5 Lessons from Silicon Valley that Translate to Manufacturing
For decades, Silicon Valley has been the epicenter for innovation and home to high-growth businesses. Naturally, leaders from all over the world are curious to decipher the formula for success of these tech companies—and leaders from traditional industrial firms are no exception. A reflection of this are the ongoing efforts to engage with the Silicon Valley ecosystem through educational programs and visits, collaboration with accelerators, or the opening of local satellite innovation offices or corporate venture arms.
However, the learnings derived in Silicon Valley often remain there. They get downplayed with the assumptions that they are effective only in the Silicon Valley ecosystem, or not transferable to the world of producing physical products. This way, many companies lose out on valuable learnings. While not every operating principle of Silicon Valley companies is a suitable fit for manufacturers and other industrials, there are several practical insights that shouldn’t be rejected.
Make data-driven decisions. In Silicon Valley, data trumps experience when it comes to decision-making. How does this look in practice? When a decision is being made, tech companies set up experiments, often using the now prevalent A/B testing. For example, if changes to the sales app are planned, several new designs are launched in parallel and tested with real users. The designers then iterate based on the user feedback and compare results to the control group. Ultimately, whichever design performs best is implemented at scale. If an experiment is not possible to validate the underlying hypotheses, sophisticated simulations will be run to evaluate several scenarios.
But it is not just the important decisions that are made in a data-driven way. The steady state of business follows this principle as well. This manifests itself in the fact that performance information is available in real time in the form of purposefully defined metrics at every level of the organization. This allows teams and individuals to assess the impact of any action taken (often even before it is taken).
Today, most industrial companies are not in a position to make data-driven decisions and are quite unfamiliar with business experiments. They lack the required transparency into their processes and are challenged by insufficient data integrity. To address this, every industrial company needs to develop a data strategy—thinking about which data should be collected, stored, and analyzed to drive decisions going forward. It starts by answering the question: What data is needed to inform strategic decisions and to drive improvements every day?
Clay Christensen, one of the leading thinkers on innovation, said in his book “How Will You Measure Your Life?” that you can see the true priorities of a company (or individual) by looking at how resources (time, money) are allocated. The allocation often diverges from the articulated vision and mission. Currently, leaders of industrials can’t easily spot misalignments between resource allocation and strategy. Reporting of financials and key metrics is often done “in batch” when needed, with very limited real-time information on a granular level. KPIs are usually “off the shelf” and not engineered to fit the specific business processes or to measure the specific objective of the company. The existing KPIs are often not effectively cascaded down in the organization, leaving departments and individuals without clarity on which behavior is desired.
Anticipate disruption and actively shape your destiny. Innovation is happening at an ever-accelerating pace and is often exponential. A technology that seems radical and far from mainstream one year can reach widespread adoption the next. To proactively shape the business strategy, executives need to understand emerging technologies, what drives their adoption, and how they could change the company’s ability to be the provider of choice for their customers. Companies in Silicon Valley seldomly underestimate the rate of adoption as they are immersed in a fast-moving innovation ecosystem where ideas and news quickly spread across tight-knit networks. Companies outside of Silicon Valley usually need to apply a more deliberate, proactive approach of innovation and technology scouting. Industrials have been very good in embracing technology innovation that helps them improve control over their core processes. But not all industrials have a thorough understanding of how maturing of technologies like additive manufacturing, automation (soft- and hardware), or generative design could create new opportunities for their – or their customers’ – business, or enable new, often unexpected entrants to take away market share. What can be learned from Silicon Valley is the adoption of a mindset that anything is possible and the constant push for more radical, out-of-the-box solutions to address existing challenges.
Innovate passionately but don’t be married to any project. Innovation and iteration are closely tied to each other in Silicon Valley. Innovation is informed by studying customer behavior, identifying unmet needs, and resolving those often by leveraging contemporary or soon-to-be-available technologies. Once a need has been identified, cross-functional teams are quickly deployed to pursue product development, constantly testing and adjusting. When testing shows that an idea or product is not living up to customer expectations, the team will either pivot or stop. Industrials might consider this a failure – Silicon Valley companies only see it as such when they haven’t learned any meaningful lessons from it. And to never fail equals not being aggressive enough. Industrials often fall into the trap of the sunk cost fallacy, continuing to pursue projects that haven’t demonstrated value because a significant amount of investment has already been made. This can lead to a spiral of frustration which in the long run undermines a vivid innovation culture: The hard work put into a new idea doesn’t pay off because the solution or product is dead on arrival, and this frustrates all parties involved in the development. The funds that went into driving the project to completion are now missing for new investments. Leadership gets more careful about picking the next project and is asking for an even more solid business case or quicker return of investment. Innovation ends up being very incremental or comes to a standstill. Additionally, industrials are sometimes too conservative in testing their innovation early with (potential) customers and wait for a near-perfect solution instead of getting feedback on rough prototypes – which would have the potential to redirect and save projects to match market needs.
Vigorously pursue and invest in talent. In a time of rapid change, talent that’s able to quickly learn and redirect their skill set has evolved into a key competitive advantage. Silicon Valley companies are aggressively going after that talent, offering attractive pay, career progression opportunities, flexibility, and perks. Their hiring process is tough, aiming to ensure only candidates with the desired skills make it through. The performance in the interview process often significantly impacts entry level and pay, way more than a candidate’s past degrees or work experience. The pay often consists of a significant variable part, often largely made up of company stocks which helps align objectives. But talent doesn’t like to work for Silicon Valley companies only because of the high compensation—they also like getting the chance to work on cutting-edge problems together with driven colleagues, supported by effective and lean internal processes and productivity tools; e.g., internal connectivity platforms that serve as knowledge management repository and reduce the need for email.
Industrials shouldn’t just give up on being able to secure the best talent for themselves, even in areas like data science or engineering where Silicon Valley companies are usually the employer of choice. But industrials have to actively analyze how they can make their companies more attractive for this talent by creating an environment they can succeed in. Many people in Silicon Valley would be excited to work on complex challenges that industrial companies have to offer – e.g. how to find the optimal process parameters and inputs to guarantee good quality, how to dynamically optimize the manufacturing schedule for always changing constraints or how to create a sustainable and visible supply chain that creates less burden on the planet and society. However, in contrast to Silicon Valley companies where data scientists and tech managers are often the decision makers, they lack the ownership and authority in industrial companies to drive change. They often find a lot of bureaucracy and hierarchy in industrials and face difficulties in getting their ideas heard and supported by the right sponsors. This, combined with old software infrastructure and inflexible work models, leads to short and often disappointing employment experiences on both sides.
Instead of hiring a lot of “tech talent” into the organization, industrials should explore making few strategic hires at the right seniority levels in combination with creating an operating model that allows these people to demonstrate how impactful and beneficial their skillset can be. Then the team will naturally scale with a “pull” instead of a “push” approach.
Industrials still live in the physical world. And certainly not everything is perfect in Silicon Valley. Not all of its philosophies and operating mechanisms can be or should be transferred to industrial companies. But it’s worth taking the time to understand the operating principles that made Silicon Valley companies so successful and to go through the thought exercise of, “How could that look for my company?” From this depth of understanding, leaders of industrial companies can then find these ideas, mold them, and make them their own.
Juliane Stephan is a leader in Bain's Performance Improvement practice, based in Silicon Valley. Views are her own and do not represent the views of Bain. Focused on digital transformation, she helps industrial companies reach a sustainable competitive positioning by combining her knowledge in supply chain and manufacturing operations with a deep understanding of strategy, technology, and change management. She also works with accelerators in Silicon Valley to mentor high-growth start-ups in the Industry 4.0 and IoT space.
