Mayank Gupta

Hi Veeranaga It is like jumping grade 5 and going straight to grade 6. There are some who can cope up with the jump and then there are those who want to take it one step at a time. Similarly, going directly for Black Belt is possible, but it will involve extra effort from one (brush up the basic knowledge and then do a lot of self study in order to understand the GB concepts as GB concepts will not be covered again in a Black belt course). So even though doable, i do not recommend it as I believe the foundation needs to be strong and GB is the foundation for BB and MBB.

Hi Senthil You may or may not split the process into multiple modules. The decision depends on the following parameters 1. Metric that has to be improved 2. Scope of the project (should include the modules which directly work or affect the metric) 3. Level of the project (GB or BB). Usually BB projects are more complex and cross functional in nature

Hi Senthil Green Belt is a prerequisite for Black Belt training. Green belt forms the basis for mode advanced topics that get covered in Black Belt.

Hi Bheemannadora - you may use Kano model to capture the customer requirements / stakeholder needs (VOC). You may also use it to identify the need for new machinery/tools/processes in case there is a gap in the current product features and customer needs. However, you will also need to use a tool (like QFD, CTQ drill down or NGT) to arrive at specific process / product parameters (what we also call engineering characteristics) that fulfill the customer needs

Kano Model - is a product development theory which categorizes customer requirements with respect to product features into below five segments 1. Basic (must be quality) - as the name suggests, these are the basic expectations of a customer. If present, the customers are neutral, but if absent, it would lead to customer dissatisfaction 2. Performance (one dimensional quality) - features that result in satisfaction if present and dissatisfaction if absent. These are the stated needs of the customer 3. Excitement (attractive quality) - features that result in satisfaction if present and neutral if absent. These are the delighters for the customers. These needs are usually unspoken and are the differentiators for competing products 4. Indifferent - features that the customer is indifferent to as their presence or absence neither causes satisfaction nor dissatisfaction to the customer 5. Reverse - features that result in dissatisfaction if present and satisfaction if absent. These are the features that should not be present in the product Kano model is used to assess the product features on a periodic basis as the customer demands keep on changing. E.g. something that is an 'Exciting' feature today, will eventually become a 'Basic' feature tomorrow Nominal Group Technique (NGT) - is a problem solving tool that is used to generate and evaluate possible solutions. It is called 'Nominal' as it limits the interaction between participants in the initial stages as they are expected to write the possible solutions on a piece of paper. Once all solutions are written, the ideas are collected followed by a discussion on the evaluation of ideas. Usually voting is done to select the best solution. Sometimes multiple rounds of voting may be required. Using the KANO Model, we will get to know the customer requirements with respect to product features. The required features could either be present or absent. Then, NGT could be used in following ways 1. If the feature is present: to identify solutions to keep the variations and cost of the feature to minimal 2. If the feature is absent: to identify how the internal processes need to be modified / strengthened or what new processes have to be designed to be able to provide the new features

There was an accident in the recently concluded German Grand Prix where a wheel did not go correctly and it flew and hit a cameraman. Given that the wheels need to be replaced during the flash pit stops (close to 3 seconds), it needs to be done with 100% accuracy and precision. On the face of it, it might appear that it was a manual error by the mechanic (the wheel gun slipped from his hand), but in the fast paced life of F1 safety of crew and drivers is of prime importance. This lead to the identification of a design fault on the wheel gun and Red Bull team will be revising it to ensure that such an accident doesn't occur again. An excellent example of how an RCA should be performed. Full article below: http://timesofindia.indiatimes.com/sports/racing/top-stories/F1-Red-Bull-change-pitstop-procedures/articleshow/21022022.cms

Unfortunate but it's true.....every industry has an excessive focus on automations......even in service industry there are examples where automations have not yielded the desired benefits...

The difference lies in the definition iteself. DPMO (Defects per Million Opportunities) talk about the defects while DPPM (Defectives parts per Million) talks about the defectives. Take an example: A car manufacturer produces 1000 cars with each car having 50 checkpoints. Hence Units - 1000 Opportunities - 1000*50 = 50000 Assume, 10 cars are defective while the total number of defects are 250 (i.e. these 10 cars cumulatively failed on 250 checkppoints). Hence Defectives - 10 Defects - 250 DPMO = 250/50000*1000000 = 5000 DPPM = 10/1000*1000000 = 10000 Typically in a service industry, Customer is more concerned with DPPM. For a service provider it makes more sense to look at DPMO. Even if they start working with DPPM, they will eventually have to drill down to the defects that are rendering the service as defective. Not sure if the same logic applies to manufacturing, but even though DPPM is an excellent metric to track the performance of the manufacturing unit, for any improvements you still need to look at DPMO.

Hi I'm looking for a list of metrics that will be applicable in a BPO / back office operations environment. Does anyone have it ready? Pls share with me at [email protected] thanks Mayank Gupta

Background - There are 15 different case types that we get in one process. The total volume for the process is summation of the volumes of these individual case types. A project requires me to check whether the volume mix (over the 15 case types) has been consistent or not as compared to a baselined volume mix. What test can be used?

Came across this interesting question today..... My baseline data contains 1000 units. Number of opportunities per unit is 5. A total of 400 units were audited resulting in 500 defects. So what should be used as the no. of units for DPMO calculation? Should it be 1000 (no. of units considered for baselining) or 400 (number of units audited)? Thanks Mayank Gupta

Isn't normality a pre-requisite for plotting control charts?

Related query How do I check for the stability of non-normal data? And what is the course of action in case we find that the data is not stable?

Amazing thought VK. I have a situation described as below Being in service industry, the metrics of Resource Utiliztion and Productivity are paramount for us. Like any other service industry, we do track the time an agent takes to process. We have an automated time tracker. What usually happens is an agent comes into office, starts the time tracker and leaves it on for the full day. Before leaving he signs out from the time tracker - thereby showing that he was only working on the process the whole day. Now this is IMPOSSIBLE. The challenge here is that he forgets or does not want to enter the break times / training times etc. How can this be tracked and monitored? My initial thoughts on this is to have a metric which says # of instances he did not update the tracker per the total # of times he was suppose to update it. E.g. Assuming he took 1 break and attended 1 training. Assuming that he needs to start and stop time for each activity, he will have to update the tracker 10 times (1 break, 1 training and 3 processing activities). Any suggestions???

Agree with you VK. A couple of things that needs to be kept in mind while doing hypothesis: The concept of sample and population and the fact that whenever we take a sample there are chances that the test statistic will not be equal to the population parameter. More often than not, we are more interested in the alternate hypothesis as that makes my life interesting If after hypothesis, I get a high P, means the statistic from the sample in question is probably "insignificantly" different to the population parameter. however, there could be samples where the statistic could be "significantly" different from the population parameter. Not sure if this example would fit in this case but recently came across a case where even after project completion the 2 proportion test was not showing significant improvement. The problem here was that the baseline data was for 6 months and the improvement data was for only 1 week. Once we compared the improvement data for 1 complete month, we could notice a significant improvement in the project. We concluded that we needed to have a more appropriate sample for the test to give "significant" results

Hi All Today when I will complete the training, I would have trained 100+ guys on six sigma. Every training has been a new learning experience for me. Training others have kept me closer to the concepts and provided me a better understanding of the same. I guess this comes primarily from the fact that you first need to be clear on concepts before training others. Participants have asked me questions which have forced me to revisit the books and six sigma concepts, but recently in my training somebody asked me "Why is it called Six Simga and not by any other name" and I found his question to be very innocent and relevant. Any answers for this??? Starting this topic to know how have your experience been with training people on six sigma and what's the most interesting question that people have ever asked?

That's great news VK, just goes to show the relevance of the web-site and the efforts you are putting in to make it a success. What are the other sites (i guess ASQ will be there)?

Superb

Hi All I have recently initiated a project precisely to improve the TAT for the recruitement function in my organization. So when I read the thread, it was like reading about my own project. Currently the project is in Measure phase. We identified the project as the operations was taking a hit due to non-availability of resource due to delay in hiring. Special thanks to VK outlining the steps for the proect and thanks to his training that I had thought of the same path for completing the project. Will not be able to share much, but yes as a matter of fact, there are improvement areas that I can see just by looking at the process flow for recruitement. Let's see if we can have some early wins by streamlining the process flow.

The UCL and the LCL shown are for the last data point (i,e, Defective = 12 and Volume = 470). How do we arrive at these figures is as follows 1. First calculate pbar => find out individual proportions and then take the average of the same. 2. Control limits = pbar +- 3 times (square root(pbar(1-pbar)/N), N = sample size for the last sample (if you change the last data point, you UCL and LCL would change).