競(jìng)爭(zhēng)市場(chǎng)中產(chǎn)品組合管理博弈模型(英文)

1、A game theory-based model for product portfolio management in a competitive marketA. Sadeghi, M. ZandiehExpert Systems with Applications,2010StructureIntroductionLiterature reviewDescription of the PPM problemProblem formulationExampleConclusions and future work1. IntroductionConsumers, industrial m

2、anagers, and sales and marketing people, all demand products that improve their lifestyles or to gain an edge over the competition. So, product portfolios are interesting for many people. But unlimited product variety is not a way to be successful; there has to be an optimum. It is true for most com

3、panies that the Pareto rule applies: 80% of the sales and/or prots come from 20% of the products . It is evident that a single product cannot fulll the manufacturer needs and on the other hand, for diversity there exists limitation .In todays highly competitive environment, determining an optimal pr

4、oduct portfolio is very important for the survival of a rm. Optimal product portfolio has received considerable attention,because the rates of failure of new product portfolio and their associated losses are very high . The whole product component information（產(chǎn)品構(gòu)建信息）, engineering portfolio decision

5、（工程組合決策）is very crucial for the progress of a rm ,because it is very costly and difficult to change .The key questions are, what the best product portfolio is, and how manufacturer can nd it.Product portfolio management (PPM) is a general business concept that analyze the production ability（生產(chǎn)能力） an

6、d market potential, simultaneously, and then determine the best set of products to offer.PPM is developed to direct a product and its diversity including not only attributes（屬性）, levels, and prices, but also analysis results,environmental requirements（環(huán)保需求）, manufacturing procedures（生產(chǎn)流程）, product p

7、erformance information（產(chǎn)品性能信息）, and etc.Therefore PPM has been classified as a combinatorial optimization problem. Each company strives for the optimality of its product offerings through various combinations of products .The PPM problem may develop from two perspectives:(I) For attract the opinion

8、of customers in target markets. (II) For reduce the manufacture engineering costs. First is the problem of marketing managers, and second is the problem of producer. When both of them compose with each other as reflect to utility of costumers and engineering costs, this problem becomes to miss link

9、between sale and production chain. Jiao and Zhang(2005) consider the customerengineering interaction in product portfolio planning, which aims to create product family specications(產(chǎn)品族/系列規(guī)格) for a target market segment, and proposed a maximizing surplus share model（最大剩余份額模型）.In competitive environme

10、nt, we determine our product portfolio with regard to products that offer by competitors, while the competitors manage their product portfolios in regard to our products. Game theory can be used to model this problem.The proposed model constructs product portfolio based on customerengineering intera

11、ction model in product portfolio planning which is developed by Jiao and Zhang .Present paper extends previous works in PPM with regard to customerengineering concerns and competitive environment. It is not for any specific product, and it can be applied to a diversity of products or services.object

12、ive: develop a game theory-based model as a procedure of nding optimal product portfolio.2. Literature reviewA PPM is dened as a decision making that optimizes some criteria, such as market share. The main contribution of the most researches in PPM is summarized in following issues: 1) Generating de

13、sign alternatives via multi-objective optimization（通過(guò)多目標(biāo)優(yōu)化生成設(shè)計(jì)方案）. 2) Accounting for uncertainty and competition when estimating the achievement of business goals. 3) Applying meta-heuristic algorithms（元啟發(fā)式算法） to solve a combinatorial problem during the product line design.The development of algorit

14、hms Heuristic ( identify product prole product line design) algorithms improved heuristic algorithms genetic algorithms.The development of models 1) Jiao and Zhang proposed a model to address the product portfolio planning problem,it considers customer preferences, choice probabilities and platform

15、based product costing. Also, a genetic algorithm procedure is applied. 2) Aiyoshi and Maki proposed a game problem under the constraints of allocation of product and market share simultaneously. Their research is considered several manufacturers in oligopoly market（寡頭壟斷市場(chǎng)）. This proposed model, on t

16、he one hand had the competitive circumstance, but on the other hand, did not has details such as large variety of customers preferences, customerengineering concerns, etc. 3) model in this paper considers both details and competitive circumstance.3. Description of the PPM problemConsidering the rm c

17、apabilities to produce products, a set of product portfolios have been identied. Each product has certain desirability between customers. More specically, we consider a scenario in which a set of products, have been identied, given that the manufacturer (m) has the capabilities (both design and prod

18、uction) to produce all these products, . . A product portfolio, ,is a set consisting of some selected product. Combined with the products, a set of product portfolios are created, . 相關(guān)參數(shù)For example, if manufacturer m can produce 3 product, 7 product portfolio are available:（ =7） Every product, , is

19、associated with certain engineering costs,denoted as . There are multiple market segments,S =s1, . , sg , . , sG, each containing homogeneous customers,with a denite size, Qg. The customerengineering interaction is embodied in the decisions associated with customers choices of different products. Va

20、rious customer preferences on diverse products are represented by respective utilities, (utility of the gth segment for the nth product of mth manufacturer). Product demands or market shares, (market share of the gth segment for the nth product of mth manufacturer), are described by the probabilitie

21、s of customers choosing products. Customers choose a product based on the surplus buyer rule. They have the option of not buying any products or buying competitors products.We assume that competitors respond to the manufacturers moves, meaning that, the competition react by introducing new products.

22、 4. Problem formulationThe present paper considers a market with G segments, S =s1, . , sg , . , sG, and 2 manufacturers that each of them can offer Nm products, and Jm product portfolios, . This gives the bimatrix-game（雙矩陣對(duì)策） problem with 2 players and Jm strategy for each, (m = 1 or 2).The payoff

23、for each player will of course depend on the combined actions of both players. A payoff matrix shows what payoff each player will receive at the outcome of the game. For player m (m = 1 or 2), the payoff matrix, Fm, is as follows:In summary, a J1 J2 bimatrix game is played by two players,player 1 an

24、d player 2. Player 1 has a finite set and player 2 has a nite set of pure strategies. The payoff matrixes f1( ), of player 1 and of player 2 are denoted by F1 and F2 respectively. This game is denoted by (F1, F2).Now the game (F1, F2) is played as follows. Players 1 and 2 choose, independent of each

25、 other, a strategy and respectively. Here can be seen as the probability that player 1 (2) chooses his th row ( th column). The (expected) payoff for player 1 is x1F1x2 and the expected payoff to player 2 is x1F2x2.A strategy pair ( ) is an equilibrium for the game (F1, F2) ifThe set of all equilibr

26、ia for the game (F1, F2) is denoted by E(F1, F2). By a theorem of Nash this set is non-empty for all bimatrix-games (Nash, 1950).Some methods for calculating payoff matrix arrays, ,are there (see Section 2). We used the function that proposed by Jiao and Zhang (2005). This function is based on custo

27、mer-engineering interaction model in PPM. This is as follows:Eq. (3) is the expected shared surplus by offering a product portfolio, consisting of products ,to customer segments,sg, each with size Qg. The market potentials, Qg, can be given exogenously at the outset or estimated through a variety of

28、 techniques based on historical data or test markets. The utility of the gth segment for the nth product of mth manufacturer is denoted as .This model assumes that customers only choose a product with a positive surplus. The choice probability, , that a customer or a segment, sg, chooses a product,

29、, with Ncom competing products, is defined as follows:where u is a scaling parameter（尺度參數(shù)）. According to matrix (1) and Eq. (3), let the function be defined by5. ExampleIn this section, a simple example to use the proposed model is presented. For simplicity, we consider a market with two competitor

30、(M = 2), and four different products (Nm = 4) for each. Feasible strategies, is defined as follows:產(chǎn)品組合數(shù)=24-1=15?Three segments are identified, i.e., s1, s2, and s3. Q1, Q2, and Q3 are assumed 0.2, 0.3 and 0.5, respectively. Table 1 shows the utilities of three segments to every product ( ) and cost

31、 of each ( ). Also, scaling parameter (u) is supposed 0.8. Therefore, 2 payoff matrixes F1 and F2 formed for manufacturer 1 and 2, separately. This game and obtained data from expected shared surplus values (Eq. (5) are summarized in Fig. 1.返回The optimal result for each manufacturer is derived from

32、the Nash equilibrium point of the game. A strategy pair is an alone equilibrium for the game . The related payoff pair is (0.74, 0.83).6. Conclusions and future workThis paper proposed a game theory-based model that is used to maximize the expected shared surplus for a product portfolio managed. The product portfolio management (PPM) is an impo