In manufacturing, lean techniques, more efficient hardware and machinery and Six Sigma methodologies can help eliminate waste, while also improving production quality and yield. Unfortunately, even after improving and optimizing as much as possible, there are just some cases where variability is unavoidable. Waste is, after all, a natural element of production and operation — which is especially true of industries such as pharmaceuticals, chemicals and mining.
Over time, the sector has grown more efficient and optimized. But there’s still a long way to go. Modern technologies such as data analytics, predictive tools and more granular tracking can help reduce waste dramatically. Data is incredibly important in today’s landscape, not just in regards to cost savings, but also the impact on the surrounding environment.
Data may, in fact, be the missing link when it comes to completely optimizing the manufacturing industry and related systems.
How Can Data Help?
Save for a few manufacturing systems, most production activities are incredibly complex and have varying stages, all of which influence yield and produce waste. Any number of factors can contribute to excess, some of which it’s tough to retain control over. Even more impactful is the fact that plant managers and overseers don’t have all the resources to monitor every working system or piece of machinery.
Plants today are incredibly vast — so much so that advanced systems and automated tools handle most monitoring and operation. But therein lies the benefit of modern setups. These systems can also be configured to accurately measure, track and extract operations and activity data, which then goes to a central processing system or unit.
The act of analyzing or studying data is a component of data science, often called data analytics. By using statistics, mathematical tools, data organization, extraction methods and various devices, you can glean everything there is to know about your working processes and machinery. This knowledge could effectively allow managers and administrators to identify areas where they can make further improvements and optimization. They can also accurately pinpoint the source(s) of excess waste or resource consumption.
In short, data can reveal insights about a plant, its inner workings and both the hardware and staff that are employed within. In this way, it can show important details and specifications that were otherwise off-limits, at least to the naked eye.
But it can also come in handy for more than just local properties and processes. Data can help eliminate waste — such as excess time — during other processes like transportation. Imagine how much improvement and optimization can be achieved by streamlining these external processes. More specifically, the data can reduce or mitigate travel times, better predict arrival windows, improve driver punctuality and reduce vehicle or fuel costs.
Data-Oriented Manufacturing in the Real World
According to McKinsey and Company, one pharmaceutical company used big data analytics to assess interdependencies and identify common yield factors. Ultimately, this decision allowed them to increase their production levels by as much as 50 percent, which netted them a savings of between $5 and $10 million annually. It also helped them mitigate inconsistencies in capacity and quality for their shipments, with alleviated regulatory attention and potential ramifications.
The major chipmaker Intel has leveraged big data — since as early as 2013 — to reduce quality assurance testing procedures and cut down on the time and costs associated with developing new electronics and computing chips. As a result, the company saved more than $3 million in manufacturing costs alone, for a single line of their processors. Over time, the company also expects to save $30 million or more thanks to their big data-fueled strategy.
Big data can also help companies manage supply chain risk and handling, improve overall manufacturing processes for any industry and cut down on the amount of waste produced in just as many sectors. It’s a viable solution for any company or organization looking to optimize and improve existing strategies.
Maybe it’s time to start considering big data for your own organization, if you haven’t already.
This article was written by Guest Contributor Nathan Sykes. He enjoys writing about the latest in technology and its effects on business. To read more check out his blog, Finding an Outlet