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FIGURE 15.1 Distinguishing between special and common cause variation. (Adapted from Cullen, J. and Hollingum, J., Implementing Total Quality, IFS Publications, Bedford, U.K., 1987.)

to interpret the data gathered from the process with the appropriate statistical tool. That tool is the statistical control chart.

Control chart methods provide an objective and statistically valid means to assess the nature of ongoing processes, and, as such, are particularly applicable to HACCP monitoring [3]. Control chart theory is based upon the notion that the parameter being measured, when in statistical control, will vary normally (e.g., only common cause variation) about a central value [37]. Control chart methodology is the only SPC tool that can distinguish between common cause (inherent) and special cause (unnatural) variation in a process. The control chart allows the highlighting of special cause variation, if present, when monitoring a process. If the special cause variation source can be found and eliminated in the process, then the process will exhibit only common cause variation. Only when common cause variation is the only source of variation present is the process in a state of statistical control. What makes statistical control so important? The essence of statistical control is predictability. A process is predictable when it is in a state of statistical control, and it is unpredictable when it is not in a state of statistical control [38].

So how can HACCP be made a more effective prediction tool for safety hazards in the production process? The key lies in integrating SPC methodology into the HACCP plan. Achieving process control in a HACCP monitoring system will assist the processor in systematically and predictably demonstrating control of identified safety parameters [39]. It will also provide warning signs signifying out-of-control status so that corrective action can be taken to return the system to the established safety limits [27].

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