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Acceptance Sampling also known as Sampling Inspection or Lot Quality Assurance Sampling is a statistical tool or technique for quality control, where a whole lot of products or items are to be inspected, some items from the lot are selected for inspection. If the sample of items meets the desired quality requirements, then the whole lot is accepted, else, the same lot is rejected. Thus, the basic concept of sampling here is testing the samples for acceptance or rejection.
 
Acceptance Sampling was first developed in World War II as a quick fix for manufacturing that was originally applied by the US Military to the testing of bullets during World War II. Harold Dodge who was a veteran of the Bell Laboratories quality assurance department, developed the methodology. He was acting as a consultant to the Secretary of War.
 
Advantages:
Less expensive due to less inspection. Less handling of the product hence reduced damage. Applicable to destructive testing. Less personnel required for inspection of activities. Reduces the amount of inspection errors. As it is an economical method, it is possible to carry out sample inspection at various stages. A good sampling plan can give better results than that achieved by 100 percent inspection, as there exits inspection fatigue in 100% inspection. Disadvantages:
There exists a risk of accepting bad lots and rejecting good lots. This method requires proper planning and documentation of the procedure whereas 100% inspection does not. Examples:
A production line that produces helmets rarely produces defective items, however when a defect occurs the entire lot is impacted as the machine is out of calibration. Hence Quality consultant decides testing 1 helmet in every lot of 10,000 is sufficient quality control. When a helmet is defective, the entire lot is tested and the problem is immediately escalated to quality consultant. A chain of restaurants controls quality by paying secret or surprise visits and evaluations. In a food manufacturing company, basic inspection of ingredients are done before producing a batch. However, as an additional control, the company samples for detailed testing. When a test fails, production is stopped and the issue is investigated.

Lean Coffee first started in Seattle in 2009 by Jim Benson and Jeremy Lightsmith. It is a new format of holding meetings primarily focusing at minimizing the wastage of time during meetings and to use the available time in the most efficient manner. The format is inspired from Lean thinking and akin to a meeting in an informal place like a coffee shop. So here when the employees gather for a meeting the agenda is not pre decided, thus keeping the staff interested about the discussion as everybody talks about what they want to talk about which is why Lean Coffee is best described as agenda less meeting.
 
How to Facilitate a Lean Coffee Meeting ?
 

 
Steps to Facilitate a Lean Coffee Meeting:
Nomination of a Facilitator Topics to be suggested by Participants in the first 5 minutes Topics to be explained by the Participants in brief. Prioritization of topics for discussions by voting Discussion of topics in order of popularity and timeboxing each topic of discussion End the session with a short wrap up. Examples of Situations :
Brainstorming Meetings at Work/Team Huddles Lean Coffee meet ups to create a podcast. Conferences and Open Space Events Agile Workshops Induction trainings/workshops to break ice between participants. Strategy Discussions/ Roadmap exercise in Boardroom

Control Impact Matrix is a prioritization tool that is generally used after the causes are captured using Cause and Effect tools. Here prioritization is done based on the causes that may be in our control and the impact of the respective causes on the problem or the effect.
 
Control Impact Matrix in a Six Sigma DMAIC Process
Below points explains when to use Control Impact Matrix in a Six Sigma DMAIC Process:
To prioritize between various options/ solutions/ issues. It is drafted post cause and effect analysis. Prior to hypothesis testing in Analyze phase ( DMAIC), verified with data as well. Prior to solution implementation plan. At the distillation stage of issues/ causes. Helps gains consensus on which ones are priority. The other variant to Control Impact Matrix is Cost Benefit Matrix/Effort Benefit Matrix that is generally a 2 by 2 matrix which is used in Improve Phase to assess the solution alternatives.
 
Below is an example of Control Impact Matrix:

 

It is important to have an unbiased sample concerning the population when conducting surveys so that the results and predictions made are more accurate. This may not be the case of random sampling as here the samples are always biased as it does not represent the population accurately. Hence, Stratified sampling and Cluster Sampling are preferred to overcome the bias and efficiency issues of simple random sampling.
 
Difference between Stratified and Cluster Sampling:
 

 

Work Breakdown Structure (WBS) is a technique or a tool that is used to breakdown complex work to smaller tasks , thus making the overall work more manageable and approachable. It is a very popular project management tool that helps in dividing the the project deliverables into sub deliverables and work packages that will define work, duration, costs involved to carry out each of the tasks.
 
Steps to create Work Breakdown Structure for effective project scope management:
Identify Team :  Identify the team who will work on the project deliverables that may be divided further into sub deliverables. The team generally will include project managers and subject matter experts. Gather necessary documents : Gather all the required documents such as project charter that generally includes the business case, problem statement, project scope statement, objectives. list of team members and project management plan. Highlight the necessary Phases : Divide the Project scope to multiple phases based on the nature or complexity of the project. This will help in taking the project from conception to completion. Identify key deliverables : Key deliverables of the project are identified and the same are listed out that is targeted to be completed in each phase. Divide deliverables into sub deliverables(manageable tasks) : With the help of the SMEs the identified key deliverables are further broken down to smaller parts of work also known as work packages. Assign every sub deliverable : Once the manageable tasks are laid out, the same is assigned to every team member who will ensure to carry out each of the laid out manageable tasks. Every team member is provided with the tools, resources and authority they need to get the job done. Benefits of Work Breakdown Structure:
Helps in visualizing the scope of the project, thus making it easier to do the planning. Assigning responsibilities to the project team becomes easy. Helps in identifying the project milestones and control points. Helps in visualizing the important parts of work and identifying areas of risk. Helps in setting timelines for the project and ensuring that no work is duplicated or ignored. Examples of Work Breakdown Structure:
1)    WBS Of Event Planning
 

 
2) WBS Of Construction Project
 

 
3) WBS Of Holiday Planning
 

 
4) WBS Of Software Project
 

 
 

Work Breakdown Structure (WBS) is a technique or a tool that is used to breakdown complex work to smaller tasks , thus making the overall work more manageable and approachable. It is a very popular project management tool that helps in dividing the the project deliverables into sub deliverables and work packages that will define work, duration, costs involved to carry out each of the tasks.
 
Steps to create Work Breakdown Structure for effective project scope management:
Identify Team :  Identify the team who will work on the project deliverables that may be divided further into sub deliverables. The team generally will include project managers and subject matter experts. Gather necessary documents : Gather all the required documents such as project charter that generally includes the business case, problem statement, project scope statement, objectives. list of team members and project management plan. Highlight the necessary Phases : Divide the Project scope to multiple phases based on the nature or complexity of the project. This will help in taking the project from conception to completion. Identify key deliverables : Key deliverables of the project are identified and the same are listed out that is targeted to be completed in each phase. Divide deliverables into sub deliverables(manageable tasks) : With the help of the SMEs the identified key deliverables are further broken down to smaller parts of work also known as work packages. Assign every sub deliverable : Once the manageable tasks are laid out, the same is assigned to every team member who will ensure to carry out each of the laid out manageable tasks. Every team member is provided with the tools, resources and authority they need to get the job done. Benefits of Work Breakdown Structure:
Helps in visualizing the scope of the project, thus making it easier to do the planning. Assigning responsibilities to the project team becomes easy. Helps in identifying the project milestones and control points. Helps in visualizing the important parts of work and identifying areas of risk. Helps in setting timelines for the project and ensuring that no work is duplicated or ignored. Examples of Work Breakdown Structure:
1)    WBS Of Event Planning
 

 
2) WBS Of Construction Project
 

 
3) WBS Of Holiday Planning
 

 
4) WBS Of Software Project
 

 
 

Cost Reduction Vs Cost Avoidance

 
Examples giving comparison of Cost Reduction & Cost Avoidance


Other Examples of Cost Reduction & Cost Avoidance
 
Cost Reduction:
• Reduction of Overtime in a company
• Partnerships where a company can find a partner to invest in certain assets or services in order to reduce costs.
• Location Strategy of a company – migration of work to a new location due to availability of cheap labor when compared to home location.
 
Cost Avoidance:
• Investing in new technology (one time investment) as it eliminates the need of spending on compensation costs in future for additional workers.
• Looking for value added services that can come at minimal or no cost to the company as it avoids additional spending on the same in the long run. For instance, a company looking to purchase fleet vehicles can opt for those dealers who offer free oil changes , etc.
• Repairing a vehicle/equipment rather than allowing employees operate vehicles or equipment that are in poor conditions that could also increase the chance of an accident for which the cost is way too high when compared to repairing a vehicle/equipment.

Smart Little People(SLP) in TRIZ also called as Moving Little Men(MLM) is a creativity tool that makes people to to think or analyze a problem in different way and come up with the most optimum solution. It is a technique that can be used with the separation principles or the 40 principles once the conflict has been identified especially a technique that is very useful in brainstorming sessions. The idea here is to break down the system into smaller parts or at micro level for a better understanding. In this technique one imagines the system that we are analyzing consists of many clever and brilliant people or miniature dwarfs and these dwarfs are assumed to have super natural capabilities that can fly, swim, run, jump, fight also appear and disappear, change size etc. As little people are assumed to have aforesaid features, it also helps the analysts or eases up the process of searching for solutions for a particular problem during the phase of diagram building by answering questions like 'What to do?' to avoid any conflicting situation. The answer to the question' How to do it' will be solved in the next stage of analysis using SLP.
 
The SLP tool allows to explain the the conflict that is exists in a problematic situation, as is state also presentation of an ideal state of the process addressing the problem identified earlier.
 
Approach (SLP Method is done in 4 simple steps):

 
  The above steps are applied in the below example: The Tipper Wagon
 
Step 1
 

 
 
Step 2
 
 

Step 3
 

 
 
Step 4
 
 

Conclusion: Thus the SLP method is easy to use for Creativity and Idea Mapping during brain storming sessions making the problem solving exercise more fun, enjoyable and exciting as it is easy to understand for people coming from different educational and professional background.
 
 
 
 

Introduction : In Machine Learning it has become a very common practice to test various models to find a better performing model. The resultant improvement score from these models is sometimes challenging to differentiate if the relationship in the data is captured better or we are just overfitting the data. Hence Validation techniques are used to help us to get out of this dilemma, and the same is more helpful in achieving generalized relationships.
 
What is Model Cross Validation ?
Cross Validation is a technique where a particular data set is reserved as a sample on which you do not train the model, however the same sample is used later as validation sample to test your model  before finalizing it.
Here the basic idea is to divide the data into two sets, the Training set and the Validation set.
The training set is used to train the model The validation set is used to validate the model by estimating the prediction error Common methods of Validation techniques and the Pros and Cons of using each of them :
 
1) The Validation Set Approach (Data Split): In this approach the data is randomly split into two sets. One set that is 50% of the dataset is used to train the model and the remaining 50% of the dataset is used to test the model.
 
 Pros:
The technique is useful when you have a large data set that can be partitioned.  Cons:  
The test error rate can be highly variable, depending on which observations are included are included in the training set and which observations are included in the validation set, due to which there are high chances of missing out on some interesting information about the data which will lead to a higher bias.  
2) Leave one out Cross Validation (LOOCV): In this approach only one data point from the dataset is reserved and rest of the data is used to train the model to record the test error associated with the prediction. This process is repeated for every data point. Finally compute the overall  prediction errors by taking the average of all the test error estimates recorded.
 
Pros:  
As all the data points are used hence the bias will be low. Cons:
As the process is repeated n times where n refers to number of data points, it results to higher execution time. This approach might lead to higher variation in prediction error as we test the model performance against one data point at each iteration. If some data points are outliers it can lead to higher variation as our estimation gets highly influenced by these data points.  
3) k fold Cross Validation: In this approach, the Cons of above two approaches are addressed. Here the model performance on different subset of the training data is evaluated and then the average prediction error rate is calculated.
 
Below steps are followed in this approach:
The dats is split randomly into k folds/k subsets. One subset is reserved and rest of the subsets are used to train the model. Record the error you observe on each of the predictions. The process is repeated until each of the k subsets are tested. Finally, the average of the k recorded errors are taken to compute the cross validation error that serves as the performance metric for the model. Pros:
This approach is a robust method for estimating the accuracy of a model. It generally gives more accurate estimates of the test error rate than the LOOCV method does. k fold CV is computational when compared to LOOCV. Cons:
A randomly selected fold might not adequately represent the minor class especially in cases where there are huge class imbalance. A lower value of k takes us towards validation set approach that is more biased and hence undesirable, whereas a higher value of k leads to LOOCV approach that is comparatively less biased however there are chances of high variation.
 
4) Repeated k fold Cross Validation: In this approach the process of splitting the data into k folds can be repeated n times thus resulting to n random partitions of the original sample. The results are then averaged to come up with a single estimation.
 
Pros:
This method is advantageous if the train set does not adequately represent the entire population. Selected fold can be good representation of the whole dataset.  
5)Stratified k fold Cross Validation: In this approach the data is rearranged to ensure that each fold is a good representation of the whole dataset.
 
Pros:
Better approach than dealing with both bias and variance. Selected fold can be good representation of the whole dataset. Cons:
If the selected fold is not a good representative of the whole dataset , then its advisable to use Repeated k fold Cross Validation technique.  
6) Adversarial Validation: This approach generally checks the degree of similarity between the test/validation and train sets in terms of feature distribution. If there is no much similarity then we can suspect that the datasets are quite different from each other. This can be quantified by combining train and test sets, assigning labels such as 0 - train, 1- test and evaluating a binary classification task.
 
Pros:
This technique is used when some of the cross validation techniques give scores that are not even close to ballpark of the test score, and this happens as the test and the train sets are quite different or highly dissimilar, thus using adversarial validation technique will make our validation strategy more robust. Cons:
This technique may not be useful if the distribution of the test set changes, as the validation set might no longer be a good subset to evaluate the model.  
7) Cross Validation for Time Series: A time series dataset cannot be randomly split because the time section of the data will be messed up. For a time series forecasting problem, folds for time series cross validation are created in  a forward chaining fashion.
 
Pros:
Recommended technique for a time series data set.  
8) Custom Cross Validation Techniques: There is no single method that works best for all kinds of problem statements, hence a custom cross validation technique can be created based on a feature, or combination of features that will give the user stable cross validation scores.
 
Conclusion : In order to achieve a better predictive model , we must always focus on achieving a balance between bias and variance. Cross Validation in other words is also called as Resampling method as it involves fitting the same statistical method multiple times using different subsets of the data.
 

Two Sample T Tests are statistical tests that are used to compare the mean values of two independent samples/groups to determine if there is a significant difference between the means of 2 samples in reference. Two samples are considered to be independent if the selection of individuals/objects of one sample does not influence the selection of individuals/objects in the other sample in any way. The data from both samples should be normally distributed to apply the Two Sample T Test.
 
On the other hand, a Paired T Test is a statistical test that is used to compare the mean values of two related samples/groups to determine if there is a significant difference between the means of 2 samples in reference. A Paired T test is also called as  a Dependent T Test/Repeated Measures T Test. Here the groups can be related by being the same group of people, same item, or being subjected to the same conditions. Hence by using the same participants or item eliminates variation/individual differences that occur between the participants .Thus, Paired T tests are considered to be more powerful. This implies that we are more likely to detect a difference, if one does exist using a Paired T test over a Two Sample T Test. The differences between the values of the two related groups should be normally distributed to apply the Paired T Test.
 
Hypotheses of Paired T Test:
The null hypothesis (H0) states that there is no significant difference between the means of two groups. The alternative hypothesis (H1) states that there is a significant difference between the means of two groups Examples/Applications of Paired T Test :  
Performance of a group of students in a test conducted before and after a Training course. The before and after effect of a pharmaceutical treatment on the same group of participants.
Body temperature using two different thermometers on the same group of participants.
Body Weights of a group of participants before and after an exercise-training program.
Body Weights of a group of participants before and after a diet counselling course