Several technologies have advanced in parallel over the years. The interactions of these in smart factories has set the stage for Industry 4.0
Data availability is nothing new for contemporary manufacturing processes. However, what makes a factory ‘smart’ is how accessible these data are to key stakeholders. An abundance of manufacturing data is useless unless operators can gather it, interpret it, and act on it. Further, the importance of these data correlates with the speed of this sequence. Many smart factory technologies are able to communicate their data in real time.
In an earlier blog of this series, we explored how various smart factory technologies come together to create a wholistic solution that is more than the sum of its parts. This is because each of the smart factory technologies not only solves its own intended issue, but also provides data and a remote connection back to the platform. In this paper, we will continue by exploring each of these smart factory technologies individually to better understand how they connect the smart factory.
Smarter Data Collection
At the heart of all smart factory technologies is data. Remote access to real time data allows a greater opportunity for the entire smart factory to work together. Data also provides the insights, through advanced analytics, that help to optimize manufacturing processes over time. There are several means of data collection with several options within each. These two examples differentiate conditions that managers may consider when planning to adopt smart factory technologies.
Industrial Sensors
The advancements in wireless enabled, battery operated sensors cannot be overstated. Sensors are available in hundreds of different forms. That way, technicians can easily retrofit almost any type of equipment. For example, there are bolt-size sensors that are waterproof and dust proof for industrial use. They even come with built in WiFi and a 10+ year battery life. They fit on the cap of bolts and detect movement. Then, the sensors transmit their data to the smart factory’s gateway.
Operator Input
There are still many use cases that require a human operator. In these cases, technicians often bear the burden of administrative paperwork. However, even a simple customized solution could expedite this work by digitizing it. For example, a phone’s camera can scan and convert documents instantly. Also, companies can create digital forms with benefits like auto-fill, data validation, and contextual drop-down lists.
Smart Manufacturing
- Remotely monitor shop floor
- Real-time feedback
- Smart automations and alerts
- Data driven efficiency
Data Transfer Technologies
In order for a smart factory technologies to work together, they must be able to communicate. However, when building a connected solution, there are several options for wireless transmission. Each wireless communication channel offers its own strengths and weaknesses. Two examples follow to contrast the differences between two familiar options for wireless data transfer.
WiFi
Although Wifi may be the most familiar wireless standard, it becomes more complicated when scaled to a smart factory. The choice of which of the two WiFi frequencies, 2.4GHz and 5GHz requires thoughtful consideration. Factors such as speed, interference, range, fidelity and energy usage vary based on the use case and even location intended.
Cellular 5G/LTE
Cellular capabilities not only offer longer range, but also mobility. These connections are also typically more secure. Although, they are also more expensive to operate, data restrictive, and energy intensive. Several mid-range options are emerging, but nothing as ubiquitous or wide scale as cellular LTE currently exist.
Smart Decision Making
Ironically, in smart manufacturing, the human operator is the limiting variable in terms of decision making speed. Machines are able to interpret incoming data and compare it with historical trends almost instantly [1]. All the while, these computers can also cross-check real-time consumer demand, warehouse inventory, and estimated delivery times. However, certain applications will always require human intervention for key decision points. In these cases, smart factory technologies can provide their information in a human readable format. The following examples will compare the differences assumed in each solution.
Industrial Internet of Things
IIoT is a broad term that describes the technology behind smart automation in manufacturing. The internet of things parallels the internet of people, which mainly facilitates peer to peer intercommunication. Similarly, the internet of things facilitates machine to machine intercommunications, just on a different internet space. There are several benefits to use IIoT solutions. Some include, improved security, propriety of information, complete ownership of the solution, greater customization, and reduced operational costs.
Supervisory Control and Data Acquisition
The primary focus of SCADA systems is to centralize data to a human machine interface. Automations play key roles in filtering and organizing these data to present to an operator. Some examples of such human readable formats include maps, graphs and images. Importantly, this automation also works in reverse. This gives the operator control at the top level. A single action from an operator can cause various automations to execute and carry that decision out.
Smart Factory Emergence
Up to this point, we have discussed various smart factory technologies and their place in the structure of the smart factory. As laid out above, each of these technologies has a role to play. Though, each of these smart factory technologies also contributes a small portion to a greater ability.
As mentioned earlier, data is the heart of these smart factory technologies. And, manufacturing data is intrinsically valuable. This is largely due to the law of large numbers, as factories produce volumes over time. The smart factory represents an opportunity to tap into this potential value. It represents the notion of peak efficiency.
AI Analytics
Some data sets are simply too large for humans to process. Even with the help of computers, some tasks may prove too expensive to invest human capital. Analytics solutions have a proven track record to create meaningful ROI from raw data. After all, these data are a byproduct of the smart factory in the first place.
Machine Learning
The goal of the smart factory is to achieve maximum efficiency at the lowest possible operational cost. However, this is an iterative process. After the smart factory runs for a while, it will present trends to the factory manager. This person will then make decisions based on the data. Then, the factory will run again to collect new data, and the feedback cycle will continue. However, with access to all this data, the smart factory can actually start to predict what will happen. This enables predictive maintenance and can avoid factory downtime.
Approaching the Upgrade
Industry 4.0 is possible with the combination of multiple existing and still evolving smart factory technologies. As a result, the shift to smart factory technologies is a gradual one. It starts with a big picture approach to define the possibilities. However, implementation can begin with only a few sensors and centralized computers. The benefits of smart manufacturing are outstanding. The ROI of these initial investments enable smart factories to scale quickly in their adoption of additional smart factory technologies [2].
No two factories are the same. Similarly, no two factories will have the same goals for a smart factory solution. It takes an expert to see the potential of a big picture solution. That is why Bridgera offers free consultations to explore smart factory technologies with plant managers. Our no pressure sales team understands the importance of starting off small. While at the same time, building in potential for scale and interoperability in the future.
