Learning Outcomes

The program provides methods for quality control by applying statistical tool for use in production processes, but also means of control (process, product).

This approach allows the appraiser to rule on the relevance of the observed data whether to continue, to recalibrate or to stop the process.

The control can be applied in monitoring a single to various characteristics (qualitative and/or quantitative) of production, follow up physical or chemical parameters of process such as assays of active ingredients previously stabilised or neutralised.

The  main  aim  for  attendees  is  to  grasp versatile and relevant control tools

available, and operate them in single or combined way.

Times and costs are mandatory gages to streamline the implied tools in front of a wide range of situation and the required level of control vs risk management.

    

Prerequisites

Knowledge of quality tools such as (5M, Ishikawa, brainstorming), P values and statistical significance in processing computed data.

Participants

Product managers and team leaders, Quality manager, Process technicians and staff invol -ved in controlling manufacturing processes.

  (knowledge underlying SPC subject)

- Definition of distribution (histogram)
- Measures of central tendency
- Measures of dispersion

Characteristics of Normal and

  Standard normal distribution

- Properties of Normal Distribution
- Introduction to Central Limit Theorem (CLT)
- Definition of normality and Henry's Law app

  lication (Linear regression mode).

* Case studies

Review of Probability Distribution Law

- Normal distribution-Poisson distribution and
  Binomial distribution

Statistical Process Control – Steps

   development

- Analysis of SPC flow-chart implementation
- The key steps content
- Application limits of SPC

Statistical control chart introduction

   (introduction to (Xbar) control chart)

- Transition from a single sampling to a

   multiple sampling distribution per standard

   period

- Rules for  determining statistical control by

  distinguishing between common and special

  causes.

- Parameters identification and characterisa- 

  tion: Tolerances, natural process limits, spe

  cification limits and calculating process per-

  formance metrics with target value.

- Graphical representation.

Identification, calculation of capa-

   bility parameters for implementation

- Measurement Systems Analysis and per-

  formance Process capability indices's: Cp,

  Cpk, capability ratio, and assessing pro-

  cess capability,

- Process performance indices's Pp, Ppk and

  assessing process performance,

- Holistic approach according to “Taguchi's

  loss function”.

* Case studies