To create trend charts using copied settings:You can use trend charts to analysis data collected over a period of time. By observing trends, you can determine the appropriate time to take corrective action. For example, you can create a Glazing Process collection plan to collect process quality variables such as oven temperature and voltage from a glazing process. You can use this collection plan to record five readings at random times during each shift. Once the results are collected, you can create a trend chart to graphically display the results of temperature or voltage. See: Creating and Viewing Trend Charts.
You can chart quality results using Trend Charts. Trend charts show values collected for a particular collection element over a period of time.
You can select results based on any combination of collection elements in the collection plan. For example, you can chart oven temperatures for a burn-in chamber for a particular production line this week.
You can create Trend charts from "scratch" or you can create them by copying settings from another chart, descriptive statistic view, or custom report. Copying settings allows you to view the same subset of data in different ways. See: Copy Settings.
You can save your chart settings. You can re-create charts using these saved settings, and you can change these settings to create new charts.
After creating and viewing your chart, you can optionally export the raw data that the chart was based upon. Exported data can be further analyzed using a spreadsheet or statistical analysis software package.
To create trend charts using copied settings:Navigate to the Trend Chart window.
Enter missing information or change the copied information as required (See below).
Choose the Copy Settings button. See: Copying Settings.
You can change all copied settings except the collection plan.
To create trend charts:Navigate to the Trend Chart window.
Optionally, enter the Chart Name.
To save your chart parameters, you must enter a chart name.
Select the Collection Plan to chart.
If you are creating a chart, you can select any collection plan, even those that are no longer effective. If you are changing a chart, you cannot change the collection plan.
Optionally, enter the Chart Title.
The text you enter here is displayed at the top of the chart.
Select the X-Axis Element.
The X-axis of the chart (the horizontal dimension) can be any collection plan element but is usually a collection element that represents time or groups of quality results collected consecutively over time. For example, you can view results by:
Occurrence: individual quality results or readings presented consecutively over time, from the oldest occurrence to the most recent occurrence
Collection number: a group of individual quality results or readings, grouped into a collection and identified by a collection number
Entry date: individual quality results or readings, grouped by the date in which they were entered
Select the Y-axis Element.
The Y-axis of the chart (the vertical dimension) represents the primary collection element that you want to analyze. Usually, this axis of the chart represents a variable collection element; for example, temperature or voltage.
Select the Y-axis Function if you want to group the collection element values. See: Functional Grouping and Processing.
If the X-axis represents a grouping (for example, Collection Number or Entry Date), you must select a grouping function for the Y-axis. For example, if the X-axis is Entry Date, you can select a function like Average to display average values for the collection element for each day. If you choose Occurrence for the X-axis, you cannot select a grouping function. See: Functional Grouping and Processing.
Optionally, enter the chart Description.
The text you enter here is displayed at the top of the chart, under the chart title.
To find and select quality results:Navigate to the Show Results Where region of the Control Chart window. See: Finding Quality Results.
If you do not select which quality results to chart, all results associated with the collection plan are used.
To view charts:Choose the View Chart button. The trend chart you created will be displayed in a separate window.
To save chart settings:Choose the Save Settings button if you want to save the inquiry settings.
To export chart results:Choose Export Results from the Tools menu. See: Exporting Quality Results.
Traditionally enterprises have depended on their production departments to make products and on their quality control departments to inspect and screen out items that do not meet specifications. Often this approach results in reiterative inspections in an effort to detect instead of prevent problems. Obviously this approach is wasteful because it allows time and materials to be invested in products or services that are not always usable. After the fact inspection is both uneconomical and unreliable.
Statistical process control, on the other hand, is a preventative system. Because it provides immediate feedback, it can minimize or eliminate waste. There are essentially four elements involved in SPC:
The process: The combination of people, equipment, materials, methods, and environment that work together to produce output.
Information about Performance: Process output provides qualitative and quantitative information about process performance. In a broad sense, process output includes not only the products that are produced, but also any intermediate 'outputs' that describe the operating state of the process, such as temperatures or cycle times. Collected and interpreted correctly, this data can provide the information you need to determine whether the product or process or both require corrective action.
Action on the process: Action on the process is future oriented because it prevents the production of out-of-specification products. Corrective actions can include changes in operations (e.g. operator training), raw materials, and even in the process itself. Process changes might include equipment repair and maintenance or the addition of temperature and humidity controls.
Action on the output: Action on the output is past-oriented, because it involves detecting out of specification output already produced. Unfortunately, if current output does not consistently meet customer requirements, it may be necessary to sort all products and to scrap or rework any nonconforming items.
Process control focuses on gathering process information and analyzing it so that actions can be taken to correct the process itself.
To use process control, it is important to understand the concept of variation. Some sources of variation in the process cause short term or piece to piece differences, such as backlash and clearances within a machine and its fixturing. Other sources of variation cause changes in the output over the long term. Consequently, the time period and conditions under which measurements are made have a direct affect on the amount of total variation.
They are two types of variations: common cause and special cause variations. Common cause variations occur when processes are in statistical control. They are inherent to the system and are therefore difficult to reduce or eradicate. The variability that exists within the control limits of a typical control chart is usually due to common causes. Special cause (often called assignable cause) variations can be attributed to factors or sets of factors that are external to the system. Examples of special cause variations include operator errors, poor machine maintenance, and missed process steps. Special cause variations can be detected by simple statistical techniques one of which is the control chart.
