In earlier articles, we have shown how digital and advanced analytics (DnA) technologies have significant potential to improve manufacturing operations in heavy industry. We have explained that to capture that potential at scale, companies need a systematic approach to identify, develop, and roll out the technologies and approaches that offer the most value to them. And we have shown how the best approach for a given organization will depend on its current level of digital maturity, its goals, and on the nature and distribution of those opportunities.
In this article we turn our attention to four critical enablers: talent, data, technology, and agile delivery. Companies need these enablers in some form for every digital initiative they run, and together they comprise an engine to power any heavy manufacturer’s successful DnA transformation. Carefully thinking through the development of digital muscles helps an organization avoid the need to repeat work or reinvent the wheel, aids the efficient use of resources, and promotes the adoption of standardized approaches that are easier to scale, replicate, and sustain.
Let’s look at each in turn:
Digital and analytics projects are skill-intensive activities. Recent advances in software and hardware have done much to improve the accessibility and usability of new technologies, but their successful application still requires people who understand the capabilities and limitations of digital approaches, and who know how to get the best out of the available tools.
In heavy industrial manufacturing, digital projects require new capabilities—and often new roles—in three areas of the business. They need people with expertise in the company’s products and production processes. They need technology specialists with expertise in areas such as software development, robotics, and automation. And they need digital specialists who can run agile projects or design an effective user experience.
Critically, projects also require people whose skills bridge these different groups. Data engineers develop more efficient information technology systems such as databases, fast data processing, or new and more reliable data sources. Data scientists use those systems to unlock new insights or knowledge from the data by developing analytical techniques and efficient algorithms. Translators frame business problems in a way that digital specialists understand, and use their domain knowledge to evaluate and continuously refine the resulting DnA solutions (Exhibit 1).
With the exception of tactical interventions, where most of the necessary talent may be provided by the external supplier contracted to deliver the project, talent can be a critical roadblock in digital projects. And it won’t be solved by the market alone. With a wave of digitization underway across industries, skilled data scientists and other technical specialists are in short supply globally. Furthermore, becoming much more efficient in those roles requires an in-depth understanding of manufacturing processes and other company-specific knowledge, alongside digital and analytics expertise.
While some external hiring is almost always necessary to fill capability gaps and kick-start digital programs, many critical new skills are best developed in-house (Exhibit 2). Homegrown capability-building efforts can’t meet an organization’s demand for PhD-trained data scientists, but they can produce large numbers of skilled and competent digital practitioners, who are essential for the application of new tools at scale. Heavy-industry manufacturers are well positioned in this regard, since their existing workforce is already technology savvy. For them, the shift to digital is a natural step: for example, an automation engineer already understands the building blocks of robotics technology.
