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Author: Winfried J. Steiner
Publisher:
ISBN:
Category :
Languages : de
Pages : 56
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Author: Winfried J. Steiner
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Designing and pricing new products is one of the most critical activities for a firm, and it is well-known that taking into account consumer preferences for design decisions is essential for products later to be successful in a competitive environment (e.g., Urban and Hauser 1993). Consequently, measuring consumer preferences among multiattribute alternatives has been a primary concern in marketing research as well, and among many methodologies developed, conjoint analysis (Green and Rao 1971) has turned out to be one of the most widely used preference-based techniques for identifying and evaluating new product concepts. Moreover, a number of conjoint-based models with special focus on mathematical programming techniques for optimal product (line) design have been proposed (e.g., Zufryden 1977, 1982, Green and Krieger 1985, 1987b, 1992, Kohli and Krishnamurti 1987, Kohli and Sukumar 1990, Dobson and Kalish 1988, 1993, Balakrishnan and Jacob 1996, Chen and Hausman 2000). These models are directed at determining optimal product concepts using consumers' idiosyncratic or segment level part-worth preference functions estimated previously within a conjoint framework. Recently, Balakrishnan and Jacob (1996) have proposed the use of Genetic Algorithms (GA) to solve the problem of identifying a share maximizing single product design using conjoint data. In this paper, we follow Balakrishnan and Jacob's idea and employ and evaluate the GA approach with regard to the problem of optimal product line design. Similar to the approaches of Kohli and Sukumar (1990) and Nair et al. (1995), product lines are constructed directly from part-worths data obtained by conjoint analysis, which can be characterized as a one-step approach to product line design. In contrast, a two-step approach would start by first reducing the total set of feasible product profiles to a smaller set of promising items (reference set of candidate items) from which the products that constitute a product line are selected in a second step. Two-step approaches or partial models for either the first or second stage in this context have been proposed by Green and Krieger (1985, 1987a, 1987b, 1989), McBride and Zufryden (1988), Dobson and Kalish (1988, 1993) and, more recently, by Chen and Hausman (2000). Heretofore, with the only exception of Chen and Hausman's (2000) probabilistic model, all contributors to the literature on conjoint-based product line design have employed a deterministic, first-choice model of idiosyncratic preferences. Accordingly, a consumer is assumed to choose from her/his choice set the product with maximum perceived utility with certainty. However, the first choice rule seems to be an assumption too rigid for many product categories and individual choice situations, as the analyst often won't be in a position to control for all relevant variables influencing consumer behavior (e.g., situational factors). Therefore, in agreement with Chen and Hausman (2000), we incorporate a probabilistic choice rule to provide a more flexible representation of the consumer decision making process and start from segment-specific conjoint models of the conditional multinomial logit type. Favoring the multinomial logit model doesn't imply rejection of the widespread max-utility rule, as the MNL includes the option of mimicking this first choice rule. We further consider profit as a firm's economic criterion to evaluate decisions and introduce fixed and variable costs for each product profile. However, the proposed methodology is flexible enough to accomodate for other goals like market share (as well as for any other probabilistic choice rule). This model flexibility is provided by the implemented Genetic Algorithm as the underlying solver for the resulting nonlinear integer programming problem. Genetic Algorithms merely use objective function information (in the present context on expected profits of feasible product line solutions) and are easily adjustable to different objectives without the need for major algorithmic modifications. To assess the performance of the GA methodology for the product line design problem, we employ sensitivity analysis and Monte Carlo simulation. Sensitivity analysis is carried out to study the performance of the Genetic Algorithm w.r.t. varying GA parameter values (population size, crossover probability, mutation rate) and to finetune these values in order to provide near optimal solutions. Based on more than 1500 sensitivity runs applied to different problem sizes ranging from 12.650 to 10.586.800 feasible product line candidate solutions, we can recommend: (a) as expected, that a larger problem size be accompanied by a larger population size, with a minimum popsize of 130 for small problems and a minimum popsize of 250 for large problems, (b) a crossover probability of at least 0.9 and (c) an unexpectedly high mutation rate of 0.05 for small/medium-sized problems and a mutation rate in the order of 0.01 for large problem sizes. Following the results of the sensitivity analysis, we evaluated the GA performance for a large set of systematically varying market scenarios and associated problem sizes. We generated problems using a 4-factorial experimental design which varied by the number of attributes, number of levels in each attribute, number of items to be introduced by a new seller and number of competing firms except the new seller. The results of the Monte Carlo study with a total of 276 data sets that were analyzed show that the GA works efficiently in both providing near optimal product line solutions and CPU time. Particularly, (a) the worst-case performance ratio of the GA observed in a single run was 96.66%, indicating that the profit of the best product line solution found by the GA was never less than 96.66% of the profit of the optimal product line, (b) the hit ratio of identifying the optimal solution was 84.78% (234 out of 276 cases) and (c) it tooks at most 30 seconds for the GA to converge. Considering the option of Genetic Algorithms for repeated runs with (slightly) changed parameter settings and/or different initial populations (as opposed to many other heuristics) further improves the chances of finding the optimal solution.
Author: Timothy W. Simpson
Publisher: Springer Science & Business Media
ISBN: 0387291970
Category : Technology & Engineering
Languages : en
Pages : 547
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Book Description
This book discusses how product platform and product family design can be used successfully to increase variety within a product line, shorten manufacturing lead times, and reduce overall costs within a product line. The material serves as a reference and a hands-on guide for practitioners involved in the design, planning and production of products. Real-life case studies that explain the benefits of platform based product development are included.
Author: Berend Wierenga
Publisher: Springer Science & Business Media
ISBN: 0387782133
Category : Business & Economics
Languages : en
Pages : 621
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Book Description
Marketing models is a core component of the marketing discipline. The recent developments in marketing models have been incredibly fast with information technology (e.g., the Internet), online marketing (e-commerce) and customer relationship management (CRM) creating radical changes in the way companies interact with their customers. This has created completely new breeds of marketing models, but major progress has also taken place in existing types of marketing models. Handbook of Marketing Decision Models presents the state of the art in marketing decision models. The book deals with new modeling areas, such as customer relationship management, customer value and online marketing, as well as recent developments in other advertising, sales promotions, sales management, and competition are dealt with. New developments are in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets.
Author: Kevin E. Voges
Publisher: IGI Global
ISBN: 1591407044
Category : Computers
Languages : en
Pages : 481
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"This book deals with the computational intelligence field, particularly business applications adopting computational intelligence techniques"--Provided by publisher.
Author:
Publisher:
ISBN:
Category : Computer-aided design
Languages : en
Pages : 986
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Author: Design Engineering Technical Conferences
Publisher:
ISBN: 9780791847428
Category :
Languages : en
Pages : 984
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Author: Xinfang (Jocelyn) Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We develop a branch-and-price algorithm for constructing an optimal product line using partworth estimates from choice-based conjoint analysis. The algorithm determines the specific attribute levels for each multi-attribute product in a set of products to maximize the resulting product line's share-of-choice i.e., the number of respondents for whom at least one new product's utility exceeds the respondent's reservation utility. Computational results using large commercial and simulated datasets demonstrate that the algorithm can identify provably optimal solutions to realistically sized problems.
Author: Ariel Fligler
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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In this marketing-oriented era where manufacturers maximize profits through customer satisfaction, there is an increasing need to design a product line rather than a single product. By offering a product line, the manufacturer can customize his or her products to the needs of a variety of segments in order to maximize profits by satisfying more customers than a single product would. When the amount of data on customer preferences or possible product configurations is large and no analytical relations can be established, the problem of an optimal product line design becomes very difficult and there are no traditional methods to solve it. In this paper, we show that the usage of Genetic Algorithms, a mathematical heuristic mimicking the process of biological evolution, can efficiently solve the problem. Special domain operators were developed to help the genetic algorithm mitigate cannibalization and enhance the algorithm's local search abilities. Using manufacturer's profits as the criteria for fitness in evaluating chromosomes, the usage of domain specific operators was found to be highly beneficial with better final results. We also have hybridized the genetic algorithm with a linear programming post-processing step to fine tune the prices of products in the product line. Attacking the core difficulty of canibalization in the algorithm, the operators introduced in this work are unique. Furthermore, applying our algorithm to a particular product line design problem, we find that the profile of the optimal single product is the core product of the optimal product line. The various brands in the product line are slight variations of the single product solution.
Author: Xinfang Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 119
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Book Description
Companies rely heavily on new products as a source of profit. New products are usually introduced in several configurations to comprise a product line. A product line enables a firm to satisfy the heterogeneous preferences of today's customers. Important in practice, the product line design problem has also drawn tremendous interest from academia. Since the 1970s researchers have been working on the problem of constructing an optimal product line using partworth data obtained from conjoint studies. In this dissertation, we focus on solving the share-of-choice product line problem. The objective is to determine the product attributes of a set of products for a firm to offer so as to maximize the projected market share provided by the set of products in the line; i.e., the number of respondents for whom at least one new product's utility exceeds a specific hurdle. Previous contributions to this NP-Hard problem include a series of heuristics. We present a new branch-and-price algorithm that embeds a column generation procedure within a branch-and-bound process to obtain exact optimal integer solutions to the product line problem. The dissertation provides a great deal of detail about the issues encountered in implementing the branch-and-price algorithm (e.g., pricing scheme, column management and tree traversal strategy). Computational results using real and large simulated datasets demonstrate that the algorithm is capable of identifying provably optimal solutions very quickly. We design an experiment to isolate the factors to which the solution times of the branch-and-price algorithm are most sensitive. The effectiveness of previously published heuristics has only been evaluated on small problems for which complete enumeration is possible. We benchmark the performance of the branch-and-price algorithm against two heuristics (i.e., the segment-by-segment approach and the sequential approach) in the context of preference heterogeneity using test problems of realistic size. All published heuristics treat estimated partworths as errorless instead of statistical estimates, leaving the robustness of heuristics to partworth uncertainty unknown. We analyze the robustness of the branch-and-price algorithm and the sequential method to within-person variation in estimated partworths by conducting an experiment with a test phase and a validation phase. The increase of unique of product lines indicates that the sequential method is less robust to the partworth uncertainty than the branch-and-price algorithm.
Author: Anil Mital
Publisher: Elsevier
ISBN: 0080556418
Category : Technology & Engineering
Languages : en
Pages : 444
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Book Description
Today’s product development teams have to comprise an integrated group of professionals working from the very beginning of new product planning through design creation and design review and then on to manufacturing planning and cost accounting. More graduate and professional training programs are aimed at meeting that need by creating a better understanding of how to integrate and speed up the entire product development process. This book is the perfect accompaniment. This instructional reference work can be used in the traditional classroom, in professional continuing education courses or for self-study. This book has a ready audience among graduate students in mechanical and industrial engineering, as well as in many MBA programs focused on manufacturing management. This is a global need that will find a receptive readership in the industrialized world, particularly the rapidly developing industrial economies of South Asia and Southeast Asia. First text/reference to cover product development from initial product concept and engineering design to design specs, manufacturability and product marketingReviews the precepts of Product design in a step-by-step structured processHelps the reader to understand the connection between initial design and interim and final design, including design review and materials selectionOffers insight into roles played by product functionality, ease-of'assembly, maintenance and durability, and their interaction with cost estimation and manufacturability
