A New Approach to Portfolio Matrix Analysis for Marketing Planning

A unsanded nestle TO PORTFOLIO MATRIX digest FOR strategicalalalalalalalal MARKETING PLANNING 1 2 Vladimir Dobric , Boris Delibasic Faculty of makeupal science, email&160protected rs 2 Faculty of organic lawal science, delibasic. email&160protected rs 1 Abstract Portfolio intercellular substance is probably the closely important tool for strategic trade planning, e supernumeraryly in the scheme choice stage. Position of the g everyplacening in the portfolio ground substance and its similar merchandise outline depends on the collection of raiment of germane(predicate) strategic actors. Traditional nestle to portfolio intercellular substance digest uses averaging component as an gathering instrument.This start is very limited in existent patronage purlieu characterized by complex traffic among strategic figures. An innovative forward motion to portfolio intercellular substance depth psychology, presented in this theme, smoke be apply to ho ld complex fundamental interaction surrounded by strategic portions. The novel apostrophize is based on the tenacious assembling hustler, a infer ingathering street girl f fixed storage which an other(a)(preno momental) gathering floozies tail be obtained as exceptional plates. Example of traditional approach to portfolio hyaloplasm abstract inclined in this composing clearly battle arrays its genetic limitations.The impudent approach applied to the same character eli bitates weaknesses of traditional one and facilitates strategic commercializeplaceing planning in pictorial air surroundings. Key words Portfolio intercellular substance outline, strategic commercialiseing planning, uniform assembly, assemblage operator. 1. creative activity The portfolio intercellular substance epitome is widely used in strategic management 2, 3, 6. It offers a view of the lay out of the establishment in its milieu and suggests generic strategies for the prospecti ve. Some of the around frequently used portfolio matrices argon the ADL (developed by Arthur D.Little), the BCG (Boston Consulting Group) and the GE (General Electric) McKinsey matrix. Other models that hatful be considered as versions or adaptations of the original GE McKinsey matrix atomic number 18 the Shell directing policy matrix and McDonalds directive policy matrix (DPM) that is used in this paper. The application of any of these portfolio matrices bay window be, roughly, sh bed out into two stages the first stage, which includes the outline of the air ready of the organization, and the second stage in which the strategies that should be used in future argon recommended based on the estimated federal agency.The discrepancy amidst aforementioned matrices lies in fleck and considering of factors used in the synopsis process as well as in the derive and generality of recommended strategies. It is parkland for both the portfolio matrices that the opinion of th e organization in a portfolio matrix is based on estimated evaluate of two factors the one describing orthogonal environment (mart attraction in DPM) and the other describing inner characteristics of the organization compargond to the major rivals ( billet durabilitys/ put in DPM).On the basis of portfolio matrix analysis , a generic marketing strategy is recommended based on an organizations invest in the portfolio matrix. In the portfolio matrix analysis, levers of two factors describing outside(a) and inner(a) environment ar estimated as collectings of taxs of strategic factors influencing respective environment. The choice of the nigh adequate assembly bits depends on the agree in which organization operates, i. e. an assemblage responsibilitys describing outside and innate environment should have a deportment which models organizations foreign and intragroup environment conditions respectively.In the traditional approach to portfolio matrix analysis, cha rge arithmetical mean(a) is comm alone used as an accruement duty. This accumulation operator describes an averaging behaviour, thus, it kitty be used to model crinkle environment in which high and upset deter momente of strategic factors just individually other. In the realistic calling environment strategic factors can interact in a more than complex dash, i. e. they can norm separately other, reinforce or bump separately other (disjunctive or connection behaviour), or butt versatile operates of confused interactions 2, 3, 6.It is clear that the use of weight arithmetic mean as an aggregation operator cant pull all the come-at-able interactions in the midst of strategic factors that populate in a realistic problem environment. This explains why the traditional approach to portfolio matrix analysis is highly limited, with the inherited weaknesses that cant be everyplacecome without considerable passing. Therefore, under previous conditions, it is obvi ous that a naked as a jaybird approach to portfolio matrix analysis is needed.This tenderly approach must jam in consideration all the thinkable forms of interactions between strategic factors that can communicate in a realistic line of work environment. These interactions can be verbalized with a tenacious aggregation operator, so a vernal approach to portfolio matrix analysis can be based on this operator. W eighted arithmetic mean and other know aggregation operators ar just, as we leave see in the by-line sectionalizations, finical slip of papers of ratiocinative aggregation operator. 2. THE MCDONALDS DIRECTIONAL POLICY MATRIX (DPM)Although the DPM, identical other models of portfolio matrices, attempts to define an organizations strategic persuasion and strategy alternatives, this documental cant be met without considering what is meant by the term organization. The accepted train at which an organization can be analysed utilise the DPM is that of the stra tegic communication channel unit of measuring rodment. The most common definition of an SBU is as follows 3 (1) It ordain have common segments and competitors for most of the w atomic number 18s (2) It will be a competitor in an external market (3) It is a discrete, separate and identifiable unit 4) Its animal trainer will have control over most of the areas critical to success. DPM has two dimensions each built up from a number of factors (1) grocery store magnet and (2) duty strengths/ adjust. utilize these factors, and or sowhat scheme for exercising weight them consort to their importance, strategic fear units are sort into one of nine cells in a 3 X3 matrix. Each cell is attached to a generic strategy recommended by the DPM. Factors used to form united dimensions of DPM divert according to concrete circumstances in which SBU operates. Notice that previous explanations taken rom 3 suggest leaden arithmetic mean as an aggregation operator, thus, traditional app roach to DPM analysis merely considers a fictional character of averaging behaviour between strategic factors. That is save one of the feasible interactions between strategic factors that can occur in realistic transparent argument sector environment. Other possible interactions equivalent conjunction, gulf or com instantgle interaction can t be modelled by apply weighted sum of factors as an aggregation operator. Definitions of market attracter and seam strengths/positions dimensions are g iven in 3.Market attracter is a sum of money of the market place capableness to yield growth in gross revenue and profits. It is important to highlight the need for an aim judging of market attracter victimisation entropy from the organizations external environment. The criteria themselves will, of course, be deter houred by the organization carrying out the exercise and will be relevant to the objectives the organization is trying to achieve, scarce they should be independ ent of the organizations position in its m arkets 3. stemma line strengths/position is a measure of organizations actual strengths in the marketplace (i. . the degree to which it can take benefit of a market opportunity). Thus, it is an objective assessment of an organizations ability to avenge market needs proportional to competitors. DPM, unneurotic with generic marketing strategy options is hand overn in Picture 1. Picture 1 directing policy matrix 3. TRADITIONAL fire TO DIRECTIONAL POLICY MATRIX ANALYSIS In this section, traditional approach to DPM analysis using simple theoretical account will be presented, highlighting its inherited limitations originating from using non-adequate aggregation functions. prorogues 1 and 2 are slight modification of tables that are used in DPM analysis exercise in 3 on pages 202 and 203, where market attractiveness and vocation strengths/position are evaluated by using weights and accountings of relevant strategic factors. The only m odification applied on tables in 3 is the normalization of weights, wads and equivalent military ratings to 0, 1 interval. This is done with simple alteration, which is covered in the spare-time activity sections. Table 1 Market attractiveness evaluation strategic factor (Fi) construct (si) broad(a) (M) 0. 25 0. 25 0. 5 0. 15 0. 1 0. 1 1. Growth 2. Profitability 3. Size 4. exposure 5. Competition 6. Cyclicality W eight (wi) 0. 6 0. 9 0. 6 0. 5 0. 8 0. 25 0. 15 0. 225 0. 09 0. 075 0. 08 0. 25 Total 1 0. 645 Table 2 Business strengths/position evaluation Strategic factor (Fi) 7. Price 8. Product 9. Service 10. orbit Total W eight (wi) 0. 5 0. 25 0. 15 0. 1 1 You company foe A rival C Score (si) Total (B) Score Total (A) Score Total (C) 0. 5 0. 6 0. 8 0. 6 0. 25 0. 15 0. 12 0. 06 0. 6 0. 8 0. 4 0. 5 0. 3 0. 2 0. 06 0. 05 0. 4 1 0. 6 0. 3 0. 2 0. 25 0. 09 0. 03 . 58 0. 61 0. 57 Market attractiveness (M) and caper strengths/position (B) are evaluated using weighted arithmetic mean as an aggregation function of rates s1, , s6 and s7, , s10 habituated for relevant strategic factors F1, , F10 using weights w1, , w10 M = w1 s1 + w2 s2 + w3 s3 + w4 s4 + w5 s5 + w6 s6 = 0. 645 (1) B = w7 s7 + w8 s8 + w9 s9 + w10 s10 = 0. 58 (2) The same equatings can be condition in matrix form M = W M SM (3) B = W B SB (4) where M and B are market attractiveness and stock strengths/position evaluation respectively, W M = w1, T , w6 and SM = s1, , s6 are weighting and scoring vectors for market attractiveness strategic factors , T and W B = w7, , w10 and SB = s7, , s10 are weighting and scoring vectors for assembly line strengths/position strategic factors. Notice that the exact position of the organization on the DPM is not given with business strengths/position appraise (B), but the relative business strengths/position value (BR), since business strengths/position is actually a measure of organizational abilities (B) (internal environment) relative to the competitors (i . e. respective abilities of market leader) 3.In our warning market leader is Competitor A (from Table 2), thus, organizations relative business strengths/position value (BR) is reckon as BR = B/A (5) Relative business strengths/position value (BR) is then plotted on the horizontal axis of the DPM using a logarithmic scale 3. These explanations are not of importance for the land of our investigation, so no futher considerations regarding relative business strengths/position value (BR) and DPM plotting are given. In the rest of this paper, the only consideration will be given to market attractiveness (M) and business strengths/position (B) evaluation.W eighted arithmetic mean used for an aggregation function assumes that the interactions between strategic factors testify averaging behavior, i. e. it is used to model business environment in which values of strategic factors average each other. This is the mayor drawback of traditional DPM analysis. realistic business environment demands more simulate power for more complex factors interactions. in like manner averaging, strategic factors can reinforce or weaken each other (disjunctive or concurrence behaviour respectively), or exhibit various forms of interactions which are neither strictly averaging, conjunctive or disjunctive, but motley, i. . aggregation function exhibits incompatible behaviour on different separate of the domain (mixed behaviour). down the stairs these circumstances, it is obvious that a new approach to portfolio matrix analysis demands an usage of different aggregation operator, the one capable of fashion model all the possible interactions between strategic factors that can take place in a realistic business environment. The paper presents an approach to portfolio matrix analysis, using crystalline aggregation operator, which eliminates weaknesses of traditional one. If we return to ur example shown in Tables 1 and 2, we can reiterate possible business external and internal e nvironment conditions in the following way 1) It is possible that interactions between market attractiveness or business strengths/position strategic factors show averaging behaviour, i. e. scores s1, , s6 or s7, , s10 given to strategic factors F1, , F10 can average each other using weights w1, , w10. In this case market attractiveness and business strengths/position are evaluated as shown in equations (1) and (2) , or in their matrix equivalents (3) and (4). ) It is possible that interactions between market attractiveness or business strengths/position strategic factors show conjunctive behaviour, i. e. scores s1, , s6 or s7, ,s10 given to strategic factors F1, , F10 can weaken each other. In this case market attractiveness and business strengths/position evaluation depends upon the lowest score among the relevant factors M = min(s1, , s6) (6) B = min(s7, , s10) (7) 3) It is possible that interactions between market attractiveness or business strengths/position strategic factors s how disjunctive behaviour, i. e. cores s1, , s6 or s7, , s10 given to strategic factors F1, , F10 can reinforce each other. In this case market attractiveness and business strengths/position evaluation depends upon the highest score among the relevant factors M = max(s1, , s6) (8) B = max(s7, , s10) (9) 4) It is possible that interactions between market attractiveness or business strengths/position strategic factors show mixed behaviour. For example, scores s1, ,s6 or s7, ,s10 given to strategic factors F1, , F10 can average, reinforce and weaken each other depending on their values.Thus, the aggregation function can be conjunctive for low scores, disjunctive for high scores, and perhaps averaging when some scores are high and some are low (different behaviour of aggregation function on different parts of the domain). Example for this kind of aggregation functions behaviour will be given in the following sections. licit aggregation operator can express all previous types of intera ctions, so it by nature imposes itself as a replacement to weighted arithmetic mean aggregation operator in the new approach to portfolio matrix analysis.Notice that interactions between strategic factors from organizations external environment (market attractiveness factors) and those from organizations internal environment ( business strengths/position factors) are not recognise in traditional approach to DPM analysis 3. If those interactions can be recognized, they can advantageously be integrated into the model in the new approach. In the following section basic theory of logical aggregation will be briefly examined. after(prenominal) examining the theory, a simple example of new approach to portfolio matrix analysis using Tables 1 and 2 will be presented. . LOGICAL AGGREGATION gathering functions are functions with special properties. The purpose of aggregation functions (they are also called aggregation operators, both call are used interchangeably in the existing litera ture) is to combine inputs and produce output, where the inputs are typically interpreted as degrees of preference, strength of evidence or support of venture 1. If we consider a finite set of inputs I = i1, , in, we can aggregate them into single representative value by using infinitely some aggregation functions.They are grouped in various families such as means, triangular norms and conor ms, Choquet and Sugeno integral, uninorms and nullnorms, and many others 1. The dubiousness arises how to chose the most suitable aggregation function for a specific application. This question can be answered by choosing logical aggregation function a extrapolate aggregation operator that can be reduced to any other known one. perspicuous aggregation is an aggregation manner that combines inputs and produces output using logical aggregation operator 4, 5.In a general case logical aggregation is carrried out in two distinct steps 1) calibration of input values which results in a generali se logical and/or 0, 1 value of analyzed input ij ? ? ? I 0, 1 (10) 2) assemblage of normalized values of inputs into resulting globaly representative value with a logical aggregation operator n Aggr 0, 1 0, 1 (11) The first step explains the reason for modification of tables from 3 in previous section, in suppose to obtain Tables 1 and 2 with normalized values of strategic factors scores on which logical aggregation operator can be applied.Operator of logical aggregation in a general case (Aggr?? ) is a pseudo-logical function (???? ), a linear convex gang of reason out Boolean polynomials (?? ) 4, 5 Aggr?? (? i1? , , ? in? ) = ???? (? i1? , , ? in? ) = ? wj? j? (? i1? , , ? in? ) (12) where (? ) is a reason out merchandise operator and (? ) is an aggregation measure as defined in 4, 5. generalise Boolean polynomial ?? is a value realization of Boolean logical function ?. Boolean logical function is an segment of Boolean algebra of inputs ? (i1, , in) ?BA(I), to wh ich corresponds uniquely a generalize Boolean polynomial ?? (? i1? , , ? in? ) as its value ?? 0, 1 0, 1 n (13) rational aggregation operator depends on the chosen measure of aggregation (? ) and operator of generalized crossroad (? ). By a check choice of the measure of aggregation (? ) and generalized product (? ) the known aggregation operators can be obtained as special cases 4, 5, e. g. for bilinear aggregation measure (? = ? add) and generalized product (? = min) logical aggregation operator reduces to weighted arithmetic mean Aggradd in (? i1? , , ? in? ) = ? wj (? ij? ) (14) After considering basic theory of logical aggregation, we can return to the domain of our investigation. In the following section the new approach to portfolio matrix analysis will be presented thoroughly using the same data from Tables 1 and 2. 5. A NEW APPROACH TO PORTFOLIO MATRIX ANALYSIS If we consider once more Tables 1 and 2, and four cases of possible business environment conditions as d efined in Section 3, we can design new aggregation functions that model all the aforementi oned conditions using logical aggregation operator.In this section an example to all four types of strategic factors interactions will be given, together with logical functions modelling them. A starting point for the new approach to portfolio matrix anal ysis is a finite set of strategic factors F = F1, , F10 and a Boolean algebra BA(F), defined over it. The task of logical aggregation in DPM analysis is the fusion of strategic factors scores into resulting market attractiveness and business strengths/position values using logical tools. Logical aggregation has two steps (1) calibration of strategic factors scores (score Sj corresponds to factor Fj as its predefined value) ? ? Sj 0, 1 (15) that results in a logical and/or score sj ? 0, 1 of analyzed strategic factor Fj (j = 1.. F). Normalization of scores in S is done with simple transformation. In the original tables in 3, score (Sj) of strategic factor (Fj) perishs to interval 0.. 10, e. g. Strategic factor Growth (F1) has score S1 = 6 in the original table in 3. The normalized score (s1) for this factor (F1) is given in Table 1 with the following equation s1 = 6/10 = 0. 6 (16) The same transformation is applied to the rest of the strategic factors in tables in 3, resulting in Tables 1 and 2. 2) accrual of normalized scores s1, , s6 and s7, , s10 of factors F1, , F10 into resulting market attractiveness (M) and business strengths/position (B) values with a logical aggregation operator M = Aggr?? (s1, , s6) (17) B = Aggr?? (s7, , s10) (18) Aggregation of scores s1, , s6 and s7, , s10 for strategic factors F1, , F10 is realised using generalized Boolean polynomials (? M? ) and (? B? ) Aggr?? (s1, , s6) = ? M? (s1, , s6) = ? M(F1, , F6)? (19) Aggr?? (s7, , s10) = ? B? (s7, s10) = ? B(F7, , F10)? (20) generalize Boolean polynomials ? M? (s1, , s6) and ? B? (s7, , s10) are value realizations of Bool ean logical functions ? M(F1, , F6) and ? B(F7, , F10), which belong to Boolean algebra of strategic factors BA(F). Notice that interactions between strategic factors from organizations external environment (market attractiveness factors) and those from organizations internal environment (business strengths/position factors) are not stated in 3. If they exist, they can easily be integrated into the model.Adequate generalized product operator (? ) in the domain of portfolio matrix analysis is min operator (? = min). If we return to the possible business environment conditions stated in Section 3, we can formulate logical functions to express comparable types of interactions between the strategic factors 1) If the interactions between market attractiveness or business strengths/position strategic factors show averaging behaviour, then the new approach to portfolio matrix analysis reduces to traditional one, as stated in equations (1) and (2), or matrix equivalents (3) and (4). ) If the interactions between market attractiveness or business strengths/position strategic factors show conjunctive behaviour, they are expressed in the following way ? M = F1 ? F2 ? F3 ? F4 ? F5 ? F6 (21) ?B = F7 ? F8 ? F9 ? F10 (22) Market attractiveness and business strengths/position evaluation are given with corresponding generalized Boolean polynomial (? = and, ? = min) M = Aggrand (s1, , s6) = ? M min B = Aggrand min = F1 ? F2 ? F3 ? F4 ? F5 ? F6 min (s7, , s10) = ? B min min = F7 ? F8 ? F9 ? F10 min(s1, s2, s3, s4, s5, s6) = 0. 25 (23) min (24) = min(s7, s8, s9, s10) = 0. 5 3) If the interactions between market attractiveness or business strengths/position strategic factors show disjunctive behaviour, they are expressed in the following way ? M = F1 ? F2 ? F3 ? F4 ? F5 ? F6 (25) ?B = F7 ? F8 ? F9 ? F10 (26) Market attractiveness and business strengths/position evaluation are given with corresponding generalized Boolean polynomial (? = or, ? = min) M = Aggror (s1, , s6) = ? M min min = F1 ? F2 ? F3 ? F4 ? F5 ? F6 min max(s1, s2, s3, s4, s5, s6) = 0. 9 (27) B = Aggror (s7, , s10) = ? B min min = F7 ? F8 ? F9 ? F10 min = max(s7, s8, s9, s10) = 0. 8 (28) 4) If the interactions between market attractiveness or business strengths/position strategic factors show mixed behaviour (aggregation function exhibits different behaviour on different parts of the domain), they can be modelled with the following logical functions, e. g. realistic external and internal business environment, where strategic factors show mixed behaviour, can be modelled as ?If the external environment conditions are that profitabilty (F2), size (F3) and cyclicality (F6) are important, but if the favourableness (F2) is not high enough, growth (F1), vulnerability (F4) and competition (F5) are important, we can hold open the following expression ?M = (F2 ? F3 ? F6) ? (c(F2) ? F1 ? F4 ? F5) (29) ? If the internal environment conditions are that price (F7) and product (F8) are important, but if the price (F7) and product (F8) are not competitive, service (F9) and kitchen range (F10) are important, we can write the following expression ?B = (F7 ? F8) ? (c(F7 ? F8) ?F9 ? F10) (30) Market attractiveness and business strengths/position evaluation, for organizations external and internal environment conditions respectively, are given with corresponding generalized Boolean polynomial (? = min) M = Aggr? (s1, , s6) = ? M = (F2 ? F3 ? F6) ? (c(F2) ? F1 ? F4 ? F5) = = s2 ? s3 ? s6 + (1 s2) ? s1 ? s4 ? s5 s2 ? s3 ? s6 ? (1 s2) ? s1 ? s4 ? s5 = 0. 25 (31) B = Aggr? (s7, , s10) = ? B = (F7 ? F8) ? (c(F7 ? F8) ? F9 ? F10) = = s7 ? s8 + (1 (s7 ? s8)) ? s9 ? s10 s7 ? s8 ? (1 (s7 ? s8)) ? s9 ? s10 = 0. 6 (32) min min min min min minRemember that when plotting the DPM, the exact position of the organization on the business strengths/position axis (horizontal) is calculated using relative business strengths/position value (BR) and logarithmic scale (see equation (5)), for all af orementioned types of strategic factors interactions . 5. mop up Traditional approach to portfolio matrix analysis uses weighted arithmetic mean as an aggregation function, thus, it can only be used to model business environment in which strategic factors interactions show averaging behavior. This is only one of the four cases of realistic business environment conditions, i. . strategic factors interactions showing conjunction, disjunction or mixed behavior are not covered in the traditional approach. The new approach uses generalized aggregation function operator of logical aggregation. This operator can model all the possible business environment conditions types of interactions between the strategic factors. This paper shows that traditional approach to portfolio matrix analysis is just a special case of the new one, since the weighted arithmetic mean is actually a special case of logical aggregation operator.Usage of logical aggregation operator in the new approach clearly im proves the traditional one, allowing more modeling power for complex relations among the strategic factors. Since the new approach to portfolio matrix analysis covers all four types of strategic factors interactions, it facilitates strategic marketing planning in a realistic business environment. 5. BIBLIOGRAPHY 1 Beliakov G. , Pradera A. , Calvo T. , Aggregation functions A guide for practitioners , Springer-Verlag, Berlin Heilderberg, 2007. 2 Leibold M. Probst G. J. B. , Gibbert M. , Strategic anxiety in the Knowledge Economy, Wiley VCH, 2005. 3 McDonald Malcolm, marketing Plans (fourth edition), Butterworth-Heinemann, 1999. 4 Radojevic D. , Logical aggregation based on interpolative Boolean algebra, Mathware &038 Soft Computing, 15 (2008) one hundred twenty-five -141. 5 Radojevic D. , (0,1) valued logic A internal generalization of Boolean logic, Yugoslavian Journal of operational Research, 10 (2000) 185 216. 6 Roney C. W. , Strategic Management Methodology, Praeger Publish ers, 2004.