Centerlining is an approach to reduce process variability and increase machine efficiency in manufacturing.
Production processes are designed to deliver a demanded outcome. This targeted outcome can be physical parameters to describe the geometry of a product, such as length, thickness, stiffness etc. or other output parameters for a process such as temperature, time or other measurable KPIs.
Each process, no matter if it is a production process or a service, fluctuates in its output parameters. When executing a process several times, the output parameter should be reaching the demanded target value, with fluctuations within the accepted tolerances. Figure 1 depicts two processes that should deliver the same result. While the measured process output parameter is placed on the x-axis, the y-axis depicts the frequency in which the process reached the corresponding output parameter. Both processes follow the normal distribution with a specific mean value in the middle of the curve. While the first process is perfectly within the tolerance, the second process is shifted to the left and it’s values are widespread. The methods to shift the mean of the process output parameters to the middle of its tolerances while reducing the spread is called centerlining.
To measure how good a process is centerlined, the KPI process capability is used. Using several sub-indicators the process capability enables operation engineers to derive at a conclusion about the capabilities of a process to produce parts with demanded properties. The whole framework of improving the process capability is called six sigma. The name “Six Sigma” is derived from the target of capturing six standard deviations between the tolerances of a process.
Target or output parameters are adjusted by controlling the input parameters. If for example cookies have been too long in the oven, they get burned. If they stay in the oven too short, the dough will not harden. Therefore an optimal backing time for the cookies is essential to reach the desired output. The targeted output, for example the consistency of the cookies, directly correlates with backing time. By adjusting the backing time, the consistency of the cookies can therefore be controlled.
The same is true for most production or service processes. The biggest issues within operations is the identification of the biggest input parameters that drive the targeted parameter. To derive at the biggest driver, lots of data needs to be tracked and analyzed, which in most cases does not exist or only exists in poor quality.
Today most processes include quality securing operations, such as regular measurements by operators or inline measurements. If measurements are conducted manually, the generated data is used to determine whether a machine should proceed with production or the input parameters need adjustment. Due to high manual efforts with traditional technologies, these data points and the adjustment of the machines is not tracked.
Information about machine setups and their adjustments are often stored in a printed paper-based format. Since this kind of data storage is dependent on location and requires time consuming searching to retrieve the desired information, it is not used as often as it should be.
With digital tools such as the WORKERBASE Agile Manufacturing System, operational data is collected directly from the shopfloor through the facilitation of mobile devices. Operators use phones and tablets to write their measurements directly into a database. Thereby batch numbers, material IDs and machine IDs can be stored together with quality measurements and the corresponding actions to adjust the specific machine.
Thereby the system can automatically derive adjustments using historical data and machine learning. Unskilled operators receive a recommendation to adjust the input parameters to derive at the desired output. If input parameters depend on each other, making it tough to determine the best correction of parameters, further data generation can easily facilitated by adjusting quality workflows to track further input parameters.
Thereby digital tools help to automate the centerlining process, transfer and store knowledge about processes in a structured and accessible manner. Operators from other sites can now access data to troubleshoot and centerline their processes with similar machines.
With WORKERBASE, globally available information about how other operators solved issues are available through powerful search engines, making physical lookups in paper based folders obsolete. All these measures enable operators to fulfill their task in shorter time and more efficiently, fostering improved quality and gains in operational efficiency.
For more examples on how to improve lean manufacturing with digital tools, please refer to our guide to lean digital.