Learn to select the appropriate monitoring, measuring and diagnostic tools relating to specific product and service quality situations.

Recommended learning hours: 40

Unit aim: To be able to select the appropriate monitoring, measuring and diagnostic tools relating to specific product and service quality situations. To be aware of the advantages and limitations of methods selected and to be able to design and manage the data collection and analysis processes effectively.

Assessment type: Assignment

Learning outcomes

  1. Understand the role of monitoring and measuring in making decisions relating to quality
  2. Understand the use of methods for data collection and analysis.

Every successful candidate can at the end of this unit:

  • Evaluate the use of qualitative and quantitative data in decision making for a range of situations
    • For ongoing processes
    • As part of process investigation
    • Following improvement activity.
  • Use a range of quality tools for data analysis
  • Evaluate the risks of making decisions based on incorrect process data
  • Explain criteria used in selecting a data collection method for decision making
  • Recommend appropriate collection methods taking into account data source and type
    • Evaluate and use appropriate analytical tools for diagnosis and control of variable, attribute, and qualitative data for decision making
    • Report on the outcomes of monitoring and measuring activity.

Indicative content:

  • Fundamental principles
    • Principle of ‘factual approach’ to decision making (ISO 9000)
    • Types of data:
      • Variable
      • Attribute
      • Subjective.
    • Introduction to sampling plans
    • Reducing sampling risks
  • Criteria for selection
    • When, where and how much data to collect
    • How it should be collected and by whom.
  • Types of data collection
    • Process measures
    • Key performance indicators
    • Surveys
    • Periodic sampling
    • In process inspection.
  • Tools for data collection
    • Inspection and test records.
      • Automatic data collection
      • Time based records (processing speed)
      • Database queries and reports/data mining
      • Check sheets/tally sheets.