Equipment vs. Processes: Why a Well-Planned Process Always Wins
Updated: May 27, 2020
When it comes to industrial production, the most important thing is not what an organization has, but how it uses it. In other words, the processes an organization uses matter much more than the equipment it has access to. Optimising the production process allows industrial organizations to do more with the machinery they currently have, all while cutting costs and raising profits.
Consider how many steps are involved in a single operational process. Material must flow from one machine to another, waste products need to be separated at every step of the way, and certain conditions must be constantly maintained. One small mistake can cost large amounts of money to the organization, meaning it is crucial to monitor every part of the operation closely. Therefore, processes must be created to allow operators to have the most transparent view possible. One way in which companies are doing this is by using IoT technology and industrial AI-knowledge solutions to ensure machine data can both be read by anyone in the enterprise and be used to provide step-by-step scenarios to maintain optimal production.
Another important thing to note is that maximum production does not always correspond with optimal production. While it would make sense that it’s always better for operations to maximise the amount of throughput, this is often not the case. Higher amounts of raw material entering a system means higher amounts of waste leaving it, which poses an issue when waste is being created at rates faster than it can be disposed of. Organizations, therefore, must focus on maximising performance instead of production. In this sense, a smart production process is much more effective than larger equipment, as a machine that can process materials faster is not useful if it is producing more waste than the organization can handle at a time.
The relationship between equipment and processes is not a zero-sum game, as both are essential to any industrial operation. Larger machinery permits higher levels of production only if waste can be managed effectively, while well-crafted processes can allow machinery to work better under any circumstances. Creating a strong process involves learning from data and expert feedback to allow for continuous improvements, which can be done using an AI knowledge solution. For example, while equipment failure is currently a reality at almost any industrial operation regardless of their expenditures, an organization that integrates AI knowledge into its processes will see very different results. Using predictive maintenance, AI knowledge minimises the risk of equipment failure by predicting breakdowns before they occur, in what has become known as predictive maintenance.
Industrial organizations often choose to emphasise equipment over the processes involving it. While state-of-the-art machinery is a boon to any organization able to afford it, greater results can often be achieved simply by shifting attention to the processes that use it. By ensuring their production processes are optimised, organizations can spend less on management and equipment repair while profiting more.