Using Control Charts to Monitor Financial Reporting of Public Companies
Richard B. Dull, PhD *
Clemson University
School of Accountancy & Legal Studies
301 Sirrine Hall
Clemson, South Carolina 29634
Email: rdull@clemson.edu
Tel: (864) 656-0610
David P. Tegarden, Ph.D.
Department of Accounting and Information Systems
Pamplin College of Business
Virginia Tech
Blacksburg, VA 24061
dtegarde@vt.edu
Tel: (540) 231-6099
September 2003
* Corresponding author
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Using Control Charts to Monitor Financial Reporting of Public Companies
Abstract
There is currently much being written about increasing the frequency and timeliness of
financial reporting. Comments frequently support the desirability and question the feasibility of
continuous reporting. Under the current reporting/assurance paradigm, if companies report
continuously, auditors must monitor and audit on a continuous basis. Current audit standards
generally address the outcomes, rather than the detailed procedures the auditor must follow to
meet the objectives of the audit. The purpose of this paper is to propose and demonstrate a
technique for monitoring continuous financial information using control charts of accounting
information.
In this study, financial data were collected and used to implement control charts. Part of
the selected companies had known errors, while others had no known errors. The resulting
control charts and common interpretation rules identified potential systematic problems only in
the companies with known errors. The authors suggest that when continuous data become
available, charts similar to these can be used in conjunction with statistical and analytical
techniques to detect signals that financial processes are not in control. Refinements of this
technique should assist those internal and external to organizations, who are concerned with the
reliability of information produced and reported by the organization.
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Introduction
Over the past two decades, the proliferation of technology has led to the increased availability
of financial information. Although the timing of financial reporting has not changed significantly,
currently there is much being written about increasing the frequency and timeliness of financial
reporting and assurance (Vasarhelyi 2002). Technology advances have generally increased system
capabilities enabling integrated packages to simplify and accelerate the mechanical process of financial
reporting. Generally, people agree that increasing the frequency may be useful, but have reservations
about the feasibility of the implementation of continuous reporting and assurance. For example,
Searcy, Woodroof, and Behn (2003) report the following quote from a Big 4 audit partner.
The process we have is good but is designed for the annual audit… We will need
the ability to push a button at any point in time and have the system summarize
for the engagement team process issues identified to-date that can lead to risk
that the financial statements are inaccurate, rather than rely only on a
traditional review of workpapers, manual summarizations of issues and followup.
[We need to be able to] use technology to actually audit as opposed to using
technology to automate manual auditing procedures.
Although continuous assurance was proposed over a decade ago (Groomer and Murthy 1989;
Vasarhelyi and Halper 1991; Vasarhelyi, Halper, and Ezawa 1991), to move assurance from the
current periodic model to a continuous process, auditors will need to look to non-traditional tools
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and monitoring techniques. Furthermore, Kogan, Sudit, and Vasarhelyi (1999) have called for
and suggested research to be completed that would support the development of continuous
assurance and audit.
To adequately monitor on a continuous basis, data must be provided to the auditor on a
(near) continuous basis, a state that is not currently available. With the increased use of tools
such as XBRL, the feasibility of (near) continuous data is becoming a reality. Using data from a
steady, frequent stream, the current research suggests using control charts to assist auditors and
other decision-makers in the identification of patterns in the underlying processes that produce
financial statements. Control charts have historically been used for monitoring manufacturing
processes and identifying when processes become “out of control”. Once interesting patterns are
identified, decision-makers may focus on the processes that generated the patterns.
The objective of continuous monitoring is detecting data abnormalities as near as possible
to the time of occurrence of the underlying event that generated the data. If an error or
irregularity occurs and is detected, the situation can often be corrected, and the effect quickly
mitigated. Overall, the purpose for monitoring financial information is to gain confidence that
the systems are operating as intended, through the ability to identify and resolve errors,
irregularities, or inconsistencies. The current research demonstrates a tool, the use of control
charts, to detect abnormal patterns in data, providing support for additional analysis and
detection of potential problems in the underlying system.
In this paper, the authors propose to use the Control Chart Approach (in conjunction with
other analytical techniques) to monitor the financial statements as they are published. As the
frequency of the financial statements increases and their timing approaches continuous, the
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monitoring of the near continuous statements needs to occur in a near continuous manner.
Control charts typically are not used to monitor inputs (transactions, in the case of accounting
information); they are used to monitor the output of a process (account balances).
The remainder of the paper is organized as follows. In the following section, background
literature is provided for the research described in this paper. This background includes material
addressing continuous auditing and assurance, and an overview of control charts and their use.
The next section provides a description of the methods used to transform the data for use in
control charts. The third major section discusses the results obtained. The final sections provide
descriptions of some of the limitations, future research directions, and conclusions based on this
research.
Background
Continuous Auditing and Assurance
As pointed out above, continuous audit and assurance was proposed over a decade ago.
Groomer and Murthy (1989) proposed embedding audit modules into a client’s software. By
using this method it would be possible to capture and evaluate relevant information in a more
continuous manner.
Vasarhelyi and Halper (1991) described an alternative audit approach called the
Continuous Process Audit Methodology (CPAM). In this approach, they suggest that
“continuous process auditing can be considered as a meta form of control and can be used in
monitoring control … by scanning for the occurrence of certain patterns …” (Vasarhelyi and
Harper 1991, p. 180). Furthermore, they propose that by having online systems monitored in a
continuous manner, auditing will become more of an audit by exception instead of a periodic
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audit, i.e., when an exception occurs, an audit will be required based on the nature of the
exception.
In 1997, a joint committee of the AICPA and CICA issued a report that suggested CPAs
and CAs will need to monitor the functioning of a firm’s systems in order to assure the reliability
of the data provided by the systems (AICPA 1997). They suggested that the CPA/CA would
need to “either (1) embed some level of monitoring or control in the client’s system or (2) direct
regular inquiries into client processing systems/databases” (Vasarhelyi 2002, p. 261). The
approach described in the current paper provides the auditor an aid to determine when and where
to query a client’s system.
As reported by Searcy et al. (2003), a Big 4 audit partner suggests that “the use of
technology tools that assist with auditing through our clients’ systems will become increasingly
important.” They also provide evidence that as continuous auditing of continuous reporting
increases, that the Big 4 partner’s believe that the clients’ expectations of an auditor’s ability and
responsibility will increase in the areas of (1) reporting on going concern problems in a more
timely manner, (2) detecting fraud, and (3) determining the reliability of financial information.
Based on the above, it seems that as the audit profession moves toward a more
continuous audit by exception philosophy, the auditor must be able to monitor the financial
status of a client firm in a real-time manner. In the next subsection, the use of control charts is
described, as a basis to raise alarms to notify an auditor to further investigate a possible problem
within a client’s system.
Control Charts
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The control chart, also known as a Shewhart chart, was created in the 1920s by Dr.
Walter A. Shewhart of the Bell Telephone Labs (Shewhart 1926). It was initially created to
provide a way to monitor manufacturing processes in a statistically sound manner. The control
chart continues to be one of the primary tools used in statistical quality control. Shewhart
suggested that control charts have two basic uses: as a judgment and as an operation (Deming,
1986). When used for judgment purposes, observations are reviewed after the fact to determine
whether a process was in statistical control or not. When used as an operation, the purpose is to
observe whether an ongoing process is still in control or not. From a continuous monitoring
perspective, it is the latter use that is proposed.
There are many different types of control charts; each type is essentially a run chart using
values of observations taken from a manufacturing process and then plotting those observations
around the mean. As long as the observations do not stray too far from the mean for too long, the
underlying process is considered to be under control (Brassard 1989 and Harris 1996).
Furthermore, experiments conducted by Shewhart demonstrated that the underlying distribution
of the data was not relevant when preparing and evaluating control charts (Shirland 1993).
Wheeler (1993, 29) notes that “instead of attempting to attach a meaning to each and
every specific value of the time series, the Control Chart Approach concentrates on the behavior
of the underlying process.” Since accounting irregularities are frequently based on systematic
issues, rather than on an isolated event or transaction, the Control Chart Approach may be more
appropriate than many of the traditional analytical review procedures when attempting to
uncover irregularities. In fact, process-based issues may be more difficult to determine using
traditional analytical review techniques.
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Manufacturing processes have been monitored frequently through the use of control
charts (Shirland 1993). If a process is in control, the control chart will appear as if the variation
in the control chart is randomly distributed. If a process is out of control, the data will appear to
have a pattern or some abnormality. Even if a process is in statistical control, it does not
necessarily guarantee that the process will produce a valid or usable product. However, if a
process is out of control, the likelihood of the process producing a usable product is low
(Shirland 1993). Wheeler (1993, 130) indicates that each signal identified on a control chart
provides the decision-maker with “an opportunity to gain more insight into the process.”
Financial accounting information has traditionally been produced annually (and quarterly
for public companies). This relatively long period has allowed for the postponement of
recording of many transactions, such as inventory corrections, depreciation expense, overhead
application, and other adjustments. With more frequent (continuous or near-continuous)
reporting, the process of recording adjustment transactions would enable their contemporaneous
reporting with their occurrence, providing a more complete financial picture of the organization
on a timely basis. This process will be required to produce continuous financial reports.
Metaphorically speaking, financial statement creation parallels a manufacturing process:
raw materials (data) are purchased (collected) and processed (classified and recorded), providing
the finished goods (financial reports). Real-time financial reporting should emulate the results of
the manufacturing process. Mauch (1993) indicates that traditional financial measures may not
represent the lag between changes in the manufacturing environment and financial reporting. By
decreasing the reporting period, this lag is decreased, and in the extreme case of real-time
reporting, the financial reports represent a timely representation of the production process. As
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control charts have been historically used in components of production processes, they should
also map to components of real time financial reports.
As control charts are overlaid on the financial reporting process, one should be able to
determine if the raw data or financial statement creation process is going “out of control.”
Through the application of this manufacturing metaphor in conjunction with control charts, it is
possible to identify potential problem areas within the financial statements. Such data or
processing problems could be due to information systems problems, fraud, and/or accounting
irregularities. Control charts have been previously suggested for management use within an
organization to improve the quality of accounting processes such as issuance of invoices and
preparation of tax returns; such applications would also improve the overall reliability of the
system (Walter, Higgins & Roth 1990 and Reeve & Philpot 1988).
The current research proposes the use of control charts to raise flags to notify decisionmakers that additional investigations may be necessary. Because the control charts provide an
alert of a potential problem, it is important that they operate at an appropriate sensitivity level.
Control charts that are too sensitive will raise flags when not appropriate, causing unnecessary
investigations and other problems for decision-makers. Control charts with too little sensitivity
may lead a decision-maker to overlook serious problems that may exist. As such, control charts
should be only one of many tools used by decision-makers to monitor information identified as
“high risk” to the financial health of an organization.
Analytical review and control charts
Traditional auditing literature describes the characteristics of analytical procedures,
including the identification, investigation and evaluation of differences between auditors’
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expectations and recorded values (AICPA 2001). Traditionally, trend analysis has been used as
one of these procedures. Control charts, as proposed in this research, examine the state of control
of the underlying system, not the expected value of a specific point, as done in trend analysis.
The authors do not suggest that traditional procedures be abandoned, but as reporting becomes
increasingly frequent, auditors’ use of those procedures will likely be modified to reflect the
nature of the data. Additionally, new tools will be developed to address new needs. Under the
current financial reporting paradigm, control charts would be of limited usefulness because,
based on annual data, it would take seven years (using the rule of sevens) to determine if a
process is out of control. Alternately, in a continuous environment, seven data collection
instances may be relatively close together, providing an opportunity to address an out-of-control
system issue on a timely basis. Using control charts when evaluating runs should increase in
usefulness as reporting periods decrease.
When using alternate rules (non-run) or underlying data, control charts may be beneficial
as analytical procedures by auditors of traditional financial information. For example,
identifying items that are three or more standard deviations from the mean can detect a potential
problem, although items with single period changes may be detected by traditional techniques.
Additionally, by suggesting control charts for data collection processes, the research of Walter,
Higgins & Roth (1990) and Reeve & Philpot (1988) support the use of the techniques to achieve
organizational control goals.
Methods
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To illustrate the application of control charts to monitoring of financial reporting, the
authors developed control charts from certain historical financial data collected from several
firms within three industries. The data used to develop the control charts in this paper were
collected from Compustat. Due to the increased reporting frequency over annual data, quarterly
data were selected. Currently, there is not a common source of data with more frequent
periodicity and reliable financial data available for companies and industries. The data selected
for this study encompassed the periods from first quarter 1993 through fourth quarter 2002 (ten
years).
In this paper, the authors demonstrate a method of monitoring financial information,
including the detection of anomalies. One of the potential weaknesses of the Control Chart
Approach is that control charts are susceptible to two types of errors: Type 1 errors relate to
raising false alarms and Type II errors relate to not raising an alarm when one should be raised
(Tannock, 2003). As such, for the authors’ purposes, a random sample was not viable.
Companies with known anomalies were required to demonstrate whether the Control Chart
Approach would detect those anomalies, i.e., avoid Type II errors. The authors selected financial
information relating to three companies with a history of specific financial irregularities.
To demonstrate the concept, the authors chose companies for which the symptoms of the
irregularities differed. For example, companies to be selected should have known problems with
that manifest with a balance sheet symptom, an income statement symptom, or a composite
symptom. The firms chosen were WorldCom, Rite Aid, and Oxford Health Plans. For
comparison purposes, the authors also chose two additional firms in each of the corresponding
industries of the “problem” firms. For the purpose of this paper, industry is defined as
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companies with identical SIC codes. The two additional firms that were chosen are not known to
have a history of financial irregularities. The comparison firms for WorldCom included AT & T
and BellSouth; for Rite Aid, CVS and Walgreen’s; and for Oxford Health Plans, Aetna and
CIGNA. The authors are not making a judgment regarding the overall similarities of the firms,
other than they share a SIC code and are not known to have material irregularities, i.e., no Type I
errors.
Selected account balances of the “problem” firms were also compared to the related
account average values for their respective industries. The specific accounts investigated were
based on suggestions by Mulford and Comiskey (2002) and Schilit (2002).
The accounts used to illustrate the control chart concepts varied by company, based on
the type of irregularity that was known to exist within the company. For WorldCom, the
irregularity involved improperly classifying cost of sales as long term assets (SEC 2002a); the
authors selected the cost of goods sold account to use for analysis in the current paper. Rite Aid
was charged by the SEC (2002b) with a variety of accounting irregularities. Among these
irregularities were erroneous inventory markdowns, leading the authors to use the inventory
account as the illustrative account. The accounting irregularities of the third company, Oxford
Health Plans, were apparently unintentional and based on billing issues related to a computer
upgrade (Schilit 2002). For Oxford Health, the authors looked at Net Income, an aggregation of
the nominal accounts.
For this paper, the authors extracted quarterly information for a ten year time period,
providing forty data points for the preparation of control charts and data analysis. In practice, an
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individual monitoring the financial statements ideally should have access to the actual values in a
more continuous manner.
Several procedures were used to standardize the raw extracted data. For demonstration
purposes, Table 1 shows only the results from standardization procedures for the first two years
(eight quarters) of the Cost of Goods Sold account data for the WorldCom analysis. Table 1
begins with actual values for the Cost of Goods Sold and Sales accounts. Following Schilit
(2002) and Welsh, Zlatkovich, and White (1976), the data were “common sized” by dividing the
selected accounts by the total sales, for the income statement accounts (Cost of Goods Sold for
WorldCom and comparables), or total assets, for the balance sheet accounts (Inventory for Rite
Aid and comparables), for the same period. In the Table 1 example, Cost of Goods Sold was
divided by Sales (see Common-Sized Values). Second, a moving average of four data periods
(quarters) was used to compute the base line for the control chart. Shirland (1993) recommends
subgroup sample sizes of four or five data points for such computations. As such, the authors
chose to use four points to provide one year of data in the moving average computation. This
choice ensured that seasonal variations were considered for each observation. In this case, the
first period in which a moving average was computable was for Dec-92 (see Moving Averages in
Table 1). Furthermore, 25 subgroups tend to be considered to be a minimum to set up control
charts (Shirland 1993). In this research, 36 subgroups are reported. Based on those subgroups, a
moving standard deviation was computed, that in conjunction with the moving average could be
used to compute a Z-score for each of the “4-period windows” (see Moving Std Deviations in
Table 1). Finally, a “z-transformation” of the data was used to set the base line to zero and
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standardize the periodic data (see Z-Score Transformation in Table 1). Based on the Z-Score
values, control charts were created and analyzed (See Discussion below).
There are many different rules on which to base the control chart analysis. Within this
paper, the authors investigate the use of rules regarding runs in z-transformed account values to
identify areas for additional investigation. For example, if the z-transformed value of a specific
account has a set of seven positive (or negative) values above (or below) the moving average in
seven consecutive time periods, there could be a problem with the underlying process. The
probability of seven values in a row being above (or below) the mean is less than two percent.
This is the so-called “rule-of-seven” (Shirland 1993).
Through the application of control charts and the appropriate rules to monitor financial
accounts (in a manner that the charts and rules have previously been applied in manufacturing
environments), the authors believe that it is possible to identify problem areas within the
accounts, before the accounts go “out of control” and potentially irreversible consequences
occur.
Results and Discussion
The three examples of using control charts to identify potential problem areas are
described in this section. First, control charts associated with WorldCom and its competitors and
industry are presented. Next, control charts are presented for the data associated with Rite Aid
Corporation and its competitors and industry. Finally, the results associated with Oxford Health
Plans and its competitors and industry are presented in control chart format. The control charts
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are presented in Figures 1 through 6. In Figures 1, 3 and 5, data points that fall under the rule-ofseven are identified with an oval1.
Figure 1 is a control chart demonstrating the behavior of WorldCom’s cost of goods sold
from the first quarter of 1992 through the fourth quarter of 2001 (data points identified by
diamonds). An industry comparison for values of cost of goods sold is shown on the chart with
squares. An examination of the WorldCom data shows in quarters 21 through 31 (Quarter 1,
1997 through Quarter 3, 1999 consisting of 11 data points), the data points are in “violation” of
the rule of seven, indicating that there may be some systematic problem with cost of goods sold
during the period. This pattern is consistent with the types of issues that are covered in the
allegations made against WorldCom. The company has since restated financial results from
2001 and 2002. Had auditors considered the authors’ proposed method of analysis during 1998,
it is likely that cost of goods sold would have received additional scrutiny during the period
under question. As a control, the industry data for the cost of goods sold account is also
presented (industry data points are shown as squares). Based on the stated criteria, there do not
appear to be any systematic patterns in the industry as a whole.
As an additional control, Figure 2 displays cost of good sold information for two other
companies in WorldCom’s industry, BellSouth (diamond) and AT&T (square). Analysis of this
data also shows no systematic patterns to suggest that underlying systems used to report cost of
goods sold for these companies are out of control.
The example of Rite Aid is provided in Figure 3. Rite Aid’s data are shown using the
diamond symbols, while the industry data are identified with squares. Rite Aid’s various
problems with inventory prompted its inclusion in this study. In examining the data in Figure 3,
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there are two time periods that are identified when applying the rule-of-seven. The first period
includes the seven periods from the second quarter of 1996 through the fourth quarter of 1997
(inclusive). The second includes the seven periods from the first quarter of 1998 through the
third quarter of 1999 (inclusive). The SEC (2002b) complaint against Rite Aid indicates
problems existed from May 1997 through May 1999, a period that overlaps the time periods
where Figure 3 would support additional investigation. The industry data presented in Figure 3
does not indicate the existence of a systemic problem within the industry as a whole.
Figure 4 shows data collected from the two other companies selected from within Rite
Aid’s industry, Walgreen’s (diamond) and CVS (square). The same methods and criteria that
were used to evaluate Rite Aid do not provide support for the existence of out-of-control
inventory systems at Walgreen’s or CVS.
Net income data from Oxford Health Plans (diamond) is presented with the
corresponding industry data (square) in Figure 5. Schilit (2002) describes the circumstances
surrounding a 1997 New York State Insurance Investigation ending with a $3 million fine for
Oxford and a mandated $50 million increase in insurance reserves. Also in 1997, the value of
the company’s common stock fell 62%. At the heart of the problem was a 1996 computer
upgrade that did not work properly, negatively impacting earnings. The results displayed in
Figure 5 indicate the potential for a problem in the nine periods prior to the actual problem
(second quarter, 1994 through second quarter 1996, inclusive). It is likely that these results do
not identify the actual problem, but may identify systematic issues that precipitated the need for
the system upgrades. Once again, the industry data does not provide any support for the
existence of an “industry based” problem.
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In the final example, Figure 6 presents net income data from two members of Oxford’s
industry, Aetna and CIGNA. The data provided for these companies do not indicate a problem
exists in their systems or underlying data.
For each company that was identified as having prior public disclosures of significant
problems within its financial accounting and reporting system, the figures show relevant
accounts and how using control charts and the rule-of-seven may help identify problems that
might go unnoticed for a significant amount of time, increasing financial damage to the company
and ultimately to its investors. Concurrently, industry data were presented that indicated the
potential problems were not industry-wide, but rather company specific. In addition to the
industry data, two other companies within each industry were presented to support the validity of
the rule to not only identify problems, but also to “non-identify” companies that do not have
problems.
Limitations and Future Research
This study is an initial proposal to use control charts to identify problem areas within
information produced by an accounting information system. Within the study, the authors
demonstrate the usefulness of the control chart, a tool created and refined for use in
manufacturing environments, but applied to financial accounting information systems by the
authors. Because this is an initial study, the authors recognize and acknowledge several
significant limitations of the study. Many of these limitations are being addressed in other
research currently in process by the authors.
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One of the main limitations of this research is the lack of true continuous data. Before
continuous data are available, it is not possible to comprehensively test and refine the rules for
evaluating control charts. Even though such refinements are not currently possible, it is
important that auditors have a framework for continuous monitoring that includes tools such as
control charts. When continuous data are available, much additional testing and refinements will
be required. Future research should develop simulations of continuous data, based on actual
company data, to begin this refinement process.
The lack of real continuous data also affects the sample. The sample was selected and
used to examine a range of instances including multiple variables across three industries and nine
companies, with three of the companies having known accounting irregularities. Although
quarterly data are “more continuous” than annual data, they are far from continuous in reality.
To address this issue will require corporate transparency, a requirement that is complicated by
the need for data from companies with irregularities. Typically, one would not expect a
company to agree to disclosing data if an irregularity or investigation is known to be in process.
In the near future, as technologies such as XBRL are implemented, it might be possible to
accumulate instances of near-continuous data for a study of active companies.
In addition to issues surrounding limited continuous data availability, there are also
potential limitations in applying control charts to financial processes. Since this is a new domain
for this application, the rules to interpret the charts and identify “out-of-control” systems may
need to be modified. Alternatively, new rules may need to be developed. Other common rules
(to manufacturing) that are currently under investigation by the authors include:
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
If an individual point is above (or below) three standard deviations from the
mean, then the underlying process is potentially out of control.

If the values trend in the same direction (increasing or decreasing) for seven
periods, then it is likely that the underlying process is out of control.

If the values for two or more periods are greater than two standard
deviations (Z = 2) but within the actual control limits, then it is likely that
the underlying process is out of control.

If the values for four or more periods are greater than one standard deviation
(Z = 1) but within the actual control limits, then it is likely that the
underlying process is out of control.
With any control chart rule, one must consider the frequency of reporting, examining the
usefulness of standard control charts when monitoring accounting processes at all reporting
frequencies, including continuous reporting.
It is likely that companies at different levels of maturity may need to be evaluated by
different rules. For example, a start-up company would likely be in a growth mode, making the
likelihood of a “false” out of control identification a possibility. There also may need to be
variation of rules based upon company size, if it is determined that the size of the firm has an
effect on the cost or asset structure of a firm.
Future research should be conducted to examine many of the limitations of this paper.
Researchers need to examine the effect of control charts on auditors’ abilities to detect
irregularities as well as alternative methods of notification to auditors that irregularities exist. If
control charts help auditors identify system irregularities, do they do so better than alternative
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methods? Obviously, fine tuning the rules to interpret control charts is a necessary component of
the stream of research.
As stated previously, there are many different types of control charts. Only results based
on the original work of Shewhart are presented in this paper. Recent research in manufacturing
has suggested the use of fuzzy logic and neural networks to help customize the rules associated
with control chart interpretation (Guh and Tannock, 1999; Tannock, 2003). Additionally,
concerns have been raised when the data are correlated (Wardel, Moskowitz, and Plante, 1992)
and when they are used to monitor high quality processes (Xie, Goh, and Kuralmani 2002).
However, until continuous or near continuous data are available, it is unclear which of the many
types of control charts or which rules would be most appropriate.
There are many more issues regarding the introduction of new tools into the audit
process. The current paper was planned to introduce the concept of control charts into
continuous monitoring, and begin the process of these future investigations.
Conclusion
In this research, the authors suggest that control charts may provide a way to
continuously monitor business and financial processes. Control charts have been successfully
used to monitor manufacturing processes and to identify processes when they become “out-ofcontrol”. As demonstrated by the provided examples, they may also be beneficial when
monitoring financial processes, by helping identify patterns that may indicate a problem with
underlying accounting data.
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Currently, the usefulness of the concept is probably highest for auditors, internal and
external, as an additional analytical review procedure. Ultimately, as companies increase the
frequency of reporting, the concept also may be beneficial to other decision-makers, through the
identification of systemic problems within an organization.
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Table 1: Two years of Values for Cost of Goods Sold for
AT&T, Bell South WorldCom, and the Industry
Based on Compustat Quarterly Account Data (March 1992-December 2001)
Actual Account Values
COGS – Industry
WorldCom – COGS
Bell South – COGS
AT&T – COGS
Sales – Industry
WorldCom – Sales
Bell South – Sales
AT&T – Sales
Common-Sized Values
COGS – Industry
WorldCom – COGS
Bell South – COGS
AT&T – COGS
Moving Averages
COGS – Industry
WorldCom – COGS
Bell South – COGS
AT&T – COGS
Moving Std Deviations
COGS - Industry
WorldCom - COGS
Bell South - COGS
AT&T - COGS
Z-Score Transformations
COGS - Industry
WorldCom - COGS
Bell South - COGS
AT&T - COGS
Mar-92
600.6124
105.252
2,153.10
8,578.00
1083.617
183.748
3,738.70
15,375.00
Jun-92
614.8397
112.311
2,170.10
8,788.00
1090.765
197.815
3,764.10
15,845.00
Sep-92
538.8757
116.821
2,216.10
9,023.00
1033.385
205.78
3,736.00
16,180.00
Dec-92
618.7917
123.8
2,371.20
9,713.00
1149.957
213.41
3,910.10
17,504.00
Mar-93
623.2446
129.2
2,262.00
9,517.00
1092.328
219.011
3,833.70
15,719.00
Jun-93
641.5731
148.003
2,279.60
10,263.00
1132.138
251.514
3,906.90
17,337.00
Sep-93
644.2354
158.088
2,316.70
10,284.00
1116.206
281.788
4,014.90
17,225.00
Dec-93
712.2872
222.546
2,436.30
11,571.00
1190.136
392.401
4,124.80
19,070.00
0.554266
0.572806
0.575895
0.557919
0.563678
0.567758
0.576526
0.554623
0.521467
0.567699
0.593175
0.557664
0.5381
0.580104
0.60643
0.554902
0.570566
0.589925
0.590031
0.605446
0.566692
0.588448
0.583481
0.591971
0.577165
0.561018
0.577026
0.597039
0.598492
0.567139
0.590647
0.606765
0.544378
0.572092
0.588006
0.556277
0.548452
0.576371
0.59154
0.568159
0.549206
0.581544
0.593279
0.577496
0.563131
0.579874
0.589242
0.587339
0.578229
0.576632
0.585296
0.600305
0.018571
0.005854
0.014658
0.001756
0.022776
0.010755
0.012276
0.024896
0.023486
0.010193
0.009652
0.02513
0.017238
0.013294
0.012629
0.022328
0.014184
0.014722
0.006397
0.00703
-0.33805
1.368816
1.256839
-0.78326
0.97091
1.260149
-0.12297
1.497719
0.744516
0.677388
-1.0151
0.576025
0.814154
-1.41844
-0.9673
0.43443
1.42861
-0.64481
0.83659
0.918808
22
Figure 1: Control Chart - Cost of Goods Sold
WorldCom and Industry Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
WorldCom - Cost of Goods Sold
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Industry - COGS
-0.5
-1
-1.5
-2
23
Figure 2: Control Chart – Cost of Goods Sold
BellSouth and AT&T Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
Bell South-COGS
39
37
35
33
31
29
27
25
23
21
19
17
15
13
11
9
7
5
3
1
0
AT&T - COGS
-0.5
-1
-1.5
-2
24
Figure 3: Control Chart – Inventory
Rite Aid and Industry Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
Rite Aid - Inventory
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
Industry - Inventory
-0.5
-1
-1.5
-2
25
Figure 4: Control Chart – Inventory
Walgreen and CVS Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
Walgreens - Inventory
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
CVS - Inventory
-0.5
-1
-1.5
-2
26
Figure 5: Control Chart – Net Income
Oxford Health Plan and Industry Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
OHP - Net Income
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
Industry - Net Income
-0.5
-1
-1.5
-2
27
Figure 6: Control Chart – Net Income
Aetna and CIGNA Quarterly Data (March 1992-December 2001)
2
1.5
1
0.5
AETNA INC - Net Income
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
CIGNA CORP - Net Income
-0.5
-1
-1.5
-2
28
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1
The results included in this research are valid even though the charts appear to return to a state of being in control.
The primary reason is that when continuously monitoring information is received chronologically, the future points
“going back” to control have not happened. Secondly, one cannot assume that the system has returned to “control”,
even if the run has completed. In the current research, a moving average was used for the baseline. Over time such
a baseline may adjust to the new state, obscuring the underlying change. For this reason, it is important to monitor
continuously and consider deviations, rather than waiting to see if a problem “resolves” itself.
30