What Is Bottom-Up Budgeting and Forecasting?
Bottom-up budgeting and forecasting work from the theory that the most accurate estimate of a large aggregate is best produced by estimating its parts and adding them up.
Bottom-up methodologies are employed in many analytic scenarios, such as by economists, econometricians, management scientists, financial analysts, budget analysts, securities analysts, chief financial officers (CFOs) and controllers, among others. Bottom-up processes often are used simultaneously with top-down processes, operating as checks upon each other.
Examples in Budgeting
In the production of corporate expense budgets, revenue budgets, and capital budgets, a bottom-up approach would involve first setting them at the most detailed level of each management reporting line item, for each reporting unit or department within the management reporting hierarchy. Under this approach, the aggregate budgets at each higher level of a hierarchy would be produced by adding the budgets at the level immediately below.
Additionally, in situations where a corporate budgeting department enforces a genuinely bottom-up approach, each department or business unit would have to work upwards from projecting each line item of expense and revenue. For example, a department's headcount budget might include precise salary and bonus forecasts for each individual projected to be on staff (allowing for exactly when new hires are expected to be added). Then they would drive employee benefits expense off these pay figures, and perhaps also occupancy charges, based on standard square footage assumptions per each employee (while adjusting for differences in office space related to rank, job title or salary grade).
Examples in Sales Forecasting
A bottom-up approach to sales forecasting produces estimates for each specific product or component, and possibly also by other dimensions such as sales channel, geographic region, customer type, or specific customer.
Once again, the forecasts for broader classes of products or components, as well for broader aggregates of sales channels, geographic regions, customer types, and customer categories, would be produced by rolling up the forecasts already made at much more specific levels.
Forecasting and budgeting in a bottom-up fashion have the advantage of forcing attention to specific categories of expenditure, output, and revenue, which is necessary to plan and manage the activities of individual reporting units, departments, plants, etc. Setting hiring, scheduling, and production plans, for example, requires such specificity.
In some cases, forecasts at low levels of aggregation and high levels of specificity, when rolled up into higher levels of aggregation, tend to be much less accurate than forecasts produced from the start strictly at those more highly aggregated levels. It is because errors made at the more specific levels can compound in the process of adding up the more detailed forecasts and estimates. This is particularly true if the projection errors at the more detailed levels tend to go in one direction (that is, all towards over or underestimates), rather than exhibit random patterns of over and underestimates.
To be more specific, in budgeting processes there is a built-in bias for low-level forecasts and wish lists to demand excessive spending and headcount, while projecting unduly low revenues. It is in the interest of line managers to register needs for more resources than is necessary while committing to less revenue and profit generation than they should be able to produce. This is gamesmanship related to performance benchmarking and compensation, to increase the odds that they will exceed goals and thus be rewarded accordingly.
Likewise, in sales forecasting, there is a normal bias for sales teams and product managers to enter lowball estimates, for the same reasons as articulated immediately above with respect to budgeting.
For many years, AT&T's Western Electric division, the old Bell System's equipment manufacturer, employed a sales forecasting process that its management frequently characterized as "bottom-up, top-down and middle out." In other words, a robust bottom-up methodology was compared to the results from a top-down approach. A reconciliation process ensued in which the detailed bottom-up projections were adjusted to fit the aggregates that management decided, in a manner that was more art than science, made the most sense.