639
USING SYSTEM DYNAMICS TO IMPROVE THE MANAGEMENT
OF WORKING CAPITAL IN A SMALL BUSINESS
Some Preliminary Results
Ray Thompson
University of Pittsburgh at Johnstown
Johnstown, PA 15904
R.C, Shreckengost
R.S.S, Associates, Inc.
2371 S. Queen St.
Arlington, VA 22202
ABSTRACT
Many firms use financial ratio analysis to monitor their control over
the operating cycle and to serve as the basis for policy formulation. Ratios
are based on data produced through the accounting information system which
is analyzed according to intuitively plausible concepts in order to make
normative judgment about the financial health of the firm. A model is con-
structed to simulate the operating cycle of a business which generates finan-
cial ratios in a manner analogous to the accounting system. It is shown
that noise and seasonality produce distortions in the ratio measures and
are spread throughout the system in a dynamic and complex fashion. Further
experiments reveal that plausible control policies based upon financial
ratios may make performance worse rather than better. System Dynamics ap-
pears to be a useful approach both to redesigning financial ratio measures
and testing policies which could enhance our ability to manage such systems.
SECTION ONE: INTRODUCTION AND SUMMARY
This paper discusses some early findings from a study which seeks to
apply system dynamics to issues of working capital management in the small
business. High failure rates in such enterprises have often been linked to
line authors wish to acknowledge the cooperation of colleagues at the
University of Pittsburgh, Dr. Richard Bisconb of Nova University, and Jeff
Sankovitch for invaluable student assistance.
2
an alledged inability to maintain adequate financial control of the operating
cycle. For those businesses who face seasonal demand or make extensive use
of credit the problem is an even more acute one. Failure to monitor short
“term financial rluctuations may lead to periodic crises which force harmful
adjustments to the smooth flow of operations or threaten the very existence of
the business.
We review methods commonly used to monitor the firm's financial situa-
tion -- principally financial ratios. It is shown that where there are dy-
namic forces at work such as noise and seasonality the ratios used to measure
our effectiveness in achieving financial control are quite inadequate. A
model has been ‘constructed to simulate the operating cycle of a small mer-
chandising business. It generates both the accounting data flows and the
financial ratios which can be used for control purposes. Experiments confirm
that seasonality or random fluctuations in the pattern of sales produce erro-
neous information about the company's turnover ratios; the ratios fluctuate
even though the real value of what is purported to be measured renains un-
changed. Furthermore the distorting effects of seasonality spread through-
out the financial system to affect payables, inventory, receivables, and cash
in a complex and dynamically interrelated fashion, Additional modelling
experiments demonstrate that financial ratios have an additional deficiency.
Significant changes in system parameters become obscured by quite meaningless
information about seasonality as it works its way through the financial con-
trol system.
Lyneis [1] has demonstrated the manner in which plausible policies for
achieving financial control may be implemented at the expense of harming the
640
firm's overall performance. Our results confirm this, showing how intui-
tively fashioned policies to improve turnover harm sales at a later point.
Further, the improvement of turnover is accompanied by even greater fluctua~
tions in other financial variables which might call forth further inappro-
priate corrective action. Our study is still in its early stages but sug-
gests that system dynamics has a considerable amount to contribute to the
task of designing more effective financial control systems. It may be
possible to provide enhanced control by suggesting financial ratio measures
which separate random and seasonal variations from underlying changes in
system parameters. System dynamics provides the means to test alternate
policies and redesign the system in the light of the dynamic complexity
which governs its behavior.
SECTION TWO: REVIEW OF LITERATURE
2,1 Financial Ratio Analysis
The basic purpose of ratio analysis is to reduce the vast amount of
information to be found in typical financial statements to a smaller set of
more focussed numbers. These should allow more meaningful comparisons both
across time and between firms. Financial ratios seek to delineate certain
characteristics of the firm such as its liquidity or solvency. These
characteristics, as well as the underlying ratios which purport to measure
them, are not usually defended on any firm logical or theoretical grounds
(ev (21). Typically one is left to appreciate their intuitive plausibility
through relatively simplified examples.
Consider the following diagram which financial analysts use to con-
ceptualize the operating cycle (Gitman [3]). Throughout our paper we shail
assume a merchandising business although broadly similar concepts would apply
to a manufacturing environment. In the normal course of the operating cycle
merchandise is purchased on credit, thus creating a payable and inventory.
After some time the account payable will be paid off through the use of cash
while the inventory itself is sold on credit at @ later date. After some
further period the receivable will be collected and the firm has now com-
pleted the cycle. Thus the operating cycle is characterized by a series of
accumulations and delays which are dynamically interrelated although these
characteristics are not usually explicitly recognized.
DIAGRAM 1: The Operating Cycle
Purchase sell Collect
Merchandise Goods Receivable
<}-—_—__EROHANDISE ___py<j___ accouys_ ___ in,
INVENTORY RECEIVABLE
0 0 80 130 (Time)
<p-—— Accounts D
PAYABLE
Incur Pay
Payables Payable
A number of financial ratio measures have been developed to measure
the firm's control of the operating cycle.” Liquidity ratios measure the
2enis paper concentrates on turnover ratios rather than the more
popular measures of liquidity such as “current” and "acid" ratios. This ap-
proach is adopted because turnover and aging measures are more commonly used
for monitoring short-term changes (Stone [4]). The interrelated nature of
measures of liquidity and turnover is demonstrated by Gitman [3]:
Current Ratio = Current Assets/Current Liabilities
Cash + Accounts Receivable + Inventory/Accounts Payable
Quick (Acid) Ratio = Cash + Accounts Receivable/Accounts Payable
Net Working Capital = Current Assets - Current Liabilities
Cash + Accounts Receivable + Inventory - Accounts
Payable
= Outlays/(Receivables Turnover + Inventory Turn-
over - Payables Turnover)
641
relationship between forthcoming financial obligations and financial re-
sources available to meet these obligations. Turnover ratios measure how
effectively the firm is employing its resources by comparing the dollars we
invest in areas of the business with the results these investments make
possible. To illustrate: we offer credit to customers in order to stimulate
sales so the effective firm generates many dollars of sales from its invest~
ment in receivables; this is receivables turnover. Similarly payables turn~
over examines the relation between Gredit provided by suppliers (accounts
payable) and the purchases of merchandise we make based on this credit.
Again merchandising companies hold inventory levels in order to stimulate
sales so the effective firm makes a lot of sales from its inventories. Colo-
quially, it “turns its inventory over many times". These ratios are shown
below:
Receivables Turnover = Sales/Accounts Receivable qa)
Inventory Turnover = Cost of Sales/Inventory (2)
Payables Turnover = Purchases/Accounts Payable q@)
For certain purposes it is useful to transform these activity ratios into
ages. To illustrate: Assume we turn over our inventory six times per year.
We could also say that our typical inventory is 60 days “old” when it is sold;
in other words, a dollar invested in receivables will enable us to make $6 of
sales throughout the year if we can turn it over 6 times (i.e., collect the
cash after 60 days). This is illustrated in Diagram 2.
DIAGRAM 2: The Operating Cycle - Age and Turnover Measures
(Time)
o 60. 120 180 240 300 360
$1 $1 $1 $1 $1 $1 $1
(Sales)
Receivables Age - 60 days
Receivables Turnover - 6 times per year
6
Age of Receivables? = 360/Receivables Turnover «)
‘Age of Inventory = 360/Inventory Turnover (3)
Age of Payables = 360/Payables Turnover (6)
Notice that equations (4) thru (6) describe the operating cycle in identical
terms to Diagram 1, The financial ratios we have described are used to
develop judgments concerning the operating performance of the firm. They
may be used crossectionally as when we compare, for instance, our inventory
turnover with our competitors or with industry averages published by agencies
such as Dunn and Bradstreet. Ratios may also be used for time series analy-
sis when we trace, for instance, the effectiveness of our collection efforts
by monitoring changes in the age of receivables. Of particular significance
to our later analysis is the use of “age measures" to evaluate the firm's
control of the operating cycle. It should also be understood that the day
~to-day practices of financial ratio analysis do not simply involve rigid
formulae like those of equations (1) thru (6); there are many variants or
approximations used, Thus financial ratios are calculated and analyzed
while fully utilizing the judgment and intuition of the experienced analyst
in order to produce the most meaningful analysis.
Since the ratios are used to judge the operating efficiency of the
firm, we need to ensure that our measures are adequate and unbiased ones.
Implicit in the use of ratios to judge managerial efficiency is the idea that
changes in the value of the ratios are indicative of a changing situation
facing the firm, i.e., a rise in the age of payables means it is taking us
longer on average to pay our bills. This may seem so obvious as to be
unworthy of comment but recall that there are a number of dynamic forces at
Saigo known as "Average Collection Period” or "Days Sales Outstanding".
642
work during the operating cycle and these will affect the ratios which we
caluclate.
2 Seasonality and Ratios
‘The initial analysis of the problems which seasonality can cause for
ratio measures was provided in the context of managing accounts receivable.
Stone [4] found that some 80% of the firms he surveyed used some systematic
method to project accounts receivable and that the vast majority used average
age of receivables or some other measure of receivables to sales. Aging
schedules were also commonly employed as a control device. Lewellen and
Johnson [5] demonstrated that if there was a pattern of fluctuating inter-
period sales (i.e., seasonality) a measure of the average age of receivables
is an inaccurate guide to the effectiveness of collection efforts. To
summarize:
«seseasonal variations in sales can send false signals to the
credit manager even though true collection experience is un-
changed ... [further] ... The way the credit manager perceives
the collection experience as measured by DSO will depend on
which averaging period is chosen. The AS figures can be
further distorted if payments on the most recent months are
unusually high or low. A high proportion of payments on
the most recent months will make up a higher percentage of
the end of quarter receivables even though the old receiv~
ables may be normal in relation to sales for these months.
(6, p. 343]
To understand why such distortions occur, consider the following
example. It assumes that our receivables are collected exactly two months
after sale and that we calculate our age of receivables on the first day of
each month by dividing outstanding receivables by the sales per day calcu-
lated over the past 3 months. Table 1 shows the results.
643
TABLE 1: Receivables Turnover Neasures With Seasonal Sales
Mm @) (3) 4) qs) (6)
Receivables Sales Receivables
Outstanding Past 3 per Day ‘Turnover
Sales at First Months Goa) Gol(3)
Time_ per _Month of Month Sales 30 ConCSy. “
($s) «s) ($) (ays)
Jan. 100
Feb. 50
March 25
‘ 50425 100+50+25
April 200 ae ee 1.94 38.6
254200 504254200
May 200 = 225 = 275 3.05 73.8
Thus as of April 1st we still have uncollected all of our sales from
the previous two months and we compare this with our sales averaged over the
past 3 months. we repeat the same procedures at the beginning of May and
the results are extremely strange! Recall that receivables turnover is
supposed to measure. our efficiency in collecting outstanding accounts. Des-
pite the deterioration in the receivables turnover measure we know the “true”
collection period has remained constant at 60 days. What we have is a highly
defective signalling mechanism, Lewellen and Johnson [5] denonstrate that
many commonly employed control mechanicms may signal an improvement or dete-
rioration in the status of accounts receivable even where there has been no
change in the distribution of customer payments. They also demonstrate that
significant changes in payment patterns could be obscured by the totally
erroneous distortions brought about by seasonality. Stone [4] suggests the
“weston & Brigham [6] cite 90 day averaging as being conmonly employed.
9
development of a “payments pattern approach" to overcome this; it represents
the use of a variance-type analytical framework. Although this will allevi-
ate the problem, the control reports provided are relatively complex and it
seems that few corporations use such measures, relying instead on the simpler
if inaccurate measures of receivables age.
To date analysis of the seasonality problem has concentrated on receiv-
ables and treats them in isolation from the rest of the firm's operating
parameters. Consider, however, just how pervasive the effects of seasonality
are. Not only will seasonal sales cause fluctuating receivables, they are also
likely to cause changing payables levels (as we purchase merchandise on
credit in anticipation of these sales) and fluctuating inventories (as we
would probably have to order merchandise in advance of sales). Thus a sea-
sonal pattern for sales has important and dynamically interrelated effects
on payables, receivables, and inventory. Will this in turn produce erroneous
fluctuations in measures of our ability to control these variables?
What we have is, of course, a situation familiar to the System Dynama~
cist -- a complex and dynamic feedback control system. To develop a systema~
tic analysis of this phenomenon, the operating cycle of a small business with
significant sales seasonality was modelled using system dynamics techniques.
The results are described in the following section.
SECTION THREE: SOME EXPERIMENTS WITH FINANCIAL RATIOS
The model illustrated below? has been developed to simulate the dyna-
5 see Appendix for flow diagram.
10
mics of the operating cycle using system dynamics principles. It is derived
from an analysis of a relatively small merchandising business which buys and
sells extensively on credit and whose sales exhibit seasonality. We are
interested in short-termand seasonal fluctuations rather than the underlying
forces of long-term growth and decline. (For an analysis of these see Lyneis
[1]. The médel which we have developed has elements in common with one of
Lyneis' [7] and includes the major financial and accounting variables of the
operating cycle as well as ratios of turnover and age as described in equa~
tions (1) thru (6). The policy rules which guide the model have also been
tentatively identified although the study is in its preliminary stages with
xespect to validation and much work remains to be done here. We feel
DIAGRAM 3: Financial Aspects of the Operating Cycle
‘a Snail Merchandising Business
644
a
confident, however, that the results described in the paper are not sensitive
to the forms chosen for the decision variables and that our findings may be
reasonably generalized.
3.1 The Base Model,
In our initial run sales were steady and the system operated in a
stable equilibrium close to breakeven, The model was verified by a series of
plausibility tests (e.g., since the delay for paying payables was 3 weeks the
model ought to generate an average age of payables of 27 days). To provide
comparability with previous studies a 90 day averaging period was used to
obtain the numerators in the turnover variables. Had an alternate smoothing
period been chosen it would have affected the numerical values calculated
(see Lewellen & Johnson [5]) and would have made the ratio comparisons more
difficult. It would not, however, affect the general nature of the dynamic
behavior which we describe in the following runs. The base run shows sta-
bility over time for each of our measures for control of the operating cycle
reflecting the fact that true collection experience (i.e., the length of the
days) is constant.
3,2 Seasonal Sales
In the next simulation sales were allowed to follow a sinusoidal
pattern around the same mean as the previous run, Note that the actual de-
lays between incurring and disbursing payables and receivables were held
constant in the model through the use of "boxcar" delay functions which re~
mained unchanged throughout the simulations. There will, however, be fluc-
tuations in the level of these variables as bunching of sales causes inven-
tory to fluctuate and brings about a later bunching of receivables and so on.
12
The results of such fluctuations on the financial ratios are shown below.
‘They are a clear demonstration of the distorting effects of seasonality.
Despite the fact that there is no charge in the true age of any of the items
concerned, the financial ratios fluctuate over time suggesting changes in
our ability to manage the operating cycle. Not only does this confirm
earlier findings concerning the distorting effects of seasonality on ratio
measures of receivables turnover but it also shows clearly how seasonality
causes erroneous fluctuations in the apparent ages of inventory and payables.
An examination of Diagram 4 would suggest to most observers not only that
there have been significant changes in our ability to monitor the operating
cycle over time but also that there are dynamic and complex interrelations
between the ages of receivables payables and inventory. Such a conclusion
would, of course, be erroneous since all we are seeing is the way in which
seasonality plays havoc with our financial ratios, One might, however,
DIAGRAM 4: Ages With a Seasonal Sales Pattern
Age
Payables
Bee...
‘Time (weeks)
645
13
wonder how a businessperson would respond to such drastic changes in the
ratios which purport to measure operating effectiveness.
In order to understand the causes of such superficially puzzling be-
havior consider equation (7):
Receivables _ Previous 13 Weeks Sales o)
Turnover Fresent Account Payable
Accounts are collected after precisely 4 weeks through the use of a boxcar
delay. Notice that an increase in sales and the accompanying rise in re-
ceivables will have a greater impact on the denoninator of (7) than upon the
numerator because receivables are averaged over 4 weeks where sales are
averaged over 13, . Thus receivables turnover would tend to fall (average age
of receivables to rise) at a time of rising sales and vice versa when sales
declined, The fluctuations are made especially acute because of our assump
tion that all sales are made on credit. An alternate collection pattern
would produce a similar although less pronounced and more complex pattern.
The above findings also suggest how we might be able to improve the
calculation of ratios, It can be demonstrated that the magnitude of the
fluctuations in financial ratios are related to the smoothing periods chosen.
By altering this period, making for instance the sales smoothing period equal
to the magnitude of the receivables delay, we might be able to eliminate or
reduce distortions in our ratio measures. Such an approach would represent
an interesting application of System Dynamics methodology. We would aim to
examine how well the measurement system holds up in a dynamic and complex
environment and how well it succeeds in differentiating true changes in the
parameters from erroneous fluctuations caused by seasonality. We have
14
reached only the earliest stages of our investigations in this area although
they pose some extremely interesting questions to which we shall return at
a later stage in the paper.
3.3 Random Variations in Sales
Even if interpreted sales were constant there could still be a signi-
ficant random component. What effect would this have on measures of our
ability to control the oerating cycle? To test this the sinusoidal sales
pattern was replaced by a steady one which had a significant random (noise)
component. Some 8 runs were made using different reruns of normally distri-
buted noise. Results did not differ greatly over the reruns and a typical
result is shown in Diagram 5,
DIAGRAM 5: Effect of Random Fluctuations in Sales
‘on Age of Receivables and Payables
Receivables }
BME.
Payatjles
Age
3
‘Time(weeks)
15
Once again although there is no change in the real ages of any of the
variables concerned, we have large fluctuations in their apparent magnitude,
These changes give every appearance of being dynamically linked and would
not appear to be the result of purely random forces, which is exactly what
they are.
We now begin to see why the task of managing the operating cycle of a
business can be so complex. Any significant fluctuation in sales can affect
the variables used to monitor the operating cycle. If we imagine the typical
sales pattern as exhibiting both random and seasonal behavior then we have an
even more complex situation especially since the underlying variables are
dynamically interrelated, -We stress these points because the situation we
are modelling is much simpler than that which the financial manager of a
large multiproduct firm must deal with, In such situations the fluctuations
are likely to be both subtle and highly complex and would probably defeat
any attempt at policy formulation on a purely intuitive basis. As we shall
show in a later section this is precisely where system dynamics can be of
considerable assistance,
3.4 Effect of a Change in the Age of Receivables
Lewellen and Johnson [5] demonstrated not only that seasonal sales
could cause apparent changes in the age of receivables, but also that signi-
ficant changes in the collection patterns could be obscured by seasonality.
To examine this phenomena we used a sales pattern containing both seasonal
and random elements and introduced a discrete increase in the time it takes
to collect receivables. This was accomplished by means of a “boxcar switch"
which changed the time between billing and collecting receivables from 3 to 4
647
16
weeks at time = 55, This would represent an increase in the time taken to
collect receivables from 4 weeks to 5
an important change for any busi-
ness. The way in which such a change would be measured by the accounting
system is shown in Diagram 6, The system does identify the change but it is
largely obscured by the erroneous information produced by seasonality.
Given that the increase in receivables collection is 7 days and the fact
that the accounting system distortions caused by seasonality are up to 21
days, it is easy to see how real and significant changes could be missed.
Similar conclusions to the above apply to any real changes in inventory or
payables turnover being hard to detect beacuse of distortions in the measure-
ment system, In general we are extremely sceptical of the ability of finan-
cial ratio measures to separate real changes from the effects of random and
seasonal disturbances.
DIAGRAM 6: Effect of a Change in Age of Receivables
Age of
Receivables
i Hy
Time (weeks)
7
Once again we have a problem which appears amenable to system dynamics
investigation. Forrester [8] first pointed out how smoothing changes the
sensitivity of the system to different periodicities that may exist in data
fluctuations. He also demonstrates how smoothing is a dilemma between more
smoothing to reduce meaningless noise and less smoothing to reduce the time
delay in extracting the information which is desired. Could we thus experi-
ment with alternative smoothing periods to ensure that the information pro-
duced is an unbiased representation of what is’actually taking place in the
system and that irrelevant dynamic fluctuations will not distort our mea-
sures of system performance? A system dynamics analysis which could thor-
oughly analyze the dynamics of the operating cycle could then lead to the
design of financial control measures which would reduce the effects of ran-
dom and seasonal sales fluctuations but would still draw attention to real
changes in the system's operating parameters, Such an approach would appear
to be both practical and valuable and this is another promising area for
investigation which we are currently developing.
SECTION FOUR: THE CASE FOR SYSTEM DYNAMICS ANALYSIS
So far we have demonstrated that problems of dynamics, delays, and
interrelations exist in the firm's operating cycle. Even in a relatively
small business the fluctuations and interactions among sales, receipts,
purchases, and disbursements can produce highly complex patterns. They are
likely to make “common sense" policy prescriptions hazardous at best and
counterproductive at worst. The dynamic complexity in such situations
causes accurate intuitive judgments to be beyond the capacity of even ex-
perienced practitioners, The more they rely on ratio measures produced by
an accounting sytem and the more frequently the data are collected, the
648
18
greater is the likely confusion!” Much more research on the dynamic impli~
cations of accounting measurement systems is called for and system dynamics
provides a tool capable of unique insights,
Lyneis on Financial Control
The present study owes a considerable debt to Lyneis [1], [7] who made
some of the first applications of System Dynamics techniques to financial
modelling. His model for financial strategy encompasses much more than our
current study; it incorporates the entire balance sheet where ours concen-
trates upon current assets and liabilities, Modelling differences stem from
an interest in different issues; where Lyneis addresses issues concerning
corporate growth, fixed asset acquisition, and long-term financing strategy,
we are focussing on the operating cycle and therefore have much more detailed
modelling of the relevant financial ratios, Where Lyneis is interested in
the dynamics of processes over the entire business cycle we are interested
in seasonal fluctuations. His model considers a manufacturing environment
where the dynamics of the production system are relatively complex; our
model of a merchandising situation is much simpler. Despite these differ
ences we shall demonstrate that we can draw similar conclusions from both
studies which reinforces claims as to the usefulness of system dynamics in
this particular area,
Iyneis [1] illustrates how a plausible policy for financial control of
Spuring a previous discussion of this issue, an accountant remarked
that they were aware of the problem and "allowed for it". When more closely
questioned on the interactions between payables, receivables, and inventory
it became obvious that it was “allowed for" in an extremely fragmented and
intuitive fashion.
19°
inventory can have harmful effects on the firm, In the nodel financial
control is exercised through a policy which operates as follows: If mea~
sured inventory turnover is below a predetermined level, the desired produc=
tion rate is cut back in an attempt to raise inventory turnover. Thus when
perceived inventory turnover is below target levels there are pressures to
reduce production rates. The finished goods inventory level influences
delivery delay and market share. Lyneis subjects the system to a cyclical
sales pattern finding that during a downturn inventory is rising while sales
are falling, When the company "corrects" this by reducing production and
parts ordering rate, the "improvement" in turnover comes at the expense of
market share and profits. Worse, as the upswing begins the company now has
inadequate inventories to carry it through a period of rising sales thus
causing lengthening delivery delays and even greater loss of market share.
It is demonstrated that we can alleviate these problems by altering the
period over which inventory discrepancies are corrected, enabling them to
peak at roughly the same time as customer orders thus producing relatively
stable inventory turnover measures.
4.2 The Effects of Control Policies Based on Inventory Turnover
In this simulation we model the effects of financial control of inven-
tory. It was assumed that if inventory age exceeded a desired value (i.e.,
inventory target was below some predetermined level) steps would be taken
to affect it by reducing orders of merchandise. In the model, however,
there is a delay before the level of inventory is altered. Our results are
shown in Diagram 7, Tt can be seen that the results of such a policy are
both futile and harmful. There is little improvement in the age of inventory
but sales are reduced in the upswing because of the reduced availability of
20
merchandise inventory. This lower sales level also affects the turnover of
receivables and payables in an apparently adverse fashion causing fluctua~
tions in the age of payables to be slightly more pronounced and in receiv-
ables age to be much greater. If we then imagine managerial policies which
respond to this second set of changes we have an extremely complex situation.
The policy to improve inventory turnover also increases significantly fluc-
tuations in the current ratio which would again signal that there is some-
thing amiss with the firm's financial control. Limitations of space prevent
us from analyzing policies control for financial control based upon modifying
the turnover of receivables and payables although they would also tend to
have similarly complex, and occasiorially harmful, effects upon the busi-
ness’ overall performance. Again there is considerable research to be com-
pleted into the ways in which apparently plausible policies for short-term
financial control may have serious and unintended consequences.
Receivables
wf Payables
Age...
i
Time (weeks)
649
21
SECTION FIVE: FUTURE DIRECTIONS
‘The results we have presented are the early stages of an investigation
which is broad in scope and: appears to have considerable potential for fur-
ther exploration. Our future studies will be directed toward a threefold
purpose: (1) as a means of improving the performance of the system under
investigation, (2) as an illustration of the applications of system dynamics
to financial control, (3) and finally as a contribution to an alternate
theory of working capital management.
Of immediate interest is the design of policies to help the company
perform better in times of fluctuating sales. They would enable the firm to
maintain or improve financial control of the operating cycle without sacri-
ficing other desired goals such as sales. At the same time, the development
of improved financial ratio measures which separate random and seasonal
variations from underlying changes in system parameters would clearly facil-
itate more effective control. Currently we are extending the model to in-
clude short-term borrowing and repayments; this will increase the range of
available alternatives for financial strategy. By combining policies which
make use of external resources with those alluded to earlier for improving
the internal dynamics of the operating cycle we anticipate further improve-
ments in system performance.
Our study also suggests some scope for system dynamics in a relatively
neglected area -- that of managerial and financial control systems in small
businesses. Given the increasing economies of computing and the availability
of DYNAMO for mini- and microcomputer systems such studies are now within the
range of quite sitall businesses, If such studies could be linked to efforts
22
to develop generic models of the processes involved in financial control,
then the effort would be doubly worthwhile. An approach to financial control
which explicitly recognizes the implications of uncertainties, delays, and
interactions might be found to be superior to techniques such as “common
sense" ratio analysis with all the defects such as those revealed in this
paper.
On a higher level of abstraction we view our work as @ contribution
toward a theory of working capital management -- an area which Smith [9]
argues is a relatively unsatisfactory one. Wé may envisage the working
capital management problem as being a series of interrelated and recurring
decisions to affect levels of cash, inventory, receivables, and payables
while facing a highly uncertain environment. “Analysis of the dynamic com=
plexity to be found in such situations may lead us to an understanding of
the symmetries between the structure-behavior theorems of system dynamics
and more familiar financial concepts. Financial ratio analysis remains to
be integrated into a theory of working capital management. If a generic
model of financial control could be developed it would provide the basis
for such a synthesis, We have only begun to explore this potentially fruit-
ful line of enquiry first suggested by Lyneis [7].
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Stone, BK,
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Forrester, J.W.
Smith, K.V,
651
24
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Corporate Planning and Policy Design. Cambridge,
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