Capacity Forecasting and Planning
The Capacity Forecasting and Planning (CFP) capability is the ability to model and forecast demand for IT services, infrastructure, facilities, and people.
Structure
CFP is made up of the following Categories and CBBs. Maturity and Planning are described at both the CC and the CBB level.
- AIT Capacity Modelling
- A1Model Design
Define a model or set of models to forecast resource utilization across IT services, infrastructure, facilities, and people. Agree on assumptions and methodology, as well as error tolerances for each resource type.
- A2Model Maintenance
Monitor the modelling accuracy (for example, by comparing actual to forecast resource utilization) and manage efficiency (for example, automation and data availability) of IT capacity modelling. Refine and recalibrate the model's structure, parameters, and assumptions, as required to improve performance.
- BIT Capacity Planning
- B1Input Management
Identify inputs required for IT capacity forecasting. Gather and validate input data for forecast scenarios.
- B2Production of Capacity Plans
Compile capacity plans to inform decision-making.
- B3Communication and Adaptation
Communicate the IT capacity plan to relevant stakeholders, and facilitate action to resolve shortages and/or over-capacity.
Overview
Goal & Objectives
An effective Capacity Forecasting and Planning (CFP) capability aims to:
- Understand what resources will be required to support IT services based on current and projected organizational demands.
- Increase knowledge about the IT resource capacity in order to predict bandwidth constraints.
- Scenario-model the impact of business strategies and forecasts on IT resources.
- Inform management of IT strategies — for example, about over- or under- capacity utilization, or about reassigning underutilized resources.
Scope
Definition
The Capacity Forecasting and Planning (CFP) capability is the ability to model and forecast demand for IT services, infrastructure, facilities, and people.
Improvement Planning
Practices-Outcomes-Metrics (POM)
Representative POMs are described for CFP at each level of maturity.
- 2Basic
- Practice
- Prioritize which resources should be covered by the capacity forecast models, and document any assumptions and methodologies that are used.
- Outcome
- Initial forecasting efforts are targeted at areas that are likely to deliver the most benefit.
- Metric
- Percentage of IT resources covered by capacity forecast models.
- Practice
- Develop and maintain IT capacity models.
- Outcome
- Increasing the quality of IT capacity models will increase the likelihood of them being used consistently in decision-making.
- Metrics
- Number of IT capacity models created.
- Percentage level of IT capacity model reuse.
- Practice
- Collect usage data on key IT resources.
- Outcome
- Capacity modelling for key IT resources is based on up-to-date empirical data.
- Metric
- Percentage of resources with up-to-date empirical usage data.
- Practice
- Produce and communicate IT capacity plans, including resource forecasts.
- Outcome
- Transparency is provided on the actual and future capacity of the reported IT resources.
- Metric
- Percentage of resources covered by the IT capacity plan.
- 3Intermediate
- Practice
- Ensure there are regular reviews to improve capacity forecast models based on comparison between forecasted and actual capacity.
- Outcome
- Improving the accuracy of capacity forecast models helps ensure actual outcomes are in line with what was forecasted.
- Metric
- Actual capacity versus plan.
- Practice
- Working with other business units, implement approaches to collate the organization's strategic direction, business forecasts, and operational objectives as inputs into IT capacity forecasting.
- Outcome
- A more valid suite of possible scenarios informs IT capacity forecasts.
- Metric
- Number of IT capacity scenarios agreed between IT and other business units.
- Practice
- Adopt standardized, semi-automated approaches to produce regular IT capacity plans.
- Outcome
- The production of IT capacity plans is more timely, efficient, and repeatable.
- Metrics
- Cost and time to produce an IT capacity plan.
- Percentage level of IT capacity model automation.
- 4Advanced
- Practice
- Advocate the use of an escalation process for issues relating to data availability and data quality.
- Outcome
- Forecasts of capacity requirements are more likely to be accurate when there is a process to improve the quality of data inputs.
- Metrics
- Actual capacity versus plan.
- Percentage of inputs checked for quality.
- Practice
- Regularly incorporate data on macro trends and patterns (such as industry and global economic trends) as input to capacity forecasting.
- Outcome
- IT capacity plans are informed by input from outside the organization to provide robust forecasts.
- Metric
- Number of external macro trends included in IT capacity planning.
- Practice
- Plan regular meetings with all key stakeholders to take appropriate action over potential IT resource shortages or over-capacities.
- Outcome
- Proactive discussions take place to ensure IT capacity is aligned to current and future needs.
- Metric
- Number of IT capacity-related outages and performance issues.
- 5Optimized
- Practice
- Regularly review and optimize the IT capacity models to improve the assumptions and modelling methodologies used e.g. based on error tolerances.
- Outcome
- IT capacity plans are continually validated for accuracy to meet business demands.
- Metrics
- Frequency of IT capacity model reviews.
- Percentage deviation of actual capacity from forecasted capacity.
- Practice
- Regularly review the communication approaches with all key stakeholders to identify improvements.
- Outcome
- Refined, business-centered capacity plans are regularly communicated to all stakeholders, with tailored communications for discrete audiences.
- Metric
- Frequency of review with key stakeholders.
Reference
History
This capability was introduced in Revision 19.01 as an update to Capacity Forecasting and Planning (16).