Seven Essences of Decision-Making

One thing right away: Decisions are always made at a specific point in time and by people facing specific advantages and disadvantages. Thus, a decision can only be ”right“ at this particular point in time and for that person. Whether this decision sustains for a longer period cannot be projected when it is being made. This insecurity concerning decision outcomes cannot be avoided by avoiding decisions as such. Not taking a decision also represents a decision – to do nothing. In the present economic moment dominated by instability, managers often decide to follow “visual flight rules“ – certainly the worst option.

Vancore helps its clients structure and implement breakthrough decisions. As a specialized management consultancy, we put the focus on the right issues to develop customer led solutions that clients own and are passionate about. Ultimately, clients work with us because they achieve a significantly higher Return on Decision RoD. How do I take the right decision? What do I need to consider? Whom do I have to consult at which time? Vancore has identified seven central aspects which considerably impact the quality of decisions.

1.    People
They might really exist; ingenious single-minded decision-makers, people like Ferdinand Piëch and Jack Welch, who take so-called A1 decisions (i.e., they gather all important information and then decide self-sufficiently). Even though this decision might have been right, who takes care of the next step? Think about it carefully: What kind of expert knowledge do you need? Which interest groups need to be included? Is there resistance in the way? Change Management is not to be mentioned on the final page of a concept only. Start making changes now – right at the beginning of the decision-making process. Gather together the most brilliant people on the team and work on decision-making together.

2.    Facts
NDF – Numbers, data, facts. We live in a world in which everything needs to make sense by the numbers. This leads to the result that often, a flood of data keeps overwhelming CEOs and directors. Whoever offers more data is on the safe side, is well-prepared and wins the presentation battle by pulling out slide 126.
Well, this all sounds nice. How about the relevance of data loads, however? This question is hardly being raised. Only when decision-critical findings emerge from information, the previous analysis and all the preparation were useful.

Be courageous and together with your team address the question of relevance: Which information is really important? Where do we find white spots in the map? Do we know the real causes of past problems? Do we have to know them in order to be able to decide? Go ahead and create a reliable basis of previous and current findings. Learn from the past. Set limits and try to avoid data overload. Do not take neglectful decisions but decisions that are well-balanced.

3.    Systemic Procedure
A process is a logical sequence of certain steps. In the western world, this sequence usually proceeds from left to right, for example, from analysis via conception to implementation. Surprisingly, this logical sequence is followed less frequently the higher the respective decision is located in the corporate hierarchy. In addition, decision makers usually concentrate more on options and recommendations concerning content, not on the process of decision-making.     Often, lively discussions on side-topics therefore end up being mixed with crucial debates on distribution partners, relocations, and expansion strategies.

It makes a lot of sense to define a structured process and to follow this process when making a decision with considerable impact. In the end, one will notice that many topics, if seen in combination, reveal a very different nature than in the case of single inspection. Above all, the works of nobel prize winner Daniel Kahneman show us that big decisions in iteration processes emerge from insight, criteria, and options. Such a successful process is always transparent, comprehensible, and robust enough to be repeated for a number of times. One conclusion therefore remains: A process can only function well if it is adhered to accordingly.

4.    Insight
Many leadership circles equal soccer stadiums: Holding on to the ball, i.e., the total time someone has the floor, is what counts. Options for passing the ball are often ignored. Scoring, i.e., the final decision, is often neglected that way. Force yourself and your management team to draw specific conclusions. Engaging in discussions is nice but what are we to learn from them specifically? Where is the insight? Should we engage in the Chinese market? Which risks are connected to this decision? Which key capabilities are relevant? Does our portfolio need adjustment?

At the decision-making point, the leadership team needs to put its cards on the table by advocating comprehensible and communicable learning points. This is how you structure your decision step by step – seemingly random building blocks form to a structured pyramid. The crucial thing is that you and your employees can trace the decision-making process – it is the basis of successful implementation.

5.    Authenticity
In the realm of “ringi seido“ (Japanese process of decision-making), management simply receives an impetus. Central problems are then handed down to the lower management levels to be solved. This triggers a circulation process including all the respective levels and departments in order to then seek a corporate consensus. This entire process is accompanied by “nemawashi.“ This concept aims at informally involving all necessary people previous to making a decision. In consequence, the actual decision turns into a more or less formal step.
In the West, however, decision-making is usually carried out differently. Although key decision makers and opinion leaders are being informed on a regular basis previous to a meeting, the actual decision itself is being „fought out“ in the meeting itself.
As consultants who steer this process, we notice that the really important topics are only discussed indirectly or not at all. “As long as I do not hurt you, you will not hurt me,“ is a tacit motto which reigns many of these procedures. What we are missing is the passionate discussion of issues, not of persons. Dissent and constructive criticism are healthy. Some companies, such as the BMW Group, have even included dissent as value in their guidelines. It needs to be allowed to struggle for certain key decisions. Authenticity in a discussion culture therefore represents a key factor. How often do you leave a meeting with the awkward feeling that the major roots of problems have not been targeted? This is when a team needs to be honest to itself: Are we really willing to confront the uncomfortable issues/people? Only the addressing of deeper matters and their solving can contribute to a decision-making process.

6.    Common Ground
Everyone looks after themselves first. Unfortunately, this has to be the case, since performance systems often only consider the contribution of individuals or individual functions. However, the individual performance of single people hardly ever advances a corporation. Team process, however, take time. If done well, they create intensive self-identification with the organization and increase the motivation of employees. Team building does not happen accidentally. A short retreat – “we go to the countryside, hammer nails into planks and milk cows” – can trigger such a process. But what is the real value? The crucial question is: how important are culture and values to you?

7.    Consistency
Often, we hear that companies invest 100 euros in conception, idea and strategy while only spending 10 euros for implementation planning and conducting. We do not understand why this is the case. Thus, Peter Drucker’s saying still holds true: “Culture eats strategy for breakfast.“ Paper is patient, as experience teaches us.

Success depends on consistent and stringent acting. And actions here mean sustainably consistent behavior. A strategic project needs to be prioritized within a transparent project portfolio with clear targets, milestones, and resources. This prevents the magical multiplying of “submarine“ projects in the CEO office, i.e., intransparent and costly ventures. In a world of limited resources, thus, saying “yes“ to a project at the same time means saying “no“ to many other things which one, nevertheless, would like to do.

Our Conclusion:
There is not the one recipe for successful decision-making. This is why we also discourage you from following quick and easy checklists (also see Sibony O. for McKinsey in Harvard Business Manager, September 2011).
It is possible, however, to make a decision which is right for oneself, although the final result cannot be projected. Just take our Seven Essences of Decision-Making to heart. Your company and your employees will appreciate it.

Reinhard Vanhöfen
Vancore Group GmbH & Co. KG


How Powerful is Game Theory? Part 1 – A Satanic Game?

In his new book: Ego. Das Spiel des Lebens (The Game of Life), Frank Schirrmacher, famous German columnist and editor of Frankfurter Allgemeine Zeitung, attributes both the collapse of communism and the behavior of humans in modern capitalism to a combination of game theory and advanced computing. According to Schirrmacher, game theory has turned humans into completely rational egoists, running the entire economy in IT-controlled financial markets.

Whereas Ego is quite obviously meant to be an entertaining sensation story rather than a textbook, no business school lecture or book on strategic planning would be complete without a chapter on game theory. Already in their 1944 classic Theory of Games and Economic Behavior, the developers of game theory, John von Neumann and economist Oskar Morgenstern, pointed out the concept’s applicability to corporate strategic decisions.

To estimate the impact game theory can actually have in corporate strategy (to be discussed in part 2 of this article) or in the steering of whole economies, let us take a very brief look at what game theory actually does. Here is hardly the place for a complete introduction to rather large field of game theory. Therefore, we will simply recall some important aspects needed to outline the capabilities and limitations of the concept. To refresh your knowledge in more depth, there is a multitude of resources on the web, from articles or videos to presentations or whole books. The Wikipedia article also is a good starting point on various types and applications of game theory for readers with some memory of the basics.

Game theory models a decision in the form of a game with clearly defined rules, which can be mathematically modeled. The games consist of a specified number of players (typically two players in the games cited as examples) who have to make decisions (typically just one per game), and there are predefined payoffs for each player, which depend on the decisions of all players combined. Two-player games with only one decision can be described in the form of a payoff matrix, in which columns describe the options of one player, rows the options of the other. Each matrix field contains payoffs for each player. Game theory then derives each player’s decision leading to the highest payoffs. Commonly cited examples of games are the prisoner’s dilemma, the chicken game or battle of the sexes.

Various types of complexity can be added to such a game. There can be more players who may or may not have previous knowledge of the other’s decisions. Players can have the aim of cooperating and achieving the highest total payoff, or they can compete and even try to harm each other. There can be different consecutive or simultaneous decisions to make, and the game can be played once or repeatedly. Payoff chances can be the same for each player or different; they can be partially unknown or depend on probability. For games with several rounds, complex strategies can be derived. If a strategy only specifies probabilities for each option, while the actual decisions are made randomly according to these probabilities, it is called a mixed strategy. With the complexity of the game, analytically optimizing strategies becomes increasingly difficult.

Based on these principles, can game theory really deliver what is attributed to it? How powerful is this tool? Starting with Schirrmacher’s book, can game theory be the decision machine for a whole economy he describes?

First of all, Schirrmacher ascribes the economic collapse of the Soviet Union and the whole communist block to the superior use of game theory by the US. That would, however, mean that the Soviet Union’s dwindling economic strength should have been caused by at least some kind of influence from a methodically acting outside competitor. In fact, the omnipresent problems of socialist economies around the world – misallocation of resources, inefficiency, lack of motivation, corruption and nepotism – come from within the system. Trade restrictions were limited to goods with a potential military significance, and at least the East German economy was even kept alive with credits from the West.The only factor in which American influence really massively impacted the Soviet economy was the excessive transfer of resources to the military sector in the nuclear arms race. But did the United States really need intricate decision models to try to stay ahead technologically while maintaining at least a somewhat similar number of weapons as the potential enemy? Obviously not. Did it take game theory to understand the Soviet concept of outnumbering any opponent’s weapons by roughly a factor of three? That was simply the Red Army’s success formula from World War II and easily observable from the 1950s onward. Game theory has to quantify outcomes as payoffs, often in the form of money, at least in terms of utility. Can such a model help to predict the secret decision processes, more often than not driven by personal motives, in the inner circles of the Soviet leadership? Does it contribute anything more valuable than the output of classical political and military intelligence?  There is a reason why the military was much more interested in game theory as a tool for battlefield tactics than in terms of global strategy.

So if game theory contributed little or nothing to the end of communism, how about Schirrmacher’s second hypothesis? Has game theory turned our decision makers into greedy rational egoists ignoring all social responsibility? Indeed, game theory works for decisions to be made based on the payoff matrix, and in the simplest form, the payoffs just correspond to profits. Commentators point out that game theory can lead to cooperative as well as competitive strategies, but cooperative strategies will also be aiming at maximizing individual or shared payoffs.

The actual point is that game theory in no way implies that a decision maker must or even should aim to maximize profits (although if the decision maker is a manager paid by his company’s shareholders waiting for their dividends, there are good arguments that he should, with or without game theory). Game theory attempts to show to a decision maker which strategy should lead to maximizing an abstract payoff. That payoff may be profit, or it may result from any other utility function. For military officers, the payoff may for example correspond to minimizing the loss of own casualties or to the number of civilians evacuated from a danger zone. For a sales manager, it may be the number of products sold or customer satisfaction.

Even if game theory derives a stategy as leading to the maximum payoff, it still does not mean that the decision maker has to follow that strategy. For example, even if the payoff is identical to profit, game theory can for example be used to estimate how much short term profit must be sacrificed to follow a more socially accepted strategy.

In short, game theory is simply one of many decision support tools available to managers and as firmly or loosely linked to profit maximization as any other of these tools.

Where game theory, however, always relies on maximization of the assumed (monetary or other) payoff is to guess the probable decisions to be made by other parties involved, be they competitors or cooperation partners. Without further information on the other parties’ intentions, game theory has to assume they will maximize payoffs – otherwise there will be no basis for any calculation. In a situation where everyone uses game theory, maximizing payoffs should therefore even help the competitors because it makes one’s actions predictable. What that implies for the applicabilitiy of game theory in actual strategic planning will be discussed in part 2 of this article.

Dr. Holm Gero Hümmler
Uncertainty Managers Consulting GmbH

The Role of Databases for Strategic Planning – Some General Remarks

Large databases, traditionally the domain of the financial departments, are increasingly entering the world of strategic planners. Under the label “business intelligence”, database software and data mining tools are marketed to strategic planners, and their acceptance is quite obviously on the rise. Contributing factors could be a change of generations among planners, more user-friendly tools available, increasing technological experience among those contributing data (who often have a background in marketing rather than technology) and a narrowing cultural gap between strategic management and the technology people necessarily involved in setting up and running such databases.

The main driver behind the spread of strategic management information systems, decision support systems and strategic planning cockpits, however, is the decision makers’ insatiable hunger for definitive answers, clear recommendations and solid data. Where traditional strategic conceps like portfolios or SWAT analyses are highly aggregated and deliberately vague in their conclusions, a strategic database can assign aggregated discounted cashflow numbers to a selection of potential future products, based on data from product and region experts across the company. We have to be aware, however, that the origins of such information about the future remain essentially the same: extrapolation, projection, estimates and, more often than not, educated guesses.

Working with databases in strategic planning offers some obvious advantages:

  • Databases help to avoid the chaos of versions and formats that often occurs when strategic information is traded within the company using standard office tools like tables or presentations. The data can be located on a central server or even an external cloud under the control of the corporate IT experts and governed by corporate IT security guidelines. Adequate access rights for the different users can be set individually or by standard rules.
  • Database user interfaces and data mining programs provide convenient tools to aggregate and visualize the gathered data, speeding up the process of generating bite-size information for decision makers and potentially reducing the workload in planning departments typically short of resources.
  • The standardization of data going into the database and the tools employed to fill it force contributors to address a certain minimum of questions in their planning process, adhere to common conventions and summarize their results in a predefined form.
  • Everybody discussing a decision can argue based on one agreed set of data, representing the best available, up-to-date information from experts across the company’s network, which may include external sales partners, market researchers and consultants.

These advantages, however, come at a price:

  • The clarity of versions and formats is not so much the result of the database itself, but of the strictly implemented strategic planning process that necessarily comes with it. If the thoughts behind a changed estimate in the database or a quick summary for an executive still end up being communicated in spreadsheets sent by e-mail, the advantage is eroded and the database becomes just one more data format users have to deal with.
  • The reduced workload resulting from the use of business intelligence tools has to be compared to the additional resources needed to set up and run the systems. The needed expertise will often not be available within the company, and even for the most user-friendly tools, the actual planning cockpits will in many cases be programmed by external consultants.
  • While standardized data structures to be filled define a minimum of questions to be addressed in generating the data, they also discourage any planning going beyond that, which may not fit into the database. Such standardization is particularly detrimental to any qualitative, critical or out-of the-box thinking that could be priceless as an indicator of possible yet unknown threats or as a source of ideas for future growth not included in current planning.
  • The uniform view of the future defined by a planning database tends to reduce the awareness that the actual future will always be uncertain. The fact that the one future (or, at best, the generic base/best/worst case structure) defined in the database has been built from the input of many contributors and has been agreed upon between different departments makes it particularly difficult to argue against the results and ask the necessary “what ifs”.

Some of these challenges can be addressed early in the process of setting up the database. Looking for synergies with database solutions already in use in the company, for example in controlling, can reduce the workload and accelerate the learning curve in the introduction phase. However, it also may introduce a bias towards processes and structures that are not ideal for information that contains estimates for an uncertain future rather than numbers from a well-accounted past. Leaving space for unstructured information within the database costs technical efficiency, but it may end up containing the one piece of information that avoids the need for parallel data exachange by e-mail or the decisive warning about an external threat that might otherwise have been unheard. Asking in time if an external support is to work as a consultant or merely as a programmer can save time and effort later and can avoid implementing potentially inefficient structures.

It is important to be aware that databases, data mining tools and even strategic planning cockpits can be an interesting source of information to be taken into account in a decision, but they are not decision tools. Asking the many “what ifs”, evaluating alternative strategies, testing for different external scenarios or analyzing potential competitors’ strategies can be done including information from such a database, but these, the actually decisive steps of strategic planning, are not done by the database. In most cases, the user interfaces employed are optimized for visualizing what’s in the database and are not even very well suited for interactively calculating the effects of assumptions that go beyond the scope of the underlying data structures.

It is, however, possible to develop tools to interactively calculate the impact of many different “what ifs” on the agreed planning basis, draw all the necessary information from the database and even write results for different scenarios back to the database, usually in separate but linked structures. The implementation will depend on the framework used, which will usually be either relational databases or multidimensional cubes. Furthermore, it depends on whether a separate data mining interface is used to access and visualize the data and if it should also provide the interface to the simulation and calculation tool.

In the upcoming weeks, we will look at two case studies on such interactive planning tools linked to pre-existing databases, both allowing the same scenario and strategic alternative evaluations on the same data, but in different database environments. One will be a relational database accessed through a data mining tool, the other a multidimensional cube providing its own user interface. We will look at similarities and differences of the two implementations and suggest ways to work around their respective limitations.

Dr. Holm Gero Hümmler
Uncertainty Managers Consulting GmbH

Preparing for an Uncertain Future in a Small Company: Coffee Table Talk at the World Skeptics Congress

After my talk at the World Skeptics Congress, I had an interesting conversation with the owner of a small, technology-driven company. The whole conversation lasted no longer than a cup of coffee. The main question was how a small to mid-size company can leverage the principles for dealing with uncertainty that I had outlined at the end of my conference talk. Here are the results of our talk (plus some explanations) in a few quick points:

  1. Work with the knowledge that exists in your company. If you needed outside experts to tell you how your own market works, you would have probably gone out of business already. Outside help may, however, be useful (and sometimes necessary) to moderate the planning and decision process, to calculate financial impacts and to ask critical questions. The more technical and market expertise a business leader displays, especially if he or she is also the owner or founder of the company, the more hesitant many employees may be to bring up risks they see or feel looming on the horizon.
  2. Make it clear what the purpose of the analysis is. Are there strategic decisions to make – if so, what are the options? If you want to test the viability of an ongoing strategy, what are the areas in which you could make adjustments? In addition, define a reasonable time range you want to plan. If you plan to build a production facility, that time range will be much longer than if you develop mobile phone software.
  3. Look at uncertainties inside-out, going from effects to possible causes. The number of things that can happen in the world around you is infinite. The number of significantly different impacts on your business is rather small. Start with the baseline plan for your business – you have one, explicitly or implicitly. Look systematically what could change, for example in a tree structure: Sales or cost could be impacted. On the sales side, demand or your ability to supply could change. A change in demand could come from the market size or your market share. Market size can change via volume or price level. Get rid of branches in the tree that are (even after critical questions) unrealistic, have negligible impact or would not be affected by the strategic options or adjustments you are evaluating. If you can’t prepare for it, there’s no point in planning it. Also, don’t continue into branches that have identical or very similar impact on your actual business.
  4. Identify key drivers of uncertainty and find possible values. Each branch of the tree gives you a driver of uncertainty. Compare their impact and get rid of the minor ones. You should end up with no more than ten key drivers (or ten for each market you are in, if there are several). Then assign two to five possible values each driver could have in the future. Think of normal as well al unusual developments, but try to avoid the generic base/best/worst assumptions.
  5. Condense possible developments into scenarios. The different values of the drivers open up a cone (see graph below) of possible future developments. Scenarios are roads through that cone. The real future will not be identical to any one scenario, but should be somewhere around or between them. A scenario contains one possible value for each driver. Start out by looking for reasonable combinations of values, then build scenarios around them. There should be at least three scenarios, and more than five or six are rarely necessary. At least one scenario should cover the center of the cone of possible developments mentioned above, but others should lead to the more extreme corners, as well.
    Isolate single factors that don’t fit into the scenario logic, either because they are independent from anything else (oil prices or tax rates sometimes fit in that category) or because there is a feedback (for example, in a local market, competitors may react to the strategy you choose). There should be no more than two or three such factors. Keep them separated and at the end of the whole process, check if varying them within reasonable limits changes the results of your analysis.
  6. Derive the impact on your strategy and options. In a larger company, I would develop an interactive business plan simulation to do this, but in smaller companies, a grid of results with manual calculations or estimates should do. One axis of the grid are your strategic options or your current strategy and possible adjustments. The other axis are the scenarios. Write down (with some basic numbers!) where your company will be at the end of the planning period with each combination of strategies and scenarios. If a strategy looks catastrophic in one scenario or survivable but bad in several scenarios, you may want to stay away from it. If you decide on a strategy and find that it runs into trouble in one of the scenarios, derive which of the scenario’s values in the key drivers could function as an early warning indicator.
  7. Do it! Here’s your main advantage over some of the multi-billion-Euro companies out there: Once you have come to a conclusion, actually implement it. Write down which steps you have to take to make it happen and check them off. If you have come up with early warning indicators, hang them on your office wall, put them on your computer desktop or into your calendar at regular intervals and test them. The only bad thing you can do with this analysis is to let it rot in your drawer.

Dr. Holm Gero Hümmler
Uncertainty Managers Consulting GmbH