Complex engineering start-ups are different from established businesses as they must simultaneously develop both product and business maturity. For the first of Industry Reflections series, Lio Grealou elaborates on the challenges start-ups must get right when setting up enterprise operations to support product development ambitions.
Above: Start-ups face multiple moving parts - building a product or service at the same time as building a business (image credit: PEXELS)
Start-ups are often associated with ‘high risk, high reward’ aiming to disrupt a market. They combine agile cultures of new idea incubation, creative problem solving and flat hierarchy. What makes a start-up unique is often an innovation-driven approach that leverages fluid collaboration, hands-on experience, and mutual trust.
In this article, we elaborate on the typical challenges start-ups face when setting up enterprise operations to support product development ambitions, focusing on the big things to get right early on, without boiling the ocean or simply replicating strategies from established rivals.
When it comes to complex engineering, the best known and most numerous can found in the EV automotive sector; Tesla – arguably not a start-up anymore – Rivian, Nio, Faraday Future, Lucid Motors, Proterra, Canoo, Arrival, Rimac Automobili, Lightyear, Volta Trucks etc. They usually grow by attracting venture capital and other investors, typically culminating in an IPO and transitioning towards a stable and so-called ‘established business’ once they’ve finished investing in the basics. This maturing into the ‘mainstream’ is sometimes characterised by the exit of founders.
Getting the basics right in these initial phases can take years for vehicle and aerospace manufacturers due to complex system requirements, engineering, manufacturing process development and production industrialisation. Adding to product complexity, OEMs in the automotive and aerospace industry leverage third party engineering and manufacturing partners for component supply. Other important aspects include compliance, safety, cybersecurity, software and hardware integration, legislative and certification requirements. Moreover, significant IT and network infrastructure is necessary, but not in isolation sufficient, to successfully launch products to market.
Complex engineering systems
A 'system' refers to a product; a set of components and sub-components interconnected as a 'system of systems' and organized in a purposeful manner, toward a given task. Engineering organizations and OEMs deal with complexity in multiple ways:
Multi-disciplinary product requirements: designing and managing product and associated services' requirements throughout their lifecycle.
Delivery organizational design: designing a delivery organization, typically in a matrix structure across functions and programs (product lines), with multi-disciplinary operations managing product change, BOM variant configuration and release, technical and non-technical publications, quality attributes and requirements, simulation results, standards, compliance, etc.
From simulation-based to software engineering: delivering to product design and performance expectations across all embedded functions.
Functional and technical integration: integrating support functions into operations based on the product scope (e.g., IT is now often part of the core strategic business due to proliferation of vehicle control, management and infotainment systems).
Supply chain integration: implementing outsourcing models to leverage specialist capabilities, economies of scale and scope through supplier engagement models, engineering-to-order, configuration-to-order, manufacturing-to-print, etc.
As highlighted by Mayfield et al. (ENCORE, 2018), complexity differs between engineering and engineered systems: "engineering refers to the set of processes and resources that deliver a technical solution, while engineered refers to the outcome of the engineering activity as an assembly of components with certain characteristics."
Engineered systems are produced to meet given product, commercial and other operational requirements - they must be resilient to certain criteria, from extensive configurability to ongoing maintenance and upgrades. Holistically speaking, these complex systems can be complicated (image credit: Mayfield et al., ENCORE, 2018)
Furthermore, the ENCORE whitepaper interestingly raises that "an engineered system is created with a specific purpose in mind and deliberately engineered to fulfil that purpose. (…) Complex systems are not fully predictable. Therefore, the engineering of complex systems must take full account of complexity [to] produce systems that can be trusted by society, which is increasingly dependent on such systems."
9 key challenges faced by engineering start-ups
Challenges faced by complex systems engineering start-ups are not significantly different from other start-ups, though some can be amplified by the inherent product and process complexity and compliance requirements that result in high barriers to entry, levels of investment and cross-disciplinary talent requirements.
1. Best athletes – do we have the right talent, role distribution, and skills? What do we want or need vs what can we afford?
Building a team from scratch requires some level of trust and understanding of the complex requirements at hand, especially when there is nothing to train new hires on (e.g., no business process, no mature organization structure, no mature or robust enterprise platforms, etc.). Onboarding relevant talents for the tasks means hiring the ‘best athletes’ or high performers to remain as lean and nimble as possible.
2. Right sizing – when it is the right time to introduce an operating process, and how formal or automated does it have to be?
Processes can hinder creativity and stifle swift, intuitive decision-making. There is nothing wrong in recognizing that certain things will remain broken until the time to address them is right; temporary solutions can be sufficient to operate for the next six months, though more robust solutions might be required after that.
3. Failing fast – how can we build the required operations without compromising product delivery and cashflow while simultaneously onboarding new talents or investors?
There is an ongoing balance between remaining lean and implementing the next enterprise platform to leverage industry best-practice processes. Start-ups need to be ready with the right tools and technologies to jump onto the next step of the maturity ladder, without boiling the ocean or starting too early and losing focus on what really matters. This includes quick iteration and learning from ‘fail fast’ business approaches while mitigating business risks.
4. Holistic enterprise architecture – when is the right time to scale, start building the required digital foundation, and how much planning is required for this?
Subscribing to online enterprise platforms to get started is not difficult. Consolidating and integrating such digital tools and processes can be more challenging, especially when data is scattered across multiple sources. Building and managing an ongoing capability-process-integration roadmap is essential. This requires a holistic perspective on enterprise architecture to ensure continuous optimization and alignment; what data required, by whom, at what point in time, and in what format. Also, certain things might need to be done twice to put capabilities right for the long run.
5. Syncing product and operations – when and how should we get the right team to deliver effective operations?
Product and operations strategy often require different talents and owners to ensure that one is not compromised at the expense of the other. As start-ups gear themselves up for growth, they must assess operational readiness and the maturity of operating requirements. While start-ups can start on the right foot with their operating data and digital processes, they can also rapidly spiral from greenfield to brownfield when faced with the need to transform themselves too early, or even before securing the relevant funding tranche for it.
6. Not reinventing the wheel – do we have the right industry connections to leverage partnerships and build new relationships?
It makes sense for start-ups to use templates that worked elsewhere in the industry as there is no need to re-invent the wheel. Nevertheless, it is also important not to reproduce the same mistakes of others, or to aim for the same outcome in the short-term that established organizations spent years (and millions of dollars) putting right in their own, very different, context.
7. Answering a question someone is actually asking – did we do sufficient market research with relevant competitive analysis to understand how to find the right solution, and how to make a difference in a start-up context?
Sizing up opportunities is often perceived as a combined art and science - keeping up with market trends and deciding what could constitute a competitive edge. This includes having a sound understanding of business performance, how it manages business changes, intellectual property, product development plans and core resources.
8. Not letting data maturity limit product maturity – how and when to build the right minimum viable product (MVP), and will it be sufficient to demonstrate business potential to potential investors?
Start-ups have changing requirements as they mature and develop their business and associated products or services. It is essential to assess data maturity at least one step ahead of product maturity, so that data does not become a drag on growth, and to ensure that the right data is available at the right time.
9. Progressive maturity – when do we start investing in infrastructure and enterprise solutions to create foundations for scaling the business?
Like data, it is important to have the right integrated tools and enterprise platforms implemented at the right time; taking a step-by-step approach and without doing too much too early. Approaching change with a progressive maturity management perspective can also help drive effective behaviors and outcomes. It is often a compromise between short- and medium-term perspectives; starting with limited infrastructure to get the data flowing does not mean neglecting the roadmap to connect all pieces when the time comes.
As Eisenmann (2021) put it: “doing something new with limited resources is inherently risky. But by recognizing that many failures are avoidable and follow the same trajectory, we can reduce their number and frequency. The payoff will be a more productive, more diverse, and less bruising entrepreneurial economy.”
What are your thoughts?
Eisenmann T (2021). Why Start-Ups Fail; Harvard Business Review - adapted from the book Why Start-Ups Fail: A New Roadmap for Entrepreneurial Success, by Tom Eisenmann.
Grealou L (2020). Greenfield vs Brownfield PLM Implementations; engineering.com
Mayfield M, Punzo G, Beasley R, Clarke G, Jobbins S, Holt N (2018). Challenges of Complexity and Resilience in Complex Engineering Systems; ENCORE Network Whitepaper.