askari

Dear VK If I were that person, I would FIRST estimate the current COPQ and make a striking impact on them (sr. management) by presenting as to how much bottom-line can be improved (or can be saved) without ever increasing the sales. Any company that listens to increase in profit without increase in production or sales, will (or should) immediately give the "go ahead" to that Six Sigma Professional FIRST. Ari

I agree with Mr. Khatri, In fact, a high-school education with an aptitude for maths/ stats with good professional experience (3-5 years of process knowledge) should be good enough to get into Lean / Six sigma career starting from Green belt certification. Regards, Ari

Hi Sapna, Lean is all about wastage & Turnaround Time (TAT) reduction. Hence 1. First identify areas / processes that take too long time or results in too much wastage. (Tip to do this : Just listen to Voice of Customers -both internal customers and external customers encompassing areas such as collection, testing, processing, storage and distribution - thereby contributing to better blood transfusion.) Traceability systems can be further examined - an area that has already caused great concern in many hospital blood banks. Full traceability of blood and blood components from the point of receipt by the hospital blood bank to their final destination. "Assumed fate" where a component is assumed transfused if it is not returned, however, is not acceptable; neither is "auto-fating" by the blood bank computer system. 2. Choose a process that has lots of scope for improvement and requires immediate improvement. 3. Define the problem/s in the current processes correctly. 4. Just try to apply Lean tools like 5S, VA/NVA/BVA analysis, SOP, Poka-Yoke (Mistake Proof) etc. I suggest a simple 5S and Mistake proof implementations will give you a "visible difference" in improvement which in turn should get the management buy-in to kick-off further bigger improvements. Enjoy your work. Ari

Hi Yogesh, Can you clearly explain / give more information on the situation where you came across this 1% target so that we can analyse and provide you an answer.

Hi Yogesh, I give 2 different situations with COPQ as 1.5% Situation 1: Product is Potable water : Test case : Water impurity is 1% or less. If the CTQ is the impurities in water and if you want to test the impurities present in a water sample and the CTQ meaure is the % of impurities in water and the target is 1% and if the allowable impurity ( upper spec limit) limit is 1% only, then the enitre lot of water will be rejected if the impurity present in the sample is 1.5% and your COPQ will be the cost enitre lot of water. Situation 2 : Product is paper plate: Test case: Paper plate is torn or not. Target is 1% defectives out of million plates. That is .o1x1000000. Target # of defectives = 10,000 Actual defectives are 1.5% out of Million paper plates. That is 15,000 defectives. All defective plates will become Cost of Poor Quality(COPQ) Here again, the COPQ is 1.5%. Hope this helps.

Dear Shri Khatri, Greetings. My thoughts are below: Let us first see the widely used "operational definition" of what management is: "Management is the art and science of getting things done from others by way of planning, organizing, staffing, controlling, and directing". It is obvious that the Management processes are also same as above. It is the Customer who decides if a process or a process step is Value-Adding or Non-Value adding. Now, let us ask the question to a customer: Will you accept a product or service without these processes? Strictly speaking, the customer should say "Yes" as long as you deliver the intended product or service without doing any of these processes. However, as management is an art and the process performers are human beings, there needs to be an Integrator who integrates the individual processes to get the final product or service or result. To understand it better, now, let us make an analogy to a movie song. Movie song: The Product Music composer: The Integrator (The Manager) Man-on-street : The Customer. The Management Processes: lyrics writing, composing, singing, and integrating. Without the integrator (the music composer- the manager - the management process of integrating), we won't get the pleasing song (the product) and the user / customer won't buy the song or pay for it. Hence, the process of integration, which is a management process, is essential in transforming the product to a saleable product for which the customer is willing to pay and hence is a Value-Adding process and will continue to be so until machines start managing themselves. Regards, Ari

Dear Partha & Manian: I regret my belated reply. I am very busy with my training schedules and other meetings/ tour programs.Besides, I moderate several membership groups for PM, Leadership, Six sigma etc . Hence, I will be able to post when I get some spare time and I request you to bear with my punctuated respones. Now, coming to your questions, The formula for sample size for estimating the population proportion is n = (z^2)*p*(1-p)/(E^2) SITUATION 1: Sample size for a Confidence Level of 95 % and Error margin of 5% : Using the above formula, Confidence Level = 95 % (needed to calculate the z value from standard normal dist) Margin of Error (E) = + or - 5%, then for maximum sample size, the value of p should be =0.5 and the sample size will work out to be n = 1.96^2 * 0.5 *0.5 / (0.05^2) = 384 Hence, a minimum of 384 calls are to be sampled randomly to state results at 95% Confidence level and within margin of error of + or - 5%. SITUATION 2: For CL = 95% and E = 3% ; Confidence Level = 95 % (needed to calculate the z value from standard normal dist) Margin of Error (E) = + or - 3%, Then, for maximum sample size, the value of p should be =0.5 and the sample size will work out to be n = 1.96^2 * 0.5 *0.5 / (0.03^2) = 1067 which will be normally rounded off to 1100 Hence, Sample size = 1100 for 95 %CL & 3% Error margin . Hope, this answers Manian's request for the formula and Partha's request for the SS for a margin of error of 3%. Partha may please note that the sample should be selected randomly especially using a stratified random sample to include samples from various types of calls stated by you. Regards, Ari

Manian: Is it 30 or 30%.? Partha: For unknown or very large poulation proportion ( current situation) of count data, a sample size of 384 should be sufficient for a Confidence Level of 95% and an Error Margin of 5%. If you need different confidence level and margin of error , then the sample size will be different. Further, in this case, one can safely assume the binomial distribution of the discrete count data approaching a normal distribution as the population is very large ( much like the binomial distribution of the outcomes of a dice game approaching a normal distribution for increased draws). Regards, Ari

Dear SJ, The idea we got was "to fill the bottle with less water". Perhaps, fill less by an amount equal to the spilled water. For example , if the content is 150ml and the spill water is 20ml ( due to poor bottle quality- shell thickness being too thin), then the actual content to be filled is 130 ml and price it & sell it for 130 ml only. The superiority of this mistake-proof idea lies in the fact that: 1. It does not require redesign of the bottle (increase the height or girth) 2. It does not require increasing the shell thickness (thicker bottle) 3. Existing bottles can be re-used, if necessary. 4. Less price 5. Collateral saving by avoiding water spilling over important office documents. (which mostly happens inadvertently) 6. Simplest in implementation. 7. Less water consumption instead of wasting that much water. Ari

Dear SJ, Cp is correctly defined as below; Cp = Specification width / Process width = (USL ~ LSL ) / (UCL ~ LCL) [ N.B: Often,people use the formula (USL-LSL) / (UCL-LCL) which might lead to confusion sometimes if understanding not clear] Now, Case 1: USL = + 3 SD ; LSL = - 3 SD Process width is mostly kept at 6 SD for all the cases. Cp = ( 6 SD / 6 SD ) = 1.0 A Cp of 1 corresponds to a Sigma Level of 3 as the area occupied between the USL and LSL is 99.73% ( assuming Short term capability and a Normally distributed process) which in turn equals to a yield of 99.73% which in turn equals to a DPMO of 2700 [( 100% -99.73%)x1000000 = 2700] which equals to a Sigma Level of 3. Hence , Cp of 1 equals to Sigma Level of 3 Proceeding on the same lines, Cp of 1.33 equals to Sigma Level of 4 Cp of 1.66 equals to Sigma Level of 5 Cp of 2.0 equals to Sigma Level of 6 Readers may please note that all the above Sigma Levels are Short term Sigma levels. Hope this helps. Regards, Ari

Dear SJ, Yes. I appreciate these ideas and I have seen these kind of bottles. However, don't we have to see the cost-benefit of the mistake-proof ideas/solutions.? It is better to implement only those ideas which are cost-effective. There are some more ideas which could have done it with lesser cost. Ari

Dear Shri Khatri, This is my favorite topic and basically, as a Civil & Structural Engineer, I try to mistake-proof all my operations as in Civil Engineering almost everything has to be "done right the first time". We can't construct any floor of a multistory building twice. As its success involves cooperation of many, especially after the emergence of mistake-proofing idea, I have found the soft-skills part of it rather more challenging than the creativity part of it. The last time I attended Mistake-proofing was " for a bottled water'. The problem is " how to mistake-proof a fresh bottled water from spilling over when someone opens it up ?". I came up with several ideas when brainstormed with my team. However, for the interest of the group and to invite many creative thoughts, I refrain from giving the near-proof solution now. I hope you will agree with me. Regards Ari

Dear Shri Khatri, In case somebody sets that goal and the average TAT actually reduces from 5min to 4min with standard deviation being same as before, we can say that there has been an improvement in the average TAT. My views are..... 1. Assuming the USL remains the same (i.e. 4 min), by reducing the average TAT from 5 to 4, the mean of the process has been shifted towards the USL; which means moving closer to the VoC, which implies there has been improvement in the average TAT. 2. By reducing the average TAT of the process from the current 5 min to 4 min, we make the mean coincide with the USL. Let us see the two situations "before" and "after" to draw any conclusion. BEFORE TAT reduction: Z- value = ( 4-5)/2 = -1/2 = -0.5 From a standard normal distribution curve, for z = -0.5, OK Region = 30.85% Defect Region = 69.15% AFTER TAT reduction: Z- value = ( 4-4)/2 = 0/2 = 0.0 From a standard normal distribution curve, for z = 0.0, OK Region = 50.00% Defect Region = 50.00% Since, the OK region has almost IMPROVED by 20%, we can resonably conclude that the average TAT has improved after this process improvement initiative. (Note:I have not addressed specifically the "actual capability" ( Cpk) related points as the question was just about improvement in the "potential capability" of the process per se when shifting the mean without reducing the variation (std.dev) Please let us know your thoughts / comments. Regards, Ari

I should thank Mr.Suresh for posing such an interesting situation and Mr Khatri for highlighting the importance of this situational question to evoke responses from members. It has brought to me a lots of thoughts and I submit them below for your kind consideration and comments. Firstly, Given the problem situation and proposed solution(s), one may often be wondering as to which one one should improve FIRST due to time and budget constraints. I recommend, for most of the situations, FIRST to reduce the std.deviation (s) rather than attempt to shift the mean or average. Secondly, Some have used the word "defective" to mean "defect". However, both are different and to be used carefully. Defect, statiscally speaking, is any performance data point not conforming to the specification limits. Whereas, "defective" is the product or service becoming useless or unfit for the intended use. Not all defects make or render a product defective. The classic example is a TV with scratches ( a defect) but still works fine ( not defective). Hence, a data point or an observation of a CTQ value, by exceeding the USL, becomes only a defect and not a defective. All data points BEYOND the specification limits are just defects only and may or may not make the product or service defective. Thirdly, Some one has linked the Std.Deviation to USL. These two have NO interdependencies, and are independent vlaues set or derived by two differnt stakeholders of the process namely the manufacturer ( for Std.dev) and USL ( the customer or consumer or the buyer). While Std. deviation ( in turn UCL/LCL) is the "Voice of Process" , USL/LSL are "Voice of Customer". Hence, statement "if the USL is 4 min the S.D cannot be 2 min" needs attention. Lastly, Even when attempting to reduce the std. deviation, such a drastic reduction of current value of 2 minutes to 0.33 minutes is a big dream, because it is almost 600% reduction in the deviation. This indicates that the process may first be re-looked thoroughly to obtain more information like are there many operators or only one operator to know possibilities of R&R issues. My observation is that, since the std.deviation is high, there may more than 1 operator performing this pseudo process. Ari