In today’s fast-paced manufacturing environment, improving productivity is at the top of every plant manager’s mind. Tracking Key Performance Indicators (KPIs) is vital to improving manufacturing performance. In the past, companies have relied on separate departments – think engineering, quality, finance and operations – with each gathering their own data and analyzing past performance. This can lead to outdated information and missing cross-functional correlations, making it difficult to implement meaningful change.
However, through automation and new digital technologies, companies can now harness data from across their operations to identify issues or recurring trends. This ultimately allows plant managers to make smarter, faster business decisions.
While every digital transformation journey is different based on each company’s specific goals, all contain four fundamental steps.
Steps in a Digital Transformation Journey
1. Access Data
Utilize sensors and control systems to collect information from key data sources such as connected machines
2. Unlock Insights:
Identify correlations and trends by integrating the data from all systems, aggregating this data from all devices, analyzing the sum of information, and visualizing the meaningful information via a dashboard
3. Decide on Next Steps:
View the data holistically, review options, analyze different outcomes, and determine the best solution
4. Take Action:
Adjust control systems, optimize processes, and report outcomes
How to Boost Productivity Through Emerging Technologies
The Industrial Internet of Things (IIoT) is already having a significant impact on industrial performance in two primary areas: machine performance management and operator efficiency. Technologies, previously limited by the cost of connection or implementation are now enabling the gathering of machine information that supports maintenance programs and decision systems for manufacturing operations. That means that unnecessary “routine” maintenance can be avoided, as can the neglect of equipment that subsequently fails.
Additionally, IIoT presents a great opportunity to bridge the skills gap and augment today’s workforce by putting real-time status and diagnostic information at their fingertips. Smart devices can provide simpler, easier to use, richer information to the operator, and make the plant user-centric, not just machine-centric.
As the deployment of IIoT technologies evolves, we will continue to see the convergence of operations and information technologies across the enterprise.
The operational technology of the factory floor will become tightly integrated with the information technology of upstream business systems (IT meets OT). Architectures will become flattened which will simplify the application and operation of both, independently of the automation hardware in place.
Some of the key benefits of the IIoT for manufacturing include:
- Interconnectivity for seamless, secure communication between sensors, machines, automation systems, business systems and people. These smooth interactions increase efficiencies by allowing more autonomous machine performance and streamlined manual processes.
- Operational visibility and remote access to the status of machines and their components allow plant managers to monitor systems and diagnose problems in real time before impacting machine availability, thereby, optimizing the yield.
- Predictive analytics for optimal planning of machine downtime for maintenance, reducing costs, and spare parts inventories.
Let’s look at some real-world examples in more detail.
Machine or Equipment Performance
A critical KPI in manufacturing is Overall Equipment Effectiveness (OEE). OEE is a measure of the utilization of a machine, or piece of equipment, based on the number of good parts produced versus its theoretical capacity. An OEE score of 100 percent represents perfect production: manufacturing only good parts, as fast as possible, with no downtime.
An OEE score of 85 percent is considered world-class for many manufacturers and is in most cases a long-term goal. Sixty percent is fairly typical, leaving substantial room for improvement.
Machine histories can be useful and informative, but if that’s all you have, you always end up reacting to old data and you never get ahead. Connecting high-priority assets to sensors on the Industrial Internet of Things (IIoT) and gathering live data tells you what OEE performance is like in real-time. Real-time machine condition-monitoring is key to improving OEE.
In order to ensure efficient operational processes throughout the factory floor, machine operators must quickly and easily determine the status of machines at any given time. The higher the visibility, the easier it is to identify and resolve problems and keep operations running smoothly. Traditional tower lights seen on many factory floors provide visibility wherever they are physically seen. However, production lines and machines equipped with wireless communication capabilities take real-time visibility to the next level. They can proactively send immediate alerts to various systems to identify operational problems regardless of whether the machine operator is physically present or not.
A huge source of capital expense lies within the machines that manufacturers rely on every day. The associated downtime and maintenance cost of an unplanned machine issue can be debilitating. Properly managing machine lifecycles and developing a proactive maintenance strategy is key to keeping productivity high and the opportunity for surprises low.
A power tool manufacturing line was suffering from lost output, high rework, and high waste at its assembly stations. The operators and maintenance team had tried to resolve the problem for two years with little success. They resorted to slowing the line down, restricting the output.
The manufacturer turned to a wireless connectivity strategy and the deployment of a real-time location-based system in the form of Wi-Fi radio frequency identification tags. The tags, coupled with IIoT sensors integrated with the existing Programmable Logic Controller provided the plant floor supervisors with visibility to every step of the production process, giving them the ability to adjust the speed of the processes and to monitor how effectively the employees completed their respective tasks.
As a result, the plant achieved a 10 percent increase in labor efficiency and better use of critical resources, which improved utilization rates from 80 percent to 90 percent. This also resulted in quality improvements with first-time pass defects reduced by 16 percent.
IIoT has applicability far beyond the shop floor—for example, from the moment produce is picked on the farm all the way through distribution channels, collecting data on everything from temperature to ripeness, to how long product remains in transport, can help manufacturers optimize processes across their entire lifecycle. This ensures product quality and is a fundamental tenant of the Farm to Fork (F2F) Initiative.
The food and beverage business is one that provides huge potential for demonstration of the Return on Investment (ROI) for various IIoT technologies to track authentication of the origin of food, or by reducing waste and optimizing logistics costs, and increasing quality of goods to the consumer.
Not only is quality important in terms of productivity management, but it also affects customer perception. In today’s highly competitive marketplace, maintaining a positive reputation for delivering quality products is a major competitive differentiator and can help companies maintain a competitive edge. Brand recognition is paramount and must be protected.
Though food and beverage manufacturers have been leveraging sensors to collect data for years, they’ve historically been limited to what they could achieve given the high cost associated with deployment. Consider a manufacturer of baked goods that utilizes IIoT to automatically adjust oven temperature to match the characteristics of specific grains from different suppliers to ensure quality remains consistent. This is just a simple example in which digital transformation can be leveraged for product track and trace quality assurance through a product’s lifecycle.
Another example involves a major popcorn manufacturer who lacked visibility to raw material usage. At one point, the manufacturer thought they had misplaced 12,000 lbs. of salt. In reality, too much salt was being injected into the popcorn resulting in poor quality and damaged brand recognition. The deployment of the appropriate IIoT technologies to connect processes, collect data, and correlate the relationships between disparate systems and data sets would have eliminated the waste and assured that high-quality standards were maintained.
OEE is best used to measure the performance and analyze the losses on a particular piece of equipment so that they can be understood and addressed.
The idea is to measure OEE on your constraint piece of equipment and work to elevate that. The first loss related to machine capacity is the loss of availability (i.e., the time where the machine could have run but did not). Examples of availability losses are:
- Planned maintenance
- Breakdowns and unplanned repairs
- Lack of material
In addition to real-time status monitoring, IIoT technologies can also be used to analyze large sets of data, as well as real-time streaming data from machines and sensors to predict future events. One application of predictive analytics is for maintenance purposes. Operational analysis and predictive models allow manufacturers to take a proactive role in maintaining their critical assets.
Predictive maintenance techniques offer substantial cost savings over routine or time-based preventive maintenance because tasks are performed when warranted and completed proactively
A large chocolate milk manufacturer had a critical 500HP motor on a sugar dissolver that was critical to the process. The manufacturer weighed the cost of maintaining and replacing a spare motor in the case of an outage, coupled with the lost productivity, versus the cost of a predictive maintenance program in determining how best to maximize the availability of the dissolver. By analyzing high-frequency streaming vibration data from accelerometer sensors mounted on the rotating equipment and comparing the vibration signatures against known patterns, the production manager was able to predict imminent failures and avoid the high costs of unforeseen breakdown. This maximized the process’ availability for maximum production.
Preparing for the Digital Transformation Journey
In summation, the benefits of IIoT technologies are very real. Manufacturers that are not adopting IIoT for the monitoring and control of critical assets will fail to reap the rewards of the Digital Transformation Journey.
Although the benefits seem obvious, it can be challenging to know where to begin and how to use these technologies to their fullest extent. Below are a few questions to help manufacturers prepare for the journey:
- What are the inefficiencies in your manufacturing operations?
- What type of data would help you overcome these inefficiencies?
- What are the high-value critical tools in your factory and how are they maintained today?
- What communication processes need to be in place to utilize the data in a meaningful way?
- Who are other stakeholders that can benefit from having access to your production data?
Answering these questions can help manufacturing companies identify the IIoT technologies to meet their immediate business needs and start taking advantage of the long-term benefits of Digital Transformation.