The single most fundamental flaw in the typical corporate budget is the failure to closely link capacity planning into the revenue and expense assumptions. For example, a company forecasts significant changes in revenue, but not enough additional sales staff to bring in the new sales. Even if it does forecast a sufficient number of sales personnel, it assumes that each one will be fully effective as of the first day of work. The inevitable result is only a slight increase in sales, if any. The same problem can arise in many areas of the budget. Other common problem areas are:
- Production equipment. New sales may be forecasted without an attendant increase in production capacity, or new equipment is assumed to be fully operational after an exceedingly short break-in period, or efficiency levels on existing equipment are assumed to increase beyond historical levels.
- Skilled staff. If sales are dependent upon skilled staff positions, which is common in the consulting and service industries, the budget may not provide for sufficient staff, or training time, or funding for recruiting, or may incorporate an excessively high staff utilization level in comparison to historical trends.
- Product development. Even if there are a sufficient number of engineers available to create new products, the budget rarely allows enough time for new products to be adequately tested, or for production planning and quality assurance to be completed prior to the initial rollout.
- Production support. Sure, the budget usually allows for a percentage increase in direct labor costs to match revenue increases, but it's much more difficult to predict the need for production overhead positions in such areas a purchasing, materials management, and front-line supervision.
There are several ways to mitigate these problems. At the simplest level, itemize the key bottleneck areas, and conduct a manual review of the budget to ensure that there is sufficient capacity planned for each bottleneck.
A better approach is to build a capacity planning page into the budget model that highlights key problem areas. The following table shows a highly simplified capacity planning page for sales staff:
Quarter 1 |
Quarter 2 |
Quarter 3 |
Quarter 4 |
|
| Budgeted revenue | $8,000,000 |
$10,000,000 |
$12,000,000 |
$14,000,000 |
| Budgeted sales staff | 27 |
32 |
38 |
42 |
| Budgeted sales per person | $296,000 |
$313,000 |
$316,000 |
$333,000 |
| Historical sales/salesperson | $250,000 |
$250,000 |
$250,000 |
$250,000 |
The table reveals a budget that is not likely to be achieved, since the new sales staff are expected to exceed the historical rate of sales. In reality, the sales per salesperson should decline for some time, until the new sales staff can ramp up their contacts and product knowledge.
Another example appears in the next table, where a company appears to be adequately ramping up its consulting staff to meet planned revenue levels, but its assumed utilization percentage is unrealistic:
Quarter 1 |
Quarter 2 |
Quarter 3 |
Quarter 4 |
|
| Budgeted consulting revenue | $2,106,000 |
$2,268,000 |
$2,565,000 |
$2,888,000 |
| Budgeted consulting staff | 52 |
56 |
60 |
64 |
| Budgeted billing rate/person | $90 |
$90 |
$90 |
$95 |
| Budgeted utilization percentage | 90% |
90% |
95% |
95% |
| Historical billing rate/person | $87 |
$87 |
$87 |
$90 |
| Historical utilization percentage | 82% |
80% |
84% |
73% |
In this case, the budget not only assumes far too high a utilization rate in all quarters, but especially in the fourth quarter, when the staff has traditionally taken time off for the holidays.
The examples shown here are simplistic, but can be used to highlight problems with the budget model. Building an adequate amount of ramp-up time or learning curve into the budget is much more difficult to verify through automation, and instead is best checked through a careful manual analysis of the model's underlying assumptions.