The simple observation is that when a process is within statistical control, its output is indiscernible from random variation.
So the main significance of SPC is: It guides us to the type of action that is appropriate for trying to improve the functioning of a process. Should we react to individual results from the process (which is only sensible, if such a result is signaled by a control chart as being due to a special cause) or should we instead be going for change to the process itself, guided by cumulated evidence from its output (which is only sensible if the process is in control)?
Process improvement needs to be carried out in three phases:
Phase 1: Stabilization of the process by the identification and elimination of special causes.
Phase 2: Active improvement efforts on the process itself, i.e.tackling common causes.
Phase 3: Monitoring the process to ensure the improvements are maintained, and incorporating additional improvements as the opportunity arises.
Control charts have an important part to play in each of these three Phases. Points beyond control limits (plus other agreed signals) indicate when special causes should be searched for. The control chart is therefore the prime diagnostic tool in Phase 1. All sorts of statistical tools can aid Phase 2, including Pareto Analysis, Ishikawa Diagrams, flow-charts of various kinds, etc., and recalculated control limits will indicate what kind of success (particularly in terms of reduced variation) has been achieved. The control chart will also, as always, show when any further special causes should be attended to.
Control Charts are a powerful statistical tool that may have many different applications. For example, they may be used to monitor key product variables and process parameters. They may also be used in the maintenance of process control and in the identification of special and common causes of variation. In addition, they may also be used for process improvement by showing the effects of process of change.
Difficulties associated with Control Charts:
- Poor understanding of the meaning of the chart
- Improperly defined purpose of the chart
- Uncertainty of motives for using the chart
- Insufficient understanding of motives
- Inappropriate measurements made, including method of sampling
- Lack of support in analyzing the data
- Lack of understanding of when to use control charts
- Failure to react to the plotted data trends
In addition, confusion between “control” and “capability” has to be cleared off. A process can be in control and not capable.
“Control” means that the process is stable. “Capable” means “within specification”. People also focus on “product” and not “process”. Control Charting is a process improvement tool not a quality control or conformance
to specification tool.
Overcoming the difficulties:
- Define the purpose of the chart
- Involve the operators at all stages
- State the aims of the chart clearly
- Define the data measurement criteria
- Define how the data are to be collected and by whom
- Constantly ask if data collection is adding value to the process, procedure or business
- Ensure procedures are in place to cover the changes that result from the charting exercise
Data are factsand to be of use we need to transform data into knowledge so that inferences can be made from them, such as decisions as to whether or not a component is capable of carrying out its allotted function.
Data can be separated into three categories of data (variables):
- Discrete variables, which are numerical and can only be particular numbers, such as the number of workers
in an organization (i.e. they are counted in single units)
- Continuous variables, which are dimensions of items in units of measurement such as meters, liters, volts and other units of length, volume, time.
- Attribute variables, which are descriptive e.g. a machine “on” or “off”, or an employee absent or present.
It is crucial when dealing with any problems in which statistical method is used, one can differentiate between the three types of data, because the distinctions usually dictate which form of analysis is appropriate.
The main phases in the collection of data are:
- The purpose or objective for collecting the data,
- Identification of the entire “population” from which the data are to be collected.
- Decisions on:
- method of collection, or how the data are to be collected
- sample size
- Validation of the results, this being a vital part of the collection/analysis process.
Hopefully, the above information would help us to understand importance of SPC feature in SAP ME 6.x, and good practices and safe methods while implementing them.
References: Open resource. Credits to them too.