires advanced programme planning and delivery management. Engineering activities include product definition, detailed design, technical and material research, boundary analysis, simulation, testing, supplier management, manufacturing, product integration, and assembly. Engineering start-ups are people-centric: team members are critical to bringing new ideas to reality and developing marketable products. In essence, they're starting from scratch with limited resources and learning by doing — developing a new product concept while also establishing a new business.
From the outset, design and technical creativity, business relationships, product attributes, simulation data, supply chain collaboration, and material and financial information are all vital to success. That becomes even more important as product data matures — late changes can add significant complexity and cost to a project. Programme managers have to monitor deliverables through data maturity tracking, setting up the operational governance that ensures product delivery health throughout.
This article covers how engineering start-ups embark on NPD operations, manage innovation and stage-gate processes, and implement the governance that fosters consistent on-quality, on-time, on-budget delivery.
Competitive advantage comes from the ability to innovate superior products and deliver them at a competitive price point in sufficient quantity. On-time delivery is critical — especially for start-ups seeking to secure their financial future.
Understanding how people collaborate is essential to the product creation process. It's often like-minded, passionate people who create and work within start-ups — they aren't afraid to experiment and learn fast from failure. But bringing complex new products to market also requires a matured (or maturing) NPD process to drive concurrent product and business development. That includes: what data is expected to represent the product at each development stage; how product data matures across design, engineering, and manufacturing; and how product data and cost can be controlled without hindering innovation.
Accelerating engineering success: balancing creativity, speed, and control
Early-stage start-ups are less focused on detailed planning and more on experimentation, concept development, building market awareness, and securing funding. As Eric Ries put it in The Lean Startup (2011): "to increase chances of success, [leaders must seek to] minimise time through the Build-Measure-Learn cycle" — building smarter MVPs to validate assumptions through experimentation and ongoing alignment tracking. Running a start-up necessitates swift decision-making and learning by doing — always moving toward and enabling future or imminent scaling.
A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty.Eric Ries, 2011
Lean principles aren't about opposing speed and cost control. In fact, they're less about cost than they are about product quality and delivery speed — including reducing time between iterations to allow for experimentation and value creation. That assumes iterating through design and concept requirements by leveraging customer insights, market and competitor research; influencing factors are translated into product strategy decisions and validated through prototypes.
The ability to manage uncertainty assumes a clear sense of purpose and vision — focusing on what can be controlled in the short term, remaining open-minded, and gradually adapting and executing the start-up's roadmap.
NPD and data maturity: getting ready to scale
To scale, start-ups must remain agile and build solid foundations for change across the extended enterprise. From a personnel perspective, this means a scalable, functional team combining current and forward-looking perspectives with the ability to manage changing objectives. From a process, tool, and technology perspective, this means initially adopting out-of-the-box solutions that can be further expanded and integrated across the enterprise as the start-up grows. Balancing between effectiveness and efficiency is critical — to avoid building and optimising temporary or deprioritised solutions.
There is surely nothing quite so useless as doing with great efficiency what should not be done at all.Peter Drucker, 1963
Doing the right thing at the right time is what matters most. At the enterprise level, that relates to the need for an aligned data architecture with master data flows and activity breakdown toward:
- Delivering the required product features and functions.
- Mapping product deliverables to a design verification plan (DVP) and a holistic, yet simple, sign-off process to track release and change.
- Driving quality management throughout — with the use of failure mode and effect analysis (FMEA) techniques to assess change impact across disciplines and product components.
- Building and approving change through the bill of material (BOM) for full traceability and cross-functional visibility.
- Tracking product deliverables and programme health accordingly — by leveraging data traceability and inter-dependencies.
Not all decisions can or will be data-driven from the outset. As organisations mature, data becomes more and more important to foster quality and inform timely decisions. Eventually, the business reaches a stage where successful product development and programme delivery depend on accurate, timely data.
Both information technology (enabling IT, including enterprise platforms) and operational technology (OT, part of the product development or manufacturing operations) come together in that context. That's especially relevant with the rise of electrification and software enablement requirements across both products and machines.
As IT and OT components converge, start-ups can manage ongoing alignment between the two in the early stages where limited integration is required. Early, robust integration can significantly contribute to building a robust foundation for future efficiency, as IT and OT both "contribute to data-driven value creation and optimisation (leading to competitive advantage and in turn organisational health)" (Grealou, 2021). Organisational health is typically a key enabler of medium- to long-term scalability, whereas programme health is at the core of start-up scalability.
Programme health and deliverable tracking
Operational health and programme health are two different things; they're managed concurrently and shouldn't be confused. Operational health relates to an organisation's ability to operate effectively, to drive change, and to grow as and when expected. Programme health links to the ability to effectively deliver product development expectations to quality, on time, and to budget.
Established NPD processes are great for data consistency and operational efficiency — yet they might not be effective or timely. Typical stage-gate processes are cumbersome and the least agile operating model. At the same time, start-ups can't afford to build a complete NPD framework up front, nor can they adopt ready-made solutions that might not align with their operating culture. Gateway countdowns are often tailored to a given organisation, and at times, they can be very time consuming or fail to reflect data reality because of status greenwashing (see Ian Quest's "Gateway Charge" article). The most effective solution will always be real-time data access through synchronous dashboards that represent or link to the relevant data sources — so the whole team focuses on a single version of truth.
When it comes to engineering and related data tracking, it's essential to consider what kind of information is required — when, by whom, and in what format. That also concerns the need for product integration across multi-disciplinary requirements and the technical disciplines involved in the delivery process: electrical; mechanical-CAD; software; engineering and manufacturing BOMs; bills of process; work instructions; PPM; systems engineering; product configuration, and more.
The need for data evolves throughout each product development cycle and changes with business maturity and transition readiness. Mapping data sources, creation, flows, interactions, transformations, and approval processes are all part of the fundamental enterprise architecture and operating landscape. Successful start-ups know how to drive minimum viable data analytics for their operations to flourish — without the overwhelming burden of trying to perfect the system at the start.
Better analytics, better collaboration
Different projects or programmes require different data analytics. Often, this is based on product maturity — e.g., timely prototype delivery versus right-first-time manufacturing and assembly quality. It's also linked to business maturity and the purpose of the project or programme: validating a given technology or component design, selecting a supplier, raising further funding, building awareness.
For analytics to be effective, data must be trusted as the single version of truth. That's more about the ability to understand the data — know where it came from, how it was gathered, and when — than about precision. It has to be a truth, rather than the truth, with perfect accuracy at every stage a secondary consideration to it being a singular version. Analytics need to be tailored based on the organisational context and operating culture, so data can be consumed and transformed into actionable insight throughout the development process.
Trusted and timely data availability is what contributes to effective collaboration, and is often a prerequisite for improving operational efficiency. Once understood and trusted, data mining and the associated processes can be further optimised to inform decision-making — and, in turn, secure programme outcomes.
What are your thoughts?
References
Grealou L (2021); Exploring the Intersection of PLM and Industry 4.0; engineering.com.
Ries E (2011); The Lean Startup: How Constant Innovation Creates Radically Successful Businesses; Penguin Books Ltd.
Drucker P (1963); Managing for Business Effectiveness; HBR.
