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Arthur J. Drucker provides advice, direct support and capacity building in decision and risk modeling applied to strategic management. Modeling and simulation are key tools in strategic decision-making, increasing the manager’s ability to formulate, analyze, and solve complex problems and to exploit the best opportunities for growth and profitability. The following are examples of themes to which Arthur renders support:
– Development and selection of new strategies
– Changes to existing strategies
– Prioritization and selection of markets and business opportunities; resource allocation
– Selection of staff, agents and distributors
– Expansion into new products
– Selection of production location
– Alliances, acquisitions and outsourcing across value chains
– Strategic cost reduction
– Integration of economic, social and environmental interests.
Often used in combination, Arthur’s methodologies include:
- Multi-Criteria Decision Analysis (MCDA), consists of a collection of approaches to formally evaluate decision alternatives according to a set of multiple objectives. From simplified versions (as SMART) to more elaborate ones, MCDA methodologies allow handling of conflicting objectives and of factors whose impacts are difficult to directly measure in monetary or quantitative terms. MCDA approaches can be used in the
- selection of alternatives when multiple objectives exist (e.g., choosing marketing and distribution strategies, making tactical or strategic changes in the marketing mix, selecting staff);
- prioritization of alternatives when multiple objectives exist (e.g. prioritization of markets, resource allocation, strategic cost reduction).
- Cost-Benefit Analysis (CBA) is used in the selection or prioritization of alternatives but, as opposed to MCDA, it only focuses on the economic efficiency of an alternative, i.e., it only takes into consideration factors amenable to monetization. It can be part of an MCDA.
- Uncertainty Analysis assesses the reliability of model predictions given uncertainties in model inputs and design.
- Sensitivity Analysis investigates how model outputs vary as a result of variations in model inputs. It is used in the identification of which input factors most affect decision outcomes.
- Monte Carlo Simulation helps managing business risk by modeling uncertainty. It can be part of an uncertainty analysis applied to an MCDA.
- Scenario Analysis facilitates the conception of strategies that will be robust in face of future uncertainties. Used when decision-makers are unable to assess the likelihood of crucial future events. Any strategic analysis can benefit from scenarios.
- Decision Tree Analysis is a relatively simple way of choosing the best course of action when outcomes of sequential events are uncertain.
- Influence Diagrams picture dependencies among events and acts within a decision. Easily revised and altered, influence diagrams can be used as a step to building decision trees.
- Forecasting extrapolates past performance. When there are no appropriate data for extrapolation of future performance, judgmental methods as the Delphi technique can be employed.
- Utility functions allow the decision-maker’s attitude towards risk to be taken into account in the decision model when risk and uncertainty are central concerns.
- Optimization can indicate the best solution to complex business problems subject to constraints, such as identifying the product mix that maximizes profit.
- And several other techniques, forming an arsenal that allows great flexibility in the tailoring of modeling and simulation to the needs existing in each strategic or tactical decision.