New Product Forecasting: An Applied ApproachConcise and jargon free, this is a one-step primer on the tools and techniques of forecasting new product development. Equally useful for students and professionals, the book is generously illustrated, and features numerous current real-world industry cases and examples. Part I covers the basic foundations and processes of new product forecasting, and links forecasting to the broader processes of new product development and sales and operations planning. Part II includes detailed, step-by-step techniques of new product forecasting, from judgmental techniques to regression analysis. Each chapter in this section begins with the most basic techniques, then progresses to more advanced levels. Part III addresses managerial considerations of new product forecasting, including postlaunch issues such as cannibalization and supercession. The final chapter presents an important set of industry best practices and benchmarks. |
Contents
Linking Techniques | 15 |
Assumptions and Examine Risk | 54 |
Markov Process Model | 63 |
Copyright | |
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approach assumptions-based models ATAR model benchmarking calculated CALIFORNIA SAN DIEGO category entries concept testing conjoint analysis consumer firms correlation cost reductions critical assumptions curve customer/market research Decision trees Delphi method dependent variable DIEGO THE LIBRARIES diffusion models equation estimate example executive opinion expected Figure industrial firms inventory jury of executive Kano Model launch control protocol launch scorecard lead users line extensions logistic regression market share market testing Markov process model methodology multiple n/a n/a n/a new-to-the-world products Nonlinear Regression option PDMA preference regression pretechnical evaluation prod product concept product development product fore Product Forecast Assumptions product forecasting process Product Forecasting Techniques product improvements product launch product sales purchase qualitative forecasting Quality Function Deployment R-squared regression analysis regression model relationship sales force composite sales potential SAN DIEGO CALIFORNIA scenario analysis strategy target market tion types uct forecasting unit sales forecast UNIVERSITY versus