Innovation Management Planning Product Features

Transcription

1 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

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 Slide 2

3 New Product Development How to be creative? How to plan final features of my product? Today s lecture How to test my product ideas before market launch? Lecture, 27 th of June????? How to proceed when developing new products?? How to avoid failure in product development? Last lectures Where are differences between diverse product development projects and how to handle them? Where do product ideas come from? Slide 3

4 Identification of New Customer s Needs Example: Trucks 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 Conjoint analysis Truck clinics Launch (Urban & Hauser, 2004) Slide 4

5 Setting the Final Specifications in Product Development Generate and sense many opportunities (Ulrich & Eppinger, 2011) Slide 5

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 (Ulrich & Eppinger, 2011) Slide 6

7 Process for Setting the Final Specifications 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. Example: Construction of suspension fork (Federgabel) for a bike. (Ulrich & Eppinger, 2011) Slide 7

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). (Ulrich & Eppinger, 2011) Slide 8

9 Process for Setting the Final Specifications Develop Technical Models 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) Slide 9

10 Process for Setting the Final Specifications Develop Technical Models Design Variables (model input) e.g., oil viscosity (Zähflüssigkeit) Dynamic Model of Suspension Performance (analytical) Values of metrics (model output) e.g., attenuation (Dämpfung) at 10 Hz (Hertz, measure for number of vibrations per second) Design Variables (model input) e.g., support geometry Static Model of Brake Mounting Stiffness (analytical) Values of metrics (model output) e.g., lateral stiffness Design Variables (model input) e.g., oil viscosity (Zähflüssigkeit) Fatigue Model of Suspension Durability (physical) Values of metrics (model output) e.g., Cycles to Failure Slide 10

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 (Ulrich & Eppinger, 2011) Slide 11

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 M = (P C) P C = P *(1 M ) Target Cost C = Target Cost M i = Gross profit margin at i th stage in distribution channel P = Price charged to end user n = Number of stages in distribution channel (= number of retailers) (Ulrich & Eppinger, 2011) Slide 12

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 M m is 40%, the retailer s margin M r is 45%. Direct sale from producer to end user: M m = 0,4 Target cost: C = P(1 M m ) = 250 * (1-0,4) = 150 Indirect sale via retailer: M m = 0,4 M r = 0,45 Target cost : C = P(1 M m ) * (1 M r ) = 250 * (1-0,4) * (1 0,45) = 82,50 Slide 13

14 Brainteaser 5 Minutes 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) Slide 14

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 (Ulrich & Eppinger, 2011) Slide 15

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 , ,68 (according to Ulrich & Eppinger, 2011) Slide 16

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 (according to Ulrich & Eppinger, 2011) Slide 17

18 Process for Setting the Final Specifications Refine Specifications 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 Estimated manufacturing cost in Competitive map for suspension fork Competitive product B Competitive product B 3,0 3,2 3,4 3,6 3,8 4,0 High performance Low performance Value of suspension test (Federungstest) Marginal Values Ideal Values Trade-off curve concept A Trade-off curve concept B (according to Ulrich & Eppinger, 2011) Slide 18

19 Process for Setting the Final Specifications Refine Specifications Decision for final specifications: Based on technical model, cost model and tradeoff 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) Spring preload (Vorspannkraft der Formfeder) Steertube length (Steuerrohr) db> (Dezibel = Einheit des Leistungspegels) N (Newton = Einheit der Kraft) mm 170,00 Headset sizes (Lenkkopf) inch 1,5 Unit manufacturing cost <27 (according to Ulrich & Eppinger, 2011) Slide 19

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 (Ulrich & Eppinger, 2011) Slide 20

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 (Ulrich & Eppinger, 2011) Slide 21

22 Example: Online Advisor and New Need Identification Univ.-Prof. Dr.-Ing. Wolfgang Maass

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 (Urban & Hauser, 2004) Slide 23

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 Conjoint analysis Truck clinics Launch (according to Urban & Hauser, 2004) Slide 24

25 Example: Research in Exploring Customer s Needs for New Product Development 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 (Urban & Hauser, 2004) Slide 25

26 Example: Research in Exploring Customer s Needs for New Product Development Variables Q: Set of question banks, indexed from q = 1 to N q: question bank r q : potential responses to question bank q (r q is a nominal variable with values from 1 to n q ) n q : number of possible combinations of answers (if there is more than one question in a question bank) R q-1 : Set of question banks up to (but not including) question bank q, for a given customer v j : Vehicles from 1 to V P(v j IR q-1, r q ): Likelihood that customer will prefer vehicle j after having been asked question bank q at any point in questioning sequence P(v j IR q-1 ): Recommendation probability to customer for vehicle v j before asking q th 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: (Urban & Hauser, 2004) Slide 26

27 Example: Research in Exploring Customer s Needs for New Product Development 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 (Urban & Hauser, 2004) Univ.-Prof. Dr.-Ing. Wolfgang Maass Slide 27

28 Example: Research in Exploring Customer s Needs for New Product Development 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 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 If a truck is recommended and this customer continues answering the questions: Conflicts in further answers and recommendation (Urban & Hauser, 2004) Univ.-Prof. Dr.-Ing. Wolfgang Maass Slide 28

29 Example: Research in Exploring Customer s Needs for New Product Development 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. (Urban & Hauser, 2004) Univ.-Prof. Dr.-Ing. Wolfgang Maass Slide 29

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 Slide 30

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 Slide 31

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

33 Solution of Brain Teaser Target Cost C = Target cost M i = Gross profit margin at i th stage in distribution channel P = Price charged to end user n = Number of stages in distribution channel (= number of retailers) Profit margin of producer M m : 0,35 Profit margin of wholesale dealer M w : 0,25 Profit margin of retailer M r : 0,1 Price charged to end customers P: 400 C = 400 * (1-0,35) * (1-0,25) * (1-0,1) = 175,50 (in Anlehnung an Ulrich & Eppinger, 2011) Slide 33