✕

Measurement and test data that is collected in the manufacturing environment can be invaluable for maximizing productivity and reducing costs. However, if certain key practices are not followed and executed it can be a waste of money.

There are three key actions necessary to gain value from collecting and analyzing quality data.

First, the data must be collected in real-time or as close to the time of manufacture as possible. Further, immediate analysis of the data using standard Statistical Process Control (SPC) methods to detect any trends or failures must be performed to ensure corrective action occurs as soon as possible.

Second, engineers and others responsible for the manufacturing processes inside the organization need to be made aware as soon as possible when specifications are not being met. This information must be provided in an automated and timely fashion. When provided in a timely manner this information can be utilized to problem-solve when processes deviate from specifications. Additional requirements for reporting and record-keeping need to be easily available.

Lastly, trends in deviation from specifications over time need to be analyzed and made available to stakeholders to assist with decisions on investments for upgrading equipment or modification of processes. Many root causes cannot be identified until data is viewed over time.

Taking a closer look at these key elements will reveal the importance of each step in obtaining the payback.

Collecting data in real-time has several key elements to consider. Data should be collected as close to the manufacturing point as possible and in a timely manner. Data collection for real-time SPC should be done using the same methods at all points of manufacture in a facility. It is also crucial to collect relevant traceability information such as the machine that produced the part, operator, lot number, cavity number, fixture station, etc. The importance of the collection of traceability cannot be over-emphasized. Trace data is key for the identification of root causes and quickly identifying the source of the data and surrounding conditions. Using a consistent procedure and system across an entire facility can ensure that all the data, regardless of what point in the manufacturing process, is stored in a common format. This enables a consistent set of rules for making decisions when adjusting processes to keep performance consistent. Nearly all measuring and testing equipment in use today has digital output capability. Everything from on-machine probing, CMMs, to small digital hand tools has output capable of being collected by a PC with the use of standard software. These capabilities make the collection of data a highly automated task by removing manual entry or pencil and paper. Data collected electronically can be utilized to perform SPC analysis in real-time automatically notifying operators of trends before components are out of specification. Data collection systems can monitor the timing of the data collected to ensure that it is collected on the required schedule by alerting operators when measurements are due. Measuring a prescribed number of parts at a fixed interval for real-time plotting against control limits is far more meaningful than measuring parts at random times or “catching” up with the required number at the end of a shift.

The use of a common database is key to enabling reporting of quality data across the organization in a common format. Data can be provided to interested parties in standard reports automatically sent via email or displayed on strategically placed monitors using dashboard presentations updated in real time. Dashboards have been successfully deployed to display product line, department, or plantwide views for managers to stay current on shop floor status quickly and easily. A common database also simplifies the fulfillment of customer-required documentation consistently regardless of which operation in the plant collected the data. The preparation of First Article, AS9102C, PPAP, CoA, and other customer-required documentation is simplified by the use of the common database. Data collected in a standard and real-time manner can provide valuable insight into potential unacceptable variations in processes to engineers and managers enabling prompt actions to be taken to maintain process control.

When engineers can utilize their time reviewing and not collecting and collating data their efforts will result in process improvements taking place faster and more efficiently. Many manufacturers that supply components to industries such as aerospace and medical devices are required to maintain historical records of quality data collected. Having a common database for this archive makes the retrieval of data, when needed, a simple task. This is another reason for recording traceability information as it enables simple retrieval of historical data at the machine, lot number, or serial number level instantly. The availability of a common relational database containing all quality measurement and test data provides powerful access to data in very specific areas with minimal effort.

Analysis of measurement data variations over time can provide invaluable insights into a manufacturing process. The ability to measure variations of a specific machine, raw material lot number, or other environmental conditions over time can uncover sources of variability that could never be discovered without consistently capturing data and trace field information. Long-term analysis of a process can identify conditions relating to workload volume, machine maintenance, personnel changes, and many more variables. Long-term data monitoring can also help predict maintenance requirements or confirm the need to replace or upgrade equipment. Many of these findings can aid in planning budgets and avoiding unexpected expenses. There truly is “gold” that can be mined from quality-related data if collected consistently and thoroughly. One example is an aftermarket camshaft manufacturer that was having an issue with parts being scrapped in their grinding department. They started collecting data on the dimensions being ground on an hourly basis. Initially, there was not an obvious cause however, when they began to plot the failed dimensions occurrences related to the time of day, they discovered that almost all the failures were taking place shortly after the operator’s lunch break. Further investigation found that the state in which the operators left the machine during their lunch break allowed certain systems to cool down while others stayed at operating temperatures. Some simple changes in the procedure for putting the machines in “idle” mode virtually eliminated the scrap. The first month of operating under the new procedures saw a $50,000 reduction in scrap. Measurement and test data along with the relevant trace data that is captured consistently on a defined schedule can contain extremely valuable information if some simple analysis is done. Examining the data from different perspectives, as revealed in the above example, can provide significant paybacks.

Although collecting measurement and test data is normally thought of strictly from a compliance point of view, there are many opportunities to gain real monetary paybacks. Maximizing profit and output can involve managing more than the typical machine cycle times and material handling measures. Companies that install sophisticated MES systems to monitor cycle times and machine uptime but continue to capture product measurement and test data with paper and pencil that might get entered into a system just in time to print a required shipping document are missing huge savings opportunities. The common practice of installing $100,000 + computerized measuring machines that then print out paper reports which a technician then enters into an Excel sheet at the keyboard is not an efficient use of available automation. Installing computer-based quality data collection activities takes a relatively small investment compared to the potential for huge savings.