krutibas Biswal

Dunning Kruger Effect : ▪ Type of cognitive bias that causes people to overestimate their knowledge ▪ How good are you with money? What about reading people’s emotions? How healthy you are, compared to other people you know? Knowing how our skills stack up against your competitor is useful in many ways. As per psychological research we’re not very good at evaluating ourselves accurately. In fact, we frequently overestimate our own abilities. David Dunning describes the Dunning-Kruger effect ▪ This effect explains why more than 100 studies have shown that people display illusory superiority, we judge ourselves as better than others to a degree that violates the law of math ▪ on a survey of two software company it was observed that 32% of one engineer company and 42% of other put themselves in the top 5% ▪ In an another study 88% of American drivers describe themselves as having above average driving skills ▪ On an average people tend to rate themselves better than most in disciplines ranging from health, leadership skills, ethics, and beyond ▪ what is particularly interesting those with least ability are often the most likely overrate their skills to the greatest extent.We all have pockets of incompetence we don't recognize ▪ Psychologist Dunning and Kruger first mentioned the effect in 1999, people having low competence suffer first they make mistakes and reach poor decisions and second those same knowledge gap prevent them catching their errors. In other words poor performance lack the very expertise needed ▪ How managers or supervisor can deal with team member : ⁃ First encourage feedback mechanism from other people and consider it even if its hard to hear . ⁃ second and more important keep learning the more knowledgable we become the less likely we are to have invisible holes in our competence

Heijunka or levelling is defined as balancing work by volume and variety during a period of time, typically a day. The day is further broken down into more manageable units of 2-4 hours The purpose of levelling is to ensure Work is evenly distributed amongst workers by volume and variety No work is waiting in queue No work is released upstream that is not required downstream A pull system of work is established Continuous flow is achieved A visual aid that identifies when and where work is behind schedule Example When a car is scheduled for a tune-up, the customer is given a time slot for the expected work to be done. That time slot may not fit your schedule. The scheduler will then attempt to fit you in at some agreed upon time. The Lean approach would be to increase capacity to meet customer demand by doing one or more of the following: 1.Determine historical demand, by the month, and create capacity for what the historical trend has been (tune-ups, brake problems, electrical problems, oil changes, etc.). 2.Cross-train more employees to handle the higher volume service jobs. 3.Increase daily capacity by reducing cycle times for repairs (this could be by implementing a bonus program based on the performance times of repair jobs, evening hours, additional shifts, etc). 4.Create capacity in bays where usage is not high by purchasing additional testing equipment, using part-time employees, etc. Auto service facilities have well-documented data (i.e., standard work) for servicing cars. They are ahead of many industries in that Lean tool application.

white hat : like a detective who gathers, organizes, analyses, and presents current information In the same fashion, while “wearing” a white thinking hat, you should collect known information and analyse it to reach fact-based solutions. Analysis of the gathered data will help you find gaps so you can look for ways to fill them or at least take note of them so you have a better idea of how to direct your conversations. · What do we know about this problem? · What don’t we know about this problem? · What can we learn from this situation? · What information do we need to solve this issue? · Are there potential existing resolutions that we can use to solve this problem? Yellow thinking hat : This hat represents enthusiasm and optimism. Like a bright, sunny day, the yellow hat is used to bring positive energy and life to every idea. With the this thinking hat, you may seek to find the benefits and value of ideas. You should not be hampered by limitations or boundaries, but rather believe that when there’s a will, there’s a way. What is the best way to approach the issue? What can be doe to make this happen? What are the long-term benefits of this solution? Black thinking hat The black hat is the opposite of the yellow hat and represents judgment. Wearers of this hat look for ways that the situation can go wrong. The black hat is used to expose flaws, weaknesses, and possible dangers of proposed ideas. The black hat dives below the surface to find any potential problems. The black hat is essential to keep you from jumping headfirst into a potentially disastrous situation. Questions will help on the black hat perspective can include: How will this idea likely fail? What is this idea’s fatal flaw? What are the potential risks and consequences? Do we have the resources, skills, and ability to make this work? Red thinking hat While you have the red thinking hat, your primary goal is to intuitively suggest proposals and plans of action based on feelings and hunches. This hat is open-minded and non-judgmental. Using the information gathered from feelings and emotions, you should be able to intuitively relate these feelings to the problem you are trying to solve. A red hat thinker’s objectives include: Make intuitive insights known. Seek out your team’s hunches and feelings. Reveal an idea’s hidden strengths. Use instinct to identify potential weaknesses. Find internal conflicts Red thinking hat questions may include: What is my gut feeling about this solution? Based on feelings, is there another way to fix this problem? What are our feelings about the choice we are making? Does our intuition tell us this is the right solution Green thinking hats Green hats are used for creative thinking. This thinking hat lets you think outside the box to explore more possibilities and bend the rules of problem-solving. This particular creative thinking should be free from judgment and criticism Keep in mind that as you work with the green thinking hat, you are free to express any idea that comes to mind. Even ideas that may sound crazy can have a kernel of feasibility that can put you on the right path to solving your problem The green hat may ask questions such as: Do alternative possibilities exist? Can we do this another way? How can we look at this problem from other perspectives? How do we think outside the box? Blue thinking hat This hat provides a management role and will help you analyse the situation. When wearing the blue hat, your job is to manage the thinking of the other hats to ensure that the team stays focused and works more efficiently toward a workable solution. The role makes sure the other hats are being used correctly Specifically, the blue hat seeks to: Efficiently and effectively improve the thinking process. Ask the right questions that help you direct and focus your thinking. Maintain and manage agendas, rules, goals, and tasks. Organize ideas and proposals, and draw up action plans. Questions that help in the blue hat role may include: What is the issue How do we define the problem? What is our goal and expected outcome? What will we achieve by solving the problem? What is the best method for going forward?

Quality Function Deployment - A method used to translate voice of the customer information into product requirements/CTQs and to continue deployment (for example, cascading) of requirements to parts and process requirements. This is also called as House of Quality. Methods associated with QFD • FAST - Function Analysis System Technique • FMEA - Failure Mode Effects Analysis • FTA - Fault Tree Analysis • DoE - Design of Experiments • VAVE - Value Analysis/Value Engineering • MCA - Measurement Capability Analysis • GDT - Geometric Dimensioning and Tolerancing • CpK - Process Capability Study • DFA - Design for Assembly • DFM - Design for Manufacturing • SPC - Statistical Process Control • CPR - Cost Price Ratio • CPM - Customer Process Matrix QFD Deployment Process Level A - Customer Requirements vs. Design Features • Gather customer requirements for all types of customers. • Specify performance level desired and achieved. • Provide competitive assessment of features and functions. • Relate customer requirements to design features. Level B - Design Features vs. Part Characteristics • Specify how to deliver design features. • Identify critical performance factors. • Link design features to specifications. • Track development of design maturity. Level C - Part Characteristics vs. Process Characteristics • Specify part features. • Set critical parameters. • Fix part tolerances. • Establish control limits. Level D - Process Characteristics vs. Operating System • Identifies supplier control plan. • Identifies special test equipment. • Specifies work instructions. • Identifies operator training requirements.

Sampling Strategy When Within the scope of each data collection Goal Samples save time and effort when data is collected · when it is impractical, impossible or too expensive to collect all data · when the data collection is a Cumbersome process Deriving a sampling strategy, which provides the most accurate level of information about the population being measured.so objective is to meet the goals of data collection but optimize the effort and cost The sampling strategy comprises the methodology for selecting samples as well as planning the sample size. This basic procedure can be divided into four phases : 1.The selection of samples should be entirely random 2.Choose a selection principle and a selection type different types of selection and selection principles will be driven by cost and effort criteria they vary depending on the question being asked 3.Determine a selection technique in case of random selection Non-random Selection Random Selection Quota Procedure Guideline of quotas e.g. accident repair Application: If only targeted information is needed Simple Sample All units have the same chance of being drawn Advantage: No knowledge about population necessary Disadvantage: High effort Cut-off Procedure Only a part of the population is observed, e.g. accident damage Application: If only one aspect is to be examined Cluster Sample The population is clustered in a logical way and one cluster is selected e.g. sites Advantage: Lower costs Disadvantage: Information can get Lost Haphazard Selection Example: Only the information which can be obtained easily, is collected Application: If only a first impression is to be gained e.g. for estimation of proportion or standard deviation for more precise sample size calculation Stratified Sample The population is stratified according to relevant criteria, e.g. spray-painting type, machine, location etc. Then a representative sample is removed from each stratum Advantage: Smaller sample Disadvantage: Information on the population must be available to start with 4.Determine the sample size The bigger the sample the greater the validity i.e. the quality of the statistical conclusion about the population One should therefore revert to available data (e.g. from IT systems): The data is treated like sample since the process to be improved hasn't yet been stopped When new data is collected (e.g. manual counting, surveys) an assessment of the cost of collection and desired level of confidence and precision must take place All in all three factors play a role when the sample size is determined: · the required Confidence Level which indicates the likelihood that the population mean lies within the given Confidence Interval. This value is normally a given for any organization e.g. 95% · The granularity is an indication of how precise we want to be and is usually half the width of the Confidence Interval · The costs and the duration of the data measurement increase with the sample size. When the sample sizes are calculated it is important to consider whether the requested precision is worth the costs inc Rules of thumb for sample size · Discrete100, at least 5 per category , Data : Ok/ Not Ok - Continuous - 30

Planning poker is a collaborative estimation technique used by Scrum teams to determine the story point values for each story in a sprint by gathering estimates and adjusting them after listening to the reasoning behind the low and high values team members have given. In planning poker, the team explains their estimates while they decide the number of story points for each story, and then they end up coming to an agreement on both the approach and the estimate Once the team has a prioritised list of user stories to get started with, they need to figure out how much effort it will take to build them. The team estimate the story points required to build each story out as part of the Scrum planning meeting at the start of each sprint. Most often the team knows which stories are the highest priority by looking at the backlog, so they try to commit to as many high priority stories as they can in each sprint. One way the team does this is planning poker. How planning poker used in software development : 1. The setup Each team member has a deck of cards with valid estimation numbers on each card. Usually the Scrum Master moderates the session. When the team can’t be in the same room to use cards, the team will agree on the point scale they’re going to use up front and a method for communicating estimates. Many distributed teams will have everyone provide their estimates over an instant message system to the moderator instead of using physical cards. 2. Understanding each story The team goes through each story in the sprint backlog in priority order with the Product Owner and asks questions about the story to figure out what the users need. 3. Assigning a story point value Post the discussion of the feature, each team member need to assigns a story point value by choosing a card from the deck and shares that value with the group. 4. Explaining the high and low numbers The estimates may differ between team members they assign the low number and the high number to explain their estimates. For example: 1 – 2 – 3 – 5 – 8 – 13- 21 2 is the low estimate. Maybe the person who estimated it knows a way to develop the feature faster than the rest of the team is assuming. The person who estimated 8 points might know of some complexity to the feature that the rest of the team isn’t thinking of. 3 people thought the feature was 3 points 5. Adjusting the estimates Once the team has heard the explanations, they have a chance to choose an estimation card again. If the team can’t be in the same room, they communicate their estimate using e-mail or IM to the moderator without sharing it out loud to the team. 6. Converging on an estimate In general teams begin with a significant range of estimates but that range narrows over the course of explanation and adjustment. After a few iterations through the process, the estimates converge on a number that the team is comfortable with. It takes 2-3 iterations of discussion until the team can unanimously agree on a story point value. Planning poker is very useful technique and is effective, in part because it’s collaborative. When the team estimates effort for the story point, each team member estimates the whole effort, not just his or her part of it. So even if you’re not doing the work, you’re estimating it... and that helps everyone on the team get a better understanding of the whole project.

A contradiction is when you have conflicts in what you want: either you want opposites of the same thing, or as you improve something, something else gets worse. Contradictions exist everywhere: they’ve always been around and human beings, being natural problem solvers, have found clever ways to resolve them. Some everyday examples of contradictions include: · I want a cup that keeps my coffee hot but doesn’t burn my hand. · I want a more powerful engine for my car, but I don’t want it to get · heavier. · I want to cover a large surface area quickly when I’m painting but I don’t want to make a mess. Two types of contradiction exist – technical and physical: · A Technical Contradiction is when, as you improve something, something else gets impacted. You start with a solution but when you put it into practice, you find a downside. For example, you make a car safer by using lots more material, but it reduces the mileage due to heaviness · A Physical Contradiction is when you want opposites of the same thing, like an umbrella needing to be both big and small. Examples - Example "Ice for a Drink" Standard Contradiction 01 Change in the aggregate state - Segmentation 05. Separation 18 Mediator 21 Transform damage into use Radical contradiction: INVENTING Key model: 18 Mediator – a) Use another object to transfer or transmit an action. Key idea: encapsulate the ice – the drink becomes cooler, and no water gets into the drink. Additional solution option based on the Radical Contradiction – on the (material) segmentation navigator and specialized navigator 38 Homogeneity: interacting objects should be made from the same materials – ice figurines should be made from the same drink (which is possible not for all drinks). ZOOMING Have the contradictions been removed? – Yes. – No. Super-effects: it becomes possible to decorate the drink with nicely shaped figurine Use hermetically encapsulated ice to cool drinks according to navigator 18 Mediator. Ice figurines can be made from the same drink in line with model 38 Homogeneity.

Who was Hammurabi? – Hammurabi was born Circa 1810 BC in Babylon Morden day Iraq . The lasting contribution of Hammurabi on Babylonian society was a set of laws written on 12 stones and displayed publicly for all to see. He died circa 1750 BC in Babylon. His master creation is Hammurabi’s’ code of Law. Another notable accomplishment was his expansion of city state Babylon to unite southern Mesopotamia. He created one of the first and consistent law codes The law is – an eye for an eye and a tooth for a tooth. The code of Hammurabi sought to organise people’s responsibility to each other. The king of Babylonian complied 282 laws that categorises 3 important concepts for organisation – 1) Accountability 2) Incentives and 3) Reciprocity. Accountability: He recognised the value of having “skin in the game”. The literal meaning is having people to honour the agreement is to hold the parties accountable. When the parties to an agreement have their real skin into the game they care more about outcomes and fairness Incentives Aligning incentives for Babylonian contractors per the law contractor couldn’t get away with minimum quality structures and standards rather contractors could be incentivised to make the structure sturdy and a predictable condition which can protect against high wind and earthquake. He understood Risk of Risk. Public well-being is difficult when people are cheating each other. The recent scandal in Wells Fargo would have been much less by implementing Babylon’s code where employee has created fake accounts to increase sales quota. people’s sense of risk when it comes to their own skins is calibrated. Reciprocity Communicating relevant standards is the most innovative feature of the code of law. The aim was to make the code easy to understand from a priest to a regular citizen. He had all the rules in a clay tablets and ensure it distributed across the kingdom. Organization should emulate Hammurabi code to communicate standards

When A journalist was asking about a news feed to Mark Zuckerberg, Facebook – “why is it so important a news feed and he replied, “A squirrel dying in front of your house may be more relevant to your interests right now than people dying in Africa”. The idea of relevance and the amount of information flowing in air is invisible and if you don’t pay attention to it could be a problem. How this translates for example someone is interested in political movement and economic movement in his/her country and may like those pages in Facebook feed and he/she may expect any news feed from that aspect which is basically interest area should appear as news feed whenever someone opens his/her Facebook page but you might notice suddenly it may disappear from your Facebook feed and the reason because Facebook is looking at what are the links you are clicking and it is noticing you might have clicked some shopping link or some other link which might not your interest area and without your permission your news feed will disappear , Facebook not the only place this is happening. This is kind of algorithm editing of the web, Google is doing it too for example if I search for something and someone else searching for exactly the same thing at same time, we may get very different result from search engine. As per google it looks at 57 different signals example what kind of computer you logged in, what kind of browser you are using and where you are located, and it provide tailor made personalised results. So there is no standard google anymore and important thing is this is hard to see your search results different from anyone else’s. This is not Google or Facebook there are host of the companies who is sweeping the web and doing similar kind of personalization. Business news on yahoo is now personalized where different people get different news based on their test. So this is a problem and if you all take this together this is called filter bubble, your filter bubble is kind of your personal unique universe of information that you live in online. Potential applications in Business World: Presently due to filter bubble companies may face issue to attract prospective customer through various social media channels so the strategy would be which social media will help to breakthrough these filter bubbles and increase their visibility and valuable engagements Since algorithm is controlling all this information based your personal universe and which is relevant to someone. The Social media companies must design algorithms to attract new customer and companies by creating platform basis not their personalization rather than focus on provide search result in such a way that to show the information which might : 1) Relevant 2)Important 3) Uncomfortable 4) challenging 5 ) other point of view. This will platform will help companies to introduce people to new ideas, new people and different perspective.

Information – information relieves uncertainty. Information gathering may be proactive as people use analysis to help them to reflect on an issue. Other time it is reactive as people seek information to verify other ideas. Analysis paralysis (or paralysis by analysis) describes an individual or group process when overanalysing or overthinking a situation can cause forward motion or decision making become "paralysed", meaning that no solution or course of action is decided upon. Paralysis by Analysis – The three situations in which organisations can carry out excessive formal analysis : Exchange of documents and reports between hierarchical levels within an organisation People believe they know what to do and carryout analysis to justify their project or point, but they are unable to force the issue onto the organisation agenda because participates in other level are either indifferent or opposed. Repetitive analysis on same leads to nowhere because the parties simply don’t understand each other. To avoid situation organisation could develop greater sensitivity to top management concern and organisation objective. The vicious circle Internal disagreement between department. People trying to block each other. It usually happens when there is wide participation and a diffuse of power structure. Each side of the department tries to bring large amount of analysis to convince each other on top of that senior management also perform the analysis to reach at a consensus and to clarify the direction. To avoid – Both sides of the department always need to consider whether their debate really focuses on facts and fundamental values and objectives of the organisation Decision Vacuum People who perform the analysis don’t have personal vision usually the analysis is always delegated to someone other than the decision maker. The decision maker thinking was often out of phase with the analytical work being done To avoid – to minimize the situation organization need to create nuclei of key people within the department or unit Extinct by Instinct - Group thinking as a major cause of poor decision making for example when everyone in power instinctively shares the same opinion of an issue. The key element of any strategy to avoid extinction by instinct is to implement checks and balances into system so that people must think through their ideas