The influx of data from new technologies is not just impacting the shop floor – it’s creating new standards for leadership, talent and culture that today’s manufacturing leaders need to embrace in order to thrive.
By Brenda Bazan
A perfect storm in business, much like at sea, happens when several negative or unpredictable factors come together at the same time. This era in manufacturing is experiencing a perfect storm of complexity and chaos driven by several factors – some predictable, some not so.
Perhaps the most predictable factor is data – lots and lots of data. It’s called Big Data, but it should be renamed Bigger Data because it only increases in volume every day. The rise of the Internet of Things (IoT) has generated more, different data in the past few years than has ever been produced in history.
We’ve all read the facts about the increasing amount of data created each year. Cisco Systems estimated that by the end of 2019, IoT alone would generate more than 500 zettabytes (a zettabyte is one sextillion bytes) a year. Bernard Marr, a best-selling author and futurist, predicts that there will be 200 billion IoT connected devices that are generating data in 2020.1 Autonomous, or self-driving cars are expected to generate and consume around 40 terabytes (a terabyte is one million bytes) of data every eight hours.2
And since a significant number of IoT devices are driving manufacturing production lines, the industry is the recipient of lots of data. For example, manufacturing companies now develop products that can call into service centers to signal their own repair order or driverless cars that not only understand the manufacturing processes associated, but also how to collect, manage, secure and benefit from the data collected.
Aside from handling the volume and analysis, data also has secondary effects on manufacturing companies. This article will explore how data is changing how manufacturing companies operate and leverage technology and setting off a ‘domino effect’ of changes to the future workforce, company culture, leadership and more.
As quickly as manufacturing companies want to embrace new data-driven technologies, they also have to contend with their existing technological ecosystem.
From Back Office Solutions to Data-Driven Technologies
As a result of the influx of data, manufacturers face huge challenges on three fronts: 1) ingesting large amounts of data, 2) comparing new data with historical, unconnected data and 3) the cloud. While the solutions to these data challenges depend specifically on the nature of each organization, their strategy and leadership, it’s fair to say that the sheer amount of data that’s being generated today is already exponentially larger than what current systems contain at manufacturing sites. Moving this amount of data from plant floor to an analytical engine is getting more difficult every day, and in many cases, manufacturers are faced with moving data long distances, like from a plant floor in China to a data center in Kentucky.
And when it comes to comparing production line numbers or inputs from an autonomous vehicle with information from an on-premise or cloud system, manufacturers often face substantial costs and time restraints. Their answer often is to transform their technology and redo the entire infrastructure to accommodate this new wave of data.
As quickly as manufacturing companies want to embrace new data-driven technologies, they also have to contend with their existing technological ecosystem. Today, there are hundreds of applications that could be applied to operations on the plant floor, with new ones coming to the market daily. For example, the rise of Manufacturing Execution Systems (MES) to track and document the transformation of raw materials to finished goods has given the industry a new framework and new software to manage. And that doesn’t even include front or back office solutions, such as PLM, CRM, ERP or HR, that should be connected to and integrated with the plant floor.
Managing applications effectively today requires thoughtful consideration and strategy because you need to ensure that critical data is transferred to new systems if old ones are eliminated. While new applications empower companies innovate in new ways, it doesn’t always eliminate the need to keep managing old systems and old data. Old applications remain and contribute heavily to the technical debt a company has and may also require upgrades or maintenance, which is another challenge in and of itself.
To begin with, data is the number one reason that most upgrades or system replacements fail. And, when the new system is installed, you must ensure that it is managed often alongside the old system for several months or years. That management includes handling at least one new release a year and in the case of cloud applications, updates or new releases can be daily. When cloud systems integrate with on-premise systems, managing this complex environment and making room for data-driven technologies can mean that manufacturers often need to hire more IT people than shop foremen.
Supplementing Talent with Technology
In recent years, technology has provided some solutions for talent in manufacturing. In other words, if companies can’t find or develop the right talent, technology is helping to pick up some of the slack or replacing the need for an employee altogether.
One example of how a technology solution uses data to improve processes for workers is Robotic Process Automation (RPA), which uses customized software bots to assist employees by performing rules-based tasks. Bots are autonomous programs that interact with computer systems or users to perform repetitive tasks. Think Excel macros – that’s the kind of “bot” that RPA creates. For back office processes, this might include accessing email and systems, performing calculations, and creating documents and reports. These bots not only help manufacturers streamline operations and remove human error, they also improve customer and employee experiences.
Deploying RPA bots doesn’t need to be a complex process or require technical expertise. Some RPA solutions include simple, drag-and-drop platforms that require little or no coding on the part of the manufacturer, which is a benefit because it takes the pressure off the IT department, allowing stakeholders from the business to handle it.
For example, at one major manufacturer of solar tracking equipment, data revealed that employing manual proof-of-delivery (PoD) processes was causing slow customer invoicing and high debtor days. As part of the PoD process, employees had to log in to transportation portals several times per day to track shipments, look for signed manifests that confirmed customers received their shipment and match those documents to customer orders. Only when this process was complete could the company invoice the customer.
The process was not only time-consuming, but it was prone to human error. Also, the repetitive nature of the process frustrated employees and distracted them from other responsibilities. By employing RPA technology, the employees became managers of the process, checking for appropriate outcomes instead of monitoring a tedious process. This provided the employees with a different, contextual view of the business and freed them up to tackle higher level tasks.
Employees are an important part of any business, but integral to manufacturing. Often tenured workers are the resources that know which part of the assembly line is most likely to fail and when and how to fix it. But the reality is that long-term employees are aging rapidly and that leads to another challenge.
Changing the Perception of Manufacturing Jobs
According to the National Association of Manufacturers, the workforce in manufacturing is aging rapidly. In 2019, 27% of these workers were already over the age of 55.3 In 2017, Deloitte found that workers in the manufacturing industry did not have the required skills. Their study concluded that approximately 600,000 jobs went unfilled and that was before the unemployment rate fell to unprecedented levels.4 Filling highly skilled production jobs is a manufacturer’s greatest challenge.
A key component of filling today’s manufacturing jobs is attracting millennial talent. This, too, has proven to be a challenge. The same Deloitte study revealed that only three in 10 Americans surveyed would encourage their children to pursue a manufacturing career even though 88% feel that manufacturing jobs require higher technical skillsets.
With experienced workers retiring and manufacturing failing to attract younger workers, companies must consider new ways of doing things. To start, manufacturers need to change the perception of their jobs as backbreaking labor in grimy working conditions. While high technology and cutting-edge entrepreneurship advertise celebrity status, modern working environments and strike-it-rich salaries, manufacturing still occupies the millennial mind space with 19th century assembly lines in dingy East Side tenements. In reality, manufacturing today can be more high-tech than high tech. Leaders should capitalize on millennials’ tech-driven tendencies by showing them opportunities where they can contribute to the cutting-edge technology solutions and strategies that are critical to the success of manufacturers.
Although public awareness of advancements in manufacturing appears to be growing, the Deloitte study also pointed out that executive priorities for what is critical to the future of manufacturing are not aligned. As with most organizations, leadership accounts for a large percentage of a company’s progress. In manufacturing, leadership must be involved in creating a company culture that embraces and executes on transformation for tomorrow.
Embracing the Manufacturing Culture Clash
Peter Drucker, the renowned management consultant, is famous for saying that “culture eats strategy for breakfast.” Despite recognizing this fact posited over a quarter of a century ago, most manufacturing organizations today are experiencing a clash of cultures. More than any other industry, manufacturing has suffered as the exodus to offshore efficiencies has slowed the growth of its share of employment in the United States. In fact, U.S. manufacturing employment has declined from 28% in 1960 to 8% in 2017. Today, the U.S. is the world’s second largest manufacturing country behind China.
Leaders who support and encourage data analysis create environments that can tackle the challenges of the next decade and beyond.
In contrast, process-driven Six Sigma (in all its variations), which focuses on very specific and closed-ended process issues that may be causing inefficiencies, has spawned another type of culture. But when both these mindsets are required for managing a successful manufacturing operation, they often clash when they must be combined to solve a challenge such as the redesign of a manufacturing line. Hence the rise of the Design for Manufacturing (DFM) approach which challenges companies to apply design thinking to product design and ensure that the design is easily manufactured.
This new way of approaching product design, manufacturing, and delivery can create schisms inside organizations and complicate progress. Many companies are still caught between knowing where they need to go in terms of innovation and sticking with the status quo. But to best manage this clash of cultures and prevent roadblocks to transformation, leaders should outline a clear value proposition for their approach to manufacturing and design, leaving the appropriate amount of flexibility for younger workers who are comfortable with the latest technologies.5
The Need for Data-Driven Leaders
When a perfect storm happens at sea, often the difference between life and death depends on the ship’s captain. This is no different in manufacturing. How to right the ship in a perfect storm takes leaders who have courage and conviction. They ensure that their strategy recognizes that the challenge is not technological, but a human issue.
Once leaders direct their attention and develop strategies that include and reward their employees, customers, and partners, manufacturers enter calmer waters and make greater progress.
Consider the Toyota factory in Georgetown, Kentucky, for example.6 Wil James, president of Toyota Motor Manufacturing who manages this plant, admits that “our automation ratio today is no higher than it was 15 years ago.”
“Machines are good for repetitive things,” James said, “but they can’t improve their own efficiency or the quality of their work. Only people can.” He added that Toyota has conducted internal studies comparing the time it took people and machines to assemble a car; over and over, human labor won.
Applying new technology to manufacturing also requires that leaders understand where technology ceases to be valuable and human effort is best applied.
Applying new technology to manufacturing also requires that leaders understand where technology ceases to be valuable and human effort is best applied. Though artificial intelligence (AI) hopes to one day mimic human problem solving, experts in this field agree that it may take decades for machines to apply creativity to manufacturing’s challenges. Applying AI in the most appropriate manner depends a lot on the data that the manufacturers collect and curate.
Executives who understand the data generated through IoT, as well as ordinary structured data collected on and by their customers, suppliers, and partners, are more likely to create cultures that are innovative and resilient in the face of today’s challenges. And those that understand that data is first an asset and then a revenue growth contributor can take greatest advantage of advanced analytics of the data they curate.
Advanced analytics refers to the application of statistics to business data in order to assess and improve practices and processes. In manufacturing, operations managers use advanced analytics to identify patterns and relationships among discrete process steps and inputs. Many global manufacturers have established analytics teams to help utilize real-time shop-floor data. They are taking previously isolated data sets, aggregating them, and analyzing them to reveal important insights.7 Leaders who support and encourage data analysis create environments that can tackle the challenges of the next decade and beyond.
Remembering that most of the challenges are human in nature, the greatest contribution that a leader can make to the transformation of manufacturing is in creating a diverse, innovative environment that rewards those who “think outside the box.”
Much has been written about creating an innovative mindset. Here are four very specific actions that manufacturing leaders can take.8
- Make innovation a priority. Put it at the top of your to-do list and embed it in your strategy.
- Tolerate, even encourage, failure. It’s a proven fact that most learning happens when you fail, not when you succeed. Looking at failures as lessons helps everyone feel more comfortable with taking risks.
- Organize to make innovation easier. Often, innovation is stifled by the number of organizational layers or the lack of collaboration between organizations. As a leader, you can help to minimize these issues.
- Encourage and reward innovation. Most companies reward innovation that works, but truly successful companies also encourage innovation that doesn’t. Take a careful look at what actions your company rewards. Also, look at teams that generate innovative ideas.
As a manufacturing leader, you set the direction and pace of innovation in your company. In today’s highly technical world, it’s easy to turn to technology for the answers to manufacturing problems. Recognizing that the bigger challenge is human can set you and your company apart from the competition and weather this perfect storm. M