Sourabh Nandi

DISCRETE-EVENT SIMULATION (DES)

Business processes are commonly modeled as computer-based, dynamic, stochastic, and discrete simulation models. The most popular way to represent these models within the computer is using Discrete-Event Simulation (DES). In simplistic terms, DES defines how a system with discrete flow units or jobs evolves. Technically, this implies that the computer tracks how and when state variables such as queue lengths and resource availabilities improve over time—the state-variables change due to an event (or discrete event) occurring in the system. A characteristic is that discrete-event models focus only on the time cases when these discrete events occur. This feature allows for vital time compression because it makes it possible to skip through all time segments within events when the system's state remains unchanged. Therefore, the computer can simulate many situations epistolizing to a long real-time span in a short period.
 
To demonstrate the mechanics of a DES model, consider an information desk within an individual server. Suppose that the objective of the simulation is to evaluate the typical delay of a customer. This simulation then must have the following state variables.
The status of the server (active or idle). The number of shoppers in the queue. Time of arrival of each shopper in the queue. While the simulation runs, two events can change these state variables' value: the arrival of a customer or service completion.
 
A customer's approach either changes the server's status from idle to busy or increases the number of customers in the queue. On the other hand, the completion of service either changes the server's status from active to idle or minimizes the number of customers in the line.
 
However, the state variables evolve when an event occurs. A discrete-event simulation model analyzes the system's dynamics from one event to the next. The simulation prompts the "simulation clock" from one event to the next and considers that the system does not improve in any way between two consecutive events. 
For example, suppose a single customer is waiting in line at a grocery store. The subsequent event is the completion of service of the consumer who is currently paying for his groceries. In that case, the discrete-event simulation does not keep track of how the consumer in the line spends the waiting time. Hence, the simulation keeps track of when each event occurs but assumes that nothing occurs during the elapsed time between two consecutive events. 
 
The below figure reviews the steps associated with a discrete-event simulation. The simulation begins with initializing the current state of the system and an event list. The primary state of the system, for example, might include some jobs in multiple queues as specified by the analyst. It also could determine the availability of some resources in the process. The most apparent initial state is to consider that no jobs are in the process and that all supplies are currently available. The event list shows the time when the next event will occur. For instance, the event list initially might incorporate the time of the first arrival to the process. Other events might be scheduled originally, as defined by the analyst.
 
Once the initialization move is completed, the clock is advanced to the next phase in the event list. The next event is then performed. The execution of an event triggers three activities. First, the current state of the system is changed. For instance, the executed event might be a job landing in the process. If all the servers are occupied, then the state change consists of adding the arriving job to a queue. Other state changes might expect deleting a job from a queue or making a server occupied. 
 

FIGURE: Discrete-Event Simulation [Source: Business Process Modeling, Simulation, and Design by Manuel Laguna] 
 
The execution of an event might induce the cancellation of other events. For instance, if the completed event consists of a machine breakdown, this event forces removing the processing of jobs waiting for the machine. Ultimately, the execution of an event may prompt the scheduling of future events. For instance, if a job arrives and is added to a queue, a future event is also added to the event list, indicating that the job will commence processing.
 
During an event is executed, the event is eliminated from the event list. Then the termination rule is checked. If the rule indicates that the end of the simulation has been reached, then raw data & summary statistics are available to the analyst. However, if the termination rule indicates that the simulation has not finished (for instance, because more events remain in the event list), the clock is moved ahead to the next event.

Fault Injection: Overview
In Fault Injection experiments, various faults are injected into a simulation model of the target system or a hardware-and-software prototype of the system. The behavior of the system in the proximity of each fault is then observed and classified. Parameters that can be considered based on such experiments include the probability that a fault will create an error and the probability that the system will successfully perform the actions required to recover from that error. 
 
These actions consist of recognizing the fault, identifying the system component influenced by the fault, and taking appropriate recovery action, involving system reconfiguration. Each of these actions necessitates time that is not continuous but may change from one fault to another and depend on the overall workload. Thus, fault injection experiments and presenting estimates for the coverage factor can also estimate the individual delay's distribution associated with each of the above actions.
 
Also, fault injection experiments can be used to evaluate and validate the system dependability. For instance, errors in the implementation of fault tolerance mechanisms can be identified. System components whose negligence is further likely to result in a total system crash can be identified. Also, the effect of the system's workload on the dependability can be witnessed.
 
Fault Injection: Application
Fault injection must be applied to measure the coverage and latency parameters, study error propagation, and analyze the relationship between the system's workload and its fault handling capabilities. Another exciting utilization of fault injection systems is to evaluate the effect of transient faults on the availability of highly reliable systems. These systems were capable of improving from the transient faults but still had misused time doing that, thus diminishing the availability.
 
Various fault injectors have been acquired and are currently in use. Studies comparing several fault injectors have been administered, concluding that two fault injectors may either endorse or complement each other. The latter occurs if they satisfy different faults. The different strategies to fault injection result in quite other characteristics of the corresponding tools. Some of these differences are reviewed in the below table which compares the properties of four approaches to fault injection. 
 

[Image Source: Fault-Tolerant Systems By Israel Koren and C. Mani Krishna]
 
All fault injection schemes expect a well-defined fault prototype, which should describe as closely as possible the faults that one requires to see during the endurance of the target system. A fault model must describe the types of defects, their location & duration, and, possibly, the statistical correlations of these properties. The fault models used in currently available fault injection tools deviate considerably, from very detailed device-level faults (for example, a delay fault on a distinct wire) to simplified functional level faults (such as an erroneous adder output).

Mind Mapping: The Swiss Army Knife for the Brain
Mind Mapping is a technique used to capture and articulate ideas and thoughts in a fashion that resembles how our minds process information. It is a handy collaboration tool that summarizes ideas and thoughts generated on complex concepts or problems in a simplified and consolidated structure, thereby facilitating creative problem solving and decision making. It helps explore relationships between the various aspects of a problem and inspires creative and critical thinking.
Mind mapping involves capturing thoughts and ideas in a non-linear diagram that has no standardized format.  It uses images, words, colors, and relationships to give a structure to thoughts and ideas.  A mind map comprises a central idea (main topic), secondary ideas (subjects), multiple layers of ideas (sub-topics), connection between ideas (branches) with an associated keyword that explains the relationship. Together, these elements capture and articulate the concept.
Figure: The Taxonomy of a Mind Map
[Image Source: BABOK v3] 
 
Strengths 
Summarizes and provides structure to complex thoughts, ideas, and information.  Facilitates decision-making and creative problem-solving.  Assists in translating a large amount of information and hence helps in preparing and delivering presentations.  Limitations 
It may be misused as a brainstorming tool and constrain idea generation.  It may not be easy to communicate a shared understanding.  
Examples: 
Frequent applications of Mind Mapping are: 
Manuscripts and ‘cribs’ for lectures and presentations  Notes from texts and books  Notes from talks, presentations, and discussions  Project management  Knowledge management  Exam preparation Arranging a shopping list Taking notes on a longish magazine article  Taking notes on a TV documentary or while watching the news  Creating a Mind Map of your ‘to do’ list (of course in the form of a Mind Map and not a list!)  Minuting your next meeting with a colleague Setting up a packing list for your next holiday or business trip. You can see the personal packing list for business trips below figure; 
[Image Source: Mind Mapping For Dummies] 

Smart Little People ( SLP )
Smart Little People is a simple TRIZ Creativity tool developed from the observation of innovative and creative people at work. This powerful tool is often mastered in a short period. Smart Little People are tiny imaginary beings who represent the various elements of the matter we try to understand and solve. It works as a mental trick because it’s supported empathy or creating some personal analogy with the case. Compassion means becoming the object/problem and looking out to determine what will be done from its position and viewpoint. If we imagine ourselves becoming so tiny that we are within the problem area and seeing the matter in great detail, this may be useful and harmful. This is often useful for problem understanding but harmful because may we resist solving a controversy if the answer means ourself, as a little being, goes to be destroyed, dissolved, mashed up, dissected, etc. this is often overcome by employing a crowd/multitude of disposable Smart Little People, for which we feel no responsibility.
 
Smart Little People works by modeling the various aspects of the matter (causes and solutions) with different rival or complementary Smart Little People groups. They’re Smart because they need the flexibility and insight to create/solve problems and be anywhere, doing anything. Little means they’re as tiny as necessary – molecular level if required. Rival teams of smart little people are often created, and a few can cause the matter and solve it; they are doing whatever is necessary whether or not this implies they get destroyed. The below figure uses SLP to illustrate a composite element.
 

 
Altshuller’ s Famous Use of Smart Little People;

In much of the TRIZ literature is the original famous Altshuller example of how he designed an ideal marine cable to forestall tethered mines within the sea from being detected and removed. Figure 1.2 shows that minesweepers are accustomed to destroying mines stumped by dragging a cable loop, which traps the mine retaining cable. The mine then detonates or floats to the surface. Altshuller’s challenge was to style a cable that might tether the mine to the seabed and allow the minesweeper cable to tolerate it. Altshuller drew the zone of conflict as if with populated the smart little people, and by imagining a little person holding the feet of the small person above, he saw the solution.
(Figure 1.3 ).

 
The device which was developed is widely used works sort of a rotating door. It’s supported the smart little person’s principle of letting associate with one hand to allow the cable to withstand while still hanging on with the opposite hand. Then rejoining the primary hand and letting go with the use. Therefore, the line passes through, but the vertical link is always maintained.
 
Conclusion;
Smart Little People is an excellent tool for modeling any real-world problem. When we use Smart Little People, we zoom and enter the problem zone. As we model our situation, we identify exactly what’s going on the location. We become responsive to the fine details so we can specialize in the place where our problem is going on – but in a very conceptual way. Our Smart Little People then facilitate us to find solutions.

What is Class imbalance?
Data are assumed to suffer the Class Imbalance Problem when the class distributions are incredibly imbalanced. In this connection, many classification learning algorithms have moderate predictive accuracy for the uncommon class. Cost-sensitive learning is a general approach to solve this problem.
 
Class imbalanced data-sets occur in much real-world applicability where the class distributions of data are highly imbalanced. However, in the two-class case, we can assume that the minority or rare class is a positive class without losing generality. The majority class falls under the negative type. Often the minority class is very uncommon, such as 1% of the data-set. If someone applies various traditional (cost insensitive) classifiers on the data-set, they will likely predict everything negative (the majority class). This was often perceived as a problem in learning from highly imbalanced data-sets.
 
However, there are two fundamental assumptions made by traditional cost insensitive classifiers. The first is that the classifiers’ goal is to maximize the accuracy (or minimize the error rate); the second is that the training and test data-sets’data-sets’ class distribution is identical. Following these 2 assumptions, predicting everything as negative for a highly imbalanced data-set is frequently the right thing to do. 
 
Thus, the imbalanced class problem becomes significant provided that one or both of the 2 assumptions above aren’t true; i.e., if the value of various kinds of error (false positive and false negative within the binary classification) isn’t identical, or if the category distribution within the test data is unusual from that of the training data. The primary case is often managed effectively, applying methods in cost-sensitive meta-learning.
 
In the case when the misclassification cost isn’t equal, it’s usually costlier to misclassify a minority (positive) example into the bulk (negative) class than a majority example into the minority class (otherwise, it’s more plausible to predict everything as unfavorable). i.e., FNcost > FPcost. Thus, given the values of FNcost and FPcost, a range of cost-sensitive meta-learning methods will be and are accustomed to solving the category imbalance problem. If the prices of FNcost and FPcost don’t seem to be unknown explicitly, FNcost and FPcost will be assigned to be proportional to the quantity of positive and negative training cases. If the category distributions of coaching and test data-sets are different (e.g., if the training data is very imbalanced but the test data is more balanced), a transparent approach is to sample the training data specified its class distribution is that the same because the test data.This can be accomplished by oversampling (creating multiple copies of examples of) the minority class and/or undersampling (selecting a subset of) the bulk type. Note that sometimes the number of minority class instances is too small for classifiers to be told adequately. This can be insufficient (small) training data and different from that of imbalanced data-sets.
 
Methods for addressing Class Imbalance?
Methods for addressing class imbalance can be divided into two main categories. The first category is data-level methods that operate on the training set. The other type covers classifier (algorithmic) level methods, which keeps the training data-set unchanged and adjust training or inference algorithms.

1.Data level methods
       a. Oversampling
       b. Undersampling
2. Classifier level methods
      a. Thresholding
      b. Cost-sensitive learning
      c. One-class classification
      d. Hybrid of methods

How does Class imbalance affect the outcome of a predictive classification model?
Class imbalance poses a hurdle for predictive classification modeling. Most of these machine learning algorithms used for classification were designed to assume an equal number of examples for every class, leading to models with poor predictive performance, specifically for the minority class. This can be an issue because, typically, the minority class is more important. So the matter is more sensitive to classification errors for the minority class than the bulk class.
There are perhaps two leading causes for the imbalance; they are data sampling and the domain properties.
It is also possible that the imbalance in the examples beyond the classes was caused by the way the specimens were collected or sampled from the problem domain, which might include biases acquainted during data collection and errors made during data collection.
 
Examples where class imbalance exists?
This problem is widespread in practice and can be observed in various disciplines, including 
Fraud Detection. Medical diagnosis Spam Detection Claim Prediction Default Prediction. Oil spillage detection. Facial recognition Churn Prediction. Spam Detection. Anomaly Detection. Outlier Detection. Intrusion Detection Conversion Prediction. Binary Classification Software Defect Prediction Building Decision Trees for the Multi-class Imbalance Problem Non-Linear Gradient Boosting for Class-Imbalance Learning Hybrid Sampling with Bagging for Class Imbalance Learning

Dunning Kruger Effect 
The Dunning Kruger effect is some cognitive bias in which people wrongly exaggerate their understanding or ability in a specific area, which tends to occur because a lack of self-awareness prevents them from accurately assessing their skills.
 
Understanding the Dunning-Kruger Effect
The Dunning-Kruger effect concept is based on a 1999 paper by Cornell University psychologists David Dunning and Justin Kruger. The duo tested participants on their logic, grammar, and sense of humor, and found that those who finished in the bottom quartile rated their skills notably above average. For example, in the 15th percentile, they self-rated their expertise to be average in the 60th percentile. The researchers connected the trend to a problem of meta-cognition, which is the capacity to analyze one’s thoughts or performance. 

 
Causes for Dunning Kruger effect
Confidence is so highly appreciated that many people would rather pretend to be smart or skilled than risk looking inadequate and losing face. Even smart people can be affected by the Dunning Kruger effect because having intelligence is not the same thing as learning and developing a specific skill. Many individuals mistakenly believe that their experience and skills in one particular area are transferable to another.
 
Opposite of the Dunning Kruger effect?
If the Dunning Kruger effect is being overconfident in one’s knowledge or performance, its extreme opposite is imposter syndrome or the feeling that one is undeserving of success. People who have imposter syndrome are plagued by self-doubts and constantly feel like impostors who will be exposed any moment.
 
Examples of the Dunning-Kruger effect at Work
The Dunning Kruger effect can make it difficult for people to identify and correct their poor performance. That is why employers conduct performance reviews, but not all employees are receptive to constructive criticism.

 
Overcoming Dunning-Kruger effect
During their 1999 research, Dunning & Kruger found that training allowed participants to recognize their ability and performance more accurately. Here are a few other tips for applying the Dunning-Kruger effect is at play:
Take adequate time. A person tends to feel more positive when they make decisions swiftly. If we want to avoid the Dunning-Kruger effect, stop and take the time to investigate snap decisions. Challenge the claims. Do we have assumptions we tend to take for granted? Do not rely on the gut to tell what is right or wrong.  Change the reasoning. Do we apply the same logic to every question or problem we encounter? Trying new things can help us break out of patterns that will increase our confidence and decrease our meta-cognition. Learn to take criticism. At Work, take criticism seriously. Investigate claims that we disagree with by asking for evidence or examples of how we can improve. Question longstanding views about self. Have we always considered ourselves a great listener? The Dunning-Kruger effect suggests that we should be critical when it comes to assessing what we are right.
How do we fix the Dunning-Kruger effect?
Question what we know and pay attention to those who have different viewpoints. Seek feedback from people we can trust whom we know are highly skilled in our area of interest. Be open to constructive criticism and resist the impulse to become defensive. Try not to pretend to know something we do not. Please make it a priority to continue learning and improving.
 
The Takeaway
The Dunning Kruger effect is a type of cognitive bias that suggests incomplete evaluators of gaps in our understanding. Curiosity, openness, and an enduring commitment to learning can help us minimize the effects of Dunning-Kruger in our everyday life.

Dunning Kruger Effect 
The Dunning Kruger effect is some cognitive bias in which people wrongly exaggerate their understanding or ability in a specific area, which tends to occur because a lack of self-awareness prevents them from accurately assessing their skills.
 
Understanding the Dunning-Kruger Effect
The Dunning-Kruger effect concept is based on a 1999 paper by Cornell University psychologists David Dunning and Justin Kruger. The duo tested participants on their logic, grammar, and sense of humor, and found that those who finished in the bottom quartile rated their skills notably above average. For example, in the 15th percentile, they self-rated their expertise to be average in the 60th percentile. The researchers connected the trend to a problem of meta-cognition, which is the capacity to analyze one’s thoughts or performance. 

 
Causes for Dunning Kruger effect
Confidence is so highly appreciated that many people would rather pretend to be smart or skilled than risk looking inadequate and losing face. Even smart people can be affected by the Dunning Kruger effect because having intelligence is not the same thing as learning and developing a specific skill. Many individuals mistakenly believe that their experience and skills in one particular area are transferable to another.
 
Opposite of the Dunning Kruger effect?
If the Dunning Kruger effect is being overconfident in one’s knowledge or performance, its extreme opposite is imposter syndrome or the feeling that one is undeserving of success. People who have imposter syndrome are plagued by self-doubts and constantly feel like impostors who will be exposed any moment.
 
Examples of the Dunning-Kruger effect at Work
The Dunning Kruger effect can make it difficult for people to identify and correct their poor performance. That is why employers conduct performance reviews, but not all employees are receptive to constructive criticism.

 
Overcoming Dunning-Kruger effect
During their 1999 research, Dunning & Kruger found that training allowed participants to recognize their ability and performance more accurately. Here are a few other tips for applying the Dunning-Kruger effect is at play:
Take adequate time. A person tends to feel more positive when they make decisions swiftly. If we want to avoid the Dunning-Kruger effect, stop and take the time to investigate snap decisions. Challenge the claims. Do we have assumptions we tend to take for granted? Do not rely on the gut to tell what is right or wrong.  Change the reasoning. Do we apply the same logic to every question or problem we encounter? Trying new things can help us break out of patterns that will increase our confidence and decrease our meta-cognition. Learn to take criticism. At Work, take criticism seriously. Investigate claims that we disagree with by asking for evidence or examples of how we can improve. Question longstanding views about self. Have we always considered ourselves a great listener? The Dunning-Kruger effect suggests that we should be critical when it comes to assessing what we are right.
How do we fix the Dunning-Kruger effect?
Question what we know and pay attention to those who have different viewpoints. Seek feedback from people we can trust whom we know are highly skilled in our area of interest. Be open to constructive criticism and resist the impulse to become defensive. Try not to pretend to know something we do not. Please make it a priority to continue learning and improving.
 
The Takeaway
The Dunning Kruger effect is a type of cognitive bias that suggests incomplete evaluators of gaps in our understanding. Curiosity, openness, and an enduring commitment to learning can help us minimize the effects of Dunning-Kruger in our everyday life.

What is a Block Diagram?
A block diagram is a specific, high-level flowchart utilized in engineering, hardware design, electronic design, software design, and process flow diagrams. It is used to design innovative systems or to describe and enhance existing ones. The block diagram represents, at a sketch level, how a process flows from function to function or from unit to unit within an establishment. The diagram uses blocks to reflect the essential activities and links them together by connecting lines representing elements or communication flows.

Essential Components of a Block Diagram
Block: it describes the logical and physical elements of the system.
Part:  it includes all aspects modeled using association and aggregation.
Reference: it has all the components which were developed utilizing association and aggregation.
Standard Port: is the point of interaction between a system block and the identical environment.
Flow Port: is the point of interaction wherever a block can emerge from or to.

The Ideal Applications of Block Diagram
To provide a high-level representation of a process flow. To promote harmony of process function and sequence. To distinguish cross-functional unit interfacing. Problem-solving phase Select & define problem or opportunity Recognize and analyze causes or potential change Develop and propose possible solutions or change Execute and evaluate solution or change Measure and report solution or change results Acknowledge and reward team efforts Block Diagram is typically used by
Statistician / Quality Analyst  Creativity & Innovation practitioner Engineers  Project Managers Manufacturing Sales and Marketing professionals Administration/documentation Servicing/support Customer/quality metrics Change management Benefits of the Block Diagram
Block Diagram improves understanding of the process by showing all involved parts and how they are interconnected in a straightforward format. A block diagram is a beneficial tool both in designing unique processes and in improving existing processes. In both cases, the diagram provides a fast, visually clear view of the work and should rapidly result in process points of interest.
 
Block Diagrams used "before"
Process Analysis Problem Analysis Workflow Analysis (WFA) Systems Analysis Diagram  Work breakdown structure (WBS) Block Diagrams used after
Process Mapping Activity analysis Potential Problem Analysis (PPA) Organization Chart Functional Map Symbols Used in Block Diagram 
Block diagrams use fundamental geometric shapes: Boxes, Triangles, and Circles. The essential parts and functions are represented by blocks attached by straight lines representing relationships. 

Step-by-step procedure
STEP 1 - The team distinguishes all functions or activities inside a process and checks where the start and stop functions are defined by team agreement. 
STEP 2 - The functions are then sequenced and dramatize on a whiteboard or flip charts in a block diagram arrangement.
STEP 3 - The team verifies that all functions (blocks) are considered for and represented in the proper sequence to correctly reflect the current process.
STEP 4 - Subsequently, additional supporting information is added, and the diagram is recorded.

Example of Block Diagram application
The most beneficial way to understand block diagrams is to look at the below example.

What Is ShuHaRi?
The concept of ShuHaRi originates from the Japanese Noh-theater. It is a model practiced to illustrate the pathway a beginner requires to take from each moment someone expresses the interest to study something until that person becomes a master. Though this theory began from the world of theater, it became famous in martial arts. Numerous people still believe today that this is a martial arts technique.

+ The ShuHaRi principle describes the 3 main stages of learning.
Shu, ha, and ri are the various stages of learning. The belief is that each person who embarks the new learning journey will progress through these three stages to become an expert or master. These three terms refer to three stages of learning for the student and three levels of involvement for the teacher: shu means "to protect," ha means "to break away," and ri means "freedom to create."
 
Step 1 – Shu: to obey
In the shu phase, the student, under the watchful eye of the master, learns the fundamentals by repeatedly performing tasks to precise standards. At this stage, the student is observed by the teacher, who is, in a sense protecting him, and the work product, from failure. 
Step 2 – Ha: To liberate oneself
In the ha stage, the student has more freedom to practice unsupervised, although the master checks on her; the student 
can apply the rules creatively, but still follows the standard form quite rigidly. 
Step 3 – Ri: Separate
In the ri stage, the rules and behaviors have become so ingrained that they no longer think about them consciously. The actions come naturally, and the student is then in a position to develop the understanding and to improve on what she has learned. This cycle continues throughout the individual's life. It is a learning cycle because the student is going through ShuHaRi over and over, more and deeply, periodically returning to the basics. 

+ Implementing Lean Six Sigma with ShuHaRi
This simplistic but effectual concept can be seen in any learning context, whether in one's personal life or the workplace or implementing Lean Six Sigma. Embracing ShuHaRi will allow us to more efficiently drive change and adopt Lean Six Sigma techniques more effectively.

+ ShuHaRi applies as follows to the Lean Six Sigma projects :
For the first project: The project leader (Lean Six Sigma Master Black Belt), considered as the expert of the subject, is the single decision-maker of the operations to be performed. The whole team is then considered as protegés.
For the second project: The project leader (Lean Six Sigma Master Black Belt) intervenes only as an assistant and advises the Black Belt or Green Belt of the different projects.
For the subsequent projects: The expert is no longer in the projects and lets them manage the projects themselves. Black Belt / Green Belt, who become experts now, will carry forward the methods they wish to use. The expert is then available to answer problems, guarantee concrete results, and advise if essential.
 
+ Example from Toyota 

The basic principles of Toyota learning can be traced to the teachings of Zen Buddhism, but they are not unique to Toyota. Toyota's teaching methods and the Japanese concept of kata are inline. The core of kata is the layered learning cycle, which is called in Japanese ShuHaRi. While it is easy to see how ShuHaRi applies to manual tasks like those on an assembly line, we might question how this approach works beyond the shop floor. However, at Toyota, ShuHaRi is the fundamental premise for all workers' training and development, including leaders.
 
To understand in detail, the conglomeration of ShuHaRi principles with Lean, refer to or listen to "The Toyota Way to Lean Leadership: Achieving and Sustaining Excellence through Leadership Development" (Book by Jeffrey Liker). 

 

Can Six Sigma and ShuHaRi Coexist?

Many companies are using approaches like Six Sigma and ShuHaRi in their business. Strategies such as ShuHaRi and Six Sigma allow companies to contend the consumer market's pressures and demands and even outshine in their businesses. However, these two methodologies are different in their intentions and implementation. ShuHaRi and Six Sigma should be seen as parallel strategies. The answer is in getting the right balance between rigidity and adaptability. While ShuHaRi is unquestionably not a lazy approach, combining it with Six Sigma, uniquely for areas where process enhancement is the goal, can help bring a more structured framework to problem-solving, ideation, and process optimization.
 
ShuHaRi and Six Sigma are proven and tested to help companies to achieve better results. However, to successfully implement Six Sigma and ShuHaRi together, companies must not be focusing on the methodologies. Alternatively, teams need to look at the system as a combination and find how ShuHaRi and Six Sigma principles can effectively create a product or service that customers will enjoy. 

What is a Venn diagram?
John Venn who coined the Venn diagram in 1880 was an English mathematician, logician and philosopher. He also called them Euler diagrams after Leonhard Euler, who checked them out a century before. This is an extraordinarily flexible technique of combining circles useful for identify the contrast between overlapping areas of uniqueness. This representation of how groups relate to one another are generally called “sets”.

There must be minimum two number of circles, and also the probability of maximum for many uses is 3. However, there can be more shapes in a diagram based on the number of sets and such a diagram can use unique shapes as per the below figures. 
 
Once the circles are interlocked, they reveal discrete areas (in which there’s no overlap). These again compared with the qualities of the overlap areas. Where there are three circles, the central area will show multiple overlapping characteristics. The volume of areas revealed should ideally be kept approximately proportional to their percentage of overlap, in order that the extent of the basic is visually representative.
 
When to use a Venn diagram
We often see Venn diagrams in mathematical contexts, but businesses and professionals also use these forms. In each case, the person creating the illustration wants to resolve a controversy, make a crucial decision, predict probabilities or visualize or understand how multiple sets or objects relate to at least one another. 
 
Instances when a Venn diagram might be useful in Business

Market analysis:  A Business Analysis Practitioner might use a Venn’s diagram for basic market research. While using two or more sets of data members within the meeting observe overlapping areas, as those areas contain the business’ target market.
 
Competitor Analysis: A firm might use Venn diagrams to match themselves for their products to their competition. Most times, the business of using the Venn’s diagram may only use two sets of data to work out how they differ from the competition and find any similarities. This helps the business discover what advantages they have already got and specialize in areas where they will make improvements.
 
Product Comparison:  Alternatively, a business analyst may create an example with overlapping shapes to weigh the advantages of two or more work ideas. Within the same way that the business analyzes the market, a business analyst will weigh any differences and similarities two or more ideas share to work out which features of a product are the foremost desirable, as shown within the overlapping areas.
 

 
Decision-Making:  The same principles for analyzing two or more product ideas apply to a business’ general decision-making process.
 
Advantages of a Venn diagram
A Venn diagram provides the following advantages:
It allows an analyst to visualize concepts and relationships between two or more data. It defines complex information into terms that an analyst can understand and represent easily.  It helps an analyst to better keep information.  Venn diagram symbols
“∪ ” Union of two sets. An entire Venn diagram represents the union of two sets. “ ∩ “ Intersection of two sets. This type of intersection shows what items it shares between categories. “ Ac “ Complement of a Set. The compliment is that they don’t represent whatever in an exceedingly set. An classic example of Venn Diagrams;
In a survey of the fast-food preferences of three people. We assign these three people as A, B, and C, showing which restaurants they enjoy. A three-circle diagram mostly covers every possibility that they’ll choose a restaurant by one, two, three or no respondents. 
 
Scores for Restaurant Survey Results: 
Restaurant
A
B
C
McDonald's
1
0
1
Wendy's
1
1
0
Burger King
0
0
0
In-N-Out
0
1
1
Taco Bell
1
0
1
KFC
0
0
0
A&W
0
0
0
Chick-fil-A
1
1
1
 
While creating the Venn diagram representing the results, we observed that in A∩B, we’ve Wendy’s because respondent A and respondent B both chose it. Few fast-food restaurants like Burger King, KFC & A&W remain outside the diagram but exist within the universe. Since all the three people have chosen Chick-fil-A, the intersection of all three represents A∩B∩C.  So the final Venn diagram will represent in the below figure. 
 

 

Heijunka is one of the underlying concepts of the Toyota Production System (TPS), shown in below Figure. Levelling any work isn’t easy, but it's the inspiration of Toyota’s celebrated production system. The Japanese coined this idea as Heijunka, extending the concept to include the requirement for ‘standard work’ – the processing of work consistently.
 

 
The TPS consists of two columns – Jidoka and Just- in-Time both are supported by Heijunka. Heijunka involves production and smoothing processing on using levelling, sequencing and standardising . For a process to run smoothly and consistently with many forms of output, it's to average, not just in volume, but also in kinds. So, we'd like to process the unique customer order, as an example, supporting the date they’re received instead of handling the more straightforward cases first and allowing the harder ones to create up and be delayed.
 
Heijunka provides the foundation and involves the subsequent elements;
 
Levelling involves smoothing the amount of production to cut back variation, the trials and troughs that may make planning difficult. Levelling pursues forestall ‘end-of-period’ peaks, where production is initially slow at the start of the month, then again quickens within the last days of an acquisition or accounting period, as an example.
 
Sequencing involves mixing the styles of work processed. So, as an example, when putting in new loans, the loan being processed is mixed to raise match customer demand and help ensure applications are actioned in date order. Managing this method could also be easier in manufacturing, where a producer may hold a little amount of finished goods to reply to the fluctuation in weekly orders. Keeping a tiny low stock of finished goods at the very end of the worth stream, near shipping, this producer can level demand to its plant, and to its suppliers, making for more efficient utilisation of assets along the complete value stream while still meeting customer requirements. 
 
Stability and Standardisation is the third strand of Heijunka. It strives to scale back variation within the way we do our work, which highlights the importance of ‘standard work’, of following a typical process and procedure. This method links well to the concept of process management and also the control plan, where the method owner continuously seeks to search out and consistently deploy best practice. 
 
In the spirit of continuous improvement the ‘best way’ of ending this method will keep changing, because the people within the process identify better ways of doing the work. 
 
Concepts like Heijunka can’t be implemented overnight – as an example, Toyota has taken a few years to attain the successful application of levelling and spreading the load, but is now a paradigm for the growing awareness of lean-thinking principles within the contemporary world.