Since the beginning of the Industrial Revolution, manufacturing has been the force that has pushed the industrial and societal transformations forward. Today, we’re amid another industrial revolution, as a new generation of sophisticated technologies is transforming manufacturing into a highly connected, intelligent, and ultimately, more productive industry. The manpowered shop floor of the past is being replaced by smart manufacturing facilities where tech-savvy workers, aided by intelligent robots, are creating the products of the future.
The following will explore seven emerging trends in manufacturing that we believe will help empower manufacturers to design more intelligent operations and increase the speed of doing business.
The 7 Key Manufacturing Trends:
- Businesses adjust to an evolving workforce: A new generation enters the workforce
- The growth of XaaS: Manufacturers evolve their business models
- Intelligent manufacturing: Connected intelligent systems make manufacturing smarter
- Living in the era of uncertainty: Uncertainty puts strain on businesses
- Increasing complexity of compliance: Maintaining compliance with ever-changing regulations
- Manufacturing technology evolves: New technologies are revolutionizing manufacturing
- Information and Operation Technologies converge: IT systems merge with operational technologies
Businesses adjust to an evolving workforce
Leaders in the manufacturing sector are aware of the impact that the aging workforce may have on their businesses in the years to come. Most manufacturing HR professionals characterize the impending retirement of aging employees over the next two decades as either potentially or problematic for their organization.
Not too long ago, the business world was all aflutter about how to deal with Millennials in the workplace, trying to predict what to expect from this new and seemingly exotic generation. Fast forward to 2019, and the oldest millennials are already approaching mid-career, with many assuming management roles. And with millennials taking on higher levels of responsibility earlier in their careers than was common in the past, they are increasingly in the position of managing employees older than themselves.
As a group, Millennials are typically characterized as tech-savvy, entrepreneurial, collaborative, and valuing of work-life balance, and this is impacting their approach to management. In practice, it means that they are likely to embrace the use of workplace communication and collaboration technologies to foster conversation and teamwork and that they promote a flatter hierarchy in the office, embracing good ideas from wherever they originate. And because the current IT environment makes it easier than ever for employees to work whenever and wherever they want, millennial managers are often more flexible about letting employees manage their personal matters as desired, so long as they are abreast with their work.
Looking to the next generation, the leading edge of Gen Z (also known as iGen) has just begun to enter the workforce. As they do, their beliefs, attitudes, and habits will shape how businesses operate and redefine how managers must lead in order to be successful.
The growth of XaaS
Millennials, troubled by high unemployment, low wages, and high debt, have quickly embraced new business models that offer them the latest products with greater flexibility and lower costs. In today’s world, startups have led the way with these new innovations, but manufacturers—either through acquisitions or internal development—are beginning to evolve their business models to the needs of the modern consumer.
As cloud computing becomes more ubiquitous, anything as a Service (XaaS) business models are also becoming more popular. The principle behind XaaS is that businesses can provide better, more cost-effective solutions to customers via subscriptions or pay-as-you-go models than via traditional software licensing models. The most commonly known XaaS model is Software as a Service (SaaS), which provides individual software applications and services through the cloud; however, Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) models have also gained traction as a way for technology companies to expand their footprint.
While XaaS has generally referred to cloud computing, it is gradually being used to define all service-based business models, from Manufacturing as a Service and Product as a Service to Transportation as a Service (Uber and Lyft) and Shopping as a Service (Trunk Club and Stitch Fix). Regardless of what you call it, it’s clear that customers’ needs are evolving, and businesses must adapt accordingly.
Manufacturing becomes intelligent
Intelligent manufacturing combines self-monitored manufacturing processes and machines, automated quality assurance of final products, and insights from outside the manufacturing process. In this new model for connected manufacturing, AI-enhanced computers are able to detect and report on physical processes happening in the real world and make human-like decisions in real time, sometimes referred to as a “cyber-physical production system.” And cloud-based monitoring and management enable up-to-the-minute intelligence on asset function and health, facilitating predictive maintenance and servicing to avoid breakdowns and associated downtime.
Intelligent manufacturing is not only about data, it is also about using data to make automated decisions, predictions, and real-time optimizations across the end-to-end value chain. As with earlier waves of the Industrial Revolution, Industry 4.0 promises to dramatically reshape how we make and deliver goods. This technology is applied to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed—all while making manufacturing more flexible.
Living in the era of uncertainty
Over the last few months, a string of major regulatory changes has been initiated and enacted. From GDPR to tariffs, these regulations span across a wide range of disciplines and touch nearly every business. As business leaders adapt to comply with the latest regulations, they remain concerned over the impact of additional pending regulations that could upend their operations.
From worker safety rules and emissions policies to net neutrality, tariffs, and subsidies, manufacturers across all industries are facing a great deal of regulatory uncertainty and flux. While we are currently in a period of regulatory easing, longer-term macro-trends suggest movement towards higher labor and environmental standards.
As such, manufacturers must not only grapple with the operational and financial impact of these changing regulations; they must also weigh the impact of these changes on other areas of their business as they develop both their short- and long-term strategies. These include the effects of lower worker safety standards on talent retention and healthcare costs and customer perceptions of products that are environmentally damaging.
Increasing complexity of compliance
From cybersecurity to compliance, manufacturers must address a wide range of threats to their business. Data protection and data privacy compliance are huge concerns for today’s business leaders. As many companies struggle with managing and securing their customers’ data, regulators are now making moves to empower consumers and ensure the privacy of this data.
Intelligent systems can be used to analyze large amounts of data and detect anomalies. In manufacturing, this may be used to help identify faulty products, to predict when a machine will need maintenance, or to detect potential safety issues in and around a factory. These tools are also being used to help mitigate risk by ensuring regulatory compliance and improving operations, flagging abnormal changes or anomalies for further investigation.
Reinforcing accountability of companies and public organizations that process personal data, provide increased clarity of responsibility in ensuring compliance.
Manufacturing technology evolves
A digital twin is exactly what it sounds like: a digital replica (simulation) of a real-world system. By merging data with artificial intelligence, machine learning, and software analytics, digital twins update and modify along with their physical counterparts, almost in real time. Digital twins can be made of to mirror complex pieces of machinery, predicting how they will respond to specific scenarios. They also permit manufacturers to have quicker, less expensive R&D cycles, create safer, higher-quality products, and eventually facilitate better decision-making.
Digital twins are particularly useful for large, complicated parts—such as jet engines, an incredibly complex piece of technology. Rotating blades in a jet engine operate in temperatures up to 3,000° F. (Most metals melt between 2,000–2,500° F.) Even when the right materials are used to solve for these challenges, the reality is that jet engines still require regular maintenance to protect them from ongoing wear and tear. However, the maintenance schedule for each engine is not necessarily the same; various factors such as airport conditions, the number of people on a flight, and a pilot’s flying style all impact how quickly an engine degrades.
Customization is another potential benefit of digital twins. Growing number of customers expect to be able to customize and personalize products to meet their own specifications. In the past, however, incorporating customer input into the design process has been cumbersome, time-consuming, and expensive. Digital twins allow product designers to quickly and easily test out different product variations, seeing how they would work in the “real world.” Collected usage data from IoT-enabled products also allows designers to improve upon future iterations.
Information and Operation Technologies converge
In the past, the management of industrial technology in manufacturing has been divided between information technology (IT) and operational technology (OT). Where IT provided the technology support for management and back office, OT monitored and controlled the machinery.
For the modern manufacturer, data is no longer just the purview of IT; from supply chain management to the operations floor, data is now ubiquitous across the organization. As data becomes integrated across the organization, IT and OT can no longer function independently and, as a result, are converging.
This IT/OT convergence enable smart factories, which were not possible before. With the combination of IT and OT data, business leaders get to view live dashboards that provide visibility across all parts of the organization. Industrial IoT systems can communicate to detect unbalanced load flows and automatically make corrections to prevent outages. Intelligent machines can recognize faulty parts and select new assets to restore production. And with integrated controls, production management systems, and supply chain management systems that are integrated with other IT systems, manufacturers are able to intelligently route orders and automate work streams.
The world is changing and so is manufacturing. In addition to the trends covered in this blog, many other changes, challenges, and new technologies are impacting manufacturing, including value chain execution efficiency demands, security challenges, smart sensors, wearables, and risk management. As manufacturing companies of the past turn into the advanced manufacturing businesses of the future, industry leaders must leverage technology to help bridge the gap, improve safety and operations, deliver greater transparency, and bring better products and experiences.