EXECUTIVE BRIEF
Data-savvy manufacturers manage to thrive despite fierce competition
Charles Darwin famously theorized that natural evolution depends on the survival of the fittest. While this may hold true for species on the Galapagos Islands, in today’s treacherous automotive industry, the odds favor different traits. It’s the smartest who have the best odds of surviving challenging conditions, fierce competition, and treacherous supply chain battles for microchips. In the automotive industry, where pandemic-related disruption has been especially brutal, data-driven insights are critical to adapting, thriving, and outlasting shortages of raw materials. Business intelligence drives resilience and supports companies as they evolve to keep pace with innovation around electric vehicles (EV), zero emission goals, and greater sustainability.
Challenges persist
Manufacturers worldwide have had their endurance abilities tested in the wake of the COVID-19 pandemic. Global volatility, political unrest, and shortages of critical resources, like microchips, contribute to the disruption. Buying habits have drastically changed, with consumers demanding wow experiences, personalized products, value, and sustainability.
The outlook is mixed. The disrupted supply chain is slow to rebound, as rising fuel costs make land transportation exorbitant, and cross-ocean shipping routes are being redrawn to favor political allies and suppliers closer to the consumer. Every manufacturer producing high tech components requires microchips and must compete with other manufacturers to win contracts for the critical parts. Shortages are likely to persist for years until new foundries start producing and supply catches up with demand.
The digital framework
The recent market challenges have accelerated the need for technology and digitalization, forcing OEMS and suppliers to up-level their technology investment. McKinsey explains: “The pandemic’s disruptions have underscored that integrating advanced technologies better equips organizations to achieve operational excellence—the foundation of long-term resilience to and sustained competitive advantage.”
Adopting digital strategies helps automotive manufacturers cope with COVID-related challenges, from remote connectivity to product innovation and embracing EV innovation. Turning to technology supports agility and gives leaders a taste of just how powerful digitalization can be in streamlining activities and boosting efficiency. In a survey of more than 400 global manufacturing companies, 94% indicated that Industry 4.0 helped them to keep their operations running during the crisis, and 56% said the digital transformation they undertook was essential to their pandemic responses.
Now, manufacturers are ready for more advances as they begin to address the pent-up demand and begin building new partnerships to pursue new go-to-market models and sustainable vehicles.
Big moves required
With so many challenges and options, many manufacturers find themselves in the proverbial “sink or swim” situation where critical choices must be made and bold actions taken. Investment in digital technology is the first step on the forward path, as most tactics for modernization include automation, connectivity, and visibility—on comprehensive scales. Most experts agree that major process changes are required to remain competitive in this new era. Simple pivots or subtle shifts in current strategies are inadequate to compensate for the billions in losses experienced during the pandemic and as chip shortages and other supply chain problems continue.
Surrounded by such obstacles, how can manufacturing leaders know where to launch initiatives and how to best invest in strategies that will serve them long-term? Knee-jerk reactions and vast amounts of quick fixes can do more harm than good, wasting resources and frustrating stakeholders, including front-line workers who are worried about job security. Fortunately, AI-driven technology can help manufacturers make sense of the options and choose the paths best for them.
Making smart choices
Deloitte reminds manufacturers that optimism about recovery may be short-lived if certain basic threats aren’t addressed first. “As industrial production and capacity utilization surpassed (in late 2021) pre-pandemic levels…strong increases in new orders for all major subsectors signal growth…. However, optimism around revenue growth is held in check by caution from ongoing risks. Workforce shortages and supply chain instability are reducing operational efficiency and margins. Business agility can be critical for organizations seeking to operate through the turbulence from an unusually quick economic rebound—and to compete in the next growth period.” The report goes on to suggest manufacturing leaders should “look not only to defend against disruption but strengthen their offense.”
Consequently, business intelligence and data-driven insights are critical right now. Manufacturers must carefully choose the correct path—not the one with the least resistance, but the one with the most opportunities.
Build the data-savvy workforce
When preparing for future market demands, building a right-skilled workforce that understands the value of data management is a necessary first step. This creates allies to help convey the vision, build consensus among teams, and execute plans. Unfortunately, a severe shortage of skilled workers has plagued manufacturers for a decade with no easy solutions in sight.
Forrester reports in a recent study that 40% of manufacturing leaders say recruiting people with the necessary technical skills is a top challenge. Finding workers with data management skills is especially challenging. “As manufacturers drive toward becoming data-driven decision makers, they struggle to get their existing staff to believe data over instinct (35%). Even more concerning: 39% of decision makers’ organizations struggle to find and hire staff with the right skills to gather actionable insights from data,” Forrester says.
Despite the uphill journey, making the effort to recruit top talent will pay off, most experts agree. Investing in the IT workforce can become a true differentiator, laying the foundation for future innovation and problem solving. “This will need to consist of world-class recruiting and retention-related initiatives that promote inclusivity and a lifelong learning culture,” says Jerry Kurtz, executive VP, Insights & Data, of Capgemini Americas. “The market has never been more competitive for people with AI skills, and this trend is likely to continue for years to come. Such, strategic partnerships will also be key across organizations and industries,” he adds.
AI is now top priority
Once the team is assembled, strategies can be formulated. Breaking large AI-driven projects into phases will help make execution more manageable. Organizations will learn from experience, with many still in the early stages of exploration. As VentureBeat reports. “AI is a hot topic for businesses. AI capabilities are expanding the possibilities for how businesses approach real-time engagement with their customers, manage their operations, and ensure business continuity through the pandemic. As the technology advances, companies are finding new ways to innovate and expand.”
Adopting AI strategies is a top priority for many organizations. IEEE, the largest technical professional organization, recently released the results of a study in which 76% of enterprises say they plan to prioritize AI and machine learning initiatives over other IT goals. CIOs and other technology leaders are the most enthusiastic supporters with 95% of the CIOs saying that AI will drive the majority of innovation in the next one to five years. “Expect AI engagements to become larger, more strategically significant, and more mission-critical – with a focus on long-term scalability," the association suggests. Robotics, Natural Language Processing (NLP), virtual assistants, recommendation engines, and enhanced cyber security will be among the top use cases.
But AI is early on the maturity scale
Although data analytics and the use of AI have been identified as essentials, most companies still have a long journey ahead before mastering the maturity scale, says The Enterprisers Project. AI is a broad field with many possible elements. Knowing where and how to start requires some research.
Basic starting points include how to capture and store the data. Most manufacturers are still working on building a modern data pipeline. Most AI models require massive amounts of data, often from hundreds of sources, over an extended period, and in both structured and unstructured forms. Context is always critical, as is a format that allows for data integration and analysis in a meaningful way.
Safe storage is another requirement. The large amounts of data involved need reliable storage. A data lake in the cloud is the proven data storage solution, offering flexible capacity, security, and real-time access.