Driving Improvement in Process Improvement

Progressive manufacturers utilize Statistical Process Control to “listen” to their processes so that potentially harmful changes will be quickly detected and rectified. However, not all SPC programs deliver to their highest capability as there are many elements to get right to achieve maximum utility. Highly effective SPC programs combine technical competencies, such as using the right chart and sample size for the application, with good management principles such as ensuring operator involvement.

This article identifies seven key improvements that most companies can make to their SPC program. If you wish to learn more about some of the topics addressed below links are included where more detail is available on www.winspc.com

1. Step 1: Focus on the Right Characteristics to Control.

   Clearly, controlling “everything” is not feasible or a smart use of limited resources. We must focus our efforts on controlling those process characteristics whose variation will impair product quality and/or reliability. Furthermore, process control is most beneficial when we move upstream in the process. When the significant process variables that affect a key process output are being controlled, then the process output is predictable, allowing costly (and imperfect) inspection processes to be eliminated. Determining exactly which key process input variable, if controlled, will produce predictable and consistent process outputs can be challenging. However, efficient and effective techniques such as Design of Experiments may be utilized to model the effect of input variables and their interactions on key characteristics.

   For more details click the link : How do I know what process characteristics to control?

2. Step 2: Ensure Adequate Measurement Systems are Used

   Effective use of data to drive decision-making requires adequate measurement systems. For example, when implementing statistical process control charts, we assume that a signal represents a significant change in the process and we react as such. However, inadequate measurement systems may result in inappropriate signals or even worse, charts that fail to detect important process changes. Thus, it is incumbent upon us to ensure that measurement systems are adequate for their intended use via proper assessments prior to their use. Only capable measurement systems should be utilized in data based methods such as Statistical Process Control.

   For more details click the link: Ten Ways to Improve your Measurement Systems Assessments

2. Step 3: Select the Right Chart for the Application.

   Choosing the wrong type of chart is likely to result in problems diagnosing when special causes of variation are present in the system. Many factors should be considered when choosing a control chart for a given application. These include:

   – The type of data being charted (continuous or attribute)
   – The required sensitivity (size of the change to be detected) of the chart
   – Whether the chart includes data from multiple locations or not
   – The ease and cost of sampling
   – Production volumes

   For more details, including a table that may be utilized to help select which chart to use for a given application, click the link: How do I choose the appropriate type of control chart?