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Industry Reflections - 3. Driving Effective Delivery in Complex Engineering Start-Ups

 

80 percent of new complex engineered projects are late or over budget. Why should non-delivery be the norm and what can be learned from engineering start-ups? In his third entry to the 'Industry Reflections' series, Lio Grealou discusses how engineering start-ups embark upon new product development (NPD) operations, manage innovation, stage-gate processes, and consider the types of governance that can be implemented to foster continuous to-quality, on-time and on-budget delivery.

Above: Tools and technologies are essential when creating and developing new products; they are required to manage and foster cross-functional collaboration, especially for start-ups which must make early, enabling decisions linked to enterprise investments that will shape their future operations and their ability to deliver.

 

Complex product engineering requires advanced program planning and delivery management. Engineering activities include product definition, detailed design, technical and material research, boundary analysis, simulation, testing, supplier management, manufacturing, product integration, assembly, and more. Engineering start-ups are people-centric, with team members critical to bringing new ideas to reality and developing marketable products. In essence, they are 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 attribute, simulation data, supply chain collaboration, material and financial information are vital to success. This becomes even more important as product data matures, as late changes can add significant complexity and cost to a project. Program managers must monitor deliverables through data maturity tracking, setting up the relevant operational governance to ensure product delivery health throughout.

In this article, I discuss how engineering start-ups embark upon new product development (NPD) operations, manage innovation, stage-gate processes and consider the types of governance can be implemented to foster continuous to-quality, on-time and 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 when it comes to start-ups as they seek to secure their financial future. 

Understanding how people collaborate is essential to the product creation process. It is often like-minded, passionate people who create and work within start-ups; they are not afraid to experiment and learn fast from failure. Bringing new, complex products to market requires a matured or maturing NPD process to drive concurrent product and business development. This includes considering what data is expected to represent the product at each development stage; how product data will mature across design, engineering, and manufacturing processes, 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 his book, The Lean Startup (2011): “to increase chances of success, [leaders must seek to] minimize time through the Build-Measure-Learn cycle”—building smarter MVP to validate assumptions through experimentation and ongoing alignment tracking. Running a start-up  necessitates swift decisions making and learning by doing, always moving towards 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 are not about opposing speed and cost control. In fact, they are less about cost than they are product quality and delivery speed (including reducing time between iterations to allow for experimentation and value creation). This 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 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 expended and integrated across the enterprise as the start-up grows. Balancing between effectiveness and efficiency is critical to avoid building and optimizing temporary or deprioritized 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, this relates to the need for an aligned data architecture with master data flows and activity breakdown towards:

Delivering the required product features and functions

Mapping product deliverables to a design verification plan (DVP) and a holistic, yet simple, sing-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 program health accordingly, by leveraging data traceability and inter-dependencies.

Not all decisions can or will be data-driven from the outset. As organizations mature, however, data and become more and more important to foster quality and inform timely decisions. Resultantly, the business will reach a stage where successful product development and program delivery depend on accurate and timely data. 

Both information technology (enabling-IT, including enterprise platforms) and operational technology (a.k.a. OT, part of the product development or manufacturing operations) come together in such a context. This is 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 optimization (leading to competitive advantage and in turn organizational health)” (Grealou, 2021). Organizational health is typically a key enabler of medium- to long-term scalability, whereas program health is at the core of start-up scalability.

 

Program health and deliverable tracking

Operational health and program health are two different things; they are managed concurrently and should not be confused. Operational health relates to an organization’s ability to operate effectively, to drive change and grow as and when expected. Program 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 cannot afford to build a complete NPD framework upfront, nor can they adopt ready-made solutions that might not align with operating culture. Gateway countdowns are often tailored to a given organization, and at times, they can be very time consuming or do not reflect data reality due to status greenwashing [see Ian Quest’s ‘Gateway Charge’ article]. The most effective solution will always be real-time data access through synchronous dashboards which represent or link to the relevant data sources so a whole team focuses on a single version of truth.

When it comes to engineering (and related) data tracking, it is essential to consider what kind of information is required; when, by whom and in what format. This also concerns the need for product integration across multi-disciplinary requirements and the technical disciplines involved in the delivery process, including electrical; mechanical-CAD; software; engineering and manufacturing BOMs; bills of process; work instructions; PPM; system engineering; product configuration, etc. 

Clearly, the need for data evolves throughout each product development cycle and changes with business maturity and transition readiness. Mapping data sources, creation, flows, interactions, transformation, 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 programs require different data analytics. Often, this is based on product maturity e.g., timely prototype delivery versus right first-time manufacturing and assembly quality. This is also linked to business maturity and the purpose of the project or program, e.g., validating a given technology or component design, selecting a supplier, raising further funding, awareness, and so on.

For analytics to be effective, data must be trusted as the single version of truth. This is more about the ability to understand the data, know where it comes from, how was it gathered and when. It has to be a truth, rather than the truth, with perfectly accuracy at every stage a secondary consideration to it being a singular version. Clearly, analytics need to be tailored based on the organizational context and operating culture so that 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 to improving operational efficiency. Once understood and trusted, data mining and associated processes can be further optimized to inform decision making securing program 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.