By Kevin P. Vanderslice, Director of Sales, ifm efector
Digital transformation and Industry 4.0 are common buzz words these days. They focus on real-time data and automation. But how should we approach this?
The manufacturing industry generates more data than any other sector of the economy, and because of all this data, companies are making analytics a priority. In a recent General Electric survey, 42% of manufacturers have said “big data” is the top priority, and another 45% said it is one of the three highest priorities.
Wave 1. The waters that most of us have navigated in recent decades are in wave 1. It is the classic ownership model, i.e., a customer pays a one-time price for a machine or after-sales service. As the OEM, you provide a warranty for a limited amount of time, and any service work beyond that follows the classic model of dispatching a technician or bringing in the vehicle for maintenance or repairs.
Wave 2. In wave 2’s usage-based model, a customer pays to use a machine but does not own it. Uptime becomes more critical. The customer simply may not pay you if the machine is not performing 100%. Insight into the machine’s operation to detect early wear and tear becomes a necessity. With that information, you can plan maintenance and repairs during scheduled downtime.
Wave 3. In this final stage, wave 3 evinces an outcome-based model in which the customer pays only for the finished product. Frankly, he could care less which machine you used to get there. But as you are responsible for the final product, so a full understanding of machine health and product quality is critical. The classic consumer example of this third wave is the ride-sharing service Uber. Suddenly, owning a car is not necessary; getting from A to B is all that matters, which also testifies to the impact that Uber’s transformation has had on the automotive industry.
Here are some examples.
1. Aircraft deicers at airports have a serious impact on airline operations. To avoid bottlenecks, stops to refill trucks with the deicing fluid are staggered. This requires a real-time view of current liquid levels in each truck. Additionally, tracking the usage per aircraft may be mandated by the FAA.
2. Refuse collection vehicles are constrained by weight and cycle time. Improving the speed of pickups and optimizing capacity enables trucks to pick up more refuse in less time. This is only possible by accurately measuring key body data and preventing unplanned maintenance.
3. Mobile drill rigs are a good
example of highly complex on/off
highway vehicles that utilize a large number of sensors and actuators. The operator must manage the drill
operation as well as the safety zone of the drill head. Using area surveillance to assist the operator with these tasks and display the parameters in the cab significantly improves the working conditions and safety of the job site.
4. What if farm equipment automatically identified weeds or pests and eradicated them? Besides efficiency and cost reductions, the decreased use of crop-protection chemicals and reduced emissions into the environment show the advantages of this technology, which could ultimately eradicate harmful substances from farming.
5. The traffic of cargo vessels in ports is tightly scheduled. There is little time to load or unload cargo. Automated guided vehicles have simplified and quickened the transportation of containers by knowing where and when to pick up the freight. Automated machines are helping the fast-growing needs of the ship-to-shore industry.
Industry 4.0 is often referred to as the fourth industrial revolution and represents a new stage in the organization and control of the industrial value chain. In manufacturing, Industry 4.0’s modern control systems and smart sensors have resulted in what is often called the “smart factory.” IoT devices in smart factories lead to higher productivity and improved quality. With minimal investment, quality-control personnel can monitor manufacturing processes from virtually anywhere by connecting a smartphone to the cloud. By applying machine learning algorithms, manufacturers can detect errors immediately, rather than at later stages when repair work is more expensive.
In the industrial world, while Industry 4.0 is influencing overall equipment effectiveness, there are multiple areas where there could be improvement, including:
Let’s look at two examples.
Consider some predictions.
Digital transformation allows machine builders to reimagine how they design, manufacture, and service equipment. While Industry 4.0 is still evolving, companies that are currently adopting new technologies realize its potential. These companies upskill their workforce to take on the responsibilities made possible by IoT, and they recruit new employees with the right skills.
We might not have the complete picture until we look back in 30 years. But for now, we’re making history!