Innovation Management – Planning Product Featuresiss.uni-saarland.de/workspace/documents/ivm-8_planning-product-features.pdfDevelop Cost Model - Target costing Variables: Gross profit

Innovation Management – Planning Product Features Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Economics – Information and Service Systems (ISS) Saarland University, Saarbrücken, Germany SS 2012 Wednesdays, 10:00 – 12:00 a.m. Room 0.21, B4 1

Univ.-Prof. Dr.-Ing. Wolfgang Maass

22.06.12 Slide 2

Lecture Agenda

Innovation Management 1.  Introduction 2.  Knowledge Management (1) 3.  Knowledge Management (2) 4.  Guest Lecture 5.  Strategic Innovation Management 6.  Case Study 7.  New Product Development 8.  Creativity Techniques 9.  Planning Product Features 10.  Experimentation Strategies 11.  Open Innovation 12.  Diffusion and Adoption of Innovation 13.  Diffusion and Adoption of Information Systems 14.  Business Planning and Writing


22.06.12 Slide 3

New Product Development

How to avoid failure in product

development?

? ?

? ? ?

?

Where do product ideas come from?

Where are differences between diverse product

development projects and how to handle them?

How to proceed when developing new products?

How to test my product ideas before market

launch? How to be creative?

How to plan final features of my

product?

Last lectures

Today’s lecture

Lecture, 27th of June


22.06.12 Slide 4

Identification of New Customer’s Needs Example: Trucks

(Urban & Hauser, 2004)

Disadvantages of other methods for the identification of customer’s needs •  Qualitative and ethnography interviews: Very expensive to identify customer‘s needs of a small market •  Conjoint analyses: Should only be used for small targeted number of needs (10 to 20). Not suited for very complex products,

e.g., trucks •  Adaptive methods: Not suited for large amount of needs. •  AIO studies (activities, interests, opinions): Expensive, repeated infrequently and do not gather data regarding unfulfilled

needs •  Truck clinics: Not used for exploratory studies, just for confirmatory studies.

Qualitative interviews

Truck clinics

Conjoint analysis

Launch


22.06.12 Slide 5

Setting the Final Specifications in Product Development

(Ulrich & Eppinger, 2011)

Generate and sense many opportunities


22.06.12 Slide 6

Overview

•  After selection and testing of a product concept (testing: next lecture), the final specifications can be set

•  Setting the final specifications for the selected product concept based on the target specifications

•  Target specifications are revised and stated more precisely

•  Problem: Several specifications have inverse relationships (e.g., decrease of weight of a bike increases cost of material). Trade-offs need to be made.

•  Trade-offs between technical performance metrics •  e.g., bike: inverse relationships between mechanics of fork and functioning of break

•  Trade-offs between technical performance metrics and costs •  e.g., bike: mass of fork can be reduced by using expensive titanium instead of steel

•  Task: Find optimum of trade-offs



22.06.12 Slide 7

Process for Setting the Final Specifications

Example: Construction of suspension fork (Federgabel) for a bike.


5 step process for setting the final specifications

①  Develop technical models of the product.

②  Develop cost model of the product.

③  Refine the specifications, making trade-offs

④  Flow down specifications as appropriate.

⑤  Reflect on results and process.


22.06.12 Slide 8

Process for Setting the Final Specifications Develop Technical Models

①  Develop technical models of the product

•  Technical model: Tool for prediction of values of metrics for a certain product design (prediction of product performance)

•  Used to assess technical feasibility •  Physical approach: Tangible prototype of product •  Analytical approach: Nontangible prototype of product (e.g., encoding system of equations on

spreadsheet)

•  Objective: Testing of technical feasibility of several different sets of specification (e.g., ideal target values)

•  Using different combinations of design variables (input)

•  Development of several technical models for certain sub-parts of a product concept. •  Technical model is always valid for one certain product concept (cannot be transferred

to other concepts).



22.06.12 Slide 9


Example: •  Team has decided for one suspension fork concept earlier in the development

process •  e.g., concept using oil for suspension fork of the bike

•  Different partial models are developed for different parts of concept (sub-parts) •  Different design variables with different values are needed for partial models •  Values of metrics for different design variables are calculated

•  e.g. model for suspension performance of suspension fork

(according to Ulrich & Eppinger, 2011)


22.06.12 Slide 10


Static Model of Brake Mounting Stiffness (analytical)

Design Variables (model input) e.g., support geometry

Values of metrics (model output) e.g., lateral stiffness

Fatigue Model of Suspension Durability (physical)

Design Variables (model input) e.g., oil viscosity (Zähflüssigkeit)

Values of metrics (model output) e.g., Cycles to Failure

Dynamic Model of Suspension Performance (analytical)

Design Variables (model input) e.g., oil viscosity (Zähflüssigkeit)

Values of metrics (model output) e.g., attenuation (Dämpfung) at 10 Hz (Hertz, measure for number of vibrations per second)


22.06.12 Slide 11

Process for Setting the Final Specifications Develop Cost Model

②  Develop cost model of the product

•  Calculation of target cost of the product •  Objective: Ensure production of product at target costs

•  Target cost Setting of value of manufacturing cost specification dependent on the customer’s willingness-to-pay for the product (estimate), but also with adequate profit margin for producer. •  Cost are calculated depending on customer and profit margin instead of material

and working costs (these need to be reduced in order to achieve target cost level). •  If several retailers in distribution channel resell the product: Target cost become

lower than when direct sale from producer to customer



22.06.12 Slide 12

Process for Setting the Final Specifications Develop Cost Model - Target costing

Variables: Gross profit margin •  M = Gross profit margin at a stage in distribution channel •  P = Price this stage charges to its customer •  C = Cost this stage pays for product it sells

Target Cost •  C = Target Cost •  Mi = Gross profit margin at ith stage in distribution channel •  P = Price charged to end user •  n = Number of stages in distribution channel (= number of retailers)

M =(P −C)P


C = P*(1−M )


22.06.12 Slide 13

Process for Setting the Final Specifications Develop Cost Model - Target costing

Example: The price the end users will for pay for a mountain bike is estimated at € 250. The producer‘s margin Mm is 40%, the retailer’s margin Mr is 45%. Direct sale from producer to end user: Mm= 0,4 Target cost: C = P(1 – Mm)

= 250 € * (1 - 0,4) = 150 € Indirect sale via retailer: Mm= 0,4 Mr= 0,45 Target cost : C = P(1 – Mm) * (1 – Mr)

= 250 € * (1-0,4) * (1 – 0,45) = 82,50 €


22.06.12 Slide 14

Brainteaser

A producer of computer monitors has determined that end customers are willing to pay 400 € for its latest monitor. The desired gross profit margin for the producer is 35%. The product is sold to the end customers via a wholesale dealer (profit margin: 25%) and a retailer (profit margin: 10%). •  Calculate the target cost for the producer.

•  One student will present his solution! (papers will be collected)

5 Minutes


22.06.12 Slide 15

Process for Setting the Final Specifications Develop Cost Model – Bill of Materials

For estimation of cost, a bill of materials is prepared (list of all product parts needed) •  Purchase price or manufacturing cost are estimated for each single part •  First calculation, not yet detailed

•  Problems: •  Only estimation of price or cost (experts needed, e.g., production engineers) •  Not all parts needed are already known (calculation may change) •  Probably many small parts for product

•  Usually different estimates for prices or cost taken into account •  Calculation of costs with low and high estimates for each part •  Range of uncertainty of costs becomes visible •  Only major parts are listed, not all small parts

•  Objective: Prediction of cost performance of a product •  Updated regularly during development process



22.06.12 Slide 16

Process for Setting the Final Specifications Develop Cost Model – Bill of Materials

Example: Bill of materials for suspension fork with cost estimates


Component Quantity per fork

High purchase price

per piece in €

Low purchase price

per piece in €

Total cost (high purchase prices)

per fork in €

Total cost (low purchase

prices) per fork in €

Steertube (Steuerrohr) 1 2,50 2,00 2,50 2,00 Boot (Schutzmanschette) 2 1,00 0,75 2,00 1,40 Lower tube (unteres Rohr) 2 3,00 2,00 6,00 4,00 Upper tube (oberes Rohr) 2 5,50 4,00 11,00 8,00 Slide bushing (Gleitlager) 4 0,20 0,18 0,80 0,72

Spring (Formfeder) 2 3,00 2,50 6,00 5,00

Oil, in liters 0,1 2,50 2,00 0,25 0,20 ... Total for suspension fork

... ... ... ... 104,19

...

78,68


22.06.12 Slide 17

Process for Setting the Final Specifications Refine Specifications

③  Refine the specifications, making trade-offs

•  Final specifications can be made using technical performance models and costs model

•  Iterative process (e.g., by team meetings): Take technically feasible product designs and examine impacts on costs.

•  Best specifications: Best position of product compared to competitors with best satisfaction of customer needs and appropriate profit margins.

•  Finding of optimum trade-offs



22.06.12 Slide 18


Value of suspension test (Federungstest)

High performance Low performance

Estimated manufacturing cost in €

50

60

70

80

90

100

3,0 3,2 3,4 3,6 3,8 4,0

Ideal Values

Marginal Values

Trade-off curve concept A

Trade-off curve concept B

Competitive product B

Competitive product B

Competitive map for suspension fork

•  Competitive map (trade-off map): Tool for supporting the decision-making •  Shows trade-off curves for different products with varying design variables •  Shows competitive products (not possible for radical innovations) •  Data can be taken from competitive benchmarking chart (competitive products)

•  Making trade-offs: Support performance advantage of product compared to competitor’s products.

•  Reduction of cost or performance at less important variables.

•  Ideal combination of specifications can be identified

•  curve intersecting area of ideal values •  e.g., manufacturing cost of 65 €, value

of suspension test of 3,2

•  Position of competitive products can be seen



22.06.12 Slide 19



•  Decision for final specifications: Based on technical model, cost model and trade-

off curves •  Supposed to meet customer’s needs and provide a profit margin for company.

•  Example: Final specifications for suspension fork (extract)

Metric Unit Value

Attenuation at 10 Hz (Dämpfung)

dB> (Dezibel = Einheit des Leistungspegels) 12

Spring preload (Vorspannkraft der Formfeder)

N (Newton = Einheit der Kraft) 600-650

Steertube length (Steuerrohr) mm 170,00

Headset sizes (Lenkkopf) inch 1,5

Unit manufacturing cost € <27


22.06.12 Slide 20

Process for Setting the Final Specifications Flow down specifications

④  Flow down specifications as appropriate

•  Establishment of specification is very complex when designing a complex product with several sub-systems and when working in several teams.

•  Defining of specifications for •  product as a whole •  each subsystem

•  e.g., car: breaking system, engine, body, transmission

•  Flow down specifications of whole product to specifications of sub-systems •  Need to fit to each other, cannot be opposite •  Specifications of sub-systems need to meet product specifications as a whole •  Specifications for different sub-systems need to be equally difficult to achieve

•  otherwise, the cost will rise

•  Understanding of relationship between different sub-systems and product as a whole needs to be achieved



22.06.12 Slide 21

Process for Setting the Final Specifications Reflect on Results

⑤  Reflect on results and process

•  Reflecting final product and specification process •  Will final product meet the customer’s needs and resist competition?

•  If not, return to concept generation necessary •  Otherwise, abandonment of project

•  How certain are technical and cost models? Is there a high level of uncertainty in the estimated data?

•  Refinement of technical or costs model might be necessary •  Does the selected concept still fit to the target market?

•  Final product might be more suited for another market



Example: Online Advisor and New Need Identification


22.06.12 Slide 23

Identification of New Customer’s Needs

•  Requirement for long-term survival of a company: Discovering new product features •  Known features are used for products in most of the cases

•  New product features: Identification of new customer needs and combination of them to new product features.

•  Opportunities to gain long-term profits



22.06.12 Slide 24

Example: Research in Exploring Customer‘s Needs for New Product Development

Advantages of “listening in”, compared to other methods •  Completes existing methods •  Uses inexpensive data that already exists •  Explores large amount of customer’s needs to

discover unfulfilled needs •  Quantitative as well as qualitative questions

can be used •  Data exploration runs all the time •  Updated regularly with new needs and new

products •  New combinations of needs are discovered

very early

“Listening in” can be used in combination with other methods,

•  e.g., for the identification of opportunities of a new truck platform:

“Listening in”

Qualitative interviews

Truck clinics

Conjoint analysis

Launch

(according to Urban & Hauser, 2004)


22.06.12 Slide 25


Tools for identifying needs in the study: ①  Bayesian Adviser: Giving product recommendations to customers ②  Opportunity Trigger Mechanism: Discovering of new customer need

combinations


Bayesian Adviser •  Basic concepts of a Bayesian Adviser

•  selection of questions (question banks) to customers. Answers should provide information for product recommendation

•  Updated probability of product preference likelihood of a customer after having answered a question bank


22.06.12 Slide 26


•  Variables •  Q: Set of question banks, indexed from q = 1 to N •  q: question bank •  rq: potential responses to question bank q (rq is a nominal variable with values from 1 to nq) •  nq: number of possible combinations of answers (if there is more than one question in a question

bank) •  Rq-1: Set of question banks up to (but not including) question bank q, for a given customer •  vj: Vehicles from 1 to V •  P(vjIRq-1, rq): Likelihood that customer will prefer vehicle j after having been asked question bank q

at any point in questioning sequence •  P(vjIRq-1): Recommendation probability to customer for vehicle vj before asking qth question bank. •  Conditional probabilities of how customers, who prefer a certain vehicle, will answer to a question

bank: Taken from previous surveys.

Updated recommendations, based on Bayes’ theorem:



22.06.12 Slide 27


Simplification: •  left-hand side: current recommendation, question bank and parts of the answer •  right-hand side: probability that customer will purchase the recommended vehicle

Example: •  After second question bank (engine

size), customer’s answer is “four cylinders”

•  If customer now stops answering questions and requests recommendation:

•  Answer would be: Mazda B2300 with purchasing probability of 0,0735

•  Here: Probability of purchase increases after each question bank



22.06.12 Slide 28



②  Opportunity Trigger Mechanism

•  Identification of customer’s needs that are not fulfilled by currently available vehicles.

•  For customers for which an existing truck will satisfy their needs: Updated recommendation probabilities will be like previously described

•  For customers for which there is no truck yet satisfying their needs: Inconsistencies in answers to question banks

•  If a truck is recommended and this customer continues answering the questions: Conflicts in further answers and recommendation

Example •  Mazda B2300 is fits best to customer’s

answers •  Further questions: Maximum

recommendation probability drops •  If this occurs with many customers with

the same combinations: •  New combination of features discovered


22.06.12 Slide 29


Further investigation due to drop in maximum recommendation probability: •  Null hypothesis: existing trucks satisfy (almost all) customer-needs combinations. •  Implication: If two truck characteristics are positively correlated among existing

trucks, they are expected to be positively correlated among customers’ preferences.

•  Identification of the needs combinations that caused the drop possible: Examination of negative correlations of expected answers for customers with probability drop.

•  Customers with probability drop: Want combinations of needs that are negatively correlated.

•  Opportunity of new combination of features •  79 features and questions are derived.



22.06.12 Slide 30

Lecture Agenda

Innovation Management 1.  Introduction 2.  Knowledge Management (1) 3.  Knowledge Management (2) 4.  Guest Lecture 5.  Strategic Innovation Management 6.  Case Study 7.  New Product Development 8.  Creativity Techniques 9.  Planning Product Features 10.  Experimentation Strategies 11.  Open Innovation 12.  Diffusion and Adoption of Innovation 13.  Diffusion and Adoption of Information Systems 14.  Business Planning and Writing


22.06.12 Slide 31

Literature

Books: •  Ulrich, K. T. & Eppinger, S. D. (2011), Product Design and Development, McGraw-Hill/Irwin.

Papers: •  Urban, G.L. & Hauser, J.R. (2004), ”“Listening In” to Find and Explore New Combinations of Customer Needs“, Journal of

Marketing, Vol. 68, pp. 72–87.


Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Information and Service Systems Saarland University, Germany


22.06.12 Slide 33

Solution of Brain Teaser

Target Cost •  C = Target cost •  Mi = Gross profit margin at ith stage in distribution channel •  P = Price charged to end user •  n = Number of stages in distribution channel (= number of retailers)

(in Anlehnung an Ulrich & Eppinger, 2011)

•  Profit margin of producer Mm: 0,35 •  Profit margin of wholesale dealer Mw: 0,25 •  Profit margin of retailer Mr: 0,1 •  Price charged to end customers P: 400 €

•  C = 400 * (1 - 0,35) * (1 - 0,25) * (1 - 0,1) = 175,50 €

Documents

Innovation Management – Planning Product Featuresiss.uni-saarland.de/workspace/documents/ivm-8_planning-product-features.pdfDevelop Cost Model - Target costing Variables: Gross profit