Strategy #29
Personnel Allocations
Incorporate the use of industry-accepted staffing formulae as a means of determining proper staffing needs, acquisitions, and allocations.
Background
The role of law enforcement is constantly changing. Agencies are and will be experiencing decreases in authorized strength at the same time that calls for service increase. Whether adding new levels of responsibility, or being forced to phase out existing operations due to budget and personnel limitations, adequate staffing to effectively meet changing responsibilities remains a daunting challenge. An accepted staffing formula that takes into account relevant demographics, current responsibilities and personnel resources, and other validated concerns, will do much to help justify additional personnel hiring and allocations.
Actions
- Conduct literature search and analyze current and relevant research in the area of law enforcement staffing formulas.
- Consider traffic safety components in all staffing formulae.
- Consider existing personnel allocation models, e.g., the Northwestern University Traffic Institute Personnel Allocation Model.
- Test selected formulas for applicability to specific agency needs and requirements.
- Lobby for acceptance of the formula and the results it generates among those responsible for funding personnel resources.
- Educate agency personnel in the selected staffing formula to help ensure acceptance.
Benefits
- A staffing formula based on the operational needs of the agency will do much to help justify changes in personnel resource allocations.
- Staffing based on application of accepted formulas provides an objective basis for allocating resources toward identified problems and needs.
Other Considerations
- Use of formulas may yield unrealistic staffing needs that are unattainable in the current political and economic climate.
- Industry-wide acceptance of a single staffing formula may not exist.
- Models need to be customized to agency needs. Issues of civilian staffing may not be considered in existing models.