Optimizing Operations Through Data
In the world of CNC machine shops, the primary goal is always efficiency. Every machine shop today is faced with the task of attempting to figure out how to increase efficiency and profitability in their day-to-day operations. But without the necessary tools to gather, store, and interpret machine data most shops have to rely on paper records or manual entry into their ERP as their source for machine data. This lack of insight results in shops missing out on full machine optimization and even further profitability. With proper machine monitoring shops can gather more effective data and turn these numbers into actionable insights. Shops can do this by utilizing data collection and analytics; allowing shops to anticipate future needs and optimize machine usage. Throughout this article, we’ll explore the predictability of data and display how these numbers can establish pathways to success in the world of CNC machining.
What is Data Collection and Data Analysis?
Data collection and analysis for CNC machining is the process of gathering information from a CNC machine, such as machine runtime and interpreting it into metrics, such as optimization. Data collection expands to all aspects of the shop – from machine runtime, labor tracking, quality control, and material usage, all the way to tool management. Data collection is a common practice for CNC machinists, whether it be an Excel sheet or pen and paper; but without proper storage the machine history can quickly be lost. If the data is properly stored it can be analyzed to gain insights into machine optimization and shop efficiency.
How Do Shops Today Gather and Interpret This Data?
Most shops today utilize one of three methods to gather data; pen and paper, Excel, or an Enterprise Resource Planning (ERP) system. All three are unique but have various benefits:
Pen and Paper:
Pen and paper is an age-old practice that is commonly done while running a machine. There are several problems with pen and paper; firstly, it’s manually entered data which can quickly lead to misrepresented information and overall bad data. Secondly, it’s also challenging to store and access paper data which can be an issue when you need to look back at machine history. Finally, attempting to interpret pen and paper data can be challenging for a multitude of reasons, the previous machinist might have illegible handwriting or you might be missing vital data records from that machine.
Excel:
Excel is another popular method for tracking and storing machine data. But like pen and paper, Excel is manually entered data and can result in incorrect representations of the machine data. Additionally, while Excel is easier to store than pen and paper it still requires a certain level of management to ensure the integrity of the data. And while you can perform some amount of data analysis in Excel it is not a streamlined process and can result in further misrepresentations of the data if there is any user error.
ERP:
Machine shops today are utilizing ERPs to conduct their data collection and storage. ERPs allow the machinist to manually track machine metrics such as job run time and have it stored in a centralized system that is secure and easily accessible. But ERPs also require manual data entry from the machine operators which as previously mentioned can lead to problems with data accuracy. Additionally, while machinists can manually track their time on a job, the actual aggregated machine data is left out, this information is vital to understanding machine utilization. With the aggregated data shops can track when a machine is running, if it was interrupted, or if the machine is down. Without an automated way to track this data, most shops have to rely on what the machinist entered.
While all of these solutions are easily accessible they all share one common problem, they rely on manual entry. Manual entry can quickly become problematic for any machine shop that’s trying to improve its metrics; even simple human error or misrepresentation can quickly account for lost efficiency and optimization. However, when data is properly collected and stored, data analytics can drastically improve shop profitability.
Benefits of Data Analytics
Data analytics provides shops with invaluable insights into shop operations. These insights can allow shops to anticipate future needs, prevent potential problems, and reduce machine downtime. Some other benefits include:
- Waste reduction
- More accurate forecasting
- Predictive maintenance
- Improved efficiency
- Process optimization
- Decrease in labor costs
- Increased machine utilization
- Data control
- Downtime reduction
Clearly, data analytics gives shops a competitive edge that allows them to reduce costs and increase efficiency. This data allows shops a predictive ability to anticipate any future concerns or needs way before they arrive. The ability to find these patterns in the data and create actionable insights is key to any shop that wants to succeed in the world of CNC machining. CNC machine shops that have adopted data analytics into their processes are empowered to make innovative and data-driven decisions, increasing the overall success of the shop.
About the Author: Katie Ruffing
