Mayank Gupta

Hi Natwar Thank you for the encouraging words. Keep contributing with your answers.

Hi Shraddha Not sure if I understood the question correctly. Could you elaborate what you imply by loss and waste analysis?

Hi Manoj If you are calculating Quality metric basis Defective calculation, you could use the Binomial Process Capability in Minitab.

Hi Manoj The calculations seem to be right. However, one could question the USL of 100%? If it becomes the USL, then you will never strive for 100% quality Anyways, you could verify your calculations by using our Cp, Cpk calculator at the below link as well https://www.benchmarksixsigma.com/calculators/process-capability-calculator/

Hi Manoj Cp and Cpk calculations are done for Continuous data type when both Lower and Upper Specification Limits are available. Now, I am not sure how you are calculating Quality, but in a typical scenario Quality % is Discrete in nature. Even if you consider it to be continuous, you will still need both the specification limits. However, for Quality %, we usually only have the Lower Specification Limit. E.g. customer says i want at least 95% quality. Here 95% becomes the Lower Specification Limit, but there is no Upper Specification Limit. For your specific case, I would recommend you to check the Binomial or Poisson capability function (depending on whether Defectives or Defects are tracked) in Minitab. Hope this helps.

The chosen best answer is of Natwar Lal for clearly explaining and highlighting the need for Bessel's Correction using a data set. Also go through the answer provided by Mr. Venugopal - Benchmark's in-house expert.

Hi Arun You will have to use data for all 25 months. There are guidelines on removing a data point while doing analysis, however looking at the description provided by you, I do not feel a need to remove any of the data points. Cp and Cpk are typically calculated for Continuous Data types, however since your metric i.e. Defects is Discrete, it is better to calculate DPU or DPMO. You may refer our calculator at the below link to calculate the current Sigma Level of the process. Enter the total sample size, the number of total defects found and number of opportunities (if known) https://www.benchmarksixsigma.com/calculators/sigma-level-calculator-discrete-data-defects/ Alternatively you could also run the Poisson Process Capability analysis in Minitab. Path is as follows Stat --> Quality Tools --> Capability Analysis --> Poisson Here you will have to enter data in columns. One for sample size and the other for defect count. Hope this helps.

Hello Ankit Since you mentioned rejections, I am assuming that you are considering Defectives. Hence you will consider the Defectives and total quantity of transactions (or items produced) to calculate Yield and subsequently the Sigma Level. You may refer to the calculator at the below link to calculate Sigma Level https://www.benchmarksixsigma.com/calculators/sigma-level-calculator-discrete-data-defectives/

Hi Vaishali Please elaborate on the intent. Currently it is unclear on what's the objective.

Hi Hemant Usually targets are given by the customers. If you are the customer and are unaware of the targets, you could probably look at your competitors or if there is a regulatory requirement for accuracy target. As a last resort and if none of the above works, then you could also set the target by looking at the process performance. Hope this helps.

Hi Larry Good to have you on our forum. It is always our endeavor to help forum members learn and grow. Accordingly we put two questions every week and seek answers from the forum fraternity. The current open question is at the below link https://www.benchmarksixsigma.com/forum/topic/35525-box-plot/ These questions are asked every Tuesday and Friday. Do attempt these questions in order to gain insights on tools and learn.

This was a slightly tricky one as the question was about why the Sigma Level or the Z score is the preferred metric for process baselining and not about what it is. The best answer has been provided by Vastupal Vashisht for highlighting the benefits of using the Sigma Level. Do read the response from our in-house expert to get more insights as to why the Sigma Level calculation is so popular.

Hi Devang Good observation. Lean Six Sigma tools can help in removing the Non-Value adding activities being performed by Supplier Management in order to avoid duplication / rework / rechecks etc. It can also help in reducing the cycle time and wait times in Supplier management tasks in order to reduce the overall Lead Time

Hi Duy There are two parts to your question. Let me try answering it part wise. Part 1 - whether bias should be compared against the reference value or the specification? During a Gage study, bias is observed in the complete range of the gauge with repeated observations. Simply stated it gives you an indication of the central tendency of the observations. This central tendency of the observations should be compared with another central tendency which in this case becomes the reference value (you may also understand the reference value as the target value). The problem with comparing it with specification is that it will not be like to like comparison. Let us take an example - suppose you are doing a gage R&R for measuring the inner diameter of a bolt. The reference value (or target) is 2.5 mm and specifications are +/- 0.1 mm. You picked a sample of 5 such bolts and did 12 trails with the operators and the study suggests that there is a bias of 0.05 mm i.e. the observed value is greater than the actual by 0.05 mm. Now if you compare it with the target, you are still within specification, however if you compare it with the specification, you might end up rejecting good bolts as well. Hence, the bias should always be compared to the reference or the target value and not with the specification. Part 2 - In cases where bias is large (>10%) and bias/specification < 10%, can the bias be accepted? Ideally speaking you should NOT accept the bias if is greater than 10%. You should first fix the measurement system. However, practically, in some projects, you may choose to accept the bias under the following conditions 1. There is only 1 sided specification limit (because in two sided specification limits, if you do not address the bias, it will start impacting the process capability as your reference value is improved) 2. Specification is too high even for the revised reference value (or the revised target) + the bias E.g. Let us assume, the moisture in a powder compound cannot exceed 13%. Your current reference value is 8% and the measurement system has a bias of 1%. If your target is 10%, you may choose to ignore the bias as you will not breach the specification of 13% with 1% bias. However, if you target is 12.5%, then you will definitely need to fix the bias before improving the process Hope this helps.

Hi Akshay As I had mentioned in my previous comment, the calculation of sum and sumsq and using it to calculate the standard deviation is not right.