Digitalization is a means to an end; implementing enterprise solutions should be carefully scoped and timed, in context of business maturity and capability requirements. In his second 'Industry Reflections' piece, Lio Grealou discusses how start-ups leverage such capabilities and associated digital tools to get on the maturity ladder, balancing experimentation with best in breed solution adoption, building the required data culture as they mature their business operations and product offering.
Above: developing complex products typically requires integrated data sets with associated end-to-end processes to collaborate across internal teams and supply chains, safely and effectively; having the right enterprise solutions to manage data creation, access and change control is essential to drive effective operations and product development delivery.
Enterprise solutions range from advanced and holistic digital platforms (PLM, ERP, MES, SCM, etc.) to self-contained specialized apps and other integrated toolsets, providing built-in business logic and configurable processes. Altogether, they bring necessary capabilities, automations, and interfaces to bridge organizational functions; they constitute the enabling ecosystem towards operation excellence, and ultimately competitive advantage:
1. Focusing on business capabilities and driving scalability, flexibility, analytics.
2. Providing automation, simplification, and speed when it comes to finding the relevant information and making informed decisions based on real-time data.
3. Delivering core data authoring and traceability functionalities, from the management of configured BOM variants, CAD / CAE and simulation lifecycle, project and program data, marketing and technical requirements, quality metrics, cost and resource forecast, logistics, supply chain performance, asset inventory, technical publications, etc. to change and release management processes and governance.
In this article, I discuss how start-ups leverage such capabilities and associated digital tools to get on the maturity ladder, balancing experimentation with best in breed solution adoption, building the required data culture as they mature their business operations and product offering.
Start-ups are typically associated to young, innovative enterprises with growth ambition, often operating under conditions of significant uncertainty such as unproven technologies or new business models. As Blank (2013) put it: “[established] companies execute a business model, start-ups look for one (…) It shapes the lean definition of a start-up: a temporary organisation designed to search for a repeatable and scalable business model.”
Building a fit-for-purpose lean start-up enterprise roadmap
Established OEMs typically seek business transformation, digital automation, learning and cost efficiency through economies of scale and scope. Start-ups are different breeds: they require swift enablement, going ‘digital now’, with room for experimentation and ongoing maturity development as they define their future products and services, as they set-up their associated operating model, looking ahead (though not too far ahead):
1. Start-ups require agility and flexibility, more than any other established businesses.
2. Start-ups can start small as they face a blank canvas of opportunities, yet they need the best of breed approach which is compatible with their ambitions, culture, and business model.
3. Start-ups don’t have the time (or budget) to launch multi-year enterprise projects; they require low entry barriers, with low-cost and adaptive entry (and transition or exit) criteria.
4. Start-ups are not afraid to compromise for effective pragmatic short-term solutions which can be swiftly deployed within weeks or months, whereas 12 months might seem like long-term for new businesses.
5. Start-ups do not have to define all requirements at once; they can look at building and maturing them over time, experiment and iterate swiftly as they grow and evolve, in a true agile sense.
6. Start-ups can learn from operating templates, lessons learned, and leverage digitalisation experience from established OEMs; many talents engaged with start-ups bring cross-industry expertise, including return of experience and contextual good practice examples.
Putting together and maintaining the enterprise development strategy and associated digitalisation roadmap is an ongoing duty that complex engineering start-ups need to take seriously from day 1. Once again, it is a means to an end: understanding how business functions and suppliers collaborate, anticipating and mapping data flows across operations, across systems, from strategic digital platforms to niche best of breed technical solutions.
“Start-ups have the double challenge of building a new business, new product concepts, new operations, new manufacturing facilities, in parallel to developing and implementing new digital strategies. They make rapid decisions to get started, decisions which they [might] have to adjust or undo later as they transition to the next stage of their business lifecycle.” Grealou (2020)
Building such a roadmap is not about “engaging in months of planning and research”; it is about summarising and validating “hypotheses in a framework called a business model canvas. Essentially, this is a diagram of how a company creates value for itself and its customers”, focusing on iterative, successive minimum viable products in “quick, responsive development” cycles, as Blank (2013) explained.
Business agility without losing sight of the big picture
Start-ups should not settle on half-house solutions: they have the opportunity to start with the best right-sized option from scratch, the more suited niche tools, based on what they can afford, and build on these as they mature. They must make the most of their digitalisation budgets, invest in the relevant prototypes to find their marks—while learning and making the relevant adjustments to fledge product development and production operations.
Above: start-ups are temporary incubators, continuously learning from markets, technologies, and customers, improving their initial ideas as they iterate through experimentation (image credit: Unsplash)
Funnelling and managing new ideas, innovation, performance, and continuous improvement come hand in hand; being innovative refers to the “systematic capacity of organisations to successfully exploit new ideas in a commercial context” (Grealou, 2015). Start-ups aim to be innovative with their new products and services; also with their operating model, they leverage “a broader variety of sources of knowledge” from their founders and their networks, building a people-centric ecosystem (Gruber et al., 2013).
Rapid digital and enabling process implementation can contribute to make or break product development roadmaps; these can be enabled by a combination of concurrent factors:
1. Leveraging SaaS and cloud-based platforms.
2. Leveraging niche technical solutions which can be rapidly adapted and integrated.
3. Adopting agile systems which are flexible and can be adapted or customised to suit niche requirements.
4. Building the right business insights, dashboards, and reports to foster the early creation of a data culture.
5. Learning from customers and suppliers, building partnerships with vendors to leverage cost-benefit collaborations.
6. Keeping everyone current on an aligned working practice and avoiding last minute integration chaos.
Robust enterprise architecture for effective business models
Enterprise architecture is about connecting the dots across business, technology, process, data, and most importantly people. Business models are important for both established organizations and start-ups. Business models are mostly for one thing: making a profit; they contribute to how new organizations attract investments, talents, customers, but also demonstrating that new ideas are viable, that products and services have the potential to create value, and that start-ups can make it happen.
Enterprise solutions include processes, applications, and technologies that organizations use to support their operations, across functions, disciplines, product lines, development, production, and commercialization phases. Assumptions and decisions made by a start-up in the early days of its existence are likely to shape how it will operate and how it achieves meaningful results. Furthermore, early manual process definition will contribute to building the best-fit solution going forward as requirements can be capture for future automation or integration—so that the operating model mature as the business matures.
As Peter Drucker put it in ‘Theory of the Business’ (1994), “assumptions that shape any organizations behavior dictate its decisions about what to do and what not to do, and define what the organization considers meaningful results. These assumptions are about markets. They are about identifying customers and competitors, their values and behavior. They are about technology and its dynamics, about a company’s strengths and weaknesses. These assumptions are about what a company gets paid for.”
Enterprise architecture brings the ‘business architecture’ notion from Drucker to the next level of detail. Enterprise architecture reaches to all underlying data, platforms, tools, and integration-related assumptions that concern how decisions are made, how data creates value across the organization.
What are your thoughts?
Grealou L (2020). Greenfield vs Brownfield PLM Implementations; engineering.com
Grealou L (2015). Framework to Successfully ‘Exploit’ New Ideas; virtual+digital.
Blank S (2013). Why the Lean Start-Up Changes Everything; Harvard Business Review.
Gruber M, MacMillan IC, Thompson JD (2013). Escaping the Prior Knowledge Corridor: What Shapes the Number and Variety of Market Opportunities Identified before Market Entry of Technology Start-Ups? Organization Science, 24(1), 280-300.
Drucker P (1994). Theory of the Business, Harvard Business Review, September/October, pp. 95-106.