Technology has always driven manufacturing growth — from the steam engine to moving assembly lines. But some are slow in adopting the latest digital technology that is often referred to as the fourth industrial revolution. Perhaps it’s because digital technology seems less tangible; you can’t watch its gears turning or see it cutting threads into a part. In the past, industrial companies considered their equipment a key differentiator, but manufacturers that fail to adopt the latest software innovations and various platforms will inevitably fall behind.
The competitive advantage that today’s technology offers manufacturers ranges from improving workflows and efficiencies across departments to increasing production and speed to market. The latest technology is expanding on initiatives to improve worker efficiencies and also focusing on increasing mechanical efficiencies with machine learning.
Technology helps manufacturers address the challenges of rising production costs and a lack of skilled labor by combining the optimal power of data, people, and equipment to produce better outcomes. Unlike trends that can come and go, the implementation of technology, the Industrial Internet of Things (IIoT), and machine learning will only increase in coming years.
When people talk about disruption in business, it often centers around processes. One of the greatest impediments to adopting new technology, however, is the need for a disruption in mindsets.
Implementing technology solutions that put an emphasis on metrics, workflows, machine learning, and integrating data across multiple departments within an organization can be met with resistance. The heightened transparency will mean more accountability, and there are those who may need to learn new skill sets or shift duties because of infrastructure changes. That’s why having internal advocates who are able to cast a vision for how technology can catapult an organization and its workforce to new heights of success is imperative.
The world is more connected than ever through various devices, sensors, and online platforms. It if has an “on” switch, chances are it is or will eventually be connected to the online world, as is evidenced by many smart devices becoming commonplace in everyday life.
“Manufacturers are expected to increase their spending on IIoT initiatives by 10% in 2018. ”
Nowhere is the power of the IoT more evident than for manufacturers, which demonstrates why they’re expected to increase their spending on IIoT initiatives by 10% in 2018.
A network of connected devices within an organization provides advanced analytics that help monitor systems, analyze workflows, and connect people in ways that were unthinkable only a decade ago. These insights help companies make faster, more agile business decisions that drive growth, so much so that 46% of the global economy is expected to benefit from the IIoT.
Enterprise Resource Planning (ERP) software is an integral part of many progressive manufacturing operations, creating a centralized view of production, machines, supply chains, logistics teams, and customers, providing real-time data to reveal additional capacity and drive outcomes. In the future, even more transparency will be possible with the power of the IIoT and ERP innovations, further breaking down barriers between manufacturers, suppliers, and end customers.
Machine Learning and Predictive Maintenance
Human behaviors and productivity can vary from one day to the next, and worker efficiencies can only be maximized to a certain extent. Machines, however, can produce predictable outcomes every time and, because of the advent of machine learning, they can become more agile and efficient over time.
Many machine learning algorithms are iterative and have the ability to adapt continually. For example, some manufacturers need to track and adapt pricing based on demand, material costs, seasonal adjustments, and other factors. Machine learning analyzes historical data related to these factors to predict demand for specific parts or products, allowing some manufacturers to reduce stock-outs, forecast accuracy, and increase production capacity by up to 20%.
“Machine learning can predict demand for specific parts or products, allowing some manufacturers to reduce stock-outs, forecast accuracy, and increase production capacity by up to 20%.”
Machine learning is also allowing for predictive analytics and the ability to prevent equipment failure. Sensors that monitor everything from temperature fluctuations to vibration help to identify variances that may indicate a potential breakdown and send out alerts or automatically adjust to mitigate risks based on set tolerances.
Additionally, newer innovations are helping manufacturers increase equipment uptime without increasing labor by leveraging the full capabilities of existing equipment and those who operate it. Many manufacturers believe they’ve exhausted the potential capacity of their equipment when, in reality, robust machine monitoring often reveals the potential to increase a machine’s capacity by as much as 15%, potentially eliminating the need for expensive capital purchases of new machines.
There are significant risks of taking a wait-and-see approach to technology and conducting “business as usual.” Worker morale and engagement will drop, leading to more turnover in an already tight labor market. Competitors focused on continuous improvement will outperform you and be able to offer higher quality products at lower prices. They’ll also be able to get products to market faster and take away your market share. Failure to adopt technology will quickly lead to your organization becoming marginalized and having to spend time, effort, and considerable resources trying to play catch-up.
American manufacturing must embrace the latest technology and innovation in order to remain competitive in their respective industries. To discuss how the latest technology innovations can be implemented in your operations, reach out to Wipfli for a no-cost consultation