Measuring Value in Product Development

“What is value in product development?” is the key question of this paper. The answer is critical to the creation of lean in product development. By knowing how much value is added by product development (PD) activities, decisions can be more rationally made about how to allocate resources, such as time and money

Measuring Value in Product Development

“What is value in product development?” is the key question of this paper. The answer is critical to the creation of lean in product development. By knowing how much value is added by product development (PD) activities, decisions can be more rationally made about how to allocate resources, such as time and money. In order to apply the principles of Lean Thinking and remove waste from the product development system, value must be precisely defined. Unfortunately, value is a complex entity that is composed of many dimensions and has thus far eluded definition on a local level. For this reason, research has been initiated on “Measuring Value in Product Development.” This paper serves as an introduction to this research. It presents the current understanding of value in PD, the critical questions involved, and a specific research design to guide the development of a methodology for measuring value. Work in PD value currently focuses on either high-level perspectives on value, or detailed looks at the attributes that value might have locally in the PD process. Models that attempt to capture value in PD are reviewed. These methods, however, do not capture the depth necessary to allow for application. A methodology is needed to evaluate activities on a local level to determine the amount of value they add and their sensitivity with respect to performance, cost, time, and risk. Two conceptual tools are proposed. The first is a conceptual framework for value creation in PD, referred to here as the Value Creation Model. The second tool is the Value-Activity Map, which shows the relationships between specific activities and value attributes. These maps will allow a better understanding of the development of value in PD, will facilitate comparison of value development between separate projects, and will provide the information necessary to adapt process analysis tools (such as DSM) to consider value. The key questions that this research entails are: · What are the primary attributes of lifecycle value within PD? · How can one model the creation of value in a specific PD process? · Can a useful methodology be developed to quantify value in PD processes? · What are the tools necessary for application? · What PD metrics will be integrated with the necessary tools? The research milestones are: · Collection of value attributes and activities (September, 200) · Development of methodology of value-activity association (October, 2000) · Testing and refinement of the methodology (January, 2001) · Tool Development (March, 2001) · Present findings at July INCOSE conference (April, 2001) · Deliver thesis that captures a formalized methodology for defining value in PD (including LEM data sheets) (June, 2001) The research design aims for the development of two primary deliverables: a methodology to guide the incorporation of value, and a product development tool that will allow direct application

Development of a Genetic Algorithm to Automate Clustering of a Dependency Structure Matrix

Much technology assessment and organization design data exists in Microsoft Excel spreadsheets. Tools are needed to put this data into a form that can be used by design managers to make design decisions. One need is to cluster data that is highly coupled. Tools such as the Dependency Structure Matrix (DSM) and a Genetic Algorithm (GA) can be of great benefit. However, no tool currently combines the DSM and a GA to solve the clustering problem. This paper describes a new software tool that interfaces a GA written as an Excel macro with a DSM in spreadsheet format. The results of several test cases are included to demonstrate how well this new tool works

An iterative method for selecting decision variables in analytical optimization problem

In this paper, we present an iterative method to assist the designer in the setting of decision variables in an optimization problem with analytical models. This method is based on the Design Structure Matrix (DSM) which allows a clear representation of interactions between variables. Such a process becomes particularly useful for complex optimal design for which choosing the right decision variables to efficiently solve the problem is not so trivial. This approach is applied to the geometrical model of a High Speed Permanent Magnet Synchronous Machine (HSPMSM

On the Design of Functionally Integrated Aero-engine Structures: Modeling and Evaluation Methods for Architecture and Complexity

The drive for airplanes with radically reduced fuel consumption and emissions motivates engine manufacturers to explore innovative engine designs. The novelty of such engines results in changed operating conditions, such as newly introduced constraints, increased loads or rearranged interfaces. To be competitive, component developers and manufacturers must understand and predict the consequences of such changes on their sub-systems. Presently, such assessments are based on detailed geometrical models (CAD or finite element) and consume significant amounts of time. The preparation of such models is resource intensive unless parametrization is employed. Even with parametrization, alternative geometrical layouts for designs are difficult to achieve. In contrast to geometrical model-based estimations, a component architecture representation and evaluation scheme can quickly identify the functional implications for a system-level change and likely consequences on the component. The schemes can, in turn, point to the type and location of needed evaluations with detailed geometry. This will benefit the development of new engine designs and facilitate improvements upon existing designs. The availability of architecture representation schemes for functionally integrated (all functions being satisfied by one monolithic structure) aero-engine structural components is limited. The research in this thesis focuses on supporting the design of aero-engine structural components by representing their architecture as well as by developing means for the quantitative evaluation and comparison of different component designs. The research has been conducted in collaboration with GKN Aerospace Sweden AB, and the components are aero-engine structures developed and manufactured at GKN. Architectural information is generated and described based on concepts from set theory, graph theory and enhanced function–means trees. In addition, the complexities of the components are evaluated using a new complexity metric. Specifically, the developed modeling and evaluation methods facilitate the following activities: \ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 identification and representation of function–means information for the component\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 representation and evaluation of component architecture\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 product complexity evaluation\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 early selection of load path architecture\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 impact assessment for the component’s functioning in the systemBy means of the methods developed in this thesis, the design rationale for a component is made explicit, and the storing, communicating and retrieving of information about the component in the future is enabled. Through their application to real-life engine structures, the usability of the methods in identifying early load carrying configurations and selecting a manufacturing segmenting option is demonstrated. Together, the methods provide development engineers the ability to compare alternative architectures. Further research could focus on exploring the system (engine) effects of changes in component architecture and improvements to the complexity metric by incorporating manufacturing information

Modeling and optimising alternatives suitable to advance the product development process

In the rapidly changing technical-economical environment time has an increasingly important role in the product development besides creativity, cost, quality criteria. In this paper we aim to reveal methods that can decrease the cost and time spent on the product development increasing the efficiency of the process. This publication presents an insight of optimising with the use of design structure matrix, genetic algorithms, and neural network. Provides the opportunity of using the methods in different areas, so it may result a profitable industrial innovation, and is suitable for creating new ideas on the field of automobile engineering projects

Summary Report: Systematic IPT Integration in Lean Development Programs

This document provides a summary report of the M.I.T. Masters Thesis, "Systematic IPT Integration in Lean Development Programs" by Tyson R. Browning. These studies argue for the inclusion of program integration principles as an essential aspect of lean enterprise product development and organization.Lean Aerospace Initiativ

A methodology for the validated design space exploration of fuel cell powered unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) are the most dynamic growth sector of the aerospace industry today. The need to provide persistent intelligence, surveillance, and reconnaissance for military operations is driving the planned acquisition of over 5,000 UAVs over the next five years. The most pressing need is for quiet, small UAVs with endurance beyond what is capable with advanced batteries or small internal combustion propulsion systems. Fuel cell systems demonstrate high efficiency, high specific energy, low noise, low temperature operation, modularity, and rapid refuelability making them a promising enabler of the small, quiet, and persistent UAVs that military planners are seeking. Despite the perceived benefits, the actual near-term performance of fuel cell powered UAVs is unknown. Until the auto industry began spending billions of dollars in research, fuel cell systems were too heavy for useful flight applications. However, the last decade has seen rapid development with fuel cell gravimetric and volumetric power density nearly doubling every 2-3 years. As a result, a few design studies and demonstrator aircraft have appeared, but overall the design methodology and vehicles are still in their infancy. The design of fuel cell aircraft poses many challenges. Fuel cells differ fundamentally from combustion based propulsion in how they generate power and interact with other aircraft subsystems. As a result, traditional multidisciplinary analysis (MDA) codes are inappropriate. Building new MDAs is difficult since fuel cells are rapidly changing in design, and various competitive architectures exist for balance of plant, hydrogen storage, and all electric aircraft subsystems. In addition, fuel cell design and performance data is closely protected which makes validation difficult and uncertainty significant. Finally, low specific power and high volumes compared to traditional combustion based propulsion result in more highly constrained design spaces that are problematic for design space exploration. To begin addressing the current gaps in fuel cell aircraft development, a methodology has been developed to explore and characterize the near-term performance of fuel cell powered UAVs. The first step of the methodology is the development of a valid MDA. This is accomplished by using propagated uncertainty estimates to guide the decomposition of a MDA into key contributing analyses (CAs) that can be individually refined and validated to increase the overall accuracy of the MDA. To assist in MDA development, a flexible framework for simultaneously solving the CAs is specified. This enables the MDA to be easily adapted to changes in technology and the changes in data that occur throughout a design process. Various CAs that model a polymer electrolyte membrane fuel cell (PEMFC) UAV are developed, validated, and shown to be in agreement with hardware-in-the-loop simulations of a fully developed fuel cell propulsion system. After creating a valid MDA, the final step of the methodology is the synthesis of the MDA with an uncertainty propagation analysis, an optimization routine, and a chance constrained problem formulation. This synthesis allows an efficient calculation of the probabilistic constraint boundaries and Pareto frontiers that will govern the design space and influence design decisions relating to optimization and uncertainty mitigation. A key element of the methodology is uncertainty propagation. The methodology uses Systems Sensitivity Analysis (SSA) to estimate the uncertainty of key performance metrics due to uncertainties in design variables and uncertainties in the accuracy of the CAs. A summary of SSA is provided and key rules for properly decomposing a MDA for use with SSA are provided. Verification of SSA uncertainty estimates via Monte Carlo simulations is provided for both an example problem as well as a detailed MDA of a fuel cell UAV. Implementation of the methodology was performed on a small fuel cell UAV designed to carry a 2.2 kg payload with 24 hours of endurance. Uncertainty distributions for both design variables and the CAs were estimated based on experimental results and were found to dominate the design space. To reduce uncertainty and test the flexibility of the MDA framework, CAs were replaced with either empirical, or semi-empirical relationships during the optimization process. The final design was validated via a hardware-in-the loop simulation. Finally, the fuel cell UAV probabilistic design space was studied. A graphical representation of the design space was generated and the optima due to deterministic and probabilistic constraints were identified. The methodology was used to identify Pareto frontiers of the design space which were shown on contour plots of the design space. Unanticipated discontinuities of the Pareto fronts were observed as different constraints became active providing useful information on which to base design and development decisions.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Nam, Taewoo; Committee Member: Parekh, David; Committee Member: Soban, Danielle; Committee Member: Volovoi, Vital

Risk Management in Lean Product Development

This whitepaper summarizes 15 years of research conducted at MIT's Lean Advancement Initiative on the topic of risk management in product design and development. It discusses current challenges in risk management for product development, presents a risk management process for PD based on ISO 31000, and summarizes 10 papers published of LAI research on risk management

LAI Paper Series: “Lean Product Development for Practitioners”: Risk Management in Lean PD

The two core challenges of risk management are finding the optimum balance a) between the cost of carrying risks vs. the cost of mitigating risks and b) between a risk that is taken with a certain development project and the return that is expected from the project. A complete absence of risk management will minimize the cost of risk mitigation measures – no backup development capacity, no review meetings, no quality control incur no direct cost. However, the project becomes very vulnerable towards uncertainties: If a development task turns out to be more complex than previously anticipated and no backup capacity can be brought to bear, the entire project might be delayed and cost incurred through idle capacities, penalty payments towards the customer for delays or opportunity cost for lost customers and market share. The same may happen for less-than-perfect coordination between different engineers and departments, or erroneous designs that would otherwise have been uncovered in review meetings or quality checks. On the other hand, excess backup capacity, reviews and quality controls bind more resources and cost more money than they save. Good risk management helps to strike the right balance between minimizing risk and the cost of doing so. After minimizing the overall risk as much as is sensible, the question remains what the right level of risk is that is still acceptable for a project to be attractive. While the goal for every single project is to minimize its overall risk, projects are in general exposed to different levels of uncertainty: Some might involve more innovative technologies or technologies that the company is not familiar with; some might address new markets where the exact customer requirements are unclear; and others might just be a lot bigger than usual and therefore have a much more significant impact if they fail. The goal is to find projects that have the right balance of risk and return, as would be the case with any other investments (e.g. a portfolio of stocks and bonds)