Strategic planning methods include a wide variety of analysis and decision making tools and techniques, all of which contribute to an organization designing its future. They are a way of answering the question, "Where should we be going and how will we get there?" Strategic planning, as distinct from other more limited forms of planning, is usually about the big decisions organizations face about their future. It is strategic in that it involves decisions and actions with major consequences that extend over long time periods, and attends to the short and long term environmental factors that may affect events.

Many of the techniques and tools described elsewhere in this guidebook would be included in virtually any discussion of strategic planning. So this section describes some useful methods we haven't already covered: scenario building, forecasting, and modeling.

Simplified reality for testing. These kinds of models provide a simplified version of reality against which to test ideas and explore consequences. They are most useful in the kind of complex situations characteristic of justice systems and their information flows.

Explore possible actions. A model can be a very powerful tool to explore possible courses of action or decisions. Consequences can be explored in hypothetical rather than real situations, so the costs of errors or bad decisions are limited.

Common understanding. The development of models also provides a way of creating a shared understanding of complex systems among those that work in them. This shared understanding can be of great value as an aid in collaboration.

Scenario building. This is a process of designing a hypothetical situation in a way that helps you predict the consequences of decisions and actions. For example, Massachusetts has proposed legislation to require all state agencies to consult a database of outstanding arrest warrants when a citizen is seeking a service or benefit. Officials could examine the possible consequences of such a new policy by creating a scenario. This scenario would assume reasonable values for the number of times the policy would generate arrests of various types, and compute the increased demand on jails or law enforcement officers.

Forecasting. This tool is also used to predict future events, but it uses calculations based on historical data. Forecasting typically uses data that have been collected on some events over time and uses them to project trends into the future. Populations, crime statistics, and budgets often have ample historical data for forecasting. The mathematical models used in forecasting may take into account the forces that influence trends to adjust the predictions.

Modeling. This includes a wide variety of techniques to represent a process or problem in some way that leads to predicting behavior or finding solutions. Graphical or qualitative models represent problems in conceptual terms, such as flows, resources, information, causal relationships, or abstract structures (such as semantic or social relationships). Quantitative models represent the problem in some mathematical form that allows calculating interactions or outputs. Qualitative models require clear identification of concepts, relationships, and interactions. Quantitative models require all that plus measurements of some kind as the basis for calculations. Models that take into account the feedback of effects from one part of a system to another are called system dynamics models. While usually quite complicated, these models provide a way to explore the dynamic interactions that are not represented in other techniques.

Require advanced technical skills. The kinds of models described here require relatively high levels of technical skill for their construction and interpretation. If these skills are not available in your organization, it will require the intervention of external experts, usually at considerable cost.

Quality depends on data. In addition, the quality of the analysis resulting from the model is no better than the model itself and the data on which it is based. Careful testing and validation are necessary to avoid conclusions or actions based on a flawed model.

Presentation, communication. Models of this sort often pose problems of presentation and communication as well. They frequently involve complex mathematical operations or graphic images that are hard to understand and explain to non-technical audiences. A well-designed interpretation and presentation must accompany the modeling work for non- technical audiences and policy makers.