Manufacturing in 2020
Manufacturing proves the adage that the more things change, the more they stay the same. At the start of the 21st Century, the industry was focused on the 4th Industrial Revolution. This revolution would combine the digital, biological, and physical innovations of the 20th century into more agile companies. The goal was to develop self-sustaining systems that were flexible enough to optimize performance, learn from existing conditions, and adapt to change.
Twenty years later, manufacturers are still struggling to create the smart factories of the 4th Industrial Revolution. For most manufacturers, converting existing facilities into smart factories takes time. Companies have to balance the need for new technologies with business needs and budget constraints. They also need a plan that puts the most crucial technologies in place first. From there, manufacturers can implement solutions that move them along the continuum of digital transformation, leading to an industry that is secure, agile, and sustainable.
Adopting New Manufacturing Technologies
Smart factories cannot exist without technology. Forbes predicted that technology would be the dominant trend in manufacturing in 2020. Despite recent events, manufacturers must still look to technology for viable solutions, because the fundamentals have not changed. Companies have to deliver products faster while maintaining product quality and reducing costs if they hope to survive. Three technologies that can help create smarter factories include:
- Industrial Internet of Things
- AI and Machine Learning
- Predictive Maintenance
Change is inevitable. The challenge is to adapt, so factories can be more proactive and predictive to avoid operational downtime and reduced productivity. Hasn’t that always been the challenge? To provide quality products at the lowest price to meet market demand.
Industrial Internet of Things (IIoT)
The IIot is the internet of things (IoT) in an industrial or manufacturing environment. The devices in both instances are anything with an on/off switch that can collect and transmit data. The information is then sent over an IP-enabled network to a central location. Once there, the data is manipulated and stored.
For example, Airbus uses RFID technology to track parts and tools in the factory. Engineers can locate their tools and parts from a phone or tablet rather than searching the factory floor. Smart tools can tell workers if the torque is correct for the intended use or when maintenance is due. Airbus is “looking at ways to improve products . . . and to reduce costs and improve production capability.”
The MPI Group found that nearly 70% of manufacturers believe the IIoT increased profitability. Using the collected data manufacturers gained actionable insights such as:
- Field service technicians can identify potential problems in customer equipment using the information received from various sensors and other endpoints. Potential failures are addressed before they occur. This leads to better customer service.
- With IIoT, products can be tracked throughout the supply chain. Personnel can be notified if a problem occurs, such as an in-transit delay or a damaged shipment, so that they can correct the problem as quickly as possible.
- Sensors on the factory floor can provide environmental data. This helps engineers make adjustments to ensure optimum operating conditions. This, in turn, extends the life of critical equipment.
As manufacturers incorporate information gained from IIoT devices into their daily operations, they will gain valuable insights into how to improve customer service and expedite decision-making.
AI and Machine Learning
For manufacturers, improving shop floor productivity has been one of the leading growth strategies for years. Companies invest in machine learning platforms because they improve production yields and product quality. According to a recent survey, machine learning has:
- Reduced unplanned downtime by 15%
- Reduced maintenance costs by 30%
- Increased production throughput by 20%
- Delivered up to a 35% increase in product quality
50% of companies that embrace AI over the next five to seven years will have the potential to double their cash flow.
Big data is the primary output of machine learning platforms, but the data must be synthesized if it is to be used. With improvements in computing capacity, it is easier to process vast amounts of data in less time. Better algorithms also lead to more accurate data mining and predictive analytics. Technological advances such as 5G wireless networks make moving volumes of data more practicable, making for successful implementations.
One area where machine learning can help manufacturing is pricing. In the past, finding the right price felt like throwing darts at a dartboard. With machine learning, manufacturers can replace the dartboard with data from multiple sources to find the perfect price. As Walmart discovered, the weather can play a significant role in what consumers are willing to pay for an item. By combining weather data with store data, Walmart was able to identify a pattern in its customers’ buying habits and adjust prices accordingly.
Manufacturers have better things to worry about than uptime, which is where smart factories come in. Smart factories have the technology to deliver data that makes predictive analytics work. They use device-level data to predict possible equipment or system failures, so downtime doesn’t happen.
Predictive maintenance uses the data from IIoT devices attached to equipment and the factory floor. Algorithms process the volumes of environmental and operational data to determine when to conduct maintenance. The technology can even predict when a system may fail, so the potential points of failure can be addressed.
Past studies have estimated that a predictive maintenance program can save a company between 8% and 12% over preventative-only programs. In the right environment, a predictive program could result in aa much as a 30% to 40% savings. These studies determined that manufacturers could realize the following:
- 70% to 75% reduction in breakdowns
- 35% to 45% reduction in downtime
- 25% to 30% reduction in maintenance costs
- 20% to 25% increase in production
Based on these percentages, the survey projected a return-on-investment of ten times the original expenditure. To some, that ROI may seem impossible, but the costs related to unexpected equipment failure are staggering. The cost of downtime is estimated at around $250,000 per hour. This includes the cost for discovery, containment, and recovery from failure as well as the costs associated with lost productivity and supply chain disruption. It doesn’t cover the cost to repair or replace the failed equipment.
Since about 80% of companies have experienced unexpected downtime in the last year, manufacturers should plan for that downtime in the next 12 months. Given that the average downtime is estimated to last four hours, manufacturers need to budget at least $1 million for annual downtime expenses. Predictive maintenance technology helps dramatically reduce unexpected downtime. This can save a manufacturer a minimum of $1 million per year.
A Smart Future for Manufacturing
Manufacturing lags behind other industries when it comes to digital transformation, but to remain competitive, manufacturers must invest in technologies that will move them towards smart factories. Specifically, manufacturing needs to:
- Understand and embrace evolving technologies that include advanced analytical tools to increase business value.
- Identify the technologies that enable them to achieve agility, efficiency, and quality for improved customer experiences.
- Use the insights that technology provides to reduce time and material costs, optimize resources, and engage customers, suppliers, and employees.
The industry needs to look beyond the short-term costs to the long-term benefits of a digital transformation. By ignoring technologies that make a smart factory, manufacturers are turning away from:
- Increased profits that 79% of competitors will realize
- Potential increases in cash flow of 50% in the next five to seven years
- Reduced downtime that costs about $250,000 per hour
The farther down the continuum a company moves, the more it will realize the potential of a smart factory. It will understand how the connectivity of the IIoT optimizes operations. It will operate more reliably with automated workflows and improved tracking and scheduling. In addition, it will increase yield, uptime, and quality while reducing costs and waste.
Real-time data is transformed into actionable insights. These insights then lead to faster decision-making. Predictive management analyzes data to decrease downtime or to identify problems throughout the enterprise before they impact the bottom line. With a smart factory, manufacturing becomes an agile enterprise that can transform itself to meet the ever-changing needs of the customer.
The closer manufacturers come to a digital transformation, the closer they are to realizing the vision of the 4th Industrial Revolution. Manufacturing becomes an enterprise that is data-driven, predictive, and easily reconfigurable. Its supply chain is responsive, responsible, and transparent, so more accurate decisions are possible.
The workforce is cross-functional, supported by digital technologies that extend from design to delivery. Most of all, the manufacturing industry is ready to adapt to new challenges and business opportunities to achieve sustained growth. By adopting digital technology, your manufacturing factory will continue to thrive.