Scenario planning, also called scenario thinking or scenario analysis , is a strategic planning method that some organizations use to make flexible long-term plans. It is in large part an adaptation and generalization of classic methods used by military intelligence.
Scenario analysis is a process of analyzing possible future events by considering alternative possible outcomes. Thus, scenario analysis, which is one of the main forms of projection, does not try to show one exact picture of the future. Instead, it presents several alternative future developments. Consequently, a scope of possible future outcomes is observable. Not only are the outcomes observable, also the development paths leading to the outcomes. In contrast to prognoses, the scenario analysis is not based on extrapolation of the past or the extension of past trends. It does not rely on historical data and does not expect past observations to remain valid in the future. Instead, it tries to consider possible developments and turning points, which may only be connected to the past. In short, several scenarios are fleshed out in a scenario analysis to show possible future outcomes. Each scenario normally combines optimistic, pessimistic, and more and less probable developments. However, all aspects of scenarios should be plausible. Although highly discussed, experience has shown that around three scenarios are most appropriate for further discussion and selection. More scenarios risks making the analysis overly complicated. Scenarios are often confused with other tools and approaches to planning. The flowchart to the right provides a process for classifying a phenomenon as a scenario in the intuitive logics tradition.
Strategic planning is an organization's process of defining its strategy, or direction, and making decisions on allocating its resources to pursue this strategy. It may also extend to control mechanisms for guiding the implementation of the strategy. Strategic planning became prominent in corporations during the 1960s and remains an important aspect of strategic management. It is executed by strategic planners or strategists, who involve many parties and research sources in their analysis of the organization and its relationship to the environment in which it competes.
Military intelligence is a military discipline that uses information collection and analysis approaches to provide guidance and direction to assist commanders in their decisions. This aim is achieved by providing an assessment of data from a range of sources, directed towards the commanders' mission requirements or responding to questions as part of operational or campaign planning. To provide an analysis, the commander's information requirements are first identified, which are then incorporated into intelligence collection, analysis, and dissemination.
The original method was that a group of analysts would generate simulation games for policy makers. The methods combine known facts about the future, such as demographics, geography, military, political, industrial information, and mineral reserves, with key driving forces identified by considering social, technical, economic, environmental, and political (STEEP) trends.
Geography is a field of science devoted to the study of the lands, features, inhabitants, and phenomena of the Earth and planets. The first person to use the word γεωγραφία was Eratosthenes. Geography is an all-encompassing discipline that seeks an understanding of Earth and its human and natural complexities—not merely where objects are, but also how they have changed and come to be.
Politics is a set of activities associated with the governance of a country or an area. It involves making decisions that apply to members of a group.
An industry is the production of goods or related services within an economy. The major source of revenue of a group or company is the indicator of its relevant industry. When a large group has multiple sources of revenue generation, it is considered to be working in different industries. Manufacturing industry became a key sector of production and labour in European and North American countries during the Industrial Revolution, upsetting previous mercantile and feudal economies. This came through many successive rapid advances in technology, such as the production of steel and coal.
In business applications, the emphasis on gaming the behavior of opponents was reduced (shifting more toward a game against nature). At Royal Dutch/Shell for example, scenario planning was viewed as changing mindsets about the exogenous part of the world, prior to formulating specific strategies.
Scenario planning may involve aspects of systems thinking, specifically the recognition that many factors may combine in complex ways to create sometime surprising futures (due to non-linear feedback loops). The method also allows the inclusion of factors that are difficult to formalize, such as novel insights about the future, deep shifts in values, unprecedented regulations or inventions.Systems thinking used in conjunction with scenario planning leads to plausible scenario storylines because the causal relationship between factors can be demonstrated. In these cases when scenario planning is integrated with a systems thinking approach to scenario development, it is sometimes referred to as dynamic scenarios.
Critics of using a subjective and heuristic methodology to deal with uncertainty and complexity argue that the technique has not been examined rigorously, nor influenced sufficiently by scientific evidence. They caution against using such methods to "predict" based on what can be described as arbitrary themes and "forecasting techniques".
Another challenge of scenario-building is that "predictors are part of the social context about which they are trying to make a prediction and may influence that context in the process".As a consequence, societal predictions can become self-destructing. For example, a scenario in which a large percentage of a population will become HIV infected based on existing trends may cause more people to avoid risky behavior and thus reduce the HIV infection rate, invalidating the forecast (which might have remained correct if it had not been publicly known). Or, a prediction that cybersecurity will become a major issue may cause organizations to implement more security cybersecurity measures, thus limiting the issue.
These combinations and permutations of fact and related social changes are called "scenarios". The scenarios usually include plausible, but unexpectedly important situations and problems that exist in some small form in the present day. Any particular scenario is unlikely. However, future studies analysts select scenario features so they are both possible and uncomfortable. Scenario planning help policy-makers and firms to anticipate change, prepare a response and create more robust strategies.
Scenarios help a firm to anticipate the impact of different scenarios identify weaknesses. When disclosed years in advance, these weaknesses can be avoided or their impacts reduced more effectively than if similar real-life problems were considered under duress of an emergency. For example, a company may discover that it needs to change contractual terms to protect against a new class of risks, or collect cash reserves to purchase anticipated technologies or equipment. Flexible business continuity plans with "PREsponse protocols" help cope with similar operational problems and deliver measurable future value-added.
Strategic military intelligence organizations also construct scenarios. The methods and organizations are almost identical, except that scenario planning is applied to a wider variety of problems than merely military and political problems.
As in military intelligence, the chief challenge of scenario planning is to find out the real needs of policy-makers, when policy-makers may not themselves know what they need to know, or may not know how to describe the information that they really want.
Good analysts design wargames so that policy makers have great flexibility and freedom to adapt their simulated organisations.Then these simulated organizations are "stressed" by the scenarios as a game plays out. Usually, particular groups of facts become more clearly important. These insights enable intelligence organizations to refine and repackage real information more precisely to better serve the policy-makers' real-life needs. Usually the games' simulated time runs hundreds of times faster than real life, so policy-makers experience several years of policy decisions, and their simulated effects, in less than a day.
This chief value of scenario planning is that it allows policy-makers to make and learn from mistakes without risking career-limiting failures in real life. Further, policymakers can make these mistakes in a safe, unthreatening, game-like environment, while responding to a wide variety of concretely presented situations based on facts. This is an opportunity to "rehearse the future", an opportunity that does not present itself in day-to-day operations where every action and decision counts.
Scenario planning is also extremely popular with military planners. Most states' department of war maintains a continuously updated series of strategic plans to cope with well-known military or strategic problems. These plans are almost always based on scenarios, and often the plans and scenarios are kept up-to-date by war games, sometimes played out with real troops. This process was first carried out (arguably the method was invented by) the Prussian general staff of the mid-19th century.
Most authors attribute the introduction of scenario planning to Herman Kahn through his work for the US Military in the 1950s at the RAND Corporation where he developed a technique of describing the future in stories as if written by people in the future. He adopted the term "scenarios" to describe these stories. In 1961 he founded the Hudson Institute where he expanded his scenario work to social forecasting and public policy.One of his most controversial uses of scenarios was to suggest that a nuclear war could be won. Though Kahn is often cited as the father of scenario planning, at the same time Kahn was developing his methods at RAND, Gaston Berger was developing similar methods at the Centre d’Etudes Prospectives which he founded in France. His method, which he named 'La Prospective', was to develop normative scenarios of the future which were to be used as a guide in formulating public policy. During the mid-1960s various authors from the French and American institutions began to publish scenario planning concepts such as 'La Prospective' by Berger in 1964 and 'The Next Thirty-Three Years' by Kahn and Wiener in 1967. By the 1970s scenario planning was in full swing with a number of institutions now established to provide support to business including the Hudson Foundation, the Stanford Research Institute (now SRI International), and the SEMA Metra Consulting Group in France. Several large companies also began to embrace scenario planning including DHL Express, Dutch Royal Shell and General Electric.
Possibly as a result of these very sophisticated approaches, and of the difficult techniques they employed (which usually demanded the resources of a central planning staff), scenarios earned a reputation for difficulty (and cost) in use. Even so, the theoretical importance of the use of alternative scenarios, to help address the uncertainty implicit in long-range forecasts, was dramatically underlined by the widespread confusion which followed the Oil Shock of 1973. As a result, many of the larger organizations started to use the technique in one form or another. By 1983 Diffenbach reported that 'alternate scenarios' were the third most popular technique for long-range forecasting – used by 68% of the large organizations he surveyed.
Practical development of scenario forecasting, to guide strategy rather than for the more limited academic uses which had previously been the case, was started by Pierre Wack in 1971 at the Royal Dutch Shell group of companies – and it, too, was given impetus by the Oil Shock two years later. Shell has, since that time, led the commercial world in the use of scenarios – and in the development of more practical techniques to support these. Indeed, as – in common with most forms of long-range forecasting – the use of scenarios has (during the depressed trading conditions of the last decade) reduced to only a handful of private-sector organisations, Shell remains almost alone amongst them in keeping the technique at the forefront of forecasting.
There has only been anecdotal evidence offered in support of the value of scenarios, even as aids to forecasting; and most of this has come from one company – Shell. In addition, with so few organisations making consistent use of them – and with the timescales involved reaching into decades – it is unlikely that any definitive supporting evidenced will be forthcoming in the foreseeable future. For the same reasons, though, a lack of such proof applies to almost all long-range planning techniques. In the absence of proof, but taking account of Shell's well documented experiences of using it over several decades (where, in the 1990s, its then CEO ascribed its success to its use of such scenarios), can be significant benefit to be obtained from extending the horizons of managers' long-range forecasting in the way that the use of scenarios uniquely does.
In the 1970s, many energy companies were surprised by both environmentalism and the OPEC cartel, and thereby lost billions of dollars of revenue by mis-investment. The dramatic financial effects of these changes led at least one organization, Royal Dutch Shell, to implement scenario planning. The analysts of this company publicly estimated that this planning process made their company the largest in the world. [ who? ] of Shell's use of scenario planning have suggested that few if any significant long term business advantages accrued to Shell from the use of scenario methodology[ citation needed ]. Whilst the intellectual robustness of Shell's long term scenarios was seldom in doubt their actual practical use was seen as being minimal by many senior Shell executives[ citation needed ]. A Shell insider has commented "The scenario team were bright and their work was of a very high intellectual level. However neither the high level "Group scenarios" nor the country level scenarios produced with operating companies really made much difference when key decisions were being taken".[ citation needed ]However other observers
The use of scenarios was audited by Arie de Geus's team in the early 1980s and they found that the decision-making processes following the scenarios were the primary cause of the lack of strategic implementation [ clarification needed ]), rather than the scenarios themselves. Many practitioners today spend as much time on the decision-making process as on creating the scenarios themselves.
Although scenario planning has gained much adherence in industry, its subjective and heuristic nature leaves many academics uncomfortable. How do we know if we have the right scenarios? And how do we go from scenarios to decisions? These concerns are legitimate and scenario planning would gain in academic standing if more research were conducted on its comparative performance and underlying theoretical premises. A collection of chapters by noted scenario plannersfailed to contain a single reference to an academic source, though this may be because academics have not caught up or do not have the resources to either do or teach scenario planning. In general, there are few academically validated analyses of scenario planning (for a notable exception, see Paul J. H. Schoemaker ). The technique was born from practice and its appeal is based more on experience than scientific evidence. Furthermore, significant misconceptions remain about its intent and claims. Above all, scenario planning is a tool for collective learning, reframing perceptions and preserving uncertainty when the latter is pervasive. Too many decision makers want to bet on one future scenario, falling prey to the seductive temptation of trying to predict the future rather than to entertain multiple futures. Another trap is to take the scenarios too literally as though they were static beacons that map out a fixed future. In actuality, their aim is to bound the future but in a flexible way that permits learning and adjustment as the future unfolds.
One criticism of the two-by-two technique commonly used is that the resulting matrix results in four somewhat arbitrary scenario themes. If other key uncertainties had been selected, it might be argued, very different scenarios could emerge. How true this is depends on whether the matrix is viewed as just a starting point to be superseded by the ensuing blueprint or is considered as the grand architecture that nests everything else. In either case, however, the issue should not be which are the “right” scenarios but rather whether they delineate the range of possible future appropriately. Any tool that tries to simplify a complex picture will introduce distortions, whether it is a geographic map or a set of scenarios. Seldom will complexity decompose naturally into simple states. But it might. Consider, for example, the behavior of water (the molecule H2O) which, depending on temperature and pressure, naturally exists in just one of three states: gas, liquid or ice. The art of scenarios is to look for such natural states or points of bifurcation in the behavior of a complex system.
Apart from some inherent subjectivity in scenario design, the technique can suffer from various process and content traps.These traps mostly relate to how the process is conducted in organizations (such as team composition, role of facilitators, etc.) as well as the substantive focus of the scenarios (long vs. short term, global vs. regional, incremental vs. paradigm shifting, etc.). One might think of these as merely challenges of implementation, but since the process component is integral to the scenario experience, they can also be viewed as weaknesses of the methodology itself. Limited safeguards exist against political derailing, agenda control, myopia and limited imagination when conducting scenario planning exercises within real organizations. But, to varying extents, all forecasting techniques will suffer from such organizational limitations. The benchmark to use is not perfection, especially when faced with high uncertainty and complexity, or even strict adherence to such normative precepts as procedural invariance and logical consistency, but whether the technique performs better than its rivals. And to answer this question fairly, performance must be carefully specified. It should clearly include some measures of accuracy as well as a cost-benefit analysis that considers the tradeoff between effort and accuracy. In addition, legitimation criteria may be important to consider as well as the ability to refine and improve the approach as more experience is gained.
A third limitation of scenario planning in organizational settings is its weak integration into other planning and forecasting techniques. Most companies have plenty of trouble dealing with just one future, let alone multiple ones. Typically, budgeting and planning systems are predicated on single views of the future, with adjustments made as necessary through variance analysis, contingency planning, rolling budgets, and periodic renegotiations. The weaknesses of these traditional approaches were very evident after the tragic attack of September 11, 2001 when many companies became paralyzed and quite a few just threw away the plan and budget. Their strategies were not future-proof and they lacked organized mechanisms to adjust to external turmoil. In cases of crisis, leadership becomes important but so does some degree of preparedness. Once the scenarios are finished, the real works starts of how to craft flexible strategies and appropriate monitoring systems.Managers need a simple but comprehensive compass to navigate uncertainty from beginning to end. Scenario planning is just one component of a more complete management system. The point is that scenario thinking needs to be integrated with the existing planning and budgeting system, as awkward as this fit may be. The reality is that most organizations do not handle uncertainty well and that researchers have not provided adequate answers about how to plan under conditions of high uncertainty and complexity.
The basic concepts of the process are relatively simple. In terms of the overall approach to forecasting, they can be divided into three main groups of activities (which are, generally speaking, common to all long range forecasting processes):
The first of these groups quite simply comprises the normal environmental analysis. This is almost exactly the same as that which should be undertaken as the first stage of any serious long-range planning. However, the quality of this analysis is especially important in the context of scenario planning.
The central part represents the specific techniques – covered here – which differentiate the scenario forecasting process from the others in long-range planning.
The final group represents all the subsequent processes which go towards producing the corporate strategy and plans. Again, the requirements are slightly different but in general they follow all the rules of sound long-range planning.
The part of the overall process which is radically different from most other forms of long-range planning is the central section, the actual production of the scenarios. Even this, though, is relatively simple, at its most basic level. As derived from the approach most commonly used by Shell,it follows six steps:
The first stage is to examine the results of environmental analysis to determine which are the most important factors that will decide the nature of the future environment within which the organisation operates. These factors are sometimes called 'variables' (because they will vary over the time being investigated, though the terminology may confuse scientists who use it in a more rigorous manner). Users tend to prefer the term 'drivers' (for change), since this terminology is not laden with quasi-scientific connotations and reinforces the participant's commitment to search for those forces which will act to change the future. Whatever the nomenclature, the main requirement is that these will be informed assumptions.
This is partly a process of analysis, needed to recognise what these 'forces' might be. However, it is likely that some work on this element will already have taken place during the preceding environmental analysis. By the time the formal scenario planning stage has been reached, the participants may have already decided – probably in their sub-conscious rather than formally – what the main forces are.
In the ideal approach, the first stage should be to carefully decide the overall assumptions on which the scenarios will be based. Only then, as a second stage, should the various drivers be specifically defined. Participants, though, seem to have problems in separating these stages.
Perhaps the most difficult aspect though, is freeing the participants from the preconceptions they take into the process with them. In particular, most participants will want to look at the medium term, five to ten years ahead rather than the required longer-term, ten or more years ahead. However, a time horizon of anything less than ten years often leads participants to extrapolate from present trends, rather than consider the alternatives which might face them. When, however, they are asked to consider timescales in excess of ten years they almost all seem to accept the logic of the scenario planning process, and no longer fall back on that of extrapolation. There is a similar problem with expanding participants horizons to include the whole external environment.
In any case, the brainstorming which should then take place, to ensure that the list is complete, may unearth more variables – and, in particular, the combination of factors may suggest yet others.
A very simple technique which is especially useful at this – brainstorming – stage, and in general for handling scenario planning debates is derived from use in Shell where this type of approach is often used. An especially easy approach, it only requires a conference room with a bare wall and copious supplies of 3M Post-It Notes.
The six to ten people ideally taking part in such face-to-face debates should be in a conference room environment which is isolated from outside interruptions. The only special requirement is that the conference room has at least one clear wall on which Post-It notes will stick. At the start of the meeting itself, any topics which have already been identified during the environmental analysis stage are written (preferably with a thick magic marker, so they can be read from a distance) on separate Post-It Notes. These Post-It Notes are then, at least in theory, randomly placed on the wall. In practice, even at this early stage the participants will want to cluster them in groups which seem to make sense. The only requirement (which is why Post-It Notes are ideal for this approach) is that there is no bar to taking them off again and moving them to a new cluster.
A similar technique – using 5" by 3" index cards – has also been described (as the 'Snowball Technique'), by Backoff and Nutt, for grouping and evaluating ideas in general.
As in any form of brainstorming, the initial ideas almost invariably stimulate others. Indeed, everyone should be encouraged to add their own Post-It Notes to those on the wall. However it differs from the 'rigorous' form described in 'creative thinking' texts, in that it is much slower paced and the ideas are discussed immediately. In practice, as many ideas may be removed, as not being relevant, as are added. Even so, it follows many of the same rules as normal brainstorming and typically lasts the same length of time – say, an hour or so only.
It is important that all the participants feel they 'own' the wall – and are encouraged to move the notes around themselves. The result is a very powerful form of creative decision-making for groups, which is applicable to a wide range of situations (but is especially powerful in the context of scenario planning). It also offers a very good introduction for those who are coming to the scenario process for the first time. Since the workings are largely self-evident, participants very quickly come to understand exactly what is involved.
Important and uncertain
This step is, though, also one of selection – since only the most important factors will justify a place in the scenarios. The 80:20 Rule here means that, at the end of the process, management's attention must be focused on a limited number of most important issues. Experience has proved that offering a wider range of topics merely allows them to select those few which interest them, and not necessarily those which are most important to the organisation.
In addition, as scenarios are a technique for presenting alternative futures, the factors to be included must be genuinely 'variable'. They should be subject to significant alternative outcomes. Factors whose outcome is predictable, but important, should be spelled out in the introduction to the scenarios (since they cannot be ignored). The Important Uncertainties Matrix, as reported by Kees van der Heijden of Shell, is a useful check at this stage.
At this point it is also worth pointing out that a great virtue of scenarios is that they can accommodate the input from any other form of forecasting. They may use figures, diagrams or words in any combination. No other form of forecasting offers this flexibility.
The next step is to link these drivers together to provide a meaningful framework. This may be obvious, where some of the factors are clearly related to each other in one way or another. For instance, a technological factor may lead to market changes, but may be constrained by legislative factors. On the other hand, some of the 'links' (or at least the 'groupings') may need to be artificial at this stage. At a later stage more meaningful links may be found, or the factors may then be rejected from the scenarios. In the most theoretical approaches to the subject, probabilities are attached to the event strings. This is difficult to achieve, however, and generally adds little – except complexity – to the outcomes.
This is probably the most (conceptually) difficult step. It is where managers' 'intuition' – their ability to make sense of complex patterns of 'soft' data which more rigorous analysis would be unable to handle – plays an important role. There are, however, a range of techniques which can help; and again the Post-It-Notes approach is especially useful:
Thus, the participants try to arrange the drivers, which have emerged from the first stage, into groups which seem to make sense to them. Initially there may be many small groups. The intention should, therefore, be to gradually merge these (often having to reform them from new combinations of drivers to make these bigger groups work). The aim of this stage is eventually to make 6–8 larger groupings; 'mini-scenarios'. Here the Post-It Notes may be moved dozens of times over the length – perhaps several hours or more – of each meeting. While this process is taking place the participants will probably want to add new topics – so more Post-It Notes are added to the wall. In the opposite direction, the unimportant ones are removed (possibly to be grouped, again as an 'audit trail' on another wall). More important, the 'certain' topics are also removed from the main area of debate – in this case they must be grouped in clearly labelled area of the main wall.
As the clusters – the 'mini-scenarios' – emerge, the associated notes may be stuck to each other rather than individually to the wall; which makes it easier to move the clusters around (and is a considerable help during the final, demanding stage to reducing the scenarios to two or three).
The great benefit of using Post-It Notes is that there is no bar to participants changing their minds. If they want to rearrange the groups – or simply to go back (iterate) to an earlier stage – then they strip them off and put them in their new position.
The outcome of the previous step is usually between seven and nine logical groupings of drivers. This is usually easy to achieve. The 'natural' reason for this may be that it represents some form of limit as to what participants can visualise.
Having placed the factors in these groups, the next action is to work out, very approximately at this stage, what is the connection between them. What does each group of factors represent?
The main action, at this next stage, is to reduce the seven to nine mini-scenarios/groupings detected at the previous stage to two or three larger scenarios
There is no theoretical reason for reducing to just two or three scenarios, only a practical one. It has been found that the managers who will be asked to use the final scenarios can only cope effectively with a maximum of three versions! Shell started, more than three decades ago, by building half a dozen or more scenarios – but found that the outcome was that their managers selected just one of these to concentrate on. As a result, the planners reduced the number to three, which managers could handle easily but could no longer so easily justify the selection of only one! This is the number now recommended most frequently in most of the literature.
As used by Shell, and as favoured by a number of the academics, two scenarios should be complementary; the reason being that this helps avoid managers 'choosing' just one, 'preferred', scenario – and lapsing once more into single-track forecasting (negating the benefits of using 'alternative' scenarios to allow for alternative, uncertain futures). This is, however, a potentially difficult concept to grasp, where managers are used to looking for opposites; a good and a bad scenario, say, or an optimistic one versus a pessimistic one – and indeed this is the approach (for small businesses) advocated by Foster. In the Shell approach, the two scenarios are required to be equally likely, and between them to cover all the 'event strings'/drivers. Ideally they should not be obvious opposites, which might once again bias their acceptance by users, so the choice of 'neutral' titles is important. For example, Shell's two scenarios at the beginning of the 1990s were titled 'Sustainable World' and 'Global Mercantilism'[xv]. In practice, we found that this requirement, much to our surprise, posed few problems for the great majority, 85%, of those in the survey; who easily produced 'balanced' scenarios. The remaining 15% mainly fell into the expected trap of 'good versus bad'. We have found that our own relatively complex (OBS) scenarios can also be made complementary to each other; without any great effort needed from the teams involved; and the resulting two scenarios are both developed further by all involved, without unnecessary focusing on one or the other.
Having grouped the factors into these two scenarios, the next step is to test them, again, for viability. Do they make sense to the participants? This may be in terms of logical analysis, but it may also be in terms of intuitive 'gut-feel'. Once more, intuition often may offer a useful – if academically less respectable – vehicle for reacting to the complex and ill-defined issues typically involved. If the scenarios do not intuitively 'hang together', why not? The usual problem is that one or more of the assumptions turns out to be unrealistic in terms of how the participants see their world. If this is the case then you need to return to the first step – the whole scenario planning process is above all an iterative one (returning to its beginnings a number of times until the final outcome makes the best sense).
The scenarios are then 'written up' in the most suitable form. The flexibility of this step often confuses participants, for they are used to forecasting processes which have a fixed format. The rule, though, is that you should produce the scenarios in the form most suitable for use by the managers who are going to base their strategy on them. Less obviously, the managers who are going to implement this strategy should also be taken into account. They will also be exposed to the scenarios, and will need to believe in these. This is essentially a 'marketing' decision, since it will be very necessary to 'sell' the final results to the users. On the other hand, a not inconsiderable consideration may be to use the form the author also finds most comfortable. If the form is alien to him or her the chances are that the resulting scenarios will carry little conviction when it comes to the 'sale'.
Most scenarios will, perhaps, be written in word form (almost as a series of alternative essays about the future); especially where they will almost inevitably be qualitative which is hardly surprising where managers, and their audience, will probably use this in their day to day communications. Some, though use an expanded series of lists and some enliven their reports by adding some fictional 'character' to the material – perhaps taking literally the idea that they are stories about the future – though they are still clearly intended to be factual. On the other hand, they may include numeric data and/or diagrams – as those of Shell do (and in the process gain by the acid test of more measurable 'predictions').
The final stage of the process is to examine these scenarios to determine what are the most critical outcomes; the 'branching points' relating to the 'issues' which will have the greatest impact (potentially generating 'crises') on the future of the organisation. The subsequent strategy will have to address these – since the normal approach to strategy deriving from scenarios is one which aims to minimise risk by being 'robust' (that is it will safely cope with all the alternative outcomes of these 'life and death' issues) rather than aiming for performance (profit) maximisation by gambling on one outcome.
It is important to note that scenarios may be used in a number of ways:
a) Containers for the drivers/event strings
Most basically, they are a logical device, an artificial framework, for presenting the individual factors/topics (or coherent groups of these) so that these are made easily available for managers' use – as useful ideas about future developments in their own right – without reference to the rest of the scenario. It should be stressed that no factors should be dropped, or even given lower priority, as a result of producing the scenarios. In this context, which scenario contains which topic (driver), or issue about the future, is irrelevant.
b) Tests for consistency
At every stage it is necessary to iterate, to check that the contents are viable and make any necessary changes to ensure that they are; here the main test is to see if the scenarios seem to be internally consistent – if they are not then the writer must loop back to earlier stages to correct the problem. Though it has been mentioned previously, it is important to stress once again that scenario building is ideally an iterative process. It usually does not just happen in one meeting – though even one attempt is better than none – but takes place over a number of meetings as the participants gradually refine their ideas.
c) Positive perspectives
Perhaps the main benefit deriving from scenarios, however, comes from the alternative 'flavors' of the future their different perspectives offer. It is a common experience, when the scenarios finally emerge, for the participants to be startled by the insight they offer – as to what the general shape of the future might be – at this stage it no longer is a theoretical exercise but becomes a genuine framework (or rather set of alternative frameworks) for dealing with that.
The flowchart to the right provides a process for classifying a phenomena as a scenario in the intuitive logics tradition.
Scenario planning differs from contingency planning, sensitivity analysis and computer simulations.
Contingency planning is a "What if" tool, that only takes into account one uncertainty. However, scenario planning considers combinations of uncertainties in each scenario. Planners also try to select especially plausible but uncomfortable combinations of social developments.
Sensitivity analysis analyzes changes in one variable only, which is useful for simple changes, while scenario planning tries to expose policy makers to significant interactions of major variables.
While scenario planning can benefit from computer simulations, scenario planning is less formalized, and can be used to make plans for qualitative patterns that show up in a wide variety of simulated events.
During the past 5 years, computer supported Morphological Analysis has been employed as aid in scenario development by the Swedish Defence Research Agency in Stockholm.This method makes it possible to create a multi-variable morphological field which can be treated as an inference model – thus integrating scenario planning techniques with contingency analysis and sensitivity analysis.
Scenario planning concerns planning based on the systematic examination of the future by picturing plausible and consistent images of that future. The Delphi method attempts to develop systematically expert opinion consensus concerning future developments and events. It is a judgmental forecasting procedure in the form of an anonymous, written, multi-stage survey process, where feedback of group opinion is provided after each round.
Numerous researchers have stressed that both approaches are best suited to be combined.Due to their process similarity, the two methodologies can be easily combined. The output of the different phases of the Delphi method can be used as input for the scenario method and vice versa. A combination makes a realization of the benefits of both tools possible. In practice, usually one of the two tools is considered the dominant methodology and the other one is added on at some stage.
The variant that is most often found in practice is the integration of the Delphi method into the scenario process (see e.g. Rikkonen, 2005;von der Gracht, 2008; ). Authors refer to this type as Delphi-scenario (writing), expert-based scenarios, or Delphi panel derived scenarios. Von der Gracht (2010) is a scientifically valid example of this method. Since scenario planning is “information hungry”, Delphi research can deliver valuable input for the process. There are various types of information output of Delphi that can be used as input for scenario planning. Researchers can, for example, identify relevant events or developments and, based on expert opinion, assign probabilities to them. Moreover, expert comments and arguments provide deeper insights into relationships of factors that can, in turn, be integrated into scenarios afterwards. Also, Delphi helps to identify extreme opinions and dissent among the experts. Such controversial topics are particularly suited for extreme scenarios or wildcards.
In his doctoral thesis, Rikkonen (2005)examined the utilization of Delphi techniques in scenario planning and, concretely, in construction of scenarios. The author comes to the conclusion that the Delphi technique has instrumental value in providing different alternative futures and the argumentation of scenarios. It is therefore recommended to use Delphi in order to make the scenarios more profound and to create confidence in scenario planning. Further benefits lie in the simplification of the scenario writing process and the deep understanding of the interrelations between the forecast items and social factors.
A prediction, or forecast, is a statement about a future event. A prediction is often, but not always, based upon experience or knowledge. There is no universal agreement about the exact difference between the two terms; different authors and disciplines ascribe different connotations.
The Delphi method is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts. The technique can also be adapted for use in face-to-face meetings, and is then called mini-Delphi or Estimate-Talk-Estimate (ETE). Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.
Futures studies, also called futurology, is the study of postulating possible, probable, and preferable futures and the worldviews and myths that underlie them. In general, it can be considered as a branch of the social sciences and parallel to the field of history. Futures studies seeks to understand what is likely to continue and what could plausibly change. Part of the discipline thus seeks a systematic and pattern-based understanding of past and present, and to determine the likelihood of future events and trends.
A technology roadmap is a flexible planning technique to support strategic and long-range planning, by matching short-term and long-term goals with specific technology solutions. It is a plan that applies to a new product or process and may include using technology forecasting/technology scouting to identify suitable emerging technologies. It is a known technique to help manage the fuzzy front-end of innovation. It is also expected that roadmapping techniques may help companies to survive in turbulent environments and help them to plan in a more holistic way to include non-financial goals and drive towards a more sustainable development. Here roadmaps can be combined with other corporate foresight methods to facilitate systemic change.
Strategic foresight is a planning-oriented discipline related to futures studies, the study of the future. In a business context, a more action-oriented approach has become well known as corporate foresight.
In futurology, especially in Europe, the term "foresight" has become widely used to describe activities such as:
Futures techniques used in the multi-disciplinary field of futures studies by futurists in Americas and Australasia, and futurology by futurologists in EU, include a diverse range of forecasting methods, including anticipatory thinking, backcasting, simulation, and visioning. Some of the anticipatory methods include, the delphi method, causal layered analysis, environmental scanning, morphological analysis, and scenario planning.
Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques.
Strategic thinking is defined as a mental or thinking process applied by an individual in the context of achieving a goal or set of goals in a game or other endeavor. As a cognitive activity, it produces thought.
Backcasting is a planning method that starts with defining a desirable future and then works backwards to identify policies and programs that will connect that specified future to the present. The fundamentals of the method were outlined by John B. Robinson from the University of Waterloo in 1990. The fundamental question of backcasting asks: "if we want to attain a certain goal, what actions must be taken to get there?"
Formalized long-range business planning, in particular that taught as a discipline in business schools or just reported in business books, has a history that goes back to the mid 20th century.
Corporate foresight has been conceptualised as a set of practices, a set of capabilities and an ability of a firm. It enables firms to detect discontinuous change early, interpret its consequences for the firm, and inform future courses of action to ensure the long-term survival and success of the company.
Real-time Delphi (RTD) is an advanced form of the Delphi method. The advanced method “is a consultative process that uses computer technology” to increase efficiency of the Delphi process.
Cross-impact analysis is a methodology developed by Theodore Gordon and Olaf Helmer in 1966 to help determine how relationships between events would impact resulting events and reduce uncertainty in the future. The Central Intelligence Agency (CIA) became interested in the methodology in the late 1960s and early 1970s as an analytic technique for predicting how different factors and variables would impact future decisions. In the mid-1970s, futurists began to use the methodology in larger numbers as a means to predict the probability of specific events and determine how related events impacted one another. By 2006, cross-impact analysis matured into a number of related methodologies with uses for businesses and communities as well as futurists and intelligence analysts.
The analysis of the global environment of a company is called global environmental analysis. This analysis is part of a company's analysis-system, which also comprises various other analyses, like the industry analysis, the market analysis and the analyses of companies, clients and competitors. This system can be divided into a macro and micro level. Except for the global environmental analysis, all other analyses can be found on the micro level. Though, the global environmental analysis describes the macro environment of a company. A company is influenced by its environment. Many environmental factors, especially economical or social factors, play a big role in a company's decisions, because the analysis and the monitoring of those factors reveal chances and risks for the company's business. This environmental framework also gives information about location issues. A company is thereby able to determine its location sites. Furthermore, many other strategic decisions are based on this analysis. One may also apply the BBW model. In addition, the factors are analyzed to evaluate external business developments. It is finally the task of the management to adapt the firm to its environment or to influence the environment in an adequate way. The latter is mostly the more difficult option. There are different instruments to analyze the company's environment which are going to be explained afterwards.
Transition scenarios are descriptions of future states which combine a future image with an account of the changes that would need to occur to reach that future. These two elements are often created in a two-step process where the future image is created first (envisioning) followed by an exploration of the alternative pathways available to reach the future goal (backcasting). Both these processes can use participatory techniques where participants of varying backgrounds and interests are provided with an open and supportive group environment to discuss different contributing elements and actions.
Threatcasting is a conceptual framework used to help multidisciplinary groups envision future scenarios. It is also a process that enables systematic planning against threats ten years in the future. Utilizing the threatcasting process, groups explore possible future threats and how to transform the future they desire into reality while avoiding undesired futures. Threatcasting is a continuous, multiple-step process with inputs from social science, technical research, cultural history, economics, trends, expert interviews, and science fiction storytelling. These inputs inform the exploration of potential visions of the future.
Strategic planning and uncertainty intertwine in a realistic framework where companies and organizations are bounded to develop and compete in a world dominated by complexity, ambiguity, and uncertainty in which unpredictable, unstoppable and, sometimes, meaningless circumstances may have a direct impact on the expected outcomes. In this scenario, formal planning systems are criticized by a number of academics, who argue that conventional methods, based on classic analytical tools, fail to shape a strategy that can adjust to the changing market and enhance the competitiveness of each business unit, which is the basic principle of a competitive business strategy. Strategy planning systems are supposed to produce the best approaches to concretize long term objectives. However, since strategy deals with the upcoming future, the strategic context of an organization will always be uncertain, therefore the first choice an organisation has to make is when to act; acting now or when the uncertainty has been resolved.