Product Portfolio Models: Conceptualisation and Implementation

P r o b l e m s

b Y

Adam Koch

(Serial No. 99)

Faculty of Business Workmg Papers

SWlNBURNE UNIVERSITY OF TECHNOLOGY

Product Portfolio Models: Conceptualisation and Implementation

P r o b l e m s

Adam Koch

(Serial No. 99)

ISBN 0 85590 719 3

This paper should not be quoted or reproduced in whole or in part without the consent of the author, to whom all comments and enquiries should be directed.

O Koch, A, 1993

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Dr Adam Koch Faculty of Business Swinburne University of Technology Melbourne

Product portfolio models: conceptualisation and implementation problems.

Optimisation of the product portfolio is an issue of particular importance for diversified companies dealing in very volatile, and highly competitive economic and technical environments. In trying to ensure optimal allocation of their resources, companies seek to minimise the role of intuition in these complex decision processes by adopting, wherever possible and appropriate, more formalised approaches that enhance the quality of most decision-making processes.

This article attempts to contribute to a critical re-assessment of the capacity of existing product portfolio models to adequately support strategic planning decisions (1). In support of this aim, some deficiencies in the conceptualisation of current portfolio models (such as lack of, blurred and/or inoperational definitions of terms, procedures used in constructing composite proxies for profitability) are discussed and, in particular, the concept of the product portfolio's balance and its relationship to product portfolio optimisation examined.

Analytical methods in strategic planning

Several categories of analytical methods are commonly used by the strategic marketing planner (2):

* methods used in analysing a market and defining its boundaries (eg. Porter's model (3) using specific market dimensions and levels of market aggregation etc.),

46 methods dealing with costs dynamics over the product life cycle and their impact on competitiveness (eg. experience curve),

* methods examining the financial consequences of business strategies (eg. PIMS) and,

* portfolio models.

Notwithstanding these methods' capacity to enhance business analysis, and the resultant quality of planning and decision making, adequate degree of caution should be exercised. by their users, due to:

* problems resulting from these methods' theoretical foundations not being robust enough,

* misconceptions as to their analytical potential in specific economic, market and industry situations, that lead to some of those methods being misapplied,

Y a tendency to accept some generic strategy suggestions those methods are capable of producing, as a substitute for a strategy based on comprehensive and specific analysis; such approach compromises principles and standards of strategic planning process,

* those methods' varying analytic potential being dependent on the management and capability level at which they are used, as well as on other analytic methods used in combination with them, and

* the need to adapt them to specific uses and situations (4).

The current state of the marketing theory substantially limits the benefits of the strategic analysis for a company. In particular, very limited attention has been given so far to the fundamental issue of adequacy and exactness of marketing terms used. Numerous terms tend to be used indiscriminately; many definitions used in marketing do not meet appropriate scientific requirements, and there appears at times to be a rather disquieting proliferation of definitions by various authors that adds to overall confusion (see Tables 1 and 2 showing the results of a short literature review).

An attempt will be made in this article to contribute to the resolution of this problem with regard to some of the terms of particular meaning for the product portfolio analysis.

Implementation areas for product portfolio models

According to Day (5): the classical growth-share matrix can be used either as:

* a diagnostic aid to synthesise prior strategic judgments about the currentiprospective positions of business unitsfproduct lines, or

* a conceptual framework guiding the generation of strategic options and facilitating the development of objectives for SBUs, or indeed

*. a prescriptive guide to the choice of appropriate strategic options and resource allocations within the firm.

The matrix format of product portfolio models facilitates communication, including the sharing of judgments and assumptions about strategic matters, helping identify information gaps and setting priorities for data collection and analysis. It may encourage management teams to devote much more of their time to analysing future problems and considering changes to the portfolio composition.

The implications of adopting portfolio planning philosophy by a company are interestingly and comprehensively covered by Hapeslagh (6). He recognizes three steps in which this philosophy is put into place, with process portfolio planning being the ultimate stage.

Exh.1 Applications of Product Portfolio Models (as postulated by G.S.Day, op.cit.)

diagnostic aid to synthesise prior strategic judgments abut positions of SUUs/product

conceptual framework for the generation of stratec~ic op- tions and the negotiation of

choice of appro- priate strategic options and

cat ions

As he finds out, by the end of the 70's only 14 % of the Fortune " 1000" had reached that most advanced stage, even though capital

intensive industries, and large diversified companies, were much more likely (up to 75 %) to use product portfolio planning in such a manner. Given an immense increase in the usage of computer software in business planning and management in that period, it would be most interesting to learn how many of that group of companies had since adopted that advanced form of planning.

The performance of the classic portfolio models is, to a large extent, affected by several deficiencies they have in common (7):

Y most are designed primarily to deal with existing product markets rather than look beyond their boundaries in attempting to optimise the portfolio in absolute terms,

* the theoretical basis underlying the choice of both single and composite factors, supposed to explain most of the variance in business profitability and hence assist in optimising the product portfolio is mostly inadequate,

.N. product boundaries and business definitions in those models are subject to judgment and convention which makes it difficult to use other methods together with PPMs to attain specific analytical tasks,

* products are categorised along two or (rarely) three dimensions only (8 ) ,

* none of the models, due to their underlying assumptions, as well as forms of graphic representation adopted, have been able to adequately show the dynamic picture of changes in the positions of products,

w the notion of product portfolio optimisation has not been properly defined (operationalised) by the marketing theory (as opposed to the financial portfolio optimisation),

46 certain weaknesses of the methods are likely to invite abuse by some managers,(eg. through market boundaries manipulation or intentional distortion of market-growth forecasts),

* labels used to categorise products may lead to self-fulfilling prophecies.

The impact of some of those deficiencies or weaknesses may be lessened however: the chance of PPMs being abused through market boundaries manipulation or intentional distortion of market-growth forecasts can be significantly reduced if every strategic decision is based

on the data that can be verified objectively and formulae/algorithms are widely used in processing those data.

Redefining markets, calculating new market shares, new market attractiveness scores etc. may be necessary to present SBUs positions on portfolio model grids in other, than traditional, market boundaries. Apart from the possibility of comparing product positions in diverse market settings, that has a value in itself, such comparisons may well lead to changes in product-mix and business strategies.

Some findings shown in this article demonstrate the need to exercise extreme cautiousness when using the portfolio models for strategic planning purposes. Developing appropriate criteria and suitable, improved methods for allocating resources among market segments should therefore continue to be high on the agenda in developing the theory(9).

Evaluating product portfolio - a challenge to theory

In helping establish an optimal resource allocation (ie. one that would result in profit maximisation over determined period), traditional portfolio models use pairs of, rarely three, proxies assumed to jointly explain most, if not all, of the variance in business profitability. They help ascertain the need for changes to the product mix, and set the timing for the underlying resource allocations. As Hapeslagh points out however, for most companies formal strategic planning "is one thing and the capita! investment process quite another "( I 0).

For all the development marketing theory has witnessed over the past three decades, making judgments about the product portfolio structure does not yet seem to always follow a set of well defined, objective rules. Its complexity, few universally accepted rules and formulae applicable in resolving those problems, as well as the vital importance of performance of this functional area to the effectiveness and survival rate of businesses today, all call for a commensurate research effort aimed at examining the feasibility of:

B making synthesised judgments about product portfolio

structure (ie. judgments based on the outcomes of analyses carried out with the use of methods that adopt diverse assumptions and measurement techniques, eg. cash flow predictions and market attractiveness assessment),

* an appropriate representation (quantification and visualisation) of relationships between simple/composite factors and business performance measures (eg. ROI, ROA, ROS),

* showing both forecasts of the consequences of continuing the present strategy and the consequences of changes to strategy, on the same product portfolio grid,

Y establishing company's strategic objectives in an integrative manner, by involving all organisational functions with some relevant expertise and/or strategic roles to play, through further formalisation and computerisation of planning processes and better access to global business information systems.

The already cited study by Hapeslagh (1 1) shows that a vast majority of the Fortune "1000" used no portfolio planning or used it as a planning tool at the corporate level "with no intention to negotiate explicit strategic mission, with the managers and business strategies influenced by traditional administrative tools and profit pressures".

A particular theoretical challenge results from the need to provide strong support for the selection of appropriate simple factors for composite measures explaining most of the changes in business performance (current profit figures, NPV etc.). Towards this objective, proxies' relationships to profitability, as well as rules used in constructing multifactor proxies will need to be thoroughly investigated. Later in this paper, some aspects of proxies' selection and construction are discussed.

On some critical conceptualisation, implementation and measurement issues

A number of critical decisions regarding the conceptualisation and measurement of market share (1 2), such as:

* the unit of measurement,

* the product definition,

* the market definition,

* the time horizon, and

Y the competitive frame of reference,

have a paramount significance in defining SBUs' positions. Hapeslagh (1 3) believes that appropriate SBUs' definitions and assigning a strategic mission to every SBU, have a paramount importance in the success of product portfolio planning effort. In only 7 % of all examined cases,

SBU's boundaries cut across existing organisational lines that seems to indicate an inclination by planners to maintain existing structure for the purposes of strategic planning. Many large, highly differentiated companies treat each SBU as a portfolio itself, thus defining an SBU becomes a two- or multiple-level operation. Wind and Mahajan (14) mention five-level approach by GE: product, product line, market segment, SBU, business sector level.

Wind, Mahajan and Swire's research shows the extent to which product1SBU positions are affected by the operational definitions of the variables (dimensions). They found that the category of twelve out of fifteen SBUs randomly selected by them depended on what definitions of the dimensions' were adopted (1 5) .

Although conceiving universal prescriptions to be used in defining product marketsISBUs does not seem to be feasible, four general approaches in defining the productsISBUs as entities in product portfolio analysis deserves some mention here:

- one based on classification developed by the company,

- another that uses industry based classifications,

- another that relates to demand structure with product applications and user characteristics being the major categorisation criteria, and

- any combination of twolthree of the above-mentioned alternatives.

In applying a company's own classification one makes the job of internal data procurement easier, in that market growth, market attractiveness and some other data will be readily available in most companies. Determining relative market share and business position may however prove to be quite difficult if this approach should be adopted, due to possibility of various data classification and aggregation procedures used by other companies in the same market.

Industry based classifications increase the reliability of the market share calculations, some care needs to usually be taken however to ensure comparability of all data. Hapeslagh's research shows that during the corporate plan review process, "many companies take a disaggregate view and look within their SBUs at the relevant strategic segments, that are defined as a result of an analysis of the industryW(l6).

Using demand derived classifications helps better see who a company's real competitors are and is likely to significantly change a product's position on the growth-share and on composite factors

matrices. It follows then that it may impact on the selection of strategies with regard to individual products.

Ideally, in marketing analysis and planning applications, one should rely on demand derived classification criteria only; in reality however some combinations of all three kinds of criteria will often be used, to accommodate various forms in which the necessary market and internal data are available.

A specific, rather perplexing problem is experienced by companies that try to define their SBUs' boundaries in such a way that they can embrace all resources that should become key strategic variables for those SBUs, before their strategic missions have been determined.

As far as the time horizon is concerned, the selection decision depends primarily on the prevailing pattern of the demand changes for the product/product class/product category in question. Clearly, a different horizon will be appropriate for fad or fashion products, others - for seasonal products, others still for well-established, mature products, further - products the demand for which is determined by the level of economic activity or products on the brink of the technological breakthrough. In addition to it, two aspects of the time horizon selection need to be considered: one relates to the past data on which a prediction is based, the other - to the future period for which market growth rate, market attractiveness, market share, business position, or any other proxy needs to be assessed. The freedom to choose a horizon will hence be limited by the availability and comparability of the relevant data.

In defining the competitive frame of reference one should use demand derived criteria. Positioning businesses against an axis representing their relative competitive advantage requires a thorough analysis and a proper market definition. It appears that the definition proposed by Day ( 1 7) : business segment or product market is correctly defined if it is possible to establish and defend a competitive advantage in the segment alone, without needing to particrjoate in the related segments, may help select an operationally viable competitive frame of reference by avoiding defining the product market in too narrow terms, being a more common mistake than one of defining it too broadly. A thorough analysis of the character, origin and sustainability of the competitive advantages a company possesses would be required to this aim. Some practical hints and examples of diverse frames of reference yielding different market share values are provided by Varadarajan (1 8) as well as Wind and Mahajan (1 9).

According to Wind and Mahajan (20), the position of any business, or product, on the product portfolio grid depends on four factors:

- the operational definitions of the dimensions used in the model,

- the rule(s) used to divide a dimension into low and high categories,

- the weighting of the variables constituting the composite dimensions,

- characteristics of the portfolio model used.

Some comments will be made in this paper with regard to operational definitions of the model dimensions as well as selecting and weighting of the variables used in constructing the composite dimensions. Current forms of portfolio matrices lead to a substantial degree of arbitrariness and politicization in delimiting low, medium, and high categories. It is only through joint usage with capital budgeting and other tools of financial analysis that those product labels loose any practical meaning, ie. do not determine, nor even significantly affect resource allocations.

Exh. 2 Assessing Industry Attractiveness

Mnr-kel. Six<! !;rowl.lr I-ate r ro l i t ~ n n r q i n Market diversity 1)ernnlid cyc1ic;rlily Expwt. oppol.tunil ies CnnrneLil.ive s t ~ u c t t ~ r a Industry pmfil.nlriiily lnflatioli vrrl~rernl~ilil.y Val~re adtlad CapiLnl inl-nsity Rarv muterial nvnii:~triliLy Teclinological role E ~ r e r q y inipnct Sncinl Envivnrrrrreni.nl irrrpnct. I*PK>I I l ~ t ~ r r n n

x RATINGS" =

Sntnc rr ilmin rnnv he 9 I a (:OR40 (:O type For ernnrl~le ntnnll). f i w r , r t r r 51)0 li1.11rs \vIII*I prol~nlrly not i n w t in inrir~slriu v i e d nepativoly hy smiecy tw~v i f i l rve~e leg;11 and prntitnl~lo to do m .' "1" .Icrmle. vqry rrnnttmctiw: "5" dtnnln "try at.t.1-nctive ..-

(from: S.C.Jain, op.cit., p.3011

The fast pace of change in the external environment increases demands on the efficiency of the planning and management processes which in turn contributes to much more widespread interest in the computerised decision support systems. More intense usage of all above-

mentioned analytical methods in those systems will ensure that the impact of some of those methods' drawbacks will be gradually reduced thereby adapting them to the requirements of complex decision making processes.

In weighting individual factors used in the multifactor dimensioning of products1SBUs managers' judgment is the dominant if not only method. employed. This reflects the state of the theory, inappropriate attitudes of those involved in relevant analysis, as well as inoperational description of some methods being applied (21). The degree of subjectivity may differ however, depending on the approach of those involved in designing the relevant methodology. We propose further that many factors applied in designing multifactor measures are ill-defined, and the judgmental procedure used in selecting them does not withstand scrutiny in most instances examined.

When used conjointly with linear programming methods, product portfolio models (PPMs) are likely to show significantly enhanced analytical capacity in their industrial applications. It is easy to see that those two categories of methods complement each other well, linear programming helping calculate optimal production (or sales) structure and strengthen the argument in negotiating strategic objectives, ie. two areas in which PPMs are often implemented. On the other hand, linear programming cannot achieve its tasks before strategic options are generated in the process of product portfolio analysis.

As some most innovative methods of strategic analysis may become a source of an important competitive advantage, information on the conjoint use of these two methods is particularly difficult to come across (22).

Where model premises fail

As mentioned before, several premises upon which portfolio models are founded have attracted a fair share of criticism in the literature (23):

-E market share has a direct effect on profitability (even in those cases where it might be found to be true, a problem remains of predicting the relationship accurately enough, merely on the basis of differing market share levels in various markets),

* high-growth markets are more attractive, because gaining share in high-growth situation is easier (true, but how you can compare between various markets and even situations in the same product market ?)

* cash flow is systematically related to the position and tendency of the product/business in the matrix (false),

* interdependencies among businesses are limited to the demand of some of them for the surplus cash generated by other SBUs of the same company (false),

* models' objective is to achieve balanced or positive cash flow within the portfolio (this definition is very questionable indeed - see later in this article).

Relating factors via model proxies (multifactor dimensions) to profit is disputed in terms of its potential to produce meaningful recommendations as to the truly optimal product portfolio structure. Some authors (24) suggest considering replacing it with net present value (NPV) because of its ability to better reflect the nature of the economic choices behind any product portfolio optimisation procedure.

It should be pointed out however, that establishing the relationships between simple (or multiple) factors and NPV will not be any easier than of those existing between such factors and profit.The weakness of relevant theory and a plethora of possible implementation situations contributes to a great diversity of views and lengthy lists of factors that are recommended to be used in constructing various proxies (model dimensions) believed to be capable of explaining a significant percentage of variance in profitability.

Further, another fundamental question may be asked as to why it is commonly assumed that it is the market share that affects a company's profitability, and not otherwise. Day prefers to view a dominant market share held by a company as a result of some superiority of past strategic decisions over its competitors and advises not to treat it as an "intrinsically valuable asset to be bought or sold" (25). It is argued as well, that building an equity base, for instance, can be a more successful strategy than building market share.

Trying to quantify the relationships between market share and profitability, without referring to, and adequately analysing the entire business situation is certainly flawed. Finally to this point, with the evidence showing that as little as 10 to 20 % variance in profitability is explained by the market share (26), increasing preference for multiple factor models may ensue as a result.

Although an increase in the growth rate of a market makes it easier, and less expensive, to gain share than in the case of a stagnating market (all other things being equal), comparisons between various markets and product mixes do not seem to lead, however, to such clear-

cut conclusions. There is a substantial possibility of this relationship being neglected or trivialised when no appropriate allowance is made for the specific factors that may have helped company achieve a certain level of market share. This omission may in turn lead to a substantial underestimation of the costs of keeping or building a share in the future.

As far as the cash flow dependence of the business position is concerned, it was found (27) that while only 10 % of the variance in cash flow rates is explained by relative share and market growth, as much as 55 % of the same variance results from changes in selling prices, absolute market share, investment intensity, rate of new product introduction and marketing expense. The viability of any simple model can be further weakened by demonstrating a case of two businesses that may have identical relative market shares and operate in two different markets showing the same rate of growth (ie. have the same position in the matrix) yet demonstrate quite dissimilar profitability levels. This criticism equally applies to multifactor models. It appears therefore that the relevant assumption about the possibility to determine the level of cash flow dependence, and the changes to it, just on the basis of SBUs positions on the product portfolio grid will need to be dropped, as it does not withstand criticism.

Arbitrariness of most decisions on the rules used in establishing borderlines demarcating low, medium and high categories means that it is relatively easy to shift some products to more desirable categories by merely changing these borderlines; this may be assumed to be a rather common practice in some situations (eg. when market growth rates fluctuate wildly), a practice that is capable of affecting some resource allocation processes rather strongly.

Concentrating on the cash flows between SBUs and neglecting other dependencies between SBUs, in particular the synergies on which their competitive advantages are based, may result in some very detrimental consequences of changes to the structure of product portfolio. It is essential that the latter relationships are adequately analysed and re-evaluated in the context of the entire external and internal environment of the company.

The adequacy of a positive cash balance as the only objective of portfolio management is also often questioned. If the need to generate certain amount of surplus cash from some businesses dominates strategic thinking in the companies using simple product portfolio models, other sometimes much more salient interrelation-ships between individual products/product lines/SBUs do not get a proper share of attention, with ensuing damage to a company's long term position and viability.

Trying to maintain cash balance imposes unrealistic terms on business planning by ignoring external sources of money. Given very

fluctuating and unsynchronised demand for, and supply of cash by the SBUs, the freedom of choice is dramatically restricted by adopting this assumption. Much of this criticism may loose itq teeth, however, if the portfolio models are used conjointly with financial analysis methods which, in turn, enables lifting models' constraints at some later stage of strategy development. An adequate approach ensures that both market and finance considerations can get the kind and amount of attention they need to for the strategies developed to be valid and optimal.

It has already been mentioned that a number of crucial constructs, such as business segment, sustainable competitive advantage, balancing product portfolio, do not seem to be formulated in a rigorous and unequivocal way, that would be appropriate for strategic analysis purposes. The deficiencies in understanding and defining the construct of balanced product portfolio are discussed below.

What is a balanced product portfolio ?

To conceive and implement a method of obtaining a more balanced product portfolio one needs to clarify and operationalise this construct so as to ensure it is unequivocally understood and can be used together with other state-of-the-art strategic business analysis tools including computer based decision support systems.

Some meaningful contributions have already been made towards this aim. Hall believes (28) that total portfolio of businesses should be managed by allocating resources to serve the interests of the firm as the whole - to achieve a balanced growth in sales, earnings, and an asset mix at an acceptable and cantrolled level of risk. The portfolio should be designed and managed to achieve the overall corporate strategy objectives. Tilles argues (29) that the same objective should be applied to product portfolio planning as applies to securities planning: a balanced combination of risk, income and growth of individual assets.

Jain's approach is different; he suggests there are four distinct basic types of unbalanced product portfolios:

* those with too many losers,

* those with too many question marks,

* those with too many profit producers and

* those with too many developing winners (30).

This classification can obviously be further extended by including "mixed" categories of unbalanced portfolios (eg. those with too many

losers and question marks, or those with too many losers and developing winners).

In his book, Jain shows an example of what he believes to be a balanced product portfolio (see Exh. 3) and comments: 'With three cash cows, this company is well positioned with stars to provide growth and to yield high cash returns in the future when they mature. The company

Exh. 3 Illustration of a Balanced Portfolio

1 1 1 1 I I I I I l l 1 l I I 1 I

Question Marlts

Relative Competitive Position

(from S.C.Jain. op.cit..p.297)

has four question marks, two of which offer a good opportunity to emerge as stars at an investment that cash cows should be able to support (based on the area of the circles). The company does have dogs but they can be managed to avoid drain on cash resources1(3 1). Regrettably, Jain fails to support his assertion with an adequate methodological elaboration and complementary financial information on the company thus making it impossible to either confirm, or refute, his claim.

Day maintains (32) that "a portfolio is balanced when the need for cash can be satisfied by the sources of cash, without jeopardising their market position". Being based on the framework of classical product portfolio models' assumptions, this definition consequently ignores the external sources of cash and is therefore incompatible with standard financial analysis as well as with other methods that may constitute a part of a decision support system. It also does not guide one very clearly

as to how he should go about determining in practice whether a particular move can "jeopardise market position" of cash generating products.

The degree to which the change in productlSBU market position can be predicted is contingent on a number of factors, such as:

Y the foreseeability of the changes in company's external environment,

3~ changing market objectives for company's products,

* our ability to predict the influence of an allocation decision on individual market position, and

* the selection of method(s) with which to measure the likely impact of any resource allocation on those positions,

which demonstrates the complexity and relativity involved in evaluating the possible impact. Some ambiguity about the operational meaning of the term "product market position" (33) often makes this task even more difficult.

Both presented approaches (ie. defining the balanced product portfolio and determining and evaluating the causes for imbalance in product portfolio) are capable of producing applicable, operational definitions of a balanced product portfolio. Other approaches to resolving this particular problem should by no means be discouraged.

Following their study into the applicability of financial portfolio theory in product portfolio decisions, Cardozo & Smith (34) recommend that a modified financial model be used, providing for differences in risk and return preferences among management groups in those two major areas, a model that would also be capable of handling product-specific objectives in addition to return and risk.

It appears that any definition of a balanced product portfolio should meet following criteria, for it to be operational (ie. properly understood and used by all participators of the planning process in all possible situations):

* allow for the consideration of external sources of capital,

* relate to the criterion and methods used in evaluating product portfolio (cash flows, flows calculated in NPV terms etc.),

* relate to a certain time frame for product portfolio evaluation,

* relate to a certain state of the non-controllable environment in this time frame,

-16 avoid using labels describing products performance.

In meeting all those requirements, it is proposed here that a

product portfolio of a company be considered as balanced for a certain period of time, and with regard to the business strategy chosen by that company, when achieving company's sales, profitability and asset growth targets for that period is unlikely, even under worst scenario conditions contemplated, to expose that company to an unacceptable level of risk, as determined by appropriate methods of analysis and against relevant financial standards.

This definition may be further adaptedldiversified to suit specific situations and applications.

It follows then, that there may well be a number of possible balanced product portfolios for every company for a certain period of time, depending on the business strategy and business objectives pursued, and that those alternative portfolios may differ, inter aha, with regard to level of risk inherent in them. On the other hand, in some other business situations only one balanced product portfolio might be available. An adequate research effort into that aspect of business strategic planning would have paramount practical significance.

Evaluating product portfolio in the context of strategic objectives pursued by a company acknowledges the existence of an inextricable link between those two notions in that a different set of objectives means dissimilar business programs with different patterns of cash flows and specific risks to be expected.

Composite factors as dimensions of portfolio models

All multifactor matrices seem to be developed in a similar way in that levels and units of analysis are established in the first stage of their development; next, factors representing both dimensions are identified and weights assigned to them. This in turn enables an assessment of products' current positions, projection of their future ones (momentum forecasts) as well as evaluating the impact of the desired change of product position on the strategy and resources required. Some

companies may show preference towards customised lists of factors, believed to be best suited for their situation and planning needs.

Multifactor models are sometimes criticised for diverting attention from the specific, relatively well defined factors to somewhat obscure, aggregate measures and, more importantly - for clearly inadequate methodology used in assigning weights to those factors. The weightings in most cases seem to be set rather arbitrarily, with no relevance to any relevant empirical research (35). As a result, two businesses may score quite differently on many factors making up a composite measure (36), yet have practically the same overall scoring on relevant axis, or, this being less likely, even the same position in the portfolio matrix. The results of qualitative analysis may at the same time indicate notable differences in those businesses' potentials.

Other criticisms address insufficient understanding of both character and strength of the influence of factors on the aggregate measures chosen, as well as of the mutual relationships between those factors. Taken separately, each factor may show significant impact on either the expected level of profit for all market participants (market attractiveness) or the relative profitability of the business (competitive position), whilst pairs of simple factors, selected to show some specific implications for the choice of the strategy, may lack meaningfulness or create some serious interpretation problems. The likelihood of such occurrences increases dramatically if the factors in question are not clearly defined (or understood) and the decision to make them parts of composite measure is not preceded by an analysis of their compatibility and congruence.

The following analysis looks at certain conceptual and implementation problems of the multifactor model developed by GEIMcKinsey; One of its dimensions - market attractiveness represents the average expected level of profit for all market participants over the assumed planning range, the other - competitive position - reflects the expected profitability of the business, relative to its competitors. It is suggested that both evaluation of the market attractiveness and competitive position should focus on customer point(s) of view and be prepared independently of the company thus contributing to its greater objectivity.

Looking at some lists of factors that are used in constructing composite measures (37), one can distinguish several categories of different relationships that may exist between pairs of factors:

* mutually reinforcing, statistically significant (eg. relative share of market and promotional effectiveness),

Table 1 . Market attractiveness ffactors recommended in constructing the p r o d

Note: the factor is named differently by some of the authors quoted the factor should be renamed for it to become unambiguous or measurable.

(Rthd = Rothschild; Chn = Cohen)

Table 2. Competitive vosition (factors recommended in constructina the vroxv)

Note: the factor is named differently by some of the authors quoted 21 the factor should be renamed for it to become unambiguous or measurable.

(Rthd = Rothschiid; Chn = Cohen)

.x. unilaterally reinforcing, statistically significant (eg. price competitiveness and promotional competitiveness),

* mutually inhibiting, statistically significant (eg. relative share of market and participation in diverse segments),

* unilaterally inhibiting, statistically significant (eg. market size and growth rate),

Y pairs influencing the composite measure unidirectionally (eg. profitability and ROI),

41. pairs influencing the composite measure in an opposing way (eg. patents and substitution threats),

Y causally related pairs (eg. ability to cope with changing technology and technological position),

* other pairs (unrelated, or with no discernible mutual influences, such as product liability and inflation vulnerability) (38).

It seems that only the pairs of factors showing a causal relationship should never be used as elements of the same multifactor proxies (39). Other recommendations or cautions appear to have specific relevance only.

To minimise the impact of politicization on the resource allocation decisions, preference should be given, other things being equal, to the factors whose weights can be adequately calculated. As far as the number of proxies recommended for every individual situation is concerned, a logical rule would be to select all, and only such, factors that show statistically significant relationship to the proxy representing profitability, and whose weights can be calculated rather than decided upon through judgment.

Factors recommended by Rothschild, Day, Luck et al. and Cohen as those to be used in describing business position and market attractiveness in multifactor matrices, provide a representative selection that could be analysed in detail against criteria of explicitness, mutual compatibility and congruency with both product categorisation dimensions. Due to volume constraints this extensive task has to be left, however, as a subject for a separate discussion. It is worth a mention, though, that despite considerable numbers of factors that these authors recommend to include into composite measures (36 for market attractiveness, 40 - for competitive position)(40), it is only four of them that they unanimously agree upon (growth rate, access to raw materials, competition structure and relative market share).

It is not difficult to see that a major part of those factors that are recommended as elements to be used in building one of the model dimensions, market attractiveness, fails to meet some, or all of the above-mentioned criteria. This can be put down either to

* rather unmindful fashion in which those factors are often named (eg . nature of in vestment, political considerations, our participation in diverse segments),

* to the lack of relevant and adequate research into the nature of some relationships or,

* to the lack of clarity as to the strength of their influence on market attractiveness.

The last problem may be virtually unmanageable in some specific situations where no experiments may be made, nor the amount and relevance of shared experience is sufficient to draw any valid conclusions. Most factors appear to be fraught in this context with serious possible implementation problems; those that seem not to are few : size o f the market, its growth rate, price elasticity, c yc/icality/seasonality o f demand, investment intensity, rep resent this group.

Some other recommended factors, albeit apparently explicit enough, may cause confusion due to the similarity, if not identity, of their implied meanings: types of competitors and competition structure; regulatory climate and political considerations would be but two such examples.

Factors representing competitive position do not compare favourably against the above-mentioned criteria, either. Few of them are named in a manner that is explicit enough, equally scarce are those whose influence on the competitive position can be quantified adequately. The task of assigning weights to some of those, such as distribution system, that can sometimes have a paramount importance in determining the competitive position of the company, would be rather daunting.

There can be little doubt that the sets of factors selected to explain the variances in business position and market attractiveness are, to a large extent, product-market, situation and company-specific and change in time; it follows that their relative importance can also vary and fluctuate in time.

Conclusions

When we consider the changeability of the business environment conditions, their degree of predictability, and our ability to accurately anticipate their influence on business performance, a number of essential questions comes up:

Y How do we go about selecting and implementing an appropriate product portfolio model in the optimisation procedure ?

* Do we employ the right methods of selecting and estimating the strength of influence of individual proxies on the profitability ?

w Are all the relevant terms defined appropriately and used accordingly when optimising product portfolio ?

* Is it possible, using rigorous methods, to arrive at a solution to any particular product portfolio balancing problem ?

.E Are we capable of proving that certain product portfolio is the optimal one, or the only one that can be regarded as balanced over a certain period ?

* Are we capable of evaluating how far removed from an ideal (if such an ideal can be determined) our actual product portfolio is ?

Y Do we have adequate methods a t hand, skills in using them and all required information, both historic and about future ?

* How should we determine the length of the period for which to determine a balanced product portfolio ?

Certainly, a continuing, comprehensive and thorough discussion of these and related issues shall be strongly encouraged as further progress in the development and implementation of product portfolio models in business planning seems to be largely dependent on it.

It appears that as a diagnostic aid used in assessing the validity of prior strategic judgments about product lines/business units positions, product portfolios still need to be implemented cautiously and should neither be used independently from other methods, nor used as an ultimate argument in deciding about allocation of resources.

Most successful implementation of product portfolio models so far seems to have been in its usage as a conceptual framework that guides

the generation of strategic options and facilitates the negotiation of objectives. Much of the observed increase in strategic planning's efficiency is owed to portfolio models' capacity .to make the planning process a much more orderly one and, at the same time, reduce the impact of poiiticization in allocating resources. It is necessary however to be always conscious of the very real danger of overestimating the analytical capacity of those models, all the more that the above- mentioned conceptual framework itself requires a general, critical overhaul.

There is a considerable amount of doubt as to whether portfolio models, in their current formats, can, or should, play any significant role as a prescriptive guide to the choice of appropriate strategic options and resource allocations. It would seem, that they at best can generate some hypotheses that need to be developed and carefully tested at various stages of strategic analysis.

As long as measurement problems persist, abandoning quadrant boundaries in portfolio analysis to avoid calibration problems appears to be a logical move that holds some promise for further development of the relevant methodology. It does not mean, however, that no classification of products should be used; rather, traditional labelling of products in the way it is done in growthishare matrix should be avoided, when possible.

In view of a comprehensive, strong criticism, attempts to link the notion of product portfolio optimisation (balancing) to the expected cash flows of products in certain positions on the grid should be dropped. No position on the grid, and no change to such position may be reasonably expected to generate or require some cash without prior, appropriate marketing and financial analysis; inferences per analogiam should be used with adequate degree of discretion.

Factors used to build composite measures should be selected and formulated in such a way that there is no scope for their misinterpretation and the requirements of individual analytical methods are met. In attempting to increase the usage potential for some methods (such as linear programming) in evaluating product portfolios, one should try to express all factors used in composite measures as either quantifiable constraints or variables.

It will be best left to the users of portfolio models to decide, how many of what portfolio models should be implemented in every specific situation, this depending inter alia on the organisational level at which they are to be used. One of dilemmas they will need to resolve would be the alternative of single multifactor model or several simple models. Using tailor-made PPMs appears, for the reasons presented by Wind and Mahajan (41), a much more appropriate option.

Appropriate financial standards should also be determined, that would correspond with the situation of the company, the state and trends of its external environment, as well as the character of product portfolio analysis to be carried out.

Furthermore, portfolio models should always be used in conjunction with capital budgeting and other appropriate tools of financial analysis as a prudent safeguard against possible misuses of PPMs that might lead to some very costly strategic blunders. It is particularly important in this context, as Hapeslagh (42) points it out, that appropriate verbal and graphic language is used in discussing strategic analysis issues.

To overcome the deficiencies of present models, such as limited number of dimensions used in categorising products and showing a static, rather than a dynamic, picture of product positions, an adequate computer software would need to be developed. Computer-aided analysis of sets of product portfolio matrices should pose no serious problems.

Practical consequences of such an enhancement of the product portfolio analysis would almost certainly be far reaching, particularly for those companies that have very diversified product-mixes and act in markets characterised by their unstable structure. In many cases, such an analysis, supported by a thorough study of technological and organisational options, might result in re-defining of key success factors and sustainable competitive advantages, as new benefits and synergies become meaningful in altered competitive environment.

To enable all this, further significant development of data bases and, perhaps even more importantly, a substantial progress in marketing theory would be necessary. In particular, the impact of simultaneous, multiple changes to the elements of the competitive environment and company's strategies needs to be measured in a more sound and accurate way. As desirable as those enhancements of the analytical tools seem to be, no quick breakthrough can though be expected, as only relatively slow progress in the marketing theory seems to be possible.

It will be perhaps appropriate to conclude this discussion with a quotation from Day's book (43): whether one considers the growth-share matrix a well-defied and valid model or simply a metaphor, it does have a singular capacity to reduce the complexities of diversified fims or heterogeneous busritess units and provide order to the thinking process. It is for the marketing discipline to pursue this avenue further in attempts to refine its analytical tools and bring them to the form that would meet strategic planning needs of tomorrow.

End notes

A number of most widespread conceptual and implementation problems posed by product portfolio models is discussed , inter alia, in: Cohen, William A.(1991), The Practice o f Marketing Management, Maxwell Macmillan, Singapore; Day, George S.(1986), Analysis for Strategic Market Decisions, International Edition, West Publishing Co.; Robinson, S.J.Q., R.E.Hichens, and D.P.Wade (1 978), "The Directional Policy Matrix: Tool for Strategic Planning" Long Range Planning, pp.8-15. Day (1 986) Porter, Michael ( 1 980), Competitive Strategy. Techniques for Analysing Industries and Competitors, The Free Press, New York . compare Day (1 986), p.6 Day (1986), p.181 Hapeslagh, Philippe (1 982), Portfolio planning: uses and limits, Haward Business Review, Vo1.60, (January-February 1982), pp.59-73 Day, (1 986), p. 162 This shortcoming can be overcome either through combining several two-dimensional matrices or through use of fairly elaborate software designed to deal with three and more dimensions at once; using traditional graphical forms we are obviously restricted to three dimensions only, and even then we have problems seeing certain relationships and changes happening to the analysed product portfolio; a three-dimensional model is presented, inter alia by R.A.Proctor and J.S.Hassard (1990) in their article ("Towards a New Model for Product Portfolio Analysis", Management Decision, V01.28 No.3, pp.14-17). For a more comprehensive discussion see eg.: Day (1 986), Hapeslagh (1982) and Wind and Mahajan (1981) Hapeslagh (1 9821, p.67 Hapeslagh (1 9821, p.63 Varadarajan,P.Rajan (1 990),"Product Portfolio Analysis and Market Share Objectives: An Exposition of Certain Underlying Relationships", Journal o f the Academy o f Marketing Science, Vol. 18, No. 1, p. 18 Hapeslagh (1 982), p.65 Wind,Yoram, Mahajan,Vijay (1 981 ), "Designing Product and Business Portfolios", Harvard Business Review, Vo1.59 (January- February 1981 ), p.159 Wind, Y., Mahajan V., Swire D.J. (1 983), "An Empirical Comparison of Standardized Portfolio Models", Journal o f Marketing, Spring 1983, p.94 Hapeslagh ( 1 982) Day (1986), p.181

Varadarajan, P.R.(1990) Wind, Mahajan (1 981 ) Wind, Mahajan (1 981), p. Operational inadequacies in the description of those procedures are very common; typical examples can be found in Allen, Michael (1979), "Diagramming GE's Planning for What's WATT" in Corporate Planning: Techniques and Applications, ed . Robert J .All i o and Malcolm W.Pennington, AMACOM, New York and in Hofer, Charles W., Schendel, Dan ( 1 978), Strategy Formulation: Analytical Concepts, West Publishing, p. 33 The only such example known well enough to the author is a product portfolio optimisation algorithm that A.Koch designed and implemented in March 1982 in a large Polish company producing household appliances; one would assume such computerised systems would now be quite widespread. Compare: Day (1 986), p. 182 Day (1986) Day (1986), p.185 Day (1986), p.185 Day (1 986): p.173 Hall, William K.(1980), "Survival Strategies in a Hostile Environment" Harvard Business Review Vo1.58, September- October 1980, pp.75-85 Tilles, Seymour (1 960), "Strategies for Allocating Funds" Harvard Business Review, January-February 1960, pp. 72-80 Jain, Subhash C.(1990), Marketing Planning and Strategy, South- Western Publishing Co,, Cincinnati, p.297 Jain (1 990), p.296 An extension of a critique by Day (1 986), p.173. Obviously, market position of a product, can be understood in various ways: eg. product's market share; position it occupies on the perceptional map; how it is positioned against competing products etc.; this exemplifies the difficulties one encounters when interpreting marketing texts. Cardozo, Richard N., Smith, David K. Jr. (1983) , "Applying Financial Portfolio Theory to Product Portfolio Decisions: An Empirical Study", Journal of Marketing, Spring 1983, pp.110-119 Eg.: Allen (1 979), Jain (1 990),pp.301-302 For the discussion of such situations see: Day (1 986) and Jain (1 990) For the purpose of this brief study, examples of such lists have been taken from: Cohen (1991), pp. 31-33; Luck, David J., Ferrell, O.C., Lucas, George H. Jr.(1989), Marketing Strategy and Plans, Prentice Hall, Englewood Cliffs, pp.380 ; Rothschild, William E.,(1976), Putting It Al l Together, AMACOM, New York, pp. 141- 1 62; Day (1 986), pp. 198-1 99

(38) The pairs of factors selected as examples are believed to represent mutual relationships of the kind attributed to them in this article under, at least, most typical circumstances; within the constraints of this paper, a separate discussion of implicit assumptions made by the author was not feasible.

(39) Causal relationship would result in a very high collinearity, thus rendering at least one factor from such a pair unusable in constructing composite measure (proxy).

(40) The actual total number of factors recommended by these authors is in fact somewhat higher; however, inessential differences in the wording of some of them are ignored and therefore some of them are regarded as identical for the purpose of this analysis.

(41) Wind and Mahajan (1 981) (42) Hapeslagh (1 982) (43) Day (1 986).

SWINBURNE INSTITUTE OF TECHNOLOGY

FACULTY OF BUSINESS

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