---
title: "Managing Scalable BAU Capacity Planning in BigPicture"
canonical: "https://support.appfire.com/space/SUPPORT/3763372088/Managing%20Scalable%20BAU%20Capacity%20Planning%20in%20BigPicture"
format: markdown
---
# Managing Scalable BAU Capacity Planning in BigPicture

This article outlines the recommended approach for modeling Business As Usual (BAU) allocations in BigPicture, specifically for large resource pools (e.g., 100+ resources) where monthly dedication varies.

## Summary

This guide covers a scalable use case for managing monthly BAU dedication (e.g., 30% in Jan, 60% in Feb) across a large number of resources and skills. It recommends using **Flat-Contoured monthly work packages** rather than modifying base calendars, ensuring project managers have an accurate view of remaining capacity.

## Use Case: Scalable Monthly BAU Allocation

**Problem:** A customer needs to reflect varying monthly BAU dedication for ~120 resources and 88 skills (e.g., Jill has 30% BAU in Jan, 60% in Feb, 50% in March) so that the remaining capacity (70%, 40%, 50%) is correctly available for project work.

### Recommended Solution: Flat-Contoured Monthly Work Packages

Instead of modifying supply-side parameters (calendars or workload plans) which are difficult to maintain at scale, model the BAU effort as demand-side tasks.

#### 1. Keep Base Calendars and Workload Plans Static

Assign resources to their standard baseline [Workload Plan](https://support.appfire.com/space/DLP/2211746590) (e.g., a standard 40h/week). These should represent their employment terms, not their specific assignments.

#### 2. Model BAU as Flat-Contoured Monthly Tasks

Create a dedicated Jira task for each resource representing their planned BAU effort for a specific calendar month. Use the **Flat Contouring** mode to distribute the effort evenly.

- **Example for Resource "Jill":**
  - **January:** Task (Start: Jan 1, End: Jan 31; Original Estimate: ~48h [30% of 160h]; Contouring: Flat).
  - **February:** Task (Start: Feb 1, End: Feb 28; Original Estimate: ~96h [60% of 160h]; Contouring: Flat).
  - **March:** Task (Start: Mar 1, End: Mar 31; Original Estimate: ~80h [50% of 160h]; Contouring: Flat).

#### 3. Capacity Outcome

In the **Resources module**, BigPicture automatically spreads the flat effort across the month's working days. The resource's capacity bar will show the baseline (100%), with the BAU task consuming the specified percentage, leaving the remainder available for project assignments.

### Implementation at Scale

To manage this for 120+ resources without manual entry:

- **Bulk Import:** Maintain a monthly BAU allocation matrix in Excel/Google Sheets and perform a **Jira CSV import**. Map the following fields: `Assignee`, `Start Date`, `Due Date`, `Original Estimate` (or custom fields).
- **Automation:** Use the **Jira REST API** to automate the creation of these monthly BAU tasks.

### Pros and Cons

| Pros | Cons |
| --- | --- |
| Easy bulk import via CSV or REST API. | Requires generating many tasks (e.g., 120 tasks/month). |
| Full visibility of BAU vs. Project time. | Tasks **must** use [Flat Contouring](https://support.appfire.com/space/DLP/2211908229/Workload+contouring#Auto-flat-mode) to distribute effort daily. |
| Supports real worklogs against BAU tasks for "Planned vs. Actual" monitoring. | Tasks should be kept in a dedicated Jira project/component to avoid cluttering project backlogs. |
| Workload plans and team definitions remain stable. |  |

## Related Documentation

- [Capacity Calculation Logic](https://support.appfire.com/space/DLP/2212528792/Capacity+calculation)
- [Workload Contouring (Auto-flat mode)](https://support.appfire.com/space/DLP/2211908229/Workload+contouring#Auto-flat-mode)