Design for Quality

Question 1

What are the ABCs for Quality in Design?

Answer 1

ABC for Quality in Design

To create “Quality” (product/services that satisfies need) there are 3 steps:

1. Understand your customer
2. Understand what your customer needs
3. Deliver what they need

A. Understand your customer

• Who is your customer, which market segment (demographic, socio-economic, innovativeness)?
• Arrow’s impossibility theorem (we cannot satisfy everyone’s preference)
• Which is your target market?

B. Understand what your customer needs

• Kano-model qualities (attractive, one-dimensional, must-be, etc.)
• Understand the different pleasures in products:
  
  • Physio: Pleasures via the sensory channels (sight, touch, smell, taste, hear)
  • Socio: Pleasure via relationship with others or between product and social identity (email vs Facebook, status through products)
  • Psycho: Emotional and mental response to products, related to cognitive demand (color effects, tendency to look at ads after purchase)
  • Ideo: About taste, values, aspirations. (environmental conscious, artsy, etc.)

C. Deliver what they need and/or want

• Properties of products/services (functionality, color, price point, etc.)
• Mapping the product/services properties to the customer needs using HoQ

Question 2

What are the principles and practices in Voice of Customer Management (VOC)?

Answer 2

Principles:

• What do “they really need” vs “you think they need”?
• Why they are doing is more important than what they are doing.
• What they want to do is more important than how it is done

Practices:

• Collection and Identification
  
  • Tools: be a customer, observations, interview, survey, etc.
• Structuring/ Clustering
  
  • Tools: Hierarchy diagram, Affinity diagram
• Prioritization
  
  • Tools: Kano-model, frequency of mention

Question 3

What is a customer need? Which questions should you ask to uncover them? What rules should you use to form a requirement statement?

Answer 3

What is a customer need?

A customer need is a description, in the words of the customer, of a benefit and/or job that he, she or they would like to have fulfilled by the product.

DO ask

• Questions that are problem oriented (what makes this job difficult/challenging etc.?)

DON’T ask

• Solution oriented questions
• What customers want in products

Forming customer needs

Rules for creating a requirement statement

1. Job statements must state the task, activity or goal
2. Statements must be free from solutions and specifications
3. Statements must not include words that will cause ambiguity or confusion
4. Statement must be brief
5. Terminology used in all the statements must be consistent
6. Statements must have a consistent structure, content and format.

Question 4

What are the benefits of using the Kano model? How is satisfaction/dissatisfaction measured from Kano surveys?

Answer 4

Why use Kano model?

• To better understand customer needs
• To prioritize different customer needs
• To identify different customer segments
• KM can be used for both attributes and customer needs

Measuring satisfaction/dissatisfaction from kano surveys

• Satisfaction = ( A + O ) / ( A + O + M + I)
• Dissatisfaction = - ( M + O ) / ( A + O + M + I)

Question 5

What is Quality Function Deployment? What parts can it be divided into? How does it help with knowledge creation?

Answer 5

What is QFD?

“a method for developing a design quality aimed at satisfying the customer and then translating the customer’s demand into design targets and major quality assurance points to be used throughout the production phases”

QFD in 4 parts

QFD can be divided into four parts:

1. Market analysis to find customer needs and expectations
2. Examine competitors to asses their ability to satisfy customer needs and wants
3. Identify key factors for product success with respect to customer needs
4. Translating key factors into product and process characteristics

QFD and knowledge creation

QFD helps create improved tacit knowledge by systematically translating them into explicit knowledge. Tacit (existing) → Explicit → Tacit (improved)

Question 6

What are the strengths and weaknesses in QFD?

Answer 6

QFD Strengths and Weaknesses

Strengths

• Improves communication, knowledge transfer and unity in cross-functional groups
• lower project and product cost
• better product design
• increased customer satisfaction

Weaknesses

• more effective for developing incremental products as opposed to groundbreaking products
• QFD does not shorten time-to-market
• rather cumbersome due to the incredibly big matrices

To think about

• The market is dynamic and preferences may change during the process, the past VOC may be obsolete

Question 7

Explain the different processes (houses) in QFD

Answer 7

The QFD Process

• Product planning: Discovery of important product characteristics.
• Product design: The best design is chosen that fulfills target values.
• Process design: Critical properties are transferred to production operations and their parameters are identified.
• Production design: Production instructions are designed.

Question 8

Explain the basics (think graphically) in the house of quality

Answer 8

Question 9

What is Design Thinking? What are it’s phases??

Answer 9

What is design thinking?

Design thinking establishes a deep understanding of those we are designing for. Knowing customers as real people with real problems, understanding their emotional and rational needs and wants.

DT in 3 phases
A project will loop between these phases, particularly the first two.

1. Inspiration: the circumstances, the realization of an opportunity or a problem.
2. Ideation: the process of generating, developing, and testing ideas that may lead to a solution.
3. Implementation: Charting of the path to market.

Question 10

What are the principles in DT?

Answer 10

Principles/themes in DT

• User focus: focus on empathy building, user understanding, and user involvement. Involve users in the ideation, prototyping, and validation of ideas.
  
  • Techniques: ethnography: approach that considers people’s needs, behaviors, values etc.
• Problem reframing: Instead of trying to solve a problem presented to you the problem should be questioned in order to widen it, challenge it and reframe it. Allows for larger solution space which facilitate ideation, create as many solutions as possible.
  
  • Techniques: HMW questions, 5 whys
• Experiment & iterate: This is done in order to work on multiple solutions at once in order to move between convergence and divergence – this to avoid our tendency to lock in to the first solution we come up with.
  
  • Techniques: brainstorming, creation of flexible and physical space that supports experimentation
• Visualization: Visualization of ideas refers to making them tangible through mock-ups and crude prototypes. Helps others understand ideas and allows for better feedback. Helps test and refine ideas, enabling insights to be shared.
  
  • Techniques: mock-ups, sketching, role-play
• Diversity: Diversity can mean many things – background, experience, skills, mindset, personalities, and hierarchical position. People participating in DT have a democratic spirit and openness to different background.
  
  • Techniques: personality tests, conscious recruitment

Question 11

What is Design for delight? What are its processes?

Answer 11

Design for delight

Managers identify customer pain points through direct field research, brainstorm about how to reduce them, and swiftly prototype solutions.

Processes in D4D

• Painstorm: Project team members talk to or observe customers in order to find out their most pressing pain that they could help relieve for them.
• Soljam: The team sets out to create as many solutions as possible related to the pain. These solutions are then combined and boiled down to a shorter list for prototyping.
• Experimenting: The prototypes are given as soon as possible to the customer for testing and feedback.

Question 12

What is service logic, service dominant logic and customer dominant logic?

Answer 12

Different types of logic

• CDL (customer dominant logic): focuses on how customers embed providers in their processes rather than how firms provide customers with services.
• SDL (service dominant logic): focuses on systems and co-creation between generic actors on a societal level.
• SL (service logic): focuses on the interaction between provider and customer.

Question 13

What are the themes and implications in CDL?

Answer 13

Themes in CDL and their implications

• Business perspectives: Marketing is seen as revenue management; customer related aspects are the foundation for business.
  
  • Implication: Consider customers’ activities, experiences, preferences, goals, tasks and reasoning.
• Customer logic: The idiosyncratic reasoning customers use to evaluate how to best achieve their goals and tasks.
  
  • Implications: Influences design and provision of offerings and how they are embedded in their activities. CL may change over time.
• Offering: What the provider offers and sells to the customer, concepts such as products, services, solutions, promises and value propositions.
  
  • Implications: It is crucial to understand what it is the customer wants when designing the offering, customers’ needs as focal point.
• Value formation: The process in which value emerges, is based on use and includes physical and mental experiences. Interaction between customer and provider, and presence is the base for value formation.
  
  • Implications: Important to understand how value emerges for the customer.
• Customer ecosystem: A system of actors and elements related to the customer and relevant to a specific service, such as: providers, other customers and actors, physical and virtual structures.
  
  • Implications: Understand the position and influence you have as a provider on a customer’s ecosystem.

Question 14

What is resourceful sensemaking and it’s mechanisms?

Answer 14

Resourceful sensemaking (RS)

• Organizations experience tension between functions in terms of specialization and cooperation due to different though worlds.
• Tension can be mitigated by using three mechanisms – exposing, co-opting, and repurposing, enables better cross-functional work leading to improved NPD outcomes. Also, reduce physical barriers.
• The aim of resourceful sensemaking is to broaden the mental horizons of the participants about NPD beyond their own thought world so that a shared NPD concept that takes all sides into account.
• RS results in greater openness to insights, reduced dualism and conflicts, creation of innovative products.

Mechanisms in RS

• Exposing: Aims at creating day-to-day awareness of the other functions contribution through interpretation which creates appreciation for their efforts. Nn. Is not focused on building a common language nor creating more communication.
• Co-opting: Aims to expand horizon discourse through deliberate use of the other functions language and concepts to enhance credibility and allows the other function to make sense of views different from their own.
• Repurposing: Engagement of the other function’s practices to build a more credible conclusion on their eyes. Repurposing involves a higher level of understanding of how the other function thinks and relies on their engagement in the process.

Question 15

Explain stated and revealed preferences

Answer 15

Stated vs. Revealed preferences

• Revealed preferences: Uses market data (actual or observed choices made by consumers.
  
  • Limitations: Difficult for new products, can sometimes be overcome with real-life experiments (tips at Uber)
• Stated preference: Involves experimental design wherein consumers select a preference among a set of alternatives.
  
  • Limitations: Suffers from hypothetical bias, can be mitigated by cheap talk scripts. Involves informing the respondent of the potential risk of hypothetical bias which leads to self-correction
• When assessing WTP it is preferable to use revealed preference rather than stated preference.

Question 16

What decisions are made in an experimental design?

Answer 16

Choice experimental decisions:

1. Relevant attributes: Choose from Quality Characteristics in prototype
2. Relevant levels
3. Prior information: About attributes and levels
4. Number of attributes/profiles/choice sets in survey/respondents per survey

Question 17

What should attributes and levels in experimental design look like? What are some methods to determine them?

Answer 17

What should attributes look like?

• Every attribute that is relevant in coming to a decision should be included
• Single attribute should not dominate choice and difference in price levels should be relevant so that there is a trade-off

Methods to determine attributes and levels

• Key points: Ask people, generate mock-ups, conduct iterative piloting of surveys
• 2 phases: (i) concept development, (ii) refine wording and levels
• Methods:
  
  • In-depth or semi-structured interviews: Useful when topic is sensitive
  • Focus groups: May generate topic discussion that the researcher was not aware of prior.
  • Meta ethnography: synthesizing literature (look for similar products)

Question 18

What are some way to cluster customer segments?

Answer 18

Two popular ways of clustering:

• Hierarchical clustering: Observations sequentially grouped based
• K-means: Observations allocated to one of a prespecified set of k clusters, useful for large datasets

Question 19

What are some ways of probability sampling?

Answer 19

Probability sampling

Sampling allows one to make inferences from a sample of a population

Types of sampling:

• Simple random sample: Each unit has an equal probability of inclusion
  
  1. Define population
  2. Devise comprehensive sampling frame
  3. Decide sample size
  4. List all units
  5. Use random numbers to select
• Systematic sample: select units directly from frame
  
  1. Select a random number between 1 and 3 (proportion)
  2. Add every 3rd unit thereafter
• Stratified random sample: ensure proportional representation of different groups/units
  
  1. Stratify (group) the population based on a criterion
  2. Select a random sample from each stratum
• Cluster sample: when interviewing sample from dispersed population (group in clusters)
  
  1. Select a random sample of clusters
  2. Select a random sample of units from each selected cluster

Question 20

What are some ways of non-probability sampling?

Answer 20

Non-probability sampling

Non-probability sampling is more limited due to its subjective, useful when population is very large. Often used just because of budget constraints and in qualitative research.

• Convenience sampling: sample that is drawn close to hand (what we did in the project)
• Quota sampling: Handpick samples that reflects population based on attributes
• Purposive sampling: researcher relies on their judgment when selecting cases:

1. Typical case: cases that are average/normal.
2. Extreme/deviant case: focus on outlier that diverge from norm to better understand patterns.
3. Critical case: One case is chosen to reveal insights for other cases.
4. Maximum variation: diverse range of cases
5. Homogeneous: having a shared characteristic
6. Expert sampling: capture knowledge rooted in a particular form of expertise

Question 21

Explain the concept of Random Utility Theory?

Answer 21

Postulates:

1. Homo economicus: Every customer will act in a manner which maximizes their benefit. When they don’t, we explain it with a random factor.
2. A set of available alternatives and a set of measured attributes-
3. Each option is associated with a net utility U
4. Individuals select the alternative that gives them maximal utility

Question 22

What is Design of Experiments? What are the benefits of using DoE?

Answer 22

What is DoE?

A scientific approach of understanding how a system works by investigating how the inputs affect the outputs to reach desired outcome.

Benefits of DoE

• Find optimal settings that produce best result at lowest cost
• Identification and quantification of most/least critical key input factors
• Generate useful and valid data
• Understanding of relationship between inputs and outputs
• Reduce time and amount needed to study factors

Question 23

What are the steps for using DOE?

Answer 23

Steps for DoE

1. What is the problem?
2. What do we want to achieve?
3. What is the response variable? How to measure it? Is the measurement accurate?
4. Which factors may possibly affect the response variables? Can one control the values of those factors?
5. To what extent should we change the values of the identified input factors?
6. Which experiment strategy should we choose?
7. How should we run the experiment, how to collect the data? (randomization, blocking, replication)
8. How do we analyze the results, draw conclusion, and further improve the process?

Question 24

Explain the P-diagram and the sources for noise factors

Answer 24

P-diagram

• Response variable: E.g. satisfaction level
• Control factors (X₁, X₂, X_n): E.g. design level
• Noise factors (NF₁, NF₂, NF_n): not controlled, e.g. temperature

Sources of noise factors

• External: environmental noise factors (temp., voltage, pressure)
• Unit-to-unit variation: manufacturing sources (deviation in control parameters, variance in raw materials)
• Deterioration: physical wear out
• Customer behavior in use of products: do customers use products as intended?

Question 25

What are some fundamental principles for factorial effects?

Answer 25

Fundamental principles for factorial effects

• Sparsity principle: the number of important effects in a factorial experiment is small
• Hierarchy principle: lower order effects are more likely to be important than higher order effects
• Hereditary principle: for an interaction to be significant, one of the parents must be significant

Question 26

What’s the benefit of using a sequential experiment design?

Answer 26

Allows a greater number of attributes without increasing resources since non active factors are filtered away in the first phase. More can be learned than a simple one-a-time technique.

Question 27

Explain the testing framework

Answer 27

Testing framework

1. Create/refine hypothesis
   
   1. Ascertain that the hypothesis hasn’t been tested before,
   2. that hypothesis can generate economic value
   3. Whether the results will prompt an action
2. Design test
   
   1. Ensure substantial amount of test for statistical significance
   2. Use simulation to explore multiple strategies for creating control groups
   3. Assess whether strategies from previous experiments will suffice
   4. Conduct statistical analysis to minimize number of test cells needed
   5. Extend testing period if key metrics are highly variable
3. Execute test
   
   1. Meet with test managers and analysts to discuss what could go wrong
   2. Instruct field personnel to report abnormal events
   3. Remove sites from test if confounding events occur
   4. Adjust evaluation and compensation for managers so that they are not negatively affected by tests
4. Analyze test
   
   1. Ensure lift from interventions is statistically significant
   2. Use software to help analyze tests
   3. Determine further need for testing
   4. Examine many site attributes to see how key variables interact
5. Plan rollout
   
   1. Study attributes of test sites to determine whether rollout should be universal or differentiated
   2. Balance rollout with ease of implementation and management
6. Rollout
   
   1. Stagger the rollout and view it as a test, does it have the intended effect? If not, modify approach for later-adopting sites.
   2. Encourage site managers to share rollout strategies and tactics.
7. Learning library
   
   1. Develop summary of:
      
      1. Hypotheses
      2. Test dimensions
      3. Key results
      4. Interactions
      5. Rollout strategies and results
   2. Employ standardized taxonomy to allow for easy search in library.
   3. Make the library accessible to all employees and publicize important results to encourage testing culture.

Question 28

What are the capabilities required for testing?

Answer 28

Capabilities required for testing

• Managerial Training: managers should receive training in what constitutes a randomized test and when to employ it.
• Test-and-Learn-Software: is needed to help structure and analyze tests.
• Learning Capture: implementation of knowledge management systems in order to store knowledge and learn from what has been done.
• Regular Revisiting: human intuition must be applied to determine when factors in the environment have changed to merit a retest.
• Core Resource Group: in order to coordinate testing across an organisation there is need for a centralized support group.

Question 29

What are the 10 principles for effective data visualization?

Answer 29

Principles for effective data visualization

1. Create the simplest graph that conveys the information you want to convey.
   
   1. Less-is-more – keep it simple and reduce redundancy in properties while making sure the reader can discriminate between visualization properties.
   2. Minimizing the “data-ink-ratio” can be used to simplify. The ratio is defined as ink used to represent non-redundant data versus total amount of ink used.
2. Use the right attributes to create plots
   
   1. The human mind can quantify certain graph attributes better than others.
      
      1. Use position and length when comprehension of the data values and how they compare to each other is important.
      2. Line width, color hue or tint, or marker size are more difficult to quantify and should be used for plots that show relative comparisons or general patterns.
3. Focus on visualizing patterns or on visualizing details, depending on the purpose
   
   1. The choice requires selection between type of plot and attributes of the plot
4. Select appropriate axis ranges
   
   1. If the relative magnitude is important to convey the vertical axis should start from 0.
5. Use non-linear scale on vertical axis when appropriate for the purpose
   
   1. Using logarithmic scale on vertical axis can remove skewness in datasets that contains both very small and very large numbers.
   2. Logarithmic scale visualizes rate of change normalized to an initial value
6. Plot overlapping points in a way that density differences become apparent
   
   1. Use transparent data-points for example to that the density of the level of transparency provides information about the number of overlapping data points.
7. Use lines when connecting sequential data in time-series plot
   
   1. By doing so the continuous change of the values between data-points is perceived as linear.
8. Aggregate large datasets in meaningful ways
   
   1. Create categories when appropriate for summary-plots.
9. Keep axis ranges as similar as possible to compare variables
   
   1. Display of variables across subplots with different axis ranges hinders comparison of ranges and variability.
   2. Separation of variables with large scale differences into subplots highlights variability within individual datasets, whereas variables with similar ranges can be grouped together in single plot for comparison.
10. Use appropriate color scheme based on the type of data and purpose

Question 30

What are the benefits and drawbacks of the Network model?

Answer 30

Benefits

• More effiicient and accurate results than traditional QFD due to incorporation of NPD risks
• Reduction of human judgement error and transparent evaluation
• Improved reliability since a sensitivity analysis can be conducted at any time when using the model.

Drawbacks

Trade-off between model complexity and time to complete pairwise comparisons