Focus your investments in innovations
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The document outlines a structured methodology for managing and optimizing innovation processes within organizations to achieve business objectives. It emphasizes the importance of creativity, statistical thinking, and process optimization, while providing guidelines for idea generation, screening, and realization. Additionally, it details the roles of management tools, case studies, and critical evaluation methods to enhance innovation outcomes.
Focus your investments in innovations
- 1. Building Statistical Support for Delivering Focused Innovation: Focusing Innovation to Achieve Business Objectives without Sacrificing Innovation “Freedom”
- 2. Agenda and Topics • Opening • Evolution of Process and Products Levels and Dimensions • The Process Levels and Dimensions • The Product Levels and Dimensions
- 3. Agenda and Topics • Understanding Innovation • Definition • Process • Tools • Application of Guidelines to Real-Life Context • What to Optimize (Process, Product, or Both) • Considerations for Process Optimization • Considerations for Product Optimization • Benefit of Both
- 4. Agenda and Topics • Case Studies • Process Optimization (Brief Walkthrough) • Product Optimization (Brief Walkthrough) • Product Optimization Which Leads to Process Optimization (Detailed Walkthrough) • Wrap-up • Questions • References
- 5. Opening •Background •Tutorial flow •Definition •The Challenge •The Rationale •CMMI ML 4 & 5 PAs Recap
- 6. Background • Innovation is a key to business growth and improved results • Innovation means a new way of doing business; it may refer to incremental, radical, or even revolutionary changes in the approach to extracting value for the business (business model) • Involves a fundamental change to markets, competencies, partners, technologies, or processes • Companies that do not innovate eventually lose customers to a competitor that has found a better way.
- 7. Background • However innovations – as any other aspect of a business – require an investment and investment is about the future. • These innovation-related investments posit a new future that plays by new rules. If you make investment decisions on an extrapolated new future based on the rules in operation today then you may misjudge the future and “shut the door” on promising opportunities • Therefore these decisions require complex analyses. To make these easier, managers often use tools to help with the financial analysis. The problem with these tools is that they often value innovation and non innovation in the same terms.
- 8. Background • Innovation is more than developing new ideas, it is also adapting those ideas to the particular context of the business so that it confers a business advantage • Thus, we speak of an “innovation lifecycle,” which includes deployment of the innovation into the appropriate parts of the organization so that the organization can exploit the new source for value to the business. • Deployment is more than introducing the change, it can include further adaptation of the change and further learning to be exploited concurrent to its deployment. • Quality and cycle time are lifecycle attributes important to the innovation lifecycle just as they are to the product development lifecycle.
- 9. Background • Our view is that creativity is a process – not an accident, nor inherent. • Creativity is initiated with a challenge and “unleashed” through managing: -multiple perspectives -shared understanding -opportunities for solution reflection, brainstorming, information gathering, evaluation -overall state of the expanding dynamic -environment
- 10. Background • For this reason we have developed a structured methodology that supports the ongoing discovery and evaluation of solutions throughout the innovation lifecycle • We make use of process performance analyses as an input to three levels of statistical thinking that support the innovation process from identified needs to pilot results.
- 11. Tutorial flow • The methodology we will be presenting in this tutorial uses a cross matrix that identifies the appropriate selected methods and models in conjunction with different management and engineering disciplines as appropriate to the innovation lifecycle phase • Our statistical methodology is based on three main evaluation phases and for each we have identified different methods, to be selected as appropriate for the given situation. • Idea generation • Idea screening • Idea realization • Case studies that will demonstrate the method in real life use
- 12. Definitions • Processes are defined as "a set of interdependent tasks transforming input elements into products” • Innovation refers to a new way of doing something. It may refer to incremental and emergent or radical and revolutionary changes in thinking, products, processes, or organizations • Statistically Managed and controlled - application of the scientific method to understand behavior
- 13. The Challenge Statements • Innovations as any other aspect of a business require an investment • Innovations-related investment is about: • the future • the rules • Making investment decisions on an extrapolated new future based on today’s rules may lead to costly mistakes
- 14. The Challenge Statements • Investment and Innovation decisions can require complex analysis. • To make them easier, managers often use tools to help with the financial and proposed solution analysis. • The problem with these tools is that they often value innovation and non innovation in the same terms. • They encourage managers to make unfair demands on returns on investment for innovation projects.
- 15. The Proposed Solution Rationale • Structured methodology that supports the ongoing evaluation of innovation ideas throughout the different lifecycle phases • Prioritization, piloting, and deployment of the innovations based on statistical analysis • We make use of process performance analysis as an input to three levels of statistical thinking that support the innovation process from identified needs to pilot results. • Idea generation • Idea screening • Idea realization
- 16. CMMI ML 4 & 5 PAs Recap • Organizational Process Performance • Quantitative Project Management • Causal Analysis and Resolution • Organizational Innovation and Deployment
- 17. Specific Practices of OPP SG 1 Establish Performance Baselines and Models SP 1.1 Select Processes SP 1.2 Establish Process-Performance Measures SP 1.3 Establish Quality and Process-Performance Objectives SP 1.4 Establish Process-Performance Baselines SP 1.5 Establish Process-Performance Models
- 18. Specific Practices of QPM SG 1 Quantitatively Manage the Project SP 1.1 Establish the Project’s Objectives SP 1.2 Compose the Defined Process SP 1.3 Select the Subprocesses That Will Be Statistically Managed SP 1.4 Manage Project Performance SG 2 Statistically Manage Subprocess Performance SP 2.1 Select Measures and Analytic Techniques SP 2.2 Apply Statistical Methods to Understand Variation SP 2.3 Monitor Performance of the Selected Subprocesses SP 2.4 Record Statistical Management Data
- 19. Specific Practices of CAR SG 1 Determine Causes of Defects SP 1.1 Select Defect Data for Analysis SP 1.2 Analyze Causes SG 2 Address Causes of Defects SP 2.1 Implement the Action Proposals SP 2.2 Evaluate the Effect of Changes SP 3.2 Record Data
- 20. Specific Practices of OID SG 1 Select Improvements SP 1.1 Collect and Analyze Improvement Proposals SP 1.2 Identify and Analyze Innovations SP 1.3 Pilot Improvements SP 1.4 Select Improvements for Deployment SG 2 Deploy Improvements SP 2.1 Plan the Deployment SP 2.2 Manage the Deployment SP 2.3 Measure Improvement Effects
- 21. Evolution of Process and Products Levels and Dimensions •The Process Levels and Dimensions •The Product Levels and Dimensions
- 22. Process Levels and Dimensions • Planned and Managed Process • Architected and Engineered Process • Operationally Optimized Process
- 23. Process Levels and Dimensions Planned and Managed Process • Plan • Perform • Control
- 24. Suggested Measures Planned and Managed Process • Availability and completeness of plan • Plan for resource • Overall performing time • Omissions in performance • Compliance to plan
- 25. Process Levels and Dimensions Architected and Improved Process • Objectives • Structured • Monitored / Measured • Effective / Efficient • Process Interfaces and Integration in Lifecycle • Prioritize and Balance Resource Utilization within Larger Context
- 26. Suggested Measures Architected and Improved Process • Process productivity • Process resources utilization effectiveness • Process resources utilization efficiency • Meeting the process objectives • Other processes interfaces efficiency • Process related defects density
- 27. Process Levels and Dimensions Operationally Optimized Process • Known Capability and Stable • Defined Ingredients • Known Critical Elements • Meeting Objectives • Controlled Interfaces • Responsive / Modifiable • Resilience / “Agile” • Relevant ‘What If’s Scenarios • Accepted Tolerance / Freedom Boundaries • Predictable Outcomes
- 28. Suggested Measures Operationally Optimized Process • Influence of Critical Elements on process output • Process resources utilization ‘What If’s Scenarios • Process elements capability • Quantitative definition of process ingredients
- 29. Product Levels and Dimensions • Planned and Managed System • Architected and Engineered System • Operationally Operated and Optimized System
- 30. Product Levels and Dimensions Planned and Managed System • Requirements • Constructions and Evaluation • Deployment
- 31. Suggested Measures Planned and Managed System • Requirements Status • Change Request Status • Component Status • Increment Content - Components • Increment Content - Functions • Technical Performance • Standards Compliance • Requests for Support • Support Time Requirements
- 32. Product Levels and Dimensions Architected and Engineered System • Operational Needs and Scenarios • System Architecture • System Interfaces and Integration • Validity / Verifiability • Compliance with CONOPS
- 33. Suggested Measures Architected and Engineered System • Maintenance Actions • Technical Performance • Performance Rating • Requirements Coverage • Defect Containment • Utilization • Reuse level • Interfaces performance • Validation accuracy
- 34. Product Levels and Dimensions Operationally Optimized System • Scalability • Availability • Reliability • Serviceability • Maintainability • Supportability • Stability • Reusability • Soundness of Technology Future
- 35. Suggested Measures Operationally Optimized System • Technology flexibility • Capacity growth models • System (size) growth models • Time to Restore • Down time • MTBF • Support calls causes and density • Technology extendibility
- 36. Understanding Innovation •Definition •Process •Tools •Application of Guidelines to Real-Life Context
- 37. Innovation Requires Management Product Development Innovation Innovation The conversion of Management knowledge and ideas into new or improved products, A systematic method of processes, fostering innovation by Process Service and services to gain Improvement Development capturing, evaluating, a competitive and developing ideas to advantage. conclusion.
- 38. Process - Background • Collect together old ideas – as well as existing facts. • You need to know as much about the world in general and get a solid, deep working knowledge of the business situation that underlies the need for a new idea. • This may seem daunting or unnecessary, but facts are the raw material for innovation. And because of changes to markets, competition, regulation, and technologies, “old ideas” previously dismissed may, perhaps after further adaptation, take on renewed promise. • You also need to bring in perspectives and have access to areas of expertise (either on the team or available to the team) that can contribute to solution formulation and evaluation.
- 39. Process - Background • It is important to approach innovation and its evaluation through a broad appreciation for causality • All processes and outputs are connected and there are relationships (synergies and tradeoffs) between all performance results. • Instead of taking a narrow focus to evaluating processes, outputs, and performance results, which hinders progress; approached more broadly, this “causality web” serves as a basis for identifying and evaluating innovations. • Ideas can be rearranged into endless new combinations. The only practical limit is your knowledge of the facts and your ability to see relationships between them.
- 40. Process - Background • The final key evaluation step is to determine how to make the innovation practical and profitable. • At this point, many ideas stop looking so attractive. • They start looking like a lot of hard work with no certain reward. • In this phase, valid historical data can help you determine whether you have the assets, including skills, necessary to successfully deploy an innovation. • A deep understanding of the business situation may also help you more fully flesh out the candidate innovation by resolving potential barriers and identifying potential partners and other resources that can help make the candidate innovation effectively and economically deployable.
- 41. Process – Steps - Idea generation • Idea generation • In this phase, an analysis of performance results and more broadly the business situation will help in identifying those business / operational areas that require more than just incremental improvements. • Experience in the systems and system-of-systems arena demonstrate that idea generation best takes place through a broader view of the “causal web” in which a business finds itself, which in turn drives identification of the criteria, measures, and analysis that will be needed for evaluating ideas
- 42. Process – Steps - Idea screening • Idea screening • In this phase, our prediction and simulation models and techniques support a deeper evaluation of the appropriate idea for feasibility and appropriateness to the business and the broader delivery capability
- 43. Process – Steps - Idea realization • Idea realization • since in this phase the innovation is maturing and being transitioned to a ‘new’ project, methods that support its management and further evaluation (and further adaptation) are applied toward achieving a higher degree of confidence relative to the impacts to the business and achievement of businesses objectives
- 44. Suggested Methods • Brainstorming • Brainstorming is a group creativity technique designed to generate a large number of ideas for the solution of a problem. In 1953 the method was popularized by Alex Faickney Osborn • Although traditional brainstorming does not increase the productivity of groups (as measured by the number of ideas generated), it may still provide benefits, such as boosting morale, enhancing work enjoyment, and improving team work. Thus, numerous attempts have been made to improve brainstorming or use more effective variations of the basic technique • Ground Rules • Focus on quantity • Withhold criticism • Welcome unusual ideas • Combine and improve ideas association.
- 45. Suggested Methods • Brainstorming • Method • Set the problem • Create a background memo • Select participants • Create a list of lead questions • Session conduct • The process • Evaluation • Variations • Nominal group technique • Group passing technique • Team idea mapping method • Electronic brainstorming • Directed brainstorming • Individual brainstorming
- 46. Suggested Methods • TILMAG's Five Steps for Solving Innovative Problems • The transformation of ideal solution elements through associations (TILMAG) is a leading method for a dominant class of issues that arise in innovation thinking • The steps • Define the problem • Identify the ISE ideal solution elements • Build the TILMAG matrix • Generate solutions • Consolidate and prioritize •
- 47. Suggested Methods • QFD • Quality Function Deployment (QFD) is a systematic process for motivating a business to focus on its customers. It is used by cross- functional teams to identify and resolve issues involved in providing products, processes, services and strategies which will more than satisfy their customers. • A structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those needs. The "voice of the customer" is the term to describe these stated and unstated customer needs or requirements •
- 48. Suggested Tools • Reliability • Ability of an equipment, machine, or system to consistently perform its intended or required function or mission, on demand and without degradation or failure. • Probability of failure-free performance over an item's useful life, or a specified timeframe, under specified environmental and duty-cycle conditions. Often expressed as mean time between failures (MTBF) or reliability coefficient. Also called quality over time. • Consistency and validity of test results determined through statistical methods after repeated trials
- 49. Suggested Tools • Validity • Degree to which an instrument, selection process, statistical technique, or test measures what it is supposed to measure. • Effectiveness • Degree to which objectives are achieved and the extent to which targeted problems are resolved. In contrast to efficiency, effectiveness is determined without reference to costs and, whereas efficiency means "doing the thing right," effectiveness means "doing the right thing." • Piloting • Small-scale campaign, survey, or test-plant commissioned or initiated to check the conditions and operational details before full scale launch
- 51. Application Guidelines • Considers the Real-Life Context • Considers the Innovation System Frame • Considers Innovative Capacity • Examines what are here termed Technological Innovations Systems, referring to a particular strand of innovation theory. • Discusses issues of policy with regard to the integration of environmental concerns in innovation • Discusses cultural determinants of innovation
- 52. Managed Process for Innovation Strategize Capture Formulate Evaluate Define Select Deliver Define IMO Business Strategy Reviews Idea Prioritize Run Portfolio Business Strategy Analysis Approval Capture Idea Enterprise Build Project Team Search Publish Idea to Execute Project Portal Design- Market Potential- Legal Evaluation- Develop Business Case Customer Feedback Strategic Impact - Market Potential - Finalize Design Financials - SWOT- Document Publish Business Case Approval Community Ratings and Reviews
- 53. Process Success Factors • Reveals emerging expectations with minimum effort and investment. • Reveals expectations customers will appreciate. • Reveals emerging expectations to anyone using the innovation system without needing special talent. • Reveals emerging expectation whenever needed. • Reveals emerging expectations that won’t quickly face competition. • Every emerging expectation is an opportunity for commercial success. • Reveals emerging expectations early enough to develop & deliver new products exactly when customers begin expecting them. • Generates the ideas with minimum effort and investment. • Generates ideas customers will like and warns of risky ideas or potential threats. • Generates new ideas whenever needed.
- 54. Process Success Factors • Generates ideas competition can’t easily copy. • Every new idea is successful. • Ideas generated early enough to allow efficient implementation. • Provides the design or reveals sources with minimal effort or expense. • Designs cover the entire range of uses. • Only provides needed uses (no need for unrealistic uses). • Logical system that anyone can use. • Competition can’t easily copy range of uses. • Enhances your existing strengths. • Every new design is successful. • Ideas are immediately converted into designs.
- 55. Process Success Factors • Designs new products so each is launched with minimum effort and investment. • Only designs products with total cost of ownership customers like. • Designs new products within needed range of total cost of ownership. • Utilizes available resources in the “standardized” way. • Uses resources competition can’t easily use. • Every new design successfully uses available resources. • Making new products takes no time. • Launches new product with minimum effort and investment. • Only launches products customers like. • Launches new products only when needed.
- 56. Process Success Factors • Products launch. • New products can’t be easily repeated by competition. • Every new product is successful. • New product is delivered to the customers exactly when they begin expecting it. • Value of new product is communicated with minimum effort and investment. • Only communicates values customers like. • Only communicates values when it’s needed and only in the way needed. • Communicates values in the “standardized” way. • Competition can’t easily repeat communication of values. • Every communication successfully reaches the proper Target Customers.
- 57. Process Success Factors • Values are communicated to the customers exactly when they start seeking. • Collects maximum relevant information with minimum effort. • Only collects true information. • Collects information only when needed. • Collects information in the “standardized” way. • Collects information that competition doesn’t collect and doesn’t understand its value. • Gets needed information every time it’s needed. • Provides relevant information so corrections are made exactly when the customers start expecting them. • Fits your organization’s existing systems and culture. • Provides motivation to use the innovation system.
- 58. Managed Process for Innovation Strategize Capture Formulate Evaluate Define Select Deliver $ % ∑ Analyze the Brainstorm & Business Review and Build project & Review projects Design for X business capture rationale/ score assign team Select project(s) Prototypes and Set business Research & justification Portfolio analysis Design, marketing, Assign budget & market testing drivers initial proof of Cost benefit Proof of concept legal time horizon Manufacturing Establish a strategy concept assessment funding Customer Approve & MRO Publish & share Reviews & rating feedback promote Reuse, recycle
- 59. Summary Widens the Idea Pipeline Formalizes the Innovation Process Fosters a culture of innovation Balances creativity with process Involves more of the right people at discipline the right time Ensures key decisions and actions are Facilitates collaborative participation taken at the right time Secures and manages intellectual capital Optimizes ROI and Time to Market Provides objective and strategic selection criteria Capitalize on business opportunities by improving the speed and robustness of idea selection Maximize the financial return of selected ideas Optimize budget allocation according to strategic value
- 60. What to optimize (Your Process / Product or Both)
- 61. Considerations for Process Optimization • Where are we now and where do we need to be to achieve our future performance goals • What are the performance ranges can we expect from our existing key processes • What resources do we need to “improve” our performance range to achieve future performance goals • How much can we afford/must to invest to achieve our improvements • What is our multi-stage campaign to implement our improvements
- 62. Considerations for Product Optimization • Optimization is successful when the cost of manufacturing will drop and your profit will increase • Produce high-quality products within shorter time lines • To Correct balance between time and cost versus yield and quality is essential to maximize return on investment
- 63. Considerations for Product Optimization • Demonstration of the scalability • Partial selection of what to optimize • Material • Cost of product • Design for • Scalability • Availability • Reliability • Serviceability • Maintainability • Supportability • Stability • Reusability • Sustainability of the Technology as a solution
- 64. Benefit of Both • Product development involves selecting both the product (what to build) and the approach and resources (how to build). • By expanding your innovation process to encompass both product and process, you may find new combinations of product assemblies and processes, resulting in promising products and business models • Leading to more growth for the business
- 65. Case studies
- 66. Process & Product Optimization (Brief Walkthrough) Our Objectives are To identify process best value chain; improvements and strengths To develop what to focus on for improvement (suggestions and an improvement action plan)
- 67. Business Goals • Simplified the Product Development Initiatives to clear scope and users • Identify, map and assign appropriate priorities the different stakeholders and commitments • Identify and predict the Large Complex or Global Teams coordination and alignment efforts Inventions impact on the program and other team members teams • Identify and predict processes efficiency And/or Effectiveness impact on the program and teams • Identify and predict Conflicts in Product Development Time vs. the stakeholders expectations • Identify and predict redesign Effectiveness impact on the program and teams • Identify and predict changing in teams impact on the program and teams • How to choose the right way Problem Solving, or Fire Fighting based on quantitative and prediction of impact analysis
- 68. Goal Alignment with Models - 1 • Simplified the Product Development Initiatives to clear scope and users • QFD and Dynamic Bayesian Games • Identify, map and assign appropriate priorities the different stakeholders and commitments • Quality Function Deployment • Identify and predict the New Product Initiatives / Inventions impact on the program and other stakeholders • Game Theory; Bayesian Networks and Dynamic Bayesian Games • Identify and predict the Large Complex or Global Teams coordination and alignment efforts Inventions impact on the program and other team members teams • Bayesian Networks and Dynamic Bayesian Games
- 69. Goal Alignment with Models - 2 • Identify and predict processes efficiency And/or Effectiveness impact on the program and teams • Bayesian Networks and Dynamic Bayesian Games • Identify and predict Conflicts in Product Development Time vs. the stakeholders expectations • Game Theory; Quality Function Deployment; Bayesian Networks and Dynamic Bayesian Games • Identify and predict redesign Effectiveness impact on the program and teams • Quality Function Deployment; Dynamic Bayesian Games • Identify and predict changing in teams impact on the program and teams • Dynamic Bayesian Games • How to choose the right way Problem Solving, or Fire Fighting based on quantitative and prediction of impact analysis • Bayesian Networks and Dynamic Bayesian Games
- 70. Professional Challenges (Partial list only) • Information analysis • Requirements Structure Analysis • Requirements Position in Business Environment • Requirements Value Chain • Operational System Value Chain • Development Elicitation to Requiremnts Type and Classification
- 71. Operational Challenges (Partial list only) • Product / Program Objectives Definition in Quantitative Way and Structure • Definition of 'Good Enough' Level • Differentiating Different Program Objectives and Success Factors For the Different Life Cycle Phases • Resource Usage and Adjustment Elicitation to Plan and Objectives
- 72. Completing the Graphical Model • To simplified the presentation we used a four stage New Product Development process. • The nodes indicating the potential return at selected four stage gates • To simplified this presentation, the gates are: • New Product Concept Return • New Product Design Return • Production Startup Return • Keep On Market Return
- 73. Completing the Graphical Model • We identified and selected thirteen relevant criteria that are influencing our factors, grouped into main five factors • Each of it forms a node in the network. And its Arcs from specific criteria to the relevant factors indicate the criteria, e.g.: • Sales Growth and Market Share influence Market Opportunity • And the factors Arcs stage gate (Return node) indicate that each factor influence each stage gate. • One of our assumption during the development of the causal relationships was criteria's that influence a factor do not change between NPD stages
- 75. Completing the quantitative aspects of the model • The third step in structuring decisions is the refinement and precise definition of all the elements of the decision model. • This relates to the second step of building a BN. • The second step of building the BN is to associate probabilities with the causal relationships defined in the previous slides.
- 76. Defining States • First action in the quantitative modelling phase is to define appropriate states for each of the nodes • Due to the large number of possible states in the model (explained later) it was decided to use numerical intervals • for all criteria to have three states 1, 2 and 3. These states can be interpreted appropriately from worst to best for each of the criteria
- 77. Defining States • Factors states are determined by the criteria that influences each state. • It was chosen to normalise any contributing criteria so that the factor values will always be between 0 and 1. • This eased the understanding of the outputs and the development of the expressions determining the probabilities of the NPD Return nodes. • Again it was chosen to have three states for each factor. The factor states indicate intervals for the result of the expression that determine the factor value. • The implemented factor states are therefore 0-0.33, 0.33- 0.66, and 0.66-1. • Again these states can be interpreted appropriately from worst to best for each of the factors
- 78. Defining States • States for the NPD Return nodes are determined by the possible states for the factors and the weightings of the relationships. • It was found that a granularity of only three states for the NPD Return did not provide sufficient resolution to aid understanding of the results. • Therefore we have decided to implement four states for the NPD Return nodes.
- 79. Model Outputs • For ease of discussion the NPD Return states of 0-23.25, 23.25 - 46.5, 46.5 - 69.75, 69.75 - 93 will be referred to as low, medium-low, medium-high and high returns respectively. Where appropriate for the factors and criteria low, medium, and high will also be used to refer to the relevant associated states • The realized model with no evidence entered, as shown in the next slide, shows a high probability for medium returns in all three stages. This is based on equal probabilities for the sixteen input criteria. • The benefit of the Bayesian network is that evidence entry is not limited to the input nodes, in this instance the criteria nodes. Evidence can be entered at any of the nodes and will propagate through the network
- 80. Model Outputs 2/4/2013
- 81. Model Results • At New Product Concept; the model results show: • 83.63% required probability for high Strategic Fit • 48.64% for Keep on Market • The Technical Feasibility is more important over the Concept and Design phases • For Concept phase 84% • For Design phase 88.84% • vs 80.82% and 47.10% respectively • Also we observe that technical feasibility also has an important part to play during production start up
- 82. Model Results • Customer Acceptance is important throughout the process but especially after product launch • Our model shows a high probability requirement for Customer Acceptance for all stages • Concept = 85.80% • Design = 91.52% • Production = 90.62% • Keep on Market = 99% • with the highest required level for Customer Acceptance (99%) at the Keep On Market stage, that is after the product has launched • The model indicates that Financial Performance importance is fairly constant over the NPD stages. A slight increase towards the later stages is in line with the paper results
- 83. Evidence Scenario Results • We found that it is very useful to use the model to run what if’s • The next scenario could be described as: • A new product of medium cost is to be developed. • The product is within the company’s niche area and would therefore leverage the company resources very well. • It is unknown whether the resource would be available and no evidence of this is entered. • The product is very well aligned with the company strategy and the window of opportunity is good but not extremely so. • It is not sure how good the market acceptance or customer satisfaction would be. • It is clear that a product of high quality can be developed. Calculation shows that the margin rate and Internal Rate of Return would be medium good. • The sales volume can not be predicted at this stage. • Both sales growth and market share is predicted to be medium
- 84. Evidence Scenario Results • The results can be interpreted as • The technical feasibility of the project is high (63% likely) to medium-high (33% likely) • The project strategic fit is perfect. Whether the customer acceptance would be high (55% likely) or medium-high (44% likely) is unsure. • The project’s financial performance and market opportunity are predicted to be medium. • All this translates into a high probability (almost 80% in all stages) of achieving a medium-high return in all stages, zero probability of achieving only a low return at any of the stage gates, and small probabilities to reach a medium-low (1.68% to 12.26%) or high return (6% to 17%)
- 86. What-if a high level of customer satisfaction could be achieved • The power of the Bayesian network lies in the ability to perform what-if analysis. In the scenario as described above one viable question that could be asked is: What-if a high level of customer satisfaction could be achieved? 91
- 87. What-if a high level of customer satisfaction could be achieved 2/4/2013
- 88. What-if a high level of customer satisfaction could be achieved • The power of the Bayesian network lies in the ability to perform what-if analysis. In the scenario as described above one viable question that could be asked is: What-if a high level of customer satisfaction could be achieved? • The results can be interpreted as follows: • For all stages the probability of achieving a medium-low return becomes zero. This is not a big influence as the original probabilities were already very low. • Increasing customer acceptance to high will almost double the probability of indicating a high return at the design stage (from 17% to 31%). • Same applies to the Production Startup stage (probability for high return changes from 13% to 24%). • Also of significance is that the 12% probability of indicating only a 93 medium-low return for the Keep on Market stage disappears
- 89. Conclusions and Recommendations • Applying decision support techniques (specifically Bayesian Networks) to the area of New Product Development will address some of the primary challenges that mangers have • Bayesian Networks can be implemented in order to develop a decision support system in the management of new product development domain • This model addresses various aspects of new product development over multiple stages • The model can deal with quantitative and qualitative input and missing data • decision support technique such as Bayesian Networks can be implemented to address our managerial problems and to support our managers with strong ‘what if’s’ • The implementation of a graphical user interface hiding the complexities of the Bayesian network
- 90. Discussion Points • Performance data • Cost of poor planning building elements • Quantifying the operational impact of support planning • Effecting and effected stakeholders mapping • Quantifying the impact of support planning on the development teams • Appling this model on other domains
- 91. Outcome(s) Predicted • Visual model that indicates the causal relationships between various aspects in the process • Will enable us to deal with uncertainty and missing data and allow the user to experiment with possible outcomes (What-if analyses). • Decision analysis that will ptovide structure and guidance for systematic thinking in difficult situations
- 92. Stakeholder Audience • Process Performers • Involved Processes • EPGs
- 93. Factors used in the Process Performance Model • Objectives • Structured • Monitored / Measured • Effective / Efficient • Process Interfaces and Integration in Lifecycle • Prioritize and Balance Resource Utilization within Larger Context
- 94. Data Collection • Due to the unique nature of data elements and related factors we have collected and analyzed the data elements and factors manually based on players stakeholders per project program • We have initiated historical data base (Excel based) and we are in the progress to build generic model • We did not use any sampling because for each project program we need to run the full method from start, therefore we have developed supporting matrix when to apply it • The current threats to data quality and integrity that we have faced • Players subjectivity • Unclear player role • Change of players (individuals) in the same position during one or more of the ‘game’ (project program) instance • We are currently running postmortem on past project to clean and understand our percentage of measurement error
- 95. Tool Used • Process Simulation Tool • Bayesian network
- 96. What Worked Well • What worked well • Senior staff commitments • Stakeholders acceptance of the balancing results • Stakeholders acceptance of their ‘position’ and weight • Between our side benefits • ‘snow ball’ effect from other departments • Request for generic model development • Request to adjust it to strategic and multi year programs • Stakeholder inputs • Give clear world view of all aspects • Reduce the decision making and factors analysis complexity • The historical data base from past projects reduce resistance • Model development team member inputs • Create more clear understanding on the • The historical data base from past projects reduce development time
- 97. Discussion Points • Process performance data • Cost of poor planning elements • Quantifying the operational impact of the process • Effecting and effected stakeholders mapping • Quantifying the impact of the optimized process • Appling this model on other domains
- 98. Wrap-up • Innovation is fundamental to continued business growth and success • Requires investment, understanding business environment • Needs to be evaluated differently than a known business investment • Is a business process with its own rules • Follows a lifecycle • Needs to be focused and measured
- 99. Wrap-up • Innovation should be thought of as: • Consisting of a set of process steps (Strategize, Elicit, Screen, …) • Having both product and process dimensions • A learned process (define and adopt a methodology, and improve it over time) • Derived from performance data and information • Must be a structured process supported by tools and methods • Must be managed through monitoring the performance of the innovation process itself and measured • Needs to have management focus and commitments
- 100. References • Carbonara, N., Scozzi, B., 2006, Cognitive maps to analyse new product development processes: A case study, Technovation 26: 1233- 1243. • Carbonell-Foulquםe, P., Munuera-Alemבn, J.L., Rodrםguez- Escudero, 2004, Criteria employed for go/no-go decisions when developing successful highly innovative products, Industrial Marketing Management, 33:307- 316. • Clemen, R.T., Reilly, T., 2001, Making hard decisions with Decision Tools, Duxbury: Pacific Grove. • Cooper, R.G., Edgett, S.J., Kleinschmidt, E.J., New Product Portfolio Management: Practices and Performance, 1999, J Prod Innov Manag, Vol. 16:333-351. • Cooper, R.G., Edgett, S.J., Kleinschmidt, E.J., 2001a, Portfolio Management - Fundamental to New Product Success, Working Paper No. 12.
- 101. Contacts • Kobi Vider – Picker • K.V.P Consulting • Kobi.vider@hotmail.com • Michael Konrad • SEI • mdk@sei.cmu.edu