Constructing industry scenarios

An industry scenario is an internally consistent view of an indus­ try’s future structure.  It is based on a set of plausible assumptions about the im portant uncertainties that might influence industry struc­ ture, carried through to the implications for creating and sustaining competitive advantage. An industry scenario is not a forecast but one possible future structure.  A set of industry  scenarios is carefully chosen to reflect the range of possible (and credible) future industry structures with important implications for competition.  The  entire set of scenar­ ios, rather than the most likely one, is then used to design a competitive strategy. The  time   period   used   in   industry  scenarios   should  reflect the time horizon of the most im portant investment decisions.

An industry typically faces many  uncertainties  about the future. The important uncertainties are those that will influence industry struc­ ture, such as technological breakthroughs,  entry  of  new competitors, and interest rate fluctuations. External factors such as macroeconomic conditions and government policy affect competition through, and not independently of, industry structure. Structural change almost always requires adjustments  in strategy and  creates the greatest  opportunities for competitors to shift their  relative  positions.

The five competitive forces described in Chapter 1 constitute the conceptual foundation for constructing industry scenarios. Uncertain­ties that affect any of the five competitive forces will have implications for competition, and therefore m ust be considered in constructing sce­ narios. Constructing industry scenarios begins by analyzing current industry structure  and identifying all the uncertainties  that  may affect it. These uncertainties are then translated into a set of different future industry   structures.  An   overview of the process is shown   in Figure 13-1.

The process shown in Figure   13-1   is deceptively simple. Con­structing industry scenarios requires several iterations and is a judg­ mental process. It can be difficult to determine fully what uncertainties are the most im portant for strategy until a number  of preliminary scenarios have been analyzed,   hence   the   feedback   loops   on   Figure 13-1.

The  process shown in   Figure   13-1   postpones  the   introduction of competitor behavior into each scenario until the industry  structure and requirements for competitive advantage  have been developed, de­ spite the fact that competitor behavior can influence structure and is often a source of uncertainty in its own right. However, predicting competitor behavior  in a scenario poses a nearly impossible task without some understanding of the structural environment in which competitors will operate. Expected behavior of competitors under a scenario may serve to modify industry structure; uncertainty about competitor behavior may lead to additional scenarios.

I will use an extended example drawn from the U.S. chain saw industry in describing how to construct industry scenarios. It is neces­ sary to provide some background  on the U.S. chain saw industry  so that the scenarios can be better  understood.  The  chain saw industry had  enjoyed a   stable   and   profitable   structure  for   decades   prior  to the 1970s.   In   the   early   1970s,   however,   there  were indications that the industry  might  be on the threshold  of a m ajor  structural change. It was believed that  sales of small chain  saws to homeowners  and other “casual users” might enter a period of explosive growth. If this happened, a m ajor structural change in the industry would be triggered which could proceed in several directions.

In   the early   1970s, the   great  m ajority  of chain   saws were sold to professional users including loggers, farmers,  and  others  for whom chain   saws were a   primary  tool   of  their  trade.   Professionals   tended to use saws heavily and valued   durability,  comfort  and   reliability. They purchased saws primarily through chain saw dealers that  also provided service and spare parts. Dealers tended  to carry  the product lines of relatively few manufacturers. Most  chain saws were large, powerful gasoline saws assembled by manufacturers  from a combina­ tion of purchased  and   m anufactured  parts.   Suppliers   of parts  such as chains, bars, and sprockets were high-volume producers that enjoyed scale economies and some bargiaining power. Electric saws were a potential substitute for gasoline saws, but were inadequate for most professional applications.

The m ajor competitors in the early 1970s were Homelite (a divi­ sion of Textron), McCulloch, and Stihl, followed by Roper,  Remington and Beaird-Poulan. Industry rivalry was moderate,  centering on qual­ ity, features, dealer network, and brand reputation. Homelite had the largest market share, followed by McCulloch. Both  pursued differentia­ tion strategies. Stihl competed by focusing on the premium-quality segment,   and   differentiated   itself   on   quality,   durability,   and   service. By 1973, some m ajor  uncertainties  loomed. The  initial spurt in demand for chain saws by casual users had been brought  on by the energy crisis, the do-it-yourself movement,  and  other  causes. Casual users were much less sophisticated than professionals and used their saws less intensively   for   less demanding  applications.   Casual  users also did not necessarily purchase saws from servicing dealers, and distribution channels for chain saws were broadening to include hard­ ware chains, catalog showrooms and departm ent stores, among others. This encouraged new entrants into the market.  Black & Decker  ac­ quired McCulloch, and Beaird-Poulan was acquired by Emerson Elec­ tric. These acquisitions had created the potential for an injection of resources into competitors that had previously been financially con­ strained. In the remainder of this chapter,  I will construct  a set of industry scenarios for the chain saw industry  while describing the principles that underlie scenario building.

1. Identifying Industry  Uncertainties

Identifying uncertainties  with   the   most  im portant  ramifications for competition  lies at the heart  of the industry  scenario   technique. Yet the sources of uncertainty can be hard to recognize and managers may find it difficult to detect discontinuous changes or shed their conventional wisdom.   To identify uncertainties  each element of indus­ try structure must be examined and placed into one of three categories: constant, predetermined, and uncertain. Constant elements of industry structure are those aspects of structure that are very unlikely to change. Predetermined elements of structure are areas where structure  will change, but  the change  is largely predictable.  Predetermined  trends may well proceed  faster   or   slower depending  on   the   scenario.   Often a variety of structural changes are predetermined if a thoughtful indus­ try analysis is done. Uncertain  elements  of structure  are those aspects of future structure which depend  on unresolvable  uncertainties. Con­ stant and predetermined structural variables are part of each scenario, while uncertain structural variables actually determine the different scenarios.

A way to begin determining which elements of industry structure fall into each category is to list all apparent industry trends and any possible   m ajor  industry  changes   that  have been   discussed   internally or mentioned  by industry  observers. While  only   those   uncertainties that might affect   structure  will be   im portant  to   scenario-building,   it is important initially to identify all the uncertainties to avoid omitting important variables. Uncertainties with a low probability of occurrance but with a potentially large impact on structure must also not be overlooked. Each trend or possible change is then analyzed to deter­-mine whether it could have a significant impact on industry  structure, and how uncertain  or predictable  its   impact  is.   Such   a procedure will tend to produce  a list of uncertainties  that  mixes causes and effects.

By considering only apparent  trends,  however, im portant discon­ t in u i tie s may be overlooked. Scenarios built  only on apparent  trends may reflect conventional  wisdom and  may not  provide  insights into future structure not available to competitors. One way to guard against overlooking discontinuities  is   to   uncover   observers   of  the   industry who might  foresee new possibilities. Soliciting opinions  from outsiders new to the industry, who can view it objectively, provides another mechanism for overcoming  conventional  wisdom.

A wide range of environmental factors can lead to both predeter­ mined and unpredictable  industry  changes, including technological trends, government policy shifts, social changes, and unstable economic conditions. Environmental changes are not  im portant  for their  own sake, but because of their possible effect on industry structure.  A num­ ber of underlying  evolutionary  processes,   at work  in every industry, are shown in Table  13 -1.3 These provide  a census of the forces that drive industry  structural change. Each  should be examined  to see if and how it might affect the industry. Sometimes the evolutionary pro­ cesses will proceed in predictable ways, while in other cases their speed and direction will be uncertain  and  lead to uncertainty  about some elements of structure.

The possible industry  changes  that are most difficult to anticipate are frequently those that  originate outside an industry.  For  example, many firms in industries that had little or no previous contact with electronics were taken by surprise by the development of microcompu­ ters. New entrants also have less predictable and often more profound impacts on industry structure than do start-up competitors.

In some industries, therefore, scenarios are best constructed  by starting inside the industry  and looking outward  for additional sources of uncertainty.  In other  industries, it is more  appropriate  to begin with macroscenarios and then narrow the focus to the industry. Mac­ roscenarios can provide im portant  insights into possible industry changes.   They  can expose possible shifts in macroeconomic,  political, or social variables that  are not  foreseen in a more  industry-centered view of the external environment. Another way of identifying uncer­ tainties is broadly  based   technological  forecasting.   A   systematic look at how any of the technologies in the firm’s value chain (Chapter  5) might be affected by outside developments can sometimes help to reveal changes unforeseen by technical personnel inside a firm.

In constructing  scenarios,   it is im portant  to try   to identify one or more   m ajor  discontinuities   that  would  have   a significant   impact on structure, such as a revolutionary technological change. If major discontinuities   have a material  probability  of occurring,  they   should be treated as one of the important uncertainties in developing scenarios. If a m ajor discontinuity will have a fundamental impact on structure but is very remote, it is usually best treated separately from the normal scenarios.

To illustrate the application of these ideas, Table 13-2 lists the uncertain elements of structure in the chain saw industry in 1973.

Significant uncertainties existed in all of the five forces except for suppliers. Since each uncertain  element  of structure  can serve as the basis for several scenarios and the list of uncertainties  can be quite long, as it is in the chain saw industry,  these sources of uncertainty must be distilled   into   the few scenarios   that  will be truly   important to strategy.

2. Independent Versus Dependent Uncertainties

Converting the list of uncertain structural elements into scenarios begins by dividing them into independent and dependent uncertainties:

  • Independent uncertainties. Those elements of structure whose uncertainty is independent of other elements of structure. The sources of the uncertainty may be inside the industry (e.g., competitor behavior) or outside the industry (e.g., oil prices).
  • Dependent uncertainties.  Those elements of structure  that  will be largely or completely determined by the independent uncer­ tainties. In chain saws, for example, the future  level of advertis­ ing on television is quite uncertain  but  will be primarily  a function of the size of casual user demand.  Casual users are receptive to television advertising while professional and farm buyers are best reached through specialized magazines.

Independent uncertainties are the scenario variables on which scenarios are based. Only independent  uncertainties are an appropriate basis for constructing scenarios because they are true sources of uncer­ tainty. Dependent uncertainties  are resolved once assumptions  about the independent  uncertainties  have been made, and thus  become part of each scenario.

Independent and dependent uncertainties often differ only in de­ gree, because many industry structural characteristics will be deter­ mined in part by independent  uncertainties  and partly influenced by other  industry  characteristics.   Industry  concentration,  for   example, is largely based on the height of entry  barriers  and hence dependent, but is also a function of independent factors such as an unexpected acquisition or entry  by a strong  competitor.  Thus,  as in all phases of scenario construction, one must attem pt to assess the most significant factors influencing   each   uncertain   variable   and   use   them   to   classify it as either dependent or as a true  scenario variable. It is often not apparent in the beginning of an analysis which uncertainties are depen­ dent.   Once   scenarios   are   analyzed,   it   may   be   necessary   to   modify the way a particular element of structure is classified.

Separating  uncertain  elements of industry  structure  into   those that are scenario variables and those that  are dependent  requires that the causal factors  of uncertain  elements of structure  be identified. Causal factors determine the future state of each uncertain structural element. For example, the level of casual  user demand  in chain saws will be a function of such causal factors as energy prices, the rate of household formation,  how many  new houses are built with fireplaces, and so on.

Practical  considerations  may dictate  not  going all the way back to the most fundamental causal factors determining an uncertain varia­ ble since they may be numerous and hard to measure.  However, causal­ ity must be traced back far enough to separate scenario variables from dependent variables. Causal  factors also are im portant in determining the appropriate range of assumptions that should be made about each scenario variable. If the level of casual  user demand  is strongly influ­ enced by energy prices, for example, then forecasting the range of possible energy prices is necessary to understand  the range of feasible levels of demand.

Table 13-3 identifies the scenario variables in the chain saw indus­ try from the full list of uncertainties  that were shown  in Table  13-2 and ranks  them in terms  of their  importance  to industry  structure. The scenario variables in the chain saw industry are relatively few, because many  of the uncertainties  in the industry  will be resolved once the demand for casual user saws and the channels through  which they are sold become  clear. Future  safety regulations  by government, for example, will probably be introduced if casual user demand grows and the number of accidents increases with the number of less sophisti­ cated users. M arketing activity will also increase sharply  and shift toward television advertising if casual user sales grow.

Table 13 -4 shows the causal factors  for the four important sce­ nario variables in the chain saw industry. Several causal factors underlie each variable, as is typically   the   case.   Causal  factors   reflect forces both within and  external  to the industry.  It is also apparent  from Table 13-4 that some of the causal factors reflect other  aspects of industry structure or competitor behavior. Casual user demand, for example, is partly  determined  by the intensity of marketing  activity and pricing behavior  of competitors.  The  future  mix of channels will in turn be influenced by the level of casual  user demand,  because casual users prefer different channels than professionals. It is not un­ common  for scenario variables   to   have   some internal  causes along with external ones, and the analysis of scenarios must reflect such interdependencies.

The uncertainty that surrounds the causal factors of each scenario variable leads to scenarios. Assumptions  about  the scenario variables will determine the outcome of dependent uncertainties.  Predetermined and constant elements of structure are then added to the scenario to complete the profile of the future structure of the industry, recognizing that the rate of change of predetermined trends may be different under each scenario. Figure 13-2 illustrates the process schematically.

Constructing  a useful scenario involves   developing   a   logic for how the various elements of industry structure interrelate, separating true scenario variables from dependent and predetermined  industry changes. A scenario must seek to expose second-order effects of struc­ tural changes that  result from one industry  change  affecting others. Such a logic   for how   various aspects of  industry  structure  interrelate is at   the   heart  of   the   usefulness   of  the   scenario   technique  because it is usually im portant  to understanding  the implications of scenarios for strategy.

An industry scenario is based on a set of plausible assumptions about  each of the scenario variables, derived from   the causal factors. The consequences of this set of assumptions  for industry  structure flows from the process diagrammed in Figure  13-2. A scenario emerges as an internally consistent view of the future industry  structure under one   set   of assumptions.  The  range  of plausible   assumptions  about the potential outcomes of scenario variables determines the appropriate set of scenarios for analytical purposes.

Constructing a set of industry scenarios would be relatively simple once the scenario   variables   had  been   determined  if there  was only one scenario variable. If the only scenario variable in the chain saw industry was the level of casual user demand, for example, then a manageable number of scenarios could be constructed by making sev­ eral plausible assumptions about demand. However, the number of relevant scenario variables is greater than one in most industries. The number of combinations generated by differing assumptions about each scenario variable can multiply rapidly, and with it the number  of scenarios that might be analyzed. W ith four scenario variables in chain saws, for example, dozens of scenarios could easily be constructed. There are two ways to limit the proliferation of scenarios— reduc­ing the number of scenario variables and reducing the number  of assumptions made  about  each   one.   The  first step   is to ensure  that the scenario variables are   all truly   uncertain  and   independent.  This test may lead to the elimination of some  variables. Another  way to reduce the number  of scenario variables is to   concentrate  on only those with a significant potential  impact  on structure.  While many factors will have   some   impact  on   future  structure,  fewer   will have an impact significant enough to influence competitive strategy. Some­ times the impact  of  a variable on   structure  only becomes clear once the analysis of scenarios has begun. In the chain saw industry, however, all four scenario variables  are  important.

Figure   13-2.    Determinants of Future Industry   Structure

The next step   in   determining  the   set of  scenarios   to   analyze is to specify the different assumptions to be made about  each scenario variable. The appropriate range of assumptions  will depend  on the extent to   which   its causal factors could  differ.   Scenario   variables can be discrete   or continuous.  When  a   scenario   variable   is discrete (e.g., a regulation is either signed into law or it is not), the choice of assump­ tions is relatively clear. When the scenario  variable is continuous (e.g., the level of casual user demand), a question  arises as to how to make the appropriate assumptions about its value.

The choice of assumptions  should  be governed  by four factors: the need to bound the uncertainty, regularity of the impact on struc­ ture, managers’ beliefs, and practicality. Assumptions about a scenario variable should bound  the feasible range of values that variable could take, exposing the important differences in possible industry structure. Since scenarios are not  meant  to be forecasts, it is im portant  that those with a low   probability   of occurrence  not  be ignored.   The   use of extreme values   can   increase   the   understanding   of   the   directions in which industry  structure could evolve. Wide differences in the level of casual user demand,  for example,   will strongly  influence   the   path of evolution in the chain saw industry.  However,  this does not mean that very unlikely values for a variable should be used in constructing scenarios unless these very unlikely outcomes would lead to industry structures that differ substantially from more likely outcomes. The credibility of scenarios can be damaged if they are based on highly implausible assumptions.

Having bounded  the   feasible   range  of uncertainty,  the   number of assumptions in between about each variable must  be selected. If changes in the value of a scenario variable affect structure in a predict­ able way between its extremes, the number  of  assumptions  can be small. If this is not the case, however, the range of assumptions  must reflect major  discontinuities.   In   chain  saws, for example, a medium level of casual user demand  is likely to have an impact on structure that  is not simply between that  of very   low   and   very   high   demand. A medium level of demand provides room for only one or two new efficient-scale manufacturing  facilities, and thus  raises the possibility that several competitors will expand simultaneously, leading to overca­ pacity. An even more striking case of irregularity  of impact is in the extent of sales through servicing dealers. It is possible that the percent­ age of saws sold through dealers will fall rapidly as casual user demand grows, but recover once first-time chain  saw buyers trade up to larger saws and require service. This has quite different structural implications from scenarios featuring low or high dealer share.

A third consideration in choosing  what  assumptions  to make about  each scenario variable   is the beliefs held   by senior management. It is important to build at least one scenario around assumptions that reflect their commonly  held beliefs. This lends credibility to the scen­ ario building process. Scenarios reflecting managers’ assumptions  can also be useful in exposing the differences in the assumptions of various senior managers, as well as in testing the overall consistency of assump­ tions that managers have made independently about  each scenario variable.   If the scenario that  results from combining  these assumptions is implausible, managers’ thinking  about  the future  may be changed. All this is also im portant in demonstrating the validity of employing multiple scenarios rather than a single scenario.

A final consideration in choosing the number of assumptions about each scenario variable is the practical limit on the number of scenarios that can be meaningfully analyzed. A proliferation of scenarios beyond three or four may make  analysis so onerous  that  the strategic issues are   obscured.   Thus  compromises  may  well be necessary   to   reduce the number of assumptions examined. Since scenarios can be added, eliminated, or combined later in the analysis, it is im portant  not to impose this constraint too strongly.

Table 13-5 shows the range of assumptions chosen for the scenario variables in the chain  saw   industry.  Except  for the   level of casual user demand and mix of dealer versus nondealer sales, two assumptions about each variable are sufficient to expose the implications for industry structure.  The  key distinction   in the   shape  of the   penetration  curve is whether it rises smoothly,  or rises rapidly and then levels off (peak­ ing). Peaking raises the risk of excessive capital investment by competi­ tors. The  extent  of private   label sales   is im portant  in   determining the bargaining power of buyers and the relative position of well-known brands such as McCulloch and Homelite (manufacturers’ brands are clearly not as im portant in private label sales). Each assumption shown in Table 13-5 can be quantified.

3. Consistency of Assumptions

A scenario should be an internally consistent view of what future industry   structure  could   be.   Internal  consistency   is   partly   ensured by separating the scenario variables from the dependent ones. Another critical requirement,  however, is the consistency of the assumptions made about each scenario variable with each other.

Often scenario variables affect one another, and thus some combi­ nations of assumptions about them are not  internally  consistent. This can lead to the elimination of some scenarios. Figure  13-3 and 13-4 illustrate the process in the chain saw industry.  Figure  13-3 compares the level of casual user demand and the shape  of the casual user penetration curve. It is unlikely that  the penetration  curve will be peaked unless casual user demand is high. Hence, two combinations of assumptions (cells in Figure 13-3) are inconsistent. Figure  13-4 compares the four consistent combinations of assumptions about de­ mand and the shape of the penetration curve against the mix of chan­ nels. Once again, some combinations of assumptions are not mutually consistent and can be eliminated.   Servicing dealers   will dominate  only if casual user demand  is not  high. Nondealers  will not  gain a high share of sales unless casual demand  is medium  or high. A short-term shift to nondealers  is likely only if demand  for casual users is high and peaked, leading to an increase in nondealer share followed by a fall-off in casual demand and the migration  of the more serious casual users to dealers. The  fourth  scenario variable, the percentage  of saws sold under  private   label versus sold branded  through  nondealers,   is not  shown on   Figure   13-4.   However,  high   private label penetration is clearly inconsistent with dom ination by the dealer channel.

Figure 13-3. Consistency of Casual User   Demand   and   the   Penetration   Curve in Chain Saws

Thus we can reduce  the num ber  of consistent  scenarios in the chain saw industry to the ten shown in Figure 13-5. These ten combina­ tions of assumptions  about the scenario variables are internally consis­ tent and,   therefore,   are   candidates  for   further  analysis.   The  process of determining  internally  consistent  assumptions is vitally important to constructing industry scenarios, because consistency in viewing the future is one of the principal benefits of the scenario technique.

4. Analyzing   Scenarios

The  next step in scenario planning  is to analyze  the implications of each scenario for competition.  The  analysis of a scenario involves the following:

  • determining future industry structure under the scenario
  • developing the implications of the scenario for industry struc­ tural attractiveness
  • identifying the implications of the scenario for the sources of competitive advantage

To determine the implications of a scenario for future industry structure, the process diagrammed in Figure 13-2 is carried out. As­ sumptions about  scenario variables determine  the dependent  elements of structure. These are combined with the predetermined and constant elements of structure  to complete the scenario. Predetermined  changes in structure may be speeded up or slowed down for different scenarios. Each scenario   will   provide   a   picture  of the   five forces   representing the industry’s structure in the event that the assumptions  about  the scenario variables come true.

This future structure will be more or less attractive in terms of profitability. The industry structure under each scenario will determine, and possibly shift, the sources of competitive  advantage.  For  example, a scenario characterized by low casual user demand  will imply very different requirements for competitive advantage than a scenario where casual user demand is high and the mix of channels shifts away from servicing dealers. In the latter  scenario, differentiation   will be based more  on advertising and having   light,   compact  saw   designs   rather than  on   traditional  sources   of uniqueness  such   as excellent dealers and durable saws. The  analysis of each industry  scenario must specify the resulting implications for competitive advantage in the value chain.

Scenarios may differ in:

  • the relative importance of value activities
  • the appropriate configuration of the value chain
  • the drivers of cost or uniqueness
  • the importance of interrelationships
  • the sustainability  of different sources of competitive advantage
  • the choice of generic strategies

Table 13-6 illustrates the analysis of two of the chain saw industry scenarios. Scenario 1 results in a structure quite similar to the current industry structure, while Scenario 7 results in a very different structure with quite different requirements for competitive advantage.

An important part of analyzing  a scenario is determining  when it will become clear that  the particular  scenario   has come to pass.

Sometimes a scenario   emerges   quickly.   In   chain  saws,   however,   a year passed before the uncertainty over the level of casual user demand was reduced. Several more years went by before the peaking of casual user demand was known.  Competitors must choose between commit­ ting to a strategy early or waiting until better  information  becomes available. Therefore, a firm must estimate when uncertainties will be resolved in order to predict competitor behavior and to set the firm’s own strategy.

5. Introducing  Competitor Behavior into Scenarios

If the firm has a dominant position in its industry or if competitor behavior has little potential effect on structure, the analysis of each scenario can stop at the industry level. In most industries, however, competitors can affect industry structure and their strategies will influ­ ence a firm’s options and likely success. Thus the analysis of scenarios must include competitors. In industries where a few powerful competi­ tors exist, competitor analysis can be the most im portant part of analyz­ ing each scenario.

The future industry  structure  under  each scenario will usually have different consequences for different competitors.  Increased casual user demand, for example, will greatly benefit firms with existing casual user models and representation in mass distribution channels compared to firms that  exclusively serve professionals.   Competitors  will respond to structural change in ways that reflect their goals, assumptions, strate­ gies, and capabilities. For  example, Beaird-Poulan’s response to grow­ ing casual user demand is likely to be aggressive, conditioned  by its parent company’s (Emerson Electric) aggressive growth goals. The behavior of competitors  may, in   turn,  affect the   speed and   direction of structural  changes  in the scenario  through  a feedback loop. In chain saw Scenario 7, for example, aggressive investment in new capac­ ity by Beaird-Poulan and McCulloch would enhance rivalry compared to the situation where one or both chose a conservative stance.

The full arsenal  of competitor  analysis tools   should  be brought to bear in predicting how competitors will behave under different sce­ narios. Strategic mapping is often a useful tool for integrating  predic­ tions of likely competitor’s  responses under  a scenario.4 The  axes of the map are chosen to reflect the critical sources of sustainable competi­ tive   advantage  implied   by   the   scenario.   Since   each   scenario   implies a different future industry structure, the variables that will most influ­ ence the relative position of competitors may vary. For example, since industry structure under chain saw Scenario 7 will shift toward  more price rivalry,   manufacturing  scale   will become  an   important  source of competitive advantage though  it is not as im portant under Scenario 1.

Strategic mapping allows the simultaneous display of all competi­tors’ expected behavior  under  a scenario. It also facilitates the analysis of interactions among competitors and their responses to each others’ moves.   If all competitors  are   forecast to   move in one direction   under a scenario, for example, some may modify their strategies as the sce­ nario unfolds to avoid a head-on confrontation.

Often competitor  behavior  is difficult to predict. If the behavior of one or more im portant competitors under a scenario is both uncer­ tain and likely to have an im portant impact on competition, this intro­ duces an additional scenario variable into the scenario. Scenarios under which   there are key uncertainties  about  competitors  must  then be split into two or more additional scenarios based on assumptions about how competitors’ behavior will differ. This same approach may be necessary to deal with   uncertainty  about  the   likelihood   of potential new entrants with differing resources and  skills from existing competi­ tors.

Figure 13-6 illustrates the most  im portant competitor uncertain­ ties in the chain saw industry scenarios analyzed in Table 13-6. Under Scenario 1, the m ajor uncertainty is whether McCulloch and Beaird- Poulan will aggressively invest in new capacity  and  advertising even though  the casual user market  never  fully materializes. Both   have strong proclivities to be aggressive given their new parent companies. Moreover, it will not be immediately clear to either firm which scenario is coming to   pass.   Under  Scenario   7, Homelite’s  strategy poses the key uncertainty because McCulloch and Poulan’s actions are quite predictable. Homelite  may choose to pursue  the entire casual user market despite the shift in strategy implied, or remain in its traditional segments and earn high profits at the expense of share. While Home­ lite’s parent company may be prone to demand high profits, the oppor­ tunity for rapid growth could also influence Homelite’s choice.

6. The Number of  Scenarios To Analyze

Since the analysis of a scenario is often complex and time consum­ ing, scenarios should be analyzed in a sequence that yields the necessary insight for the selection of a strategy  without  requiring  full blown analysis of every possible scenario. A good starting  point is to analyze the polar, or most widely separated, scenarios first. The polar scenarios typically lead to the most different industry  structures  and thus will help bound the range of strategic options.  The  stark contrast between polar scenarios is often very stimulating  to strategic thinking.  In the chain saw industry,  Scenarios 1 and  7 were the polar  scenarios on Figure 13-5.

The  next scenario analyzed  after  the   polar  scenarios should be one where the structural  outcome  is expected to differ significantly from the polar scenarios. The scenario  deemed  most  likely to occur should also be analyzed. This   process   should  continue  until   the   way in which the scenario variables determine  future  structure  is under­ stood. In addition, m ajor discontinuities with a low probability of occurring  should be included  as special scenarios which are analyzed less extensively but factored into strategic choices.

Figure 13-7 shows the analysis of an intermediate  chain saw scenario, Scenario 9. This  is very different scenario from   Scenarios   1 and 7, because it assumes that casual user saws are a fad and private label sales will   never catch  on.   Thus  Scenario   9 creates   a   dilemma for firms about how to capitalize on the short-lived casual user boom without  alienating   dealers or sacrificing   key   competitive  strengths.   It is clear from Figure  13-7 that the determinants of competitive advan­ tage and likely competitor behavior will be quite different under Sce­ nario 9 than under the other  two scenarios. Briefer consideration  of other chain saw scenarios suggests that  these three  industry  scenarios are representative of the impact of uncertainty on competition.

The purpose of scenarios is to understand  the different ways in which industry  and   competitive   conditions  might  change.   Forecasts are frequently not accurate,  and  scenarios attem pt instead to illustrate the logical outcomes of a range of  forecasts. The  scenarios actually analyzed will be but a few of the almost  infinite number of possible futures that might occur in an industry.  However, well-chosen scenar­ ios will illuminate the range of futures germane to strategy formulation. Scenarios should be chosen to communicate, educate, and stretch man­ agers’ thinking about the future.

7. Attaching  Probabilities to Scenarios

Rarely will each scenario be equally likely to occur. Industry scenarios are not intended to cover all possible outcomes— they are devices for exploring the strategic implications of possible future indus­ try structures. However,  the strategic  implications  of scenarios are partly a function of their likelihood of occurring. It is important  to determine the relative probabilities of outcomes that are broadly  similar to each scenario. If the scenarios analyzed are well chosen, they will represent the range of industry  outcomes  that  might  occur. In chain saws, outcomes close to Scenario 1 are the least likely to occur, while outcomes close to Scenarios 7 and 9 are about equally likely.

Attaching probabilities to scenarios is beset  with problems of bias and conventional wisdom. It is im portant  to find unbiased ways to assess the probabilities of scenarios, based on the underlying  causal factors of each scenario variable. M anagers’ own implicit probabilities should be identified and confronted  if they vary widely or contradict those indicated by industry analysis.

8. Summary Characteristics of Industry  Scenarios

A number of characteristics distinguish industry scenarios. Each scenario is, in effect, a full analysis of industry structure, competitor behavior, and  the sources of competitive advantage under a particular set of assumptions about the future. The whole range of techniques available for analyzing industries and competitive advantage must be brought to bear; the scenario tool is merely a framework for identifying the key uncertainties and analyzing them,  not an end in itself. The process of understanding how uncertainty affects future industry struc­ ture is as im portant as the scenarios that are actually constructed.

The successful analysis of industry  scenarios hinges on judgment and compromise.  Constructing  scenarios is a process of abstracting those elements of uncertainty that will drive strategic choices. Selecting and analyzing a few scenarios from the range of future industry struc­ tures requires picking the most im portant cases and  simplifying them. The process is nearly always iterative, because the analyst will better understand the relationship between the key uncertainties and industry structure as the analysis proceeds.

Finally, it should be clear that a m ajor purpose of industry scenar­ ios is to ensure internal consistency of a firm’s view of the future. A scenario aims to create a view of future industry structure that recog­ nizes the interactions among  variables and the need for consistency among assumptions about different industry characteristics. Scenarios provide a way of linking uncertain trends together into a number of alternate but consistent  views of the future. Thus  scenarios emphasize the ways in which industry  trends and competitor behavior will interact or reinforce each other. Scenarios aim to   reduce  the   chances   that actions taken to deal with one element  of uncertainty  in an industry will unintentionally worsen a firm’s position vis-a-vis other uncertain­ ties.

Source: Porter Michael E. (1998), Competitive Advantage: Creating and Sustaining Superior Performance, Free Press; Illustrated edition.

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