The term “smart factory” (a.k.a. "digital factory" or "intelligent factory") was initially coined at the Hanover Fair in 2011. However, the ideas and practices known as "smart manufacturing" today have been developing for decades.
The same essential components of a traditional manufacturing plant apply to a smart factory as well. What makes a smart factory “smart” is its high level of digitalization in controlling machinery and production processes. It utilizes sensors, artificial intelligence (AI), machine learning (ML), cloud computing, and other Industrial Internet of Things (IIoT) technologies to enable real-time data gathering, transfer, and analysis.
When thinking of the smart factory, many picture a clean and highly efficient operation where all things work in unison. Downtime, defects, and other production disruptors are minimal. It's a manufacturing environment where plant and IT leadership plan and execute based on real-time data.
For most manufacturers, the experience is anything but perfect harmony. The questions then become: What’s possible? And what’s realistic?
Manufacturing leaders recognize that ignoring the smart factory opportunity (maintaining the status quo) will only leave them in the rearview mirrors of their competition. On the flip side, the attempt to transform the entire plant through one big technology push is where most forward-thinking manufacturers get off course.
The rise of the smart factory represents significant opportunities for gains in efficiency and productivity, but it must be approached pragmatically. More on this below.
Smart manufacturing refers to factory operations where digital technologies are applied to connect machines, people, and processes together. The ultimate goal is to produce more efficiently. This combination of hardware, cloud computing, and big data should provide a clearer picture of where opportunities exist for improvement.
A smart factory, on the other hand, is simply a term describing a facility that enables smart manufacturing operations. It incorporates hardware and software elements into a cyber-physical system.
Smart factory and smart manufacturing fall under the umbrella of Industry 4.0. They're examples of digital transformation of the manufacturing environment.
Broadly speaking, the benefits of a smart factory include improvements in efficiency, quality, safety, and cost reduction. Ultimately, these benefits should lead to increased competitiveness and profitability in the marketplace.
Common benefits can be broken into four categories:
By connecting assets such as machines and facilities through IIoT devices, data can be collected and analyzed to reveal asset performance issues. As the most costly issues are identified and resolved, plants can reduce machine downtime and optimize production capacity.
However, it’s important to understand that more data doesn't automatically translate to greater efficiency and productivity.
Data must be pulled into a smart manufacturing system, such as L2L’s manufacturing operations platform. The ability to view the data and get to the root of the most costly issues is critical to realizing the desired efficiency gains.
There are a variety of causes that lead to poor quality, including environmental, machine, and human error. The key is detecting these issues early and often so they can be resolved before they bloom into bigger problems. Catching and resolving issues early typically improves scrap rates, lead times, and yield.
Better quality means fewer defects. In industries like automotive, this can mean avoiding devastating product recalls and potential tragedies for customers.
As processes are optimized, the efficiency gains impact the bottom line of the factory. Even if processes or workflows aren't machine-related, they still represent the potential for production disruption when issues occur.
Smart technology enables visibility and issue prioritization. Plant workers can isolate and solve problems that have the highest negative impact on production. This clarity and focus provide the direction. When coupled with responsive action, it translates into improvements and lower operational costs.
The smart factory unlocks the opportunity for the workforce to raise its level of contribution. As automation and AI play a larger role on the plant floor, it reduces the need for humans to perform menial, repetitive, and labor-intensive tasks. It also increases the need for higher-level data analysis and decision-making, even at the lowest levels.
Smart factory technology provides access to more and better real-time data on the shop floor. Subsequently, it empowers the machine operators to identify, prioritize, and resolve issues that can disrupt production performance.
ADAC Automotive is a supplier of engineered products to the automotive industry. Historically, the company's process recording mechanisms were painstakingly manual. It relied on paper-based systems that wasted time and gave limited real-time visibility into its plant floor operations. ADAC lacked a clear picture of how its plants were operating at any given time. When something broke, there was no connected way to notify, triage, and fix the issue.
“Machine downtime has a large impact on direct and indirect labor variance and lost production throughput, so it was essential ADAC make a shift to a smart factory concept to maximize machine availability.” – Brent Warren, Director of Assembly Operations
ADAC used L2L’s connected manufacturing operations platform to unify its data, empower its workforce, and increase efficiency. The team started with small, highly measurable projects and scaled up as they found success. ADAC was able to fully integrate assembly assets and molding machines while automatically reporting descriptive machine downtime events into L2L. The company now has a standard real-time process for monitoring and responding to these issues quickly on over 200 production lines in four Michigan-based facilities. This smart factory platform has enabled ADAC to embrace cycle-based preventative maintenance and increase machine utilization.
Results after 12 months:
Siemens Electronics Works Amberg (EWA) is a Siemens facility that produces circuit boards, controllers, and other electrical devices. With 17 million components produced per year, the EWA utilizes an equally impressive level of technology.
For example, the facility's production equipment employs artificial intelligence in analyzing data picked up by sensors. This system uses an AI-based algorithm that automatically evaluates the quality of soldered portions of a circuit board.
Other applications of smart manufacturing are evident in the facility's warning systems. Anomalies in the operating conditions of particular machines can predict downtime and failure events. Through the cloud, this information is easily disseminated to relevant plant operators, allowing them to respond in a timely manner.
Cloud computing leverages internet connections to access web-based software applications that are hosted on external servers. This allows manufacturers to deploy SaaS (Software as a Service) applications at a fraction of the cost of on-premise solutions that require servers and other hardware to deploy. Due to macro forces, the adoption of cloud-based systems has been accelerated in recent years. According to Gartner, “the worldwide infrastructure as a service (IaaS) market grew by 40.7% in 2020.” This trend certainly holds true in the manufacturing sector and will continue to accelerate in the future.
A few benefits of cloud technology for manufacturers include:
Sensors are an important part of a smart factory because they allow for real-time monitoring of potential disruptors to production. These can include optical sensors that use image capture to detect abnormalities in machine or materials processes, as well as vibration, pressure, proximity, contact, and other measurements that can indicate a deviation from normal operating conditions.
These IIoT sensor technologies are becoming more affordable and efficient to deploy every year, and can be a powerful tool when deployed pragmatically and purposefully (like tracking a specific failure mode).
Machine learning (ML) is a type of artificial intelligence (AI) intended to extract additional insights or patterns out of data that’s already being collected in the factory. With large volumes of data, patterns can be detected that can be used to train sophisticated algorithms to identify when these patterns are likely to be repeated.
For manufacturers, ML is often applied to achieve predictive maintenance, where machine or other breakdowns can be predicted and prevented. How equipment is used in different factories will vary greatly, so getting to a point where maintenance is performed just when it’s needed, not at some time interval, can add up to huge cost savings. ML can also be applied to improve the efficiency of power and other utilities consumption, predicting inventory levels and needs, and feeding other production consumption or quality data back to the factory to influence product improvements.
Additive manufacturing, also known as 3D printing, refers to the technology that builds three-dimensional objects by applying superfine layer upon layer of melted or liquified material. This process of adding layers until the object is completed is done by computer-aided-design (CAD) software that guides a print head that can precisely apply the material needed for each layer. This level of precision allows for 3D printed objects to be dependably used for important industries such as automotive, aerospace, and medical device manufacturing. 3D printed products can be made from materials such as thermoplastics, ceramics, metals, and even biochemicals.
Augmented reality can have several applications inside a smart factory. Wearable technologies such as Google Glass provide a “small, lightweight wearable computer with a transparent display for hands-free work.” These smart technologies allow the user to have an enhanced view of their surroundings that includes superimposed information or visuals related to completing their job. One powerful example is asset maintenance. For a maintenance technician, having this information in front of their eyes while on-site at a machine breakdown can improve the speed of the fix and reduce human errors, leading to improved efficiency and reduced costs.
In manufacturing, a digital twin is a virtual representation of a product, equipment, or process. As data is collected around products or processes, this data can be used to gain insights that will drive improvement for future iterations. As data is gathered from the original object or process, adjustments can be made to the digital twin to test different changes or improvements and their impacts before implementing them in real life.
The Industrial Internet of Things (IIoT) refers to sensors, machines, instruments, and other devices that are interconnected or networked together and leveraged through cloud computing.
This connectivity facilitates the collection of data from each of these sources, which can be aggregated and visualized in software and dashboards. Analysis of this data can (or should) lead to clarity about plant floor operational performance, and it can help identify opportunities for improvements.
IoT is used for a variety of benefits on the plant floor, including visibility to production performance, monitoring for quality issues, and triggering of material needs.
The most common example involves the deployment of connected devices such as sensors on the factory floor to monitor machine performance and health. This opens up the path for condition-based maintenance (CbM), where issues are detected by sensors and addressed as they occur; and Predictive Maintenance (PdM), where sensor measurements are applied to formulas to determine when maintenance should be performed in the future, and schedule the activities to avoid the predicted issue.
Most manufacturers collect large amounts of data, and the quantity is accelerating. The industry is generally good at using this data for tracking purposes, but fails to tap its potential for driving operational improvements.
This can be a challenge because, in order to gain reliable insights that can be used for decision making, different data sets typically must be merged and visualized in a way that humans can make sense of the data. Plant leadership and even the workforce need to know what the data means and what it’s telling them to do.
Instead, in many manufacturing plants, large amounts of data are collected and ultimately end up in the “data graveyard,” never to be seen again.
Having a system that can take various data sets and make them actionable, resulting in improved operations, is key to smart manufacturing success.
A smart manufacturing software solution is a platform that unlocks the value of smart manufacturing technologies for the factory. As mentioned above, if large amounts of data are collected from sensors and various systems (Big Data), but that data is never used to make better decisions and drive improvements, then the data is ultimately useless.
Therefore, a smart manufacturing platform is one that provides five key capabilities that are foundational for smart factory success:
1. Visibility at each level: Real-time visibility into plant floor operations (what issues are occurring, where, how often, etc) is the first requirement to understanding where to deploy smart technologies to solve specific problems.
2. Prioritization insight: Once you know what problems are occurring, where they’re occurring, and how often, then you can couple that with cost and impact data to quickly prioritize which problems are bleeding the company the most, therefore should be solved first. This might sound obvious, but focusing attention on solving the RIGHT problems is incredibly important.
3. Resolution structure: Once issues are identified and prioritized, manufacturers need a resolution structure that notifies the resources required to fix the issue, escalates unresolved issues when necessary, and provides transparency into how the issues were ultimately resolved and by whom. Then the solution to the problem can be turned into a best practice and shared with other lines and plants within the company.
4. Speed to value: This pragmatic approach to deploying smart factory technologies is key to success. To take a technology-led approach where sensors are implemented throughout the plant, without first understanding where they can generate the quickest ROI, is a common pitfall. On the contrary, knowing where the biggest problems are and deploying technologies to solve them can generate both quick and significant ROI. Taking this approach creates the speed to value that manufacturers need to drive competitive advantage.
5. Employee empowerment: In the end, the above capabilities and the corresponding benefits cannot be achieved without human action. By connecting your workforce to actionable data and empowering them to act on it, you unlock the greatest resource for improvement that you have - the human mind and desire to make a difference.
Success is measured by return on investment (ROI) and how quickly it can be achieved. With these five capabilities, the factory now has the foundation for a successful smart factory initiative that will positively impact the bottom line.
To learn more about this pragmatic approach to the smart factory and the five key capabilities, we recommend the ebook: A Smarter Approach To The Smart Factory.
"What makes L2L so unique is the fact that the product was developed by real manufacturing users. People that truly understand the day-to-day issues and concerns that drive the production floor."
