Business Analytics. MEAN STANDARD DEVIATION NORMAL CURVE DISTRIBUTION RAND. back2basics. HANDS ON. Process Capability. Product Specifications Preset product or service dimensions, tolerances e.g. bottle fill might be 16 oz. ±.2 oz. (15.8oz.-16.2oz.)
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Cp = 1, as in Fig. (a), process
variability just meets specifications
Cp ≤ 1, as in Fig. (b), process not capable of producing within specifications
Cp ≥ 1, as in Fig. (c), process
exceeds minimal specifications
One shortcoming, Cpassumes that the process is centered on the specification rangeRelationship between Process Variability and Specification Width
(USL = 16.2 & LSL = 15.8)
σ of 0.1 oz.
Six-sigma quality standard is now a benchmark in many industries
Before design, marketing ensures customer product characteristics
Operations ensures that product design characteristics can be met by controlling materials and processes to 6σ levels
Other functions like finance and accounting use 6σ concepts to control all of their processes±6 Sigma versus ± 3 Sigma
Background & Objectives
To understand which fundamentals drive outcomes like satisfaction, favorability etc.
* Fundamentals consists of Speed/ Performance, Security, Reliability, Navigation/Ux, Software Compatibility, Hardware Compatibility, Battery Life
**H2 Target Segments: Extreme Enthusiasts, Go Getters, Savvy Socials
An industrial engineer has suspected that the filling volume variability could be caused by three main factors;
feed rate of containers, temperature of yogurt, and the length in time the machine has been in operation since the start of the shift.
The industrial engineer suspects that there could be other factors affecting the filling volume; but only the stated factors can be easily controlled.
He understands that the filling volume will not be the same if it is repeated with the same values of feed rate, temperature, and operation duration.
The variability in the filling volume is caused by a random error, that for practical purposes is not important. But, he knows very well that, in regression analysis, the error is supposed to be normally distributed with mean zero and constant variance.
This error includes the effects of factors that are not included in the analysis; such as the age of the machine, viscosity of the liquid, etc.
This first step in the analysis is to collect data. What he has done was to observe the filling operation at different values of the control variables
To make scientifically based conclusions, he has to test some relevant hypotheses, like
1. how the feed rate, temperature, and operation duration collectively contribute to the variability or change in the filling volume.
2. how the filling volume is affected by the individual control variables.
3. how good the regression model in its entirety.