Whether you design automobiles, work as an aerospace engineer or sell medical equipment, there comes a time when you’re likely to have exhausted the traditional levers for growth. It’s a bit of a confounding moment and it requires a new range of creative solutions to make progress.
Naturally, this causes some anxiety, and while there is realization that digital tools can be a gamechanger, there is still reticence to embrace them fully. Reasons vary – from management mindset, to increased workload, to lack of skills to manage big data or develop new digital tools. Readiness even varies significantly by variables such as company, region and role.
Venkat Atluri is a senior partner in the Chicago office of McKinsey & Co. He has worked in product development and management for General Electric and 3M. (Image source: McKinsey &Co.)
Our studies have found leading companies were more likely to use digital tools at every phase of the research and development (R&D) process. The laggards might use digital tools for technical activities but not for management activities, such as planning and project management. Those in the vanguard were particularly likely to use product data management (PDM) and product lifecycle management (PLM) systems, citing the benefits such systems provide in reducing both product and development costs.
In a recent compendium of work produced by McKinsey & Company, including The Trillion Dollar Question for Advanced Industries: How to Extract Full Value from Technology, we take a closer look at how technology can enable transformations that improve margins and return growth.
What prevents companies from diving into the deep end of the pool? Four main factors stand out:
- Management mind-set: Managers either mistakenly believed they already had all the necessary capabilities in-house or were reluctant to make the leap to untried and potentially incomplete new approaches.
- Organizational and cultural issues: Most R&D staffs were struggling to fulfill their basic mission and management thought the move to digitization would be an added burden.
- Skillset: Most companies lack the capabilities to make this transition, especially when it comes to managing big data or developing new digital tools.
- Risk averse: Some companies are reluctant to make large investments in unproven technologies or where standards have yet to emerge.
In a time of economic uncertainty, the fact remains that there exists tremendous opportunity for established companies to dive into the deep end of the pool and reap the rewards – as much as $2 trillion in total return to shareholders.
Though it is true that advanced industries, and in particular, R&D, have been slower to digitize than many other sectors, there remains a sometimes-overlooked silver lining. By simply introducing technology and transparency into decision-making and operations, engineers and R&D staff are often more empowered, enjoy a better experience and can focus on what they like most -- building & designing.
Satya Rao is a partner in the in the Chicago office of McKinsey & Company. He holds a master’s degree in computer engineering and has directed integration of hardware and software of handsets. (Image source: McKinsey &Co.)
In an essay, we have examined the challenges and opportunities that come with tech-enabled transformations. Among them is that companies can take a piecemeal approach, leading to suboptimal outcomes. This occurs when they are adopting artificial intelligence, machine learning, cloud services and a host of other technologies on a case-by case basis, instead of selecting technologies to serve their strategy or meet specific goals. Success depends on a holistic approach to transformation. However, the opportunities and path to success is clear: Define your aspirations, link them to sources of business value, work out which technologies you will transform. Double down on that bet.
In 2017, we surveyed executives developing Internet of Things (IoT) solutions and found that half had been running customer pilots for two years and more than 25 percent ran those pilots even longer. This is what we call “pilot purgatory.”
Pilot Purgatory: How to Escape
Though our data demonstrates that “pilot purgatory” is a common occurrence, it doesn’t have to be. We’ve hatched an escape plan for pilot purgatory:
- Use closed-loop processes to generate customer insights
- Translate them into product features and services
- Rapidly deploy these elements with the customer
- Test the impact
- Repeat as necessary
These steps effectively describe what we call “design thinking.”
- Companies should find partners to share data and insights and create mutually-shared value, or what we call an “ecosystem”
- Understand your current situation and set a baseline. When doing so, be realistic about your starting point and digital maturity
- Take a step back and consider what you want to achieve. It can be easy for a company to get overwhelmed, so evaluate your whole business to see where technology could unlock the greatest value
- Be bold and transform. It would be easy to settle for incremental gain, but instead set a bold aspiration to ensure the changes you make don’t just reinforce the status quo
Unlocking Profit from Research & Development
Traditional approaches to R&D efficiency—peer benchmarks, lean engineering, trial and error—are producing diminishing returns and ceasing to confer competitive advantage. The fundamentals of tech-enabled R&D efficiency are a shift to agile iterative product-development cycles and the rapid deployment of digital- and analytics-based productivity techniques.
Consider a typical company where engineers use ten or more systems in a typical day’s work, ranging from timesheets, emails, and project plans to bills of materials and suppliers’ systems. By integrating data from all these disparate systems into a common structure, the company can use machine-learning algorithms to track metrics dynamically and extract powerful insights that provide a fact-based, granular guide to sources of value.
One aerospace and defense company applied advanced analytics to identify productivity drivers and metrics in its software engineering. It began by creating a data lake that combined data from a dozen or so sources, including the enterprise value-management system, software code tracking, timesheets, and Microsoft Exchange. Then it ran multivariate algorithms to identify factors that correlated to productivity metrics. It found, for instance, that replacing late-stage software testing with early-stage testing using automated scripting would improve productivity by five percent. Finally, the company created a business case and action plan to address target initiatives. This entire process was completed in just 16 weeks, thanks to a sprint-based approach that combined traditional engineering practices with advanced analytics. As a result, the company reduced software defects by 35 to 50 percent and increased engineering capacity by 20 percent.
Success in Five Steps
- Listen to your customers. They know what they want when they see it, even though they may not be able to articulate it in advance. Invest heavily in customer insights to identify pain points in the user experience, and pressure-test your new offerings with customers to ascertain what they are willing to pay for.
- Place big bets. It’s fine to fail fast, but avoid spreading your investment across too many ideas. Successful organizations prioritize a few big bets that get the lion’s share of management attention. Having identified your big bets, consider novel ways to organize around them. Some tech-enabled industrial companies use a VC-like governance structure with a digital unit reporting directly to a “digital board” comprising the CEO, CTO, and CFO. Such a structure ensures that funding is based on reaching milestones, that issues are resolved quickly, and that the core business stays focused on the core.
- Adopt agile product development. Set up small, autonomous, cross-functional teams that can get close to customers, fail fast, and pivot to the next opportunity. Traditional product development cycles are a recipe for failure, as they can’t keep up with advances in technology and data.
- Build out your ecosystem. Commercial as well as technological partnerships are essential to moving fast and scaling effectively. Building and maintaining a robust ecosystem of partners demands dedicated resources.
- Establish the right go-to-market capabilities. Selling tech-enabled products is nothing like selling traditional hardware. It requires knowledge of consultative selling, software bundling, and unfamiliar sales cycles and solution architectures. Expecting your traditional sales channels to convert customers quickly or bolting a digital sales group onto a traditional organization could spell disaster. Instead, develop a clear customer interaction model and overhaul your sales structure, processes, enablement strategies, and incentives.
Successful innovation relies not only on sound data and technology but on a deep understanding of how to use them to tap into new sources of value.
Though tech-enabled transformations in advanced industries are still in the early stages, companies have no time to lose. An early mover with the right strategy could not only reduce costs across the board, but leapfrog competitors. Smart companies can capture disproportionate value by gaining market share from peers or by being the first to respond to radical shifts in customer behavior.
For established companies, this is a critical juncture. Many companies will stick to their core values, protect market share and struggle. The few with vision and courage are taking the plunge into a future that can be at once profitable, provide measured growth and transformation.
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