Practical Profitability Analysis
By Frank Warthen
. Overview:
A recent business intelligence ad by a leading vendor of Business Intelligence
software asks the question: "Does your enterprise software turn data into insight?"
This sums up the real ROI question when it comes to the investment and energy
required to mine BI data.
One of the most data intensive areas of management interest that is capable of
turning up valuable nuggets is product profitability analysis. If there were ever one
area that could provide a big payoff for BI, product profitability analysis is
certainly it. This is often where Business Intelligence and management accounting
collide. This confusing collision provides much more heat than light.
In many cases, the correct BI tool for the task may be the simple spreadsheet. The
spreadsheet is capable of providing understandable answers to management;
without management understanding of the algorithms provided as well as their full
implications, the BI effort is doomed to failure. Spreadsheets can provide a clear
communication vehicle between management and the BI specialists. The
spreadsheet is also a clear winner when it comes to cost effectiveness.
. Profitability analysis of "Actual vs. Budget" for Gross Profit Analysis:
. Responsibility Accounting
Management accounting's major charter is the development of meaningful insight
from mountains of financial data. In no other area is this task more significant or
difficult than in analyzing the actual realization of gross profit as opposed to the
budgeted amounts. Gross profit is the engine that drives the entire profit and loss
statement.
Product profit centers are responsibility centers and are organized hierarchically.
Indeed, the entire company is an accumulation of lower level profitability centers
of management responsibility. The responsibility accounting task here involves
the addition of variances from lower level responsibility centers; this is a far
different task than mere addition of budgeted and actual sales amounts.
. Traditional Volume and Mix Variances (2-Dimensional Analysis of
Variance)
. Volume Variance affects Gross Profit
Volume variance (see Analytical Notes) is the portion of sales which affects
(increases or decreases) the gross profit amount.
. Mix Variance affects Gross Profit Profit Rates
Mix variance (see Analytical Notes) is the portion of sales which affects (increases
or decreases) the gross profit rate.
. Rollup of Data:
. Problems with the Traditional 2-Dimensional Analysis of Variance
The problem with variance accumulation across multiple responsibility centers is
that profit rates themselves are not directly additive numbers. Profit rates must be
calculated at each level and, when this happens, the traceability of profit rate
variances attributable to lower levels is lost.
. Introduction of the "Rollup Variance" (3-Dimensional Analysis of
Variance)
. Addresses the Navigation of Hierarchical Data Aggregations
The rollup variance is a third variance calculated at each hierarchical level. It is in
addition to the volume and mix variances normally calculated by the traditional 2dimensional analysis of variance.
. Rollup of Lower Level Mix Variances (Enables "Drilldown")
Rollup variance is merely the summation of all mix variances (not volume
variances) attributable to lower levels of the product hierarchy. In this manner,
mix variances can be rolled up. Any variance of profit rate at any level of the
product hierarchy is subject to "drilldown".
. Tools:
An important question relates to the appropriateness of the various tools which can
be applied to product profitability analysis. They vary greatly in their data
scalability and cost effectiveness. The following tools are examined in terms of
their position on the data scalability question as well as their relative levels of cost
and intellectual investment.
. Spreadsheet
The spreadsheet is the most popular data analysis tool on the planet. In addition to
its standalone application, the spreadsheet functions as an interface for BI data. A
recent article in TechNewsWorld by David Hatch and Andrew Stamer
(01/08/2009) explores this subject in some detail:
http://www.technewsworld.com/story/Microsoft-BI-Just-Do-It-in-Excel65595.html. Spreadsheets can be applied in practically all companies of any size
and level of transactional data capability. Management might well ask the
question: "If an understandable answer cannot be provided by spreadsheet
analysis, what is the ROI of the more data intensive BI tools?"
. Extensible Markup Language
XML is next up in the data scalability spectrum. It is a logical choice in that it has
an intrinsic hierarchical capability. The most important data in profitability
analysis is also hierarchically structured. XML is capable of handling far more
data than the spreadsheet.
. Relational Database
Relational database management employing hierarchical data navigation
techniques is worthy of consideration. It is, however, very difficult to navigate
multiple levels of hierarchical data using techniques such as adjacency lists,
multiple cardinalities and reflexive joins. The big problem is that it is difficult to
contort relational data (particularly variances) into a hierarchical model.
. Business Intelligence
BI cubes provide an appropriate dimension to the hierarchical data problem of
problem of profitability analysis. BI is capable of providing analytical results on
the base of massive amounts of business transactional relational data. With
appropriate techniques, the hierarchical question can be addressed by cube
dimensions. It is the ultimate top of the scalability pyramid but it does require
large financial and intellectual capital. Business Intelligence provides the best
possible form of profitability analysis providing that, most importantly, financial
management can appropriately comprehend the answers provided.
. Spreadsheet Example:
The attached spreadsheet example is designed to illustrate the principles of the
rollup variance. It consists of a hypothetical company modeled with 2 divisions,
each division comprising 5 product groups with each product group composed of
10 products. The experimenter can use this spreadsheet as an illustrative model by
simply changing budget amounts, actual amounts or budget profit rates of
individual products and following their rollup into the company consolidation.
The algorithm applied in this spreadsheet model is equally applicable to the
summation of data and variances using cube dimensions.
One important thing that the spreadsheet example illustrates is the morphing of
volume and mix variances as they roll up into higher hierarchical levels. Volume
variances split into volume and mix variances at the next higher level; mix and
rollup variances combine into rollup variances at the next higher level. The
integrity of volume, mix and rollup variances is maintained as one ascends or
descends the company's product hierarchy.
. Analytical Notes:
. Volume and Mix Variance Calculations
Budgeted Profit Rate Differential = (Budgeted Product Profit Rate Budgeted Group Profit Rate)
Product Sales Mix Variance = (Actual Product Sales Mix - Budgeted
Product Sales Mix)
Mix Variance = (Total Actual Group Sales x Budgeted Profit Rate
Differential x Product Sales Mix Variance)
Volume Variance = [(Total Actual Product Sales - Total Budgeted
Product Sales) x Budgeted Product Profit Rate] – Mix Variance
. Budgeted Profit Rates Only (Creates the "Rollup Variance")
Only budgeted profit rates are ever introduced into the calculations. Changes in
actual profit rates are indirectly inferred by the addition of mix and rollup
variances from lower levels.
. Price Variances, Cost Variances and Unbudgeted Products
Price variances, cost variances and the introduction of "unbudgeted" products
during the budget year are handled simply by setting their budget amounts equal to
0. An appropriate profit rate applicable to each of these instances can be assigned
by the analyst. Note that this is a radical departure from the traditional accounting
treatment of price and cost variances.
. Uses either $ or Units
The analytical rollup techniques illustrated are applicable equally to both dollar
sales amounts and units. The choice of dollars or units (or both) is determined by
management's analytical preferences.