Today, many maintenance strategies of manufacturing companies rely on reactive approaches to machine failures or schedule-based maintenance performed by personnel. Sometimes, this imposes significant time and financial burdens for manufacturers.
Industry 4.0- a term circulating in the industry for the last few years where companies say they are trying to adopt I4.0 practices into their manufacturing operations but have no idea what it truly means.
We realize that companies look for several factors when they look at data-collection and visualization solutions, and we have made our data-collection practices flexible enough to accommodate all these factors.
A manufacturing company that manufactures in-house wanted a data-driven decision-making system to understand the utilization of one of their machines that does repetitive manufacturing of a specific part of their product segment.
Data blindness refers to having an abundance of data, unstructured with an overload of information preventing the user from leveraging data to find valuable information to make decisions.
Within manufacturing, only around 35% of companies have a data collection system to know how efficiently their factory is running. They believe that data-driven processes are essential, but they find the systems complicated, causing hindrance to their implementation.
In order to stay competitive on the market, manufacturing companies needs to maintain high level of efficacy and convenience during the production process. An unforeseen machine breakdown can cause the company high time and financial losses.
The saw is the first machine in a series production which cuts parts for all the machines within the factory. The machine is utilised for long periods at a stretch and stays idle after stock replenishment.
Paper documentation is a common but clumsy way to track and store information. It causes efficiency issues and production losses out of which up to 60% could be avoided by digitising information flows.