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Lean Six Sigma Presentation
- 1. Process Capability, Process Capability Index and Z Value Submitted By- HARSHITA BANSAL MBE
- 2. Process Capability The capability of a process is defined as the inherent variability of a process in the absence of any undesirable special causes and the variability is due to common causes.
- 3. Process capability index measures the extent of variation a process experiences relative to its specification limits. It also helps us to compare different processes with respect to the optimal situation or if they come up to our expectations. Process Capability Index Where: USL= Upper specific Limit LSL = Lower specific Limit = Mean σ = Standard Deviation
- 4. Example The data was taken from Wooden furniture manufacturing company, in the profiling process which is done with a four-side molder machine. Aim • Either the machine can have a particular tolerance or not. • Able to prove to the client that the process is fully capable to avoid the production of the non-conform products and identification for improvement.
- 6. Calculation of Cp & Cpk
- 8. Z value Z is the unit of measure that is equivalent to the number of Standard Deviation a value is away from the mean value. Y = Value of the data point we are concerned with µ = Mean of the data points σ = Standard Deviation of the data points Z = Number of standard deviations between Y & the mean (µ)
- 9. Conclusion As Cpk value is 1.28 which is greater than 1 and less than 1.33 this implies that our process is capable at 3σ . The mean value is 34.9994, so it is very close to the target value.
Editor's Notes
- When its came to process capability The question arises is your process is Capable? Taking an example of who is more capable to Olympic. we can say that have to check our efficiency in specific limit on constant biases. Which we can see in the given diagram. Here is the most favourable situation is the first one where the variation is spread within the specified limits,i.e., USL and LSL. where mean that refers the middle of the distribution. In the second situation the variation is much more spread out but the distribution is near borderline. Though if the variation is at the specified limit that it will be accepted but if it spread little more as shown in third graph then it will get rejected
- Do the cp alone is capable to tell the the process shift which is really critical to the business , no its just give the numeric value we need to calculate Cpk which measures how close you are to your target and how consistent you are to around your average performance. Cpk measures two things: 1) how close the mean of the readings are to the center of the lower and upper spec limits (ideally, the mean of the readings must equal the center of the spec limits); and 2) how widely spread the readings are (ideally, the standard deviation of the readings should be zero). The higher the Cpk, the better is the capability of the process to meet its requirements.
- The aim of the study was to analyze the process capability of the profiling operation
- All the samples were picked up randomly from the production, after the four side moulder machine and then measured. The data was collected during more days and shifts in order to have more reliable data data is a bell-shaped that means it is normal distribution control chart is in control and free from special cause. that fullil the assumption of Cpk
- The graph represents the process capability analysis. On the left end it shows the LSL which is 34.7 and on right hand side it show USL which is 35.3. The middle of the graph shows that the mean is = to34.99 The curve spread is exactly between the specified limits where Cp = Cpk = 1.28 , the process is capable at 3 sigma. Due to the fact that the process does not require a very high precision (it is about wood industry, not automotive or aeronautic industry), the system is not bad. Still, the system can be improved further on.
- A z-score describes the position of a raw score in terms of its distance from the mean, when measured in standard deviation units. The z-score is positive if the value lies above the mean, and negative if it lies below the mean. t is useful to standardized the values (raw scores) of a normal distribution by converting them into z-scores because: (a) it allows researchers to calculate the probability of a score occurring within a standard normal distribution; (b) and enables us to compare two scores that are from different samples (which may have different means and standard deviations).
- a wood industry company and the control charts for variables was implemented in order to achieve the good control in the process.