When you hear the term variability, chances are your mind immediately goes to product variability – those fluctuations in the production process that impact the overall quality of what you’re producing. That is, of course, a key challenge to address, particularly for those organizations in the food and beverage or pharmaceutical sectors.
There is, however, another type of variability that is harder to spot but equally costly: performance variability. Equipment operating outside of typical performance parameters, frequent need to adjust steps in a batch process to compensate for changes up- or downstream, and other similar actions are all indicators that variability has found its way into your processes.
Another area where variability can be particularly costly is in heavy equipment manufacturing. This area is one of the largest and most competitive markets of the manufacturing sector, so it’s important to have as little variation in equipment performance as possible.
Take the drilling industry, for example. There’s no shortage of organizations in this space that own and operate a large fleet of drilling equipment. With such a demanding industry clientele, drilling requires a continuously operating fleet. If a piece of your fleet has to go into repair unexpectedly, because of variability during its operation, you can’t confidently project to clients when maintenance and repair cycles will occur. Unchecked variation in equipment operation can spell large and unpredictable idle time (downtime events), which can easily consume millions in capital costs.
The easiest and most effective way to identify, eliminate, and prevent variability of all kinds is through the use of data analysis and predictive modeling. An effective predictive analytics platform empowers engineers, data scientists, and operations professionals to identify and address performance concerns before they have an opportunity to impact operations. These issues could be initially imperceptible by conventional measures, but the frequency and granularity of data collected by a predictive analytics tool is designed for this very purpose.
Action leading from these discoveries could be as simple as flagging maintenance personnel to address a specific part during the next planned downtime event, or as sophisticated as assuming direct control of an industrial process in flight so the end product is not substantially impacted by challenges encountered during the production.
In the case of our drilling industry examples, you can substantially reduce the probability that crucial equipment will be placed (unexpectedly) on standby. So, not only are you improving overall fleet efficiency, but you are also saving millions of dollars annually and ultimately achieving lower cost of production for equipment operators.
To learn more about how TwinThread can prevent the loss of profit through operational variability, see a demo today.
October 29, 2020