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This area contains premium content for various aspects of police management. The material is easily accessible and is intended for those who must undertake police planning exercises, budgeting, resource allocation or workload assessments.


Software license codes available to enable discounts for the following groups 

  • Police1 readers

  • National Sheriff's Association members

  • Ohio Association of Chiefs of Police, CLEE graduates

  • Police Staffing Observatory Affiliates

Problems with Workload Analysis and Budget

Police departments continually face issues with staffing, largely owing to their inability to measure their work. A recurring, yet fundamental question pervades the industry: How many police officers do we need? This is inevitably followed by: What is the projected cost? Answering these questions is essential to any planning discussion about staffing. Police agencies often report that they “feel” understaffed, but cannot demonstrate it with empirical data (Wilson & Weiss, 2012). Lack of empirical data can directly affect department efficiency and recruitment efforts. Perceived understaffing may also undercut community policing, and problem-solving efforts, as well as negatively impact officer morale, productivity and community support (Wilson & Weiss, 2014). Even if a police agency can reasonably estimate their workload, they often do not have a rational method for linking work to the costs. This presents an “either or” conundrum for elected and appointed officials—solve the workload or the budget issue, but not both. The Ocean View, Delaware Police Department faced this issue. The police chief wanted to hire additional officers to engage in community policing, and posed this research question: If OVPD increases proactive time from 67% to 75%, then how many officers do we need and what is the projected cost? The City Council required the chief to explain the implications for both workload and costs to justify hiring additional personnel.

Previous research suggests that workload is one of the primary contributing factors to increasing staffing (Greene & Bynum, 1986) and responding to service demands (Laufs et al., 2021). The approach is to quantify the work through a more discrete unit of analysis (hours of work based on specific activities) instead of the traditional larger unit of analysis (organizational divisions) and then assign costs from the existing budget (read here). Linking activities to costs creates a clear nexus between the cost of service and resource allocation. This model comports with the rational-technical theory of organizations, which implies that organizations behave and are structured (Blau & Schoenherr, 1971; Thompson, 1967) in a manner designed to optimize efficiency and effectiveness while concurrently synthesizing the interests of powerful actors who hold sway over the organization (Crank, 2003; Crank & Langworthy, 1992; Moore, 1995). Since the model relies on actual workload and budget data, it is more rational than other staffing models, such as officer:population ratio, officer:crime ratio and minimum staffing based on labor agreements.

Conventional budgeting works well for those activities where there are well-defined input–output relationships insofar as the consumption of resources usually varies proportionately with the volume of final output of services. Flexible budgets can be used to control the costs of these activities. Budgets that are not based on well-understood relationships between activities and costs are poor indicators of performance and performance reporting normally implies little more than checking whether the budget has been exceeded. Therefore, conventional budgets provide little relevant information for managing the costs of activities (e.g., patrol, investigations, communications, traffic..etc.).

The Solution

To manage costs more effectively and evaluate the performance of activities, the principles of activity-based cost management have been extended to budgeting. The distinguishing features of activity-based budgeting (ABB) are: 1) costs are analyzed by activities and expense categories; and 2) physical output measures are identified for each activity. This model solves two recurring public administration issues in a single system: workload analysis and budgeting. This is known as an activity-base budget (ABB). For example, a police patrol division typically consumes the greatest portion of the police budget. But general police patrol is not granular enough to  exercise fiscal and managerial control over those resources. Understanding how patrol officers spend their time and the results they produce is critical to figuring out how to better use police resources. This model establishes a common method for disaggregating police work into discrete modalities for a more accurate budget based on real-world data. This model presented here is consistent with CALEA standard 21.2.4 Workload Assessments (formerly standard 16.1.2), it overcomes some of the budgeting issues identified by the  ICMA (International City/County Management Association), and is a cost-effective way to facilitate the IACP's (International Association of Chiefs of Police) operational workload assessments

The ABB model is a more comprehensive approach to determining appropriate workforce levels that considers actual police workload based on service demands. The workload approach estimates future staffing needs by modelling the current level of actual activity. This can assist with planning for additional resources or relocating existing resources (based on temporal or geographic needs), assessing individual and group performance and productivity, and detecting trends in workload that may reveal changes in activity levels. Moreover, workload assessments can be performed at every level of the agency and for all key functions, which is what the model presented here offers. By distributing time across three categories--reactive, proactive, and administrative--managers can establish a baseline minimum staffing posture, then incrementally increase effective strength based on agency priorities  (i.e., proactive time, administrative time). The baseline ABB model can be combined with fixed-position allocation (e.g., specific posts that must be staffed) and cyclical factors (e.g., holiday events, carnivals, parades, festivals) for a complete staffing picture. The ABB is an opportunity for the agency to capitalize on existing data elements (workload and budget), which facilitates better planning and decisions in public safety agencies.



  1. Ideal as a presentation tool, and conversation starter for a police executives (chief, commissioner, sheriff, superintendent, directors, command staff members), elected and appointed officials (mayors, county executives, business administrators, legislative bodies) and those responsible for workload analysis, organizational planning, resource allocation and budgeting.
  2. Excellent as a planning tool for determining costs associated with law enforcement contracting services, and consolidation efforts.
  3. Useful for grant applications between organizations including detailed planning, cost allocation, and resource optimization (Pareto analysis).
  4. Workload insights allow better decision making, and better resource allocation to support agency priorities. The model permits balancing operational requirements. Since it generates a budget from workload and resources, it can highlight sources of imbalances, inefficiencies, and other areas for improvements. This enables supervisors to correct inefficiencies.
  5. Supervisors and employees can discuss workload in operational terms instead of financial terms. Since the model provides a clearer picture of how resources and workload are related, it can help supervisors and employees understand and communicate budget information in more tangible non-financial terms to help them understand better what needs to be done to improve processes, and how to best perform their job. By improving the flow of workload and resources, the ABB makes it easier to evaluate performance by being able to specify who is accountable for certain activities that may overlap in different divisions. The ABB allows supervisors to be more agile in terms of contingency planning, decision making, performance measurement, and evaluation.
  6. Increases traceability and transparency that allows balancing capacity. With a more sophisticated operational model, the ABB enables a richer set of tools for balancing capacity. It becomes easier to adjust demands or changes in the amount of resources allocated, and it is easier to adjust the workload or resource consumption rate because of the resource capacity analysis. This increased transparency and traceability of resource consumption can enable the agency to identify capacity issues, and adjust in a timely manner.
  7. Exercise control in several ways: 1) assign personnel based upon a demonstrated need; 2) expand or contract personnel proportionately as needs change; 3) uncover waste and hidden costs; 4) view which activities are most and least expensive; 5) assess the full efficiency of the organization; 6) identify places to cut spending; 7) establish a cost baseline that may be influenced through process or technology changes that reduce effort for the activity; and 8) argue from an informed, objective position in favor of the organization’s budget.
  8. Suitable for any size law enforcement organization, correctional facility, or public safety answering point (PSAP).
  9. Share with all other organizational elements to produce an enterprise-wide budget model.
  10. Justify operations with actual workload and financial data.
  11. Smaller unit of analysis (activity level) is more flexible. Can be aggregated to a higher unit (program level). Easier to identify patterns (Pareto Analysis).
  12. Incorporate as part of your organization's business plan, performance management framework or strategic plan to document workload and costs.

About the Model

  1. Developed in MS Excel. No proprietary software needed. Basic knowledge of MS Excel is helpful. Must have MS Excel installed.

  2. Customizable and expandable. Add or delete worksheets to fit your organization.

  3. Complete with workload  and budget models for patrol, communications, traffic, investigations and prisoner processing. Also includes an example of fixed-position relief factoring and Pareto Analysis for planning purposes. 

  4. Complete with preformatted calculations.

  5. User guide is embedded in the Excel workbook.

  6. Dynamic updates. Watch the budget immediately recalculate based on your agency's priorities and estimates.

  7. Perpetual single-user license. One-time purchase. Non refundable.

  8. Easy to download and PC compatible.

  9. This is a self-directed exercise. Technical support and customization available.


  1. Blau, P. & Schoenherr, R. (1971). The Structure of Organizations. New York: NY: Basic Books;

  2. Crank, J. (2003). Institutional theory of police: a review of state of the art. Policing: An International Journal of Police Strategies and Management, 26(2): 186-207.

  3. Crank, J. & Langworthy, J. (1992). An institutional perspective of policing. Journal of Criminal Law & Criminology, 83(2):  338-63.

  4. Greene, J.R., Bynum, T.S. & Cordner, G.W. (1986). Planning and the play of power: Resource acquisition among criminal justice agencies. Journal of Criminal Justice, 14(6): 529.544. 

  5. Laufs, J., Bowers, K., Birks, D., & Johnson, S. D. (2021). Understanding the concept of ‘demand’ in policing: a scoping review and resulting implications for demand management. Policing and Society, 31(8), 895-918.

  6. Moore, M.H. (1995). Creating Public Value. Cambridge, MA: Harvard University Press.

  7. Thompson, J.D. (1967). Organizations in Action. New York, NY: McGraw-Hill.

  8. Wilson, J. M., & Weiss, A. (2012). A performance-based approach to police staffing and allocation. Washington, D.C: US Department of Justice Office of Community Oriented Policing Services, p. 14.

  9. Wilson, J.M., & Weiss, A. (2014). Police staffing allocation and managing workload demand: A critical assessment of existing practices. Policing: A Journal of Policy and Practice 8(2): 96-108.

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