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Kano Model: Dr Noriaki Kano created Kano Model in 1984 for product development and customer satisfaction and to explain different categories of customer requirements and how these requirements influence the customer satisfaction. Any product or service given by any organisation will only be considered by customers if it solves important customer problems effectively. its not necessary that all customer requirements will deliver more satisfaction. you can have two different customer needs of equally important and you will be more satisfied if one goes well and you can be neutral if other goes well. it may be possible a customer can be more satisfied with the need of less importance and can be neutral with the need of more importance. Kano Model has two axis( Refer Fig 1) , the horizontal axis represents the degree of implementation or execution, on the right side it is fully executed an don the left side its not done at all. the vertical axis represents satisfaction level of customer , on the top customer is fully satisfied and on the bottom side customer is very dis - satisfied. Dr. Kano gave total five categories of customer needs by using these sets of axis which are explained below: 1. Performance Attributes: these attributes are one dimensional and are on the top of customer's mind when they are making choices and evaluating between competitor present in the market. ( Refer Fig 1), Performance of these attributes gives more satisfaction and if they fail to perform customer will be very dissatisfied., because these are liner in nature and it will be better to execute them fully so that customer is more satisfied. in other words you can call them as satisfiers to customers. for example : the battery life of a mobile , if it goes well then customer is satisfied otherwise dissatisfied or the average claimed by a car manufacturing company is 24 and its actually coming 14 then customer is fully dis-satisfied, or the resolution in your new TV is not as per claimed by the company then customer is not satisfied. any mobile company is claiming its mobile can be used for gaming purpose but it starts to hang in simple application then customer will be fully dis- satisfied. it means that you will receive more satisfaction by customer if you are able to able to execute fully these performance attributes. 2. Threshold Attributes: these are the basic attributes and customers take them for granted and expects them to be in the product or service they are having. if these are doing well then customers might be just neutral but if these are not performing well then it may leads to customer dis-satisfaction (refer fig 1). in other words we can call them as " Must-be's" because they must be included. for example : lock of the door of the car that we are considering to buy, cleanliness of the hotel room that you booked for your trip. 3. Excitement Attributes: (Refer fig 1)these are the unexpected surprises or delights for the customer. in others words these are termed as the " WOW - factor" or the different offers that any company gives on its product to attract more customers. these are called Delighters because they do exactly what they are. they attract more customer and sometimes it happens that customer leaves some of its needs when they see such type of delighters. for example offer of 2 lac off on purchasing a brand new car so in this case customer can overcome some of its needs to grab that opportunity, or to give more service time for more period of time or for kilometers. giving road side assistance also comes into this delighters. normally these type of delighters comes on festive seasons because customers wants to buy new things. if these are not given at any point of time then there is very less that customer will be less satisfied. 4. In different attributes: (Refer fig 1), these attributes are those types that presence or absence of them does not matter to customer satisfaction. customer will be neutral if they are present or absent. for example some advanced application in mobile phone that is not used by maximum people. they provide little value to your product because majority of customers dont care about them. 5. Reverse Attribute: ( refer fig 1) this is the rarest category out of these 5 and these items are those that you dont want to offer. these requirements are of such types that their presence leads to dis-satisfaction of customers. Reverse Attributes found very rarely. Microsoft's little " paperclip helper" is a very small example of this because most of people was annoying because of it. there is presence of grey shades between five categories which are defined above. it may change from person to person and its very important to keep in mind that Kano Model is not only absolute because whatever one is describing it as an excitement attribute , it might be possible that the other one describes it as the performance attributes. these is very often and its very simple fact that there is a little difference between customer to customer and their requirements. Kano model helps any organisation to take decision which can fulfill requirements of customers and one can take decision with the help of Kano Model. As every one knows that customer needs and expectations are very dynamic in nature and for any organisation it is must to understand their nature of business and understand the pace of change of industry year by year to be in this competitive market. if you are not going with change then you will be replaced by someone else, there are many examples of it like Nokia, Blackberry, HMT Tractors. Time is a very important factor which plays a very important role in Kano Model ( Refer Fig 1). As the time passes industries changes, technologies changes and customer requirements also changes. for the excitement attributes we should know that how long they will last . generally its a saying that whatever is exciting ( Excitement Attributes) today will be definitely asked for tomorrow ( Performance attributes) and can be expected the next day( Threshold Attributes). there are various examples of this transition and companies can take decision by seeing all these factors. its reality is that it forces companies to bring innovations continuously ( Excitement Attributes) to keep themselves in this competitive edge. for example when touchscreen was offered by Apple then it was excitement attribute for the customers buy as the time passed it became threshold attribute and now every mobile company producing mobile phone with touchscreen. another example is of headphones that was used to give with every mobile phone and now a days also companies used to give but Xiomi has changed this scenario by giving more features and quality product. As Xiomi does not provide any headphone with new mobile phone but still number one company to sell its smartphone because of its performance and excitement attributes. Customers are more satisfied even without headphone and its presence does make any affect. Other examples are of AC in car now days becomes threshold, Wifi in a hotel and camera in your mobile phone and remote control for your television. Kano model helps organisation to collect data on the base of voice of customer and helps to classify that data into different categories to launch a new product in the market to satisfy more and more customer to be in the market.

OFAT vs DOE? OFAT or One Factor at a Time is a method in which the impact of change in one factor is studied on the output when all the other factors are kept constant. DOE or Design of Experiments is a method in which the impact of change in factors is studies on the output when all factors can be changed at the same time. Similarity in both techniques 1. Both require experiments to be conducted 2. Both are statistical techniques. Solutions identified from these need to be checked for practical or business sense as well Differences in both techniques 1. In OFAT, only 1 factor can be changed while in DOE, all factors can be changed in a single experiment 2. DOE can be used to screen the critical factors from among a list of multiple factors and can also be used to optimize the factors for a desirable output. On the other hand, OFAT can only be used for screening of critical factors 3. OFAT will only tell the main effect of the factor on the output. DOE will tell us both about the main effect and interaction effects (i.e. the combined effect of 2 or more factors) on the output 4. In OFAT, the project lead can decide the number of experiments that they want to do. DOE will give us the number of experiments that are required (basis the fractional or full factorial design) It is a well established fact that DOE is superior to OFAT as it can help you change multiple factors at the same time and hence allows to study the impact using less number of experiments. However, the question is that whether there is a need to change multiple factors? E.g. Let us assume the mileage of the car as the output. There are multiple inputs for this (limiting to 5 for explanation) Mileage = f(Car Condition) Mileage = f(Road Condition) Mileage = f(Fuel Type) Mileage = f(Way you drive) Mileage = f(Resistance between tyres and road) Now if a car manufacturer wants to understand which of the factors is important for mileage, they will definitely prefer DOE over OFAT. They will be able to identify the critical factors and also optimize the value of critical factors to get maximum mileage. Now, consider my situation. I have only one car (10 years old), I take the same route to office everyday, i have a fixed driving style and the tyres are also in good condition. The above things mean that except for Fuel Type every other factor is almost constant. Now if I need to maximize the mileage of my car, I dont need a DOE. I can simply do a OFAT. This is precisely what I did. I have a BP station where I refuel my car. I experimented with the Speed (97 octane) fuel as compared to the normal fuel. Now common sense would suggest that there will be a statistically significant change in the mileage. However, when i did OFAT testing, the mileages were not different (may be the car engine is old and higher octane makes no difference) and I could continue to use the normal petrol and save by not spending extra for Speed. The point that I want to highlight is that if experimentation does not cause much and you can reasonably assume the other factors to be constant, then OFAT is also useful. Otherwise, it is well established that DOE is advantageous over OFAT. P.S. The data for my fuel test is available on request (though I will have to dig it out from the hard-disk).

One Factor at A Time Design of Experiments In OFAT, we hold 1 factor as constant and alter 2nd variable level Multiple (more than 2 factors) can be manipulated It is sequential, one factor at a time Simultaneous with multiple factors Experimenter can decide upon the number of experiments to be conducted In DOE, number of experiments is selected by the design itself We CANNOT estimate interactions among the Factors Systematically interactions are estimated Design is Experimenters decision Factorial designs (Full and Fractional) Low precision in OFAT With regards to Precision, in designed experiments the estimates of each factor is High High chances of False optimum (when 2+ factors considered) which can mislead High chances of optimization Used to estimate curvature in factors If there is curvature, estimation is done by augmenting into central composite design Domino effect, If one experiment goes wrong resulting in Inconclusiveness Orthogonal design, easy to predict and make conclusions It is sensible to say DOE is superior over OFAT, as we can save time and don’t have to perform multiple tests / experiments. Let’s see how Designed Experiments take an upper hand against OFAT with an example. Let’s run an example for 3 factors in 15 runs Few interpretations, with reference to above diagram In DOE, we can estimate the interactions between the factors but not in OFAT In DOE, prediction is better as the experimental runs have better data spread compared to that in OFAT with same number of experimental runs Curvature determination is better as it covers entire spectrum in DOE compared to OFAT and for that matter Response Optimisation is also better in designed experiments.