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So far, we have discussed the voice of the customer and related performance measures.

Â We have discussed many factors that impact the value of a product or service.

Â Different customers in different markets may have

Â different preferences with respect to performances,

Â to ability, reliability, aesthetic, et cetera.

Â The next step is to develop a model that provides a single performance measure for value.

Â A performance measure that represents the value

Â for a variety of customers in different markets.

Â In the following discussions,

Â we will see some models designed to measure value,

Â and we will discuss the pros and cons of these models.

Â A very simple model is based on the quality parameters discussed earlier.

Â Each quality parameter is viewed as a criterion that is

Â measured on a specific scale using an appropriate performance measure.

Â The relative importance of each criterion is judged by

Â the customer and aggregated over all the customers by the model.

Â The end result is a summary table in which each row correspond to one of the criteria.

Â Each of the criteria is assigned a weight or relative importance.

Â And each alternative receives a score,

Â representing its performances relative to each of the criteria.

Â By multiplying all these scores in a specific alternative,

Â by the weight of the corresponding criteria,

Â and summing up over all the criteria,

Â a weighted average score is calculated for each alternative.

Â This weighted average score is based on the scores given by

Â the customers as well as the weight given to each of the criteria by the customers.

Â And therefore, it presents

Â the relative value of the alternatives as judged by the customers.

Â The main problem with this approach is how to estimate

Â the weights given by the customers to each of the criteria.

Â And in some cases, such as aesthetics,

Â how to estimate the score of each alternative with respect to the criteria.

Â In this example, an air traffic control radar system for airport is considered.

Â The first performance measure is the radar range.

Â The minimum acceptable range is 10 miles,

Â while the required range is 12 miles.

Â In addition, required values of quality and reliability are also specified.

Â Each criteria has a relative weight of presenting the voice

Â of the customer and its importance to the stakeholders.

Â Quality has the highest value of eight out of 10.

Â The specific alternatives that correspond to the side yield a range of 11.07 miles,

Â which is acceptable but only a little more than half way

Â between the minimum 10 miles and the required of 12 miles.

Â Thus, the score is a little above 50 percent or 53.5 percent exactly.

Â The quality of the proposed design is 59.59,

Â which is lower than the required quality of 75.

Â But the reliability is above the required 65.

Â And therefore, the score for reliability is 100 percent.

Â When the value of each alternative is calculated,

Â it is possible to find all the attractive feasible alternatives

Â and to perform trade-off analysis taking into account the cost schedule,

Â risk and value of each alternative.

Â The focus is on the efficient frontier

Â feasible alternatives that are not dominated by any other alternative.

Â A dominated alternative, for example,

Â an alternative with a value of 80 and a cost of 100.

Â If this cost and alternative with higher value of 90 is available,

Â they alternative is a value of 80,

Â and a cost of 100 is dominated or inferior,

Â and should not be considered any further.

Â In many real problems,

Â most alternatives are dominated.

Â And therefore, the efficient frontier can have

Â the new product development team focus on few efficient alternatives.

Â The weights and scores model is straightforward and easy to understand.

Â Its major weakness is the difficulty to find the weight of each of the criteria,

Â a weight that truly represents the voice of

Â the different customers and stakeholders in different markets.

Â In addition, there is a need to assign a score

Â to each alternative with respect to each criteria.

Â While this may be straightforward in a criterion such as

Â the range of radar system that can be measured in miles,

Â it might be very difficult in a criterion like aesthetics or style.

Â Professor Thomas Saaty of

Â the Wharton School of Business at the University of Pennsylvania

Â developed a model for finding the weights and scores based on the voice of the customers.

Â The model is known as The Analytical Hierarchy Process or AHP,

Â and is explained next.

Â The Analytical Hierarchy Process model is based on

Â linear algebra and can be implemented

Â using general purpose software like Excel and MATLAB.

Â There are special software packages that implement

Â AHP and are user-friendly and easy to use.

Â One example is Expert Choice.

Â And the following slides are taken from Expert Choice website.

Â The following presentation focuses on the theory

Â developed by Thomas Saaty to support decision making.

Â The theories implemented by the web-based application of Expert Choice,

Â a software that supports group decision making or GDSS.

Â By using Expert Choice,

Â a group of stakeholders can share their view regarding criteria,

Â relative importance, and scores of possible alternatives with respect to each criteria.

Â The Expert Choice software integrates these different views into a model that produces

Â a single measure of value or benefit associated with each possible alternative.

Â Decisions are made in different ways.

Â Some decisions are based on intuition and experience of the decision maker.

Â Other decisions are based on magic like a crystal ball that is used for fortune-telling.

Â And some decisions are based on scientific analysis like

Â decision trees and Analytical Hierarchy Process model developed by Thomas Saaty.

Â In the case of new product development projects,

Â group decision support system,

Â known as GDSS, are used,

Â and AHP is an example of GDSS.

Â Although there is no guarantee the decisions based

Â on scientific analyses are the best decisions,

Â the process of using such tools is in itself

Â important for the creation of shared understanding among the decision makers.

Â A good process leads to a consensus among

Â team members and develops their commitment to the decisions they make as a group.

Â The development of a model with

Â The Analytical Hierarchy Process starts with a discussion on three important issues.

Â What exactly we want to decide on, and consequently,

Â who are the customers and stakeholders whose voice is an important input to the process?

Â What are the needs and expectations of the stakeholders,

Â and how these needs and expectations are

Â translated into a set of criteria used to judge each alternative?

Â What are the alternatives that should be considered?

Â The name analytical hierarchic process or AHP comes from

Â the hierarchy of a tree-like model that is used to present the data on the objective,

Â the criteria and the alternatives.

Â In this model, each entity at the higher level might have

Â several entities connected below it in the next level.

Â But each entity at the lower level can have only one entity connected above it.

Â This is known as a one-to-many relationship.

Â And the entity above is called father,

Â while the entities below are called sons.

Â So there are many sons to each father and only one father to each son.

Â The top level is always the objective we want to decide on,

Â while the lowest level is always the set of alternatives.

Â There might be one or more intermediate levels that represent criteria.

Â For example, the criteria style may be broken down into shape, dimensions and color.

Â The weight of relative importance of the criteria is

Â determined by the voice of the customers and the needs and expectations of stakeholders.

Â Unlike personal decisions, where a person can use

Â intuition to decide on the relative importance of each criteria,

Â in GDSS, there is a need to integrate the needs and expectations of many individuals.

Â And, therefore, a process supported by proper tool is needed.

Â Saaty proposed pairwise comparisons as a basis for assignment of weights to the criteria.

Â The idea is to ask the stakeholders which of the two criteria

Â is more important in their opinion and by how much.

Â The individual inputs are integrated into a single way using the AHP methodology.

Â In the very simple example presented in this slide,

Â where the objective is to select a passenger car,

Â one can think of a process in which stakeholders were

Â asked whether a liability is more important than style.

Â And the number of stakeholders that responded saying yes was twice the number saying no.

Â The input is organized in a matrix,

Â in which each row and each column represent one criteria.

Â Each cell in the matrix represent the results of a pairwise comparison

Â between the criteria in

Â the corresponding column and the criteria in the corresponding row.

Â On the diagonal, each criterion is compared to itself and,

Â therefore, the entries are equal to one,

Â while the value itself below the diagonal is the

Â reciprocal of the value of the corresponding cell above the diagonal.

Â Saaty shows that the eigenvalues of

Â the pairwise comparison matrix are

Â weights that represent the perfect sense of the decision makers.

Â By finding the eigenvalues or weights,

Â the value or benefit of

Â each alternative can be calculated and used to support the decision.

Â The calculated benefit of value can be used along with the cost of each alternative to

Â find the efficient frontier of alternatives that are not dominated by other alternatives.

Â The points marked by blue dots are dominated by points marked by a red cross.

Â As for the same or lower cost,

Â higher benefits can be realized,

Â or the same or higher benefit can be achieved at a lower cost.

Â Thus, in this example,

Â there are two alternatives on the efficient frontier marked by an

Â X. AHP was used successfully on numerous projects.

Â And the results are reported in several books and articles,

Â some of which are available on the internet.

Â There are others tools similar to AHP.

Â For example, 1000minds was developed in

Â the University of Otago in New Zealand and widely used for numerous projects.

Â The motives are different,

Â but the purpose is the same: to support group decision-making

Â and to select the best alternative by a well-defined process and tools.

Â In this model, we concentrated on the question, what to develop?

Â Starting with the voice of the customer and the needs and expectations of

Â other stakeholders and translating the information into a measure of value or benefit.

Â It is possible to use the efficient frontier to select the most promising ideas.

Â The next step is to use the most promising ideas as

Â a basis for a plan of a project that can deliver the product,

Â process or service that was selected for implementation.

Â The process of translating the voice of the customer into

Â a specific product or process design is part of scope management.

Â We will define two types of scope.

Â The product scope is defined as

Â the features and functions of the product selected for development.

Â The project scope is defined as the work that needs to be

Â done to deliver the product with the required features and functions.

Â The tools presented in this lecture can help

Â us in translating the voice of the customer and

Â the needs and expectations of stakeholders into

Â a specific alternative for the product scope.

Â The next step is to translate the alternative selected into a specific project plan.

Â The basic building block of such plans are the activities of the project.

Â We will use a modification of the model called quality function deployment, QFD,

Â to transform the product scope into the project scope.

Â QFD is a process and tool developed by

Â Dr Yoji Akao to transfer the voice of the customer,

Â or VOC, to design parameters.

Â QFD is a template that has several dimensions.

Â We will concentrate on two.

Â The voice of the customer or what the customer wants,

Â also known as the whats in QFD terminology.

Â The engineering aspects or how we are going to fulfill the customer requirements,

Â also known as the hows in QFD terminology.

Â In the center of the QFD model,

Â each row corresponds to a specific requirement,

Â what the customer wants,

Â and each column correspond to a specific engineering decision,

Â how we are going to do it.

Â The cells in the center of the matrix

Â represent the relationship between the corresponding row and column,

Â in terms of the correlation between the two.

Â In the modified QFD, rows represent the project activities of the project scope,

Â and the columns represent the voice of the customer or requirements or the product scope.

Â The top cell in each column represent

Â the equation by which the requirement is calculated.

Â For example, under range,

Â we see the radar equation that tells us that

Â the range of a radar system in a function of three parameters.

Â TP, or the transmitter power, RS,

Â or the receiver sensitivity,

Â and AG, or the antenna gain.

Â The values of these three parameters

Â are determined by the corresponding three project activities.

Â TP, the transmitter power,

Â is determined by the transmitter design.

Â RS, the receiver sensitivity,

Â is determined by the receiver design.

Â And AG, the antenna gain,

Â is determined by the antenna design.

Â Given this information, we know that we should focus on

Â these three activities in order to satisfy the requirement for range.

Â