Evaluating the performance of receivable and inventory strategies

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FACULTY WORKING
PAPER NO. 89-1571

Evaluating the Performance of
Receivable and Inventory Strategies

James A. Gentry
Paid Newbcld
David T. Whitford
Jesus De La Garza

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College of Commerce and Business Administration
Bureau of Economic and Business Research
University of Illinois Urbana-Champaign

BEBR

FACULTY WORKING PAPER NO. 89-1571

College of Commerce and Business Administration

University of Illinois at Urbana- Champaign

May 1989

Evaluating the Performance of Receivable
and Inventory Strategies

James A. Gentry, Professor
Department of Finance

Paul Newbold, Professor
Department of Economics

David T. Whitford, Associate Professor
Department of Finance

Jesue De La Garza
Virginia Polytechnic Institute and State University

EVALUATING THE PERFORMANCE OF RECEIVABLE
AND INVENTORY STRATEGIES

ABSTRACT

The primary objective of this paper is to present a methodology
for evaluating the long-run performance of receivable and inventory
management. The methodology is based on a conceptual idea and a time
series technique. The model develops nine sets of conditions involved
in determining the cause of changes in accounts receivables and inven-
tories. It shows the trend of sales patterns and collection exper-
ience are responsible for changes in receivables. Also the trend of
production costs and inventory controls are the causes of changes in
inventories. The model ranks the nine sets of conditions according to
the present value that is created because of management decisions
and/or economic factors related to receivables and/or inventories.
The best strategy for receivables management improvement is to speed
up the inflow of cash, which occurs when the rate of change in
receivables is below the rate of change in sales. The best strategy
for Inventory management improvement is to improve control and reduce
inventory levels, which occurs when the rate of change in inventories
is lower than the rate of change in production cost patterns, and vice
versa. The Box, Pierce and Newbold ARIMA model determines a time
series trend of sales, receivables, production costs and inventories.
The estimated trends are used to rank the performance of a company's
receivable and/or inventory management. The methodology is tested
empirically in a recession and a post recession period. Finally,
insights from the methodology are presented.

EVALUATING THE PERFORMANCE OF RECEIVABLE
AND INVENTORY STRATEGIES1

Changes in Che amount and turnover of receivables and inventories
are directly related to the level and timing of a firm's cash inflows
and outflows. Therefore, changes in the long-run performance of
receivable and inventory management directly affects the value of a
firm [25, 26]. For example, shortening the time period involved in
collecting cash from customers without decreasing demand results in an
increase in the present value of the net cash flows, which in turn
creates shareholder value. Likewise the overall reduction in the com-
mitment to inventories without decreasing demand creates shareholder
value. When analyzing the causes of changes in the level and speed of
cash inflows and outflows, changes in accounts receivable and inven-
tory are compared to changes in sales and production, respectively.
Therefore, a model that determines the causes of changes in
receivables and inventories provides valuable information to manage-
ment, boards of directors and analysts. There are numerous finance
oriented models that focus on the control of accounts receivable,
e.g., [1, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 27]. However,
models for controlling inventories are generally found in the
accounting literature, e.g., [16, 20] or in the management science
literature.

The systems used to monitor receivables and inventories provide a
wealth of information for estimating trends and evaluating the per-
formance of receivable and inventory strategies. The performance of
receivable management has not been previously studied because, until

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recently, the cause of changes in receivables had not been fully
developed. The causes of changes in inventories are developed in this
paper. In 1985 Gentry and De La Garza (GD) [10] extended the work of
Carpenter and Miller [4] and showed there are nine possible sets of
conditions that underlie the causes of changes in accounts receivable.
GD concluded the primary causes of changes in receivables are
attributed to changes in sales patterns, collection experiences and
joint effects. Gallinger and Ifflander [9] also observed these three
effects in a variance model designed to control accounts receivables.
The overall objective of the study is to create a methodology for
evaluating the performance of receivable and inventory management.
The remaining objectives are to review briefly the GD model for
monitoring accounts receivable; to develop a model for explaining the
causes of changes in inventories; to present a methodology for ranking
the performance of receivable and inventory management; to use the
Box, Pierce and Newbold [3] ARIMA model to evaluate the receivable and
inventory management performance of 119 industrial companies; and to
analyze the performance rankings and the contribution receivable and
inventory strategies make in the creation of shareholder value.

I. MONITORING MODELS
Overview

GD identified nine sets of conditions that were needed in order
to analyze changes in accounts receivable. These conditions were
conceptualized in a 3x3 matrix based on the trend of sales patterns
(S) and collection experience (CE). Exhibit 1 is a similar 3x3 matrix
used to identify the conditions that cause changes in receivables and

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inventories . The horizontal axis shows changes in receivables are
caused by changes in sales patterns and changes in inventories are
associated with changes in production cost patterns. Changes in sales
and production are in turn related to changes in the demand for a
firm's products and changes in production schedules, respectively.
The vertical axis reflects that changes in receivables are also re-
lated to collection experience. Additionally, changes in inventories
are also related to inventory control. These changes in collection
experience are in turn related to changes in a firm's credit policies
and the changes in inventory control are related to changes in inven-
tory management and/or production policies.

Changes in sales or production cost patterns refer to monthly
changes in the level of sales or production. The pattern and trend in
sales and production can change because of seasonal, cyclical or random
events. The collection experience reflects the payment behavior of a
firm's customers and is related to a firm's credit administration
actions. Collection experience is characterized by the fraction of
credit purchases in a month that remain outstanding at the end of a
subsequent month. Inventory control exemplifies the performance of
the internal control system and the efficiency of inventory manage-
ment. Inventory control experience reflects the fraction of a firm's
production costs in a month that remain outstanding at the end of each
subsequent month. For example, if the inventory control pattern for
June is 80-50-20, it means 80% of June's production costs are embedded
in the June 30th inventory value; 50% of May's production costs are

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present in the inventory value on June 30, and 20% of April's produc-
tion costs are still outstanding in the inventory value on June 30.
The nine conditions shown in Exhibit 1 reflect the interaction
that exists between sales experience and collection pattern behavior
and between production costs and inventory control experiences. The
algorithms for taking into account these interaction effects for re-
ceivables are presented in GD [10, p. 31], and the algorithm for

2
inventories are presented in Exhibit 2. The algorithms determine the

relative amount that each component contributes to either the change

in receivables or inventories. Because receivables and inventories

are current assets, the only difference between the two algorithms is

the explanation of the variables that cause receivables or inventories

to change. The interactive relationships developed in the algorithm

are manifested in the trend of sales and receivables, as shown in

Exhibit 3, or in the trend of production costs and inventories, as

shown in Exhibit 4.

Inventory Model

Exhibit 4 provides the conceptual framework for understanding the
logic embedded in the inventory management algorithms. The relation-
ships that cause changes in receivables were developed in GD [10, p.
30], therefore, a brief overview of the conditions that cause changes
in inventories follows. The parallel lines of production cost and
inventories shown in Condition 1 of Exhibit 4 indicate there was no
change in production costs, or in inventory control for the time
period presented, therefore, there was no change in inventories.

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Under Conditions 2 and 2' there is no change in production costs,
but in Condition 2 inventory control is deteriorating while in
Condition 2' it is improving. Because inventory control is deterior-
ating in Condition 2 inventories are increasing more rapidly than the
stable production costs. Under Condition 2' the opposite set of
circumstances prevail which cause inventories to decrease while pro-
duction remains constant.

Under Condition 3 inventories change only because of changes in
production cost patterns and inventory control performance is neutral
and has no affect on inventories. In Condition 3 inventories increase
because of an increase in production costs and in Condition 3' the
decrease in inventories is caused by a decline in production costs.

Condition 4 in Exhibit 4 illustrates the case where lax inventory
controls cause raw material or goods-in-process inventories to in-
crease more rapidly than production costs. For example, a change in a
policy to carry more raw materials because of a forecasted shortage
may be an explanation for this inventory build up. Additionally, an
increase in sales can cause an increase in production costs. Like-
wise a forecasted increase in sales can create an expansion in raw
material goods-in-process or finished goods inventories. Thus an
increase in inventories may be caused by a pure production effect,
a pure inventory control effect and/or an interaction effect between
production costs and inventory control, referred to as a joint effect.

Scenario 5 expresses an opposite set of conditions. Tightened
inventory control can cause raw materials, goods-in-process or
finished goods to decline more rapidly than the declining production

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costs. Likewise a cut in production costs can result In a tightening
of inventory controls, which can cause inventories to decline more
rapidly than the production costs. Also a policy to hold less raw
materials, goods-in-process or finished goods can be carried over into
production efficiency, thereby causing production costs to decline.
Under Condition 5 inventories can be smaller because of a decline in
production costs, a production effect, an improvement in inventory
control, or a combination of the two, a joint interaction effect.

Under Conditions 6 and 7, there are opposite forces at play that
affect the change in inventories. For example, under Condition 6,
lenient inventory control practices result in an inventory build up,
simultaneously, a decline in demand causes production costs to be
reduced. The decline in demand is a countervailing force that pro-
duces an overall decline in inventories. Under Condition 7, improved
inventory control practices and policies cause inventories to decline
while increased demand causes production costs to rise. In this
circumstance, the improved inventory control practices more than
offset the increase in inventories caused by rising demand. The re-
sult is inventories increase less rapidly than production costs.

II. RANKING PERFORMANCE
The model makes it possible to analyze the long-run performance of
receivable and inventory management and, thereby, determine the effec-
tiveness of the operating strategies pursued by a company. The moni-
toring model provides a tool to rank the operating performance of
receivable and inventory management.

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Objectives of top management are to analyze and judge the per-
formance record of receivable and inventory management. Exhibit 3
shows graphically that annual changes in accounts receivable (AAR)
were related to long-run trends in sales (AS) and in collection ex-
perience (ACE). Exhibit 4 graphically shows that annual changes in
inventories (AINV) are associated with long-run trends in production
costs (AP) and in inventory control experience (AIC). Exhibits 3 and
4, respectively, provide operating frameworks for financial managers,
analysts and academic researchers to identify quickly the sets of con-
ditions and variables used in measuring the performance of receivable
and inventory management. Using the present value model as a bench-
mark, Exhibits 3 and 4, respectively, highlight from the best to the
worst set of conditions that exist in creating firm value through cash
collection or inventory control strategies. The ranking methodology
is based on the principle of creating present value for shareholders.

Receivables

The best strategy for receivable management improvement is to
speed up the inflow of cash without causing demand to decline. That
would occur when the rate of change in receivables is below the rate
of change in sales. The receivable management strategies that would
speed up the inflow of cash are strategies 7, 2' and 5 in Exhibit 3.
The worst strategy for receivable management is to slow down the
inflow of cash. That occurs when the rate of change in receivables is
greater than the rate of change in sales. These worst receivable
management strategies are in cells 6, 2 and 4 as shown in Exhibit 3.

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Finally strategies 3, 1 and 3' reflect a neutral receivables strategy
where the change in receivables is equal to the change in sales.

Inventories

The best strategy for inventory management improvement is to
improve control and reduce inventory levels without causing stockouts
and shortages. That occurs when the rate of change in inventories is
below the rate of change in production costs. The inventory strate-
gies that reduce inventory levels are strategies 7, 2' and 5 in
Exhibit 4. The worst strategy for management is to lose control of
its inventories and experience an unexpected build up in its inven-
tories. That happens when the rate of change in inventories is
greater than the rate of change in production costs. These worst
inventory management strategies are in cells 6, 2 and 4 as shown in
Exhibit 4. Finally, strategies 3, 1 and 3' reflect a neutral inven-
tory strategy, where the change in inventories is equal to a change in
production costs.

Benefits

The performance ranking system can be used by top management to
accomplish several important tasks. First, if top management observed
that receivables management was in the worst ranking performance
cells, credit policies and collection procedures could be designed to
speed up the inflow of cash, causing receivables to become a smaller
proportion of sales.

Second, top management may wish to create a hierarchy of rewards
if the performance record is deserving and creates shareholder value.
For example, Exhibit 5 shows the highest award would occur when

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consistent performance is achieved over time, which would be in cell 7
for both receivable and inventory management. The second highest
award would be for performance achievement that is consistently in the
top three strategies over time, which would be cells 7, 2' and 5 as
reflected in Exhibit 5.

Third, the performance ranking system provides top management the
information needed to track longitudinally the performance of receiv-
able and inventory management. Assuming the objective of top manage-
ment is to maximize owner's wealth, the performance ranking system
makes it possible to determine if receivable and inventory management
consistently produce results that are in the cells with the highest
ranking. If the results are not consistently in the highest ranking
cells, management is also concerned that performance is improving as
evidenced in the longer run performance trend results.

The first step in determining the performance ranking is to esti-
mate the trends of sales, receivables, production and inventories.
An explanation of the Box, Jenkins and Newbold ARIMA model follows.

III. ESTIMATING TRENDS
A frequently studied problem in time series analysis, notably in
the literature on seasonal adjustment, concerns the decomposition of
an observed series into trend, seasonal, and irregular components.
Often this decomposition is taken to be additive. Alternatively, a
multiplicative components model can be considered through the additive
decomposition of the logarithms of the observed series. A great dif-
ficulty that is faced is that, given just the observed series, the

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individual components are not uniquely Identified, unless somewhat
arbitrary assumptions about their behavior are imposed. Given a
generating process for an observed time series, there typically exists
a large number of plausible decompositions whose components can rea-
sonably be viewed as representing trend, seasonal, and irregular para-
meters. This large number of alternatives can create problems in
analyzing seasonal adjustment problem. However, Box, Pierce and
Newbold [3] have recently shown that, for a wide class of time series
generating models, although the problem of estimating components over
the sample period has no unique solution, there is a unique solution
to the problem of forecasting future values of these components. In
short, all of the observationally equivalent components' models lead
to identical forecasts for the constituent components. Thus, while
there is some ambiguity in defining and estimating trend over the
sample period, there is no ambiguity in the estimation of projected
trend. This is encouraging, since for many purposes it is precisely
this forward looking version of trend that is most relevant. For
example, if a manager is presented with a historical record of data on
sales and receivables it is reasonable to ask what these data suggest
about future trends. It is precisely this problem for which the ana-
lysis of Box, Pierce and Newbold demonstrates that a unique solution
is available.

Box, Pierce and Newbold consider a time series X , generated by a
member of the class of seasonal ARIMA models of Box and Jenkins [2].
If a member of this class of models admits a decomposition into trend,
seasonal, and irregular components, then the optimal components

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forecasts, based on observations of X , are unique. For example, one

particular member of this class which has been found to well represent

a wide array of actual series is the (0 , 1 , 1)(0 , 1, 1) model — sometimes
called the "airline model." This is

(l-B)(l-BS)Xt = (l-e,B)(l-6sBS)et

where s is the seasonal period, B the back shift operator, and e is
zero-mean white noise. As Box, Pierce and Newbold note, forecasts of
future values of series generated by this model can be written as a
linear time trend, plus seasonal dummy variables. Viewing the process
X as the sura of trend, seasonal and irregular components, the linear
trend in the forecast function constitutes the optimal prediction of
the trend component, and the dummies are the optimal predictors of the
seasonal component. (The optimal forecast of the irregular component
is zero.) Since the airline model can be fitted to observed data, and
forecasts of future values of X readily computed from the fitted
model, it is straightforward to separate out the components forecasts.
If an airline model is fitted to the logarithms of a time series, the
slope of the linear trend in the forecasts of the logarithms repre-
sents projected growth rate in the original series.

When analyzing a large number of time series on the same phenome-
non, such as corporate sales or receivables, it is common practice to
see if it is reasonable to impose the same ARIMA model structure on
every series. The model parameters are then separately estimated for
each series. We carefully examined the time series properties of a
subset of our sales and receivables series, and found in both cases

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that the airline model appeared to provide a good description of the
behavior of the logarithms of these data. Accordingly, this model was
fitted to the logarithms of all of our series on sales and receivables.
The forecasts from these fitted models were then used to estimate pro-
jected growth rates. These projected growth rates should give an
accurate picture of what management could reasonably expect about
future trends, based on recent past history of these time series.

Company Selection

In order to use the Box, Pierce and Newbold ARIMA model to measure
a time series trend of sales, receivables, production and inventories,

a sample of 119 industrial companies was selected from the quarterly

3
Compustat file. The time period of the analysis was IVQ 1975 to the

IIQ 1987. To be included in the sample it was necessary to have 47

4
quarters of continuous sales, receivables, production and inventory

observations for the period IVQ 1975 to TIQ 1987. A list of the 119

companies is presented in Exhibit 6.

IV. PERFORMANCE ANALYSIS
There are models designed to control accounts receivable and in-
ventories, however, there are no empirical studies that analyze the
performance of receivable or inventory management. Neither are there
any studies that determine the receivable or inventory strategy
pursued by a company in a recession or post recession environment.
One objective of this paper is to analyze the long-run (vis-a-vis
seasonal) performance of receivable and inventory management and the
strategies pursued in a recession and in a post recession period. The

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overall objective is to create a modei that evaluates receivable and
inventory performance. Additional objectives are to test the model
with empirical data, to interpret management performance and strate-
gies pursued in managing receivables and inventories in a recession
and post recession period.

Quarterly data for 119 industrial companies are used to estimate
the trend of sales, receivables, production and inventories in a
recession and a post recession period. There were 2 5 quarters of time
series data, 1VQ 1975 to IVQ 1981, used in the Box, Pierce and Newbold
model to estimate the trend of sales, receivables, production and
inventories for the subsequent eight quarters, i.e., 1Q 1982 to IVQ
1983. Likewise, 47 quarters, IVQ 1975 to IIQ 1987, of sales,
receivables, production and inventory data were used to estimate their
respective trends for the subsequent eight quarters, 1110 1987 to IIQ
1989. The projected two-year trends are used to assign each sample
company to the appropriate receivable or inventory performance cell in
Exhibit 1.

Receivables

If the theoretical objective of a firm is to maximize owners'
wealth and, if the firm's managers are successful in implementing a
receivable strategy to achieve that task in the face of powerful macro
economic and industry forces, the receivable performance would be
expected to be located in cells with the highest rankings in Exhibit
5. The top ranked cells are in the bottom row of Exhibit 1, where the
projected trend of receivables is always lower than the respective
sales trend. Thus if sales are increasing, the best strategy is for

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the trend in receivables to be below the sales growth, which is cell 7
in Exhibit 1. If sales are flat or declining, cells 2' and 5, respec-
tively, reflect the best strategy. The estimated trends of sales and
receivables are reported in Exhibit 6 for each of the 119 sample com-
panies.

A transition matrix is used to present the performance rankings of
the 119 companies. The vertical axis represents the receivable per-
formance ranking for the recession period. The highest rank is a 1
and the lowest is a 9. The cell location from Exhibit 1 is associated
with its appropriate performance ranking. That is cell 7 has the
highest performance rank, a 1. Cell 6 has the lowest performance rank,
a 9. The horizontal axis represents the receivable performance rankiag
for the post recession period.

The following example illustrates how to interpret the information
in Exhibit 7. The northwest corner of the matrix shows 35.7 percent
of companies that were located in cell 7 for the recession period were
also located in cell 7 in the post recession period. That is, of the
42 companies that had the highest receivable performance ranking in
the recession, where sales were increasing more rapidly than receiv-
ables, 35.7 percent (15/42) continued to have the highest receivable
performance ranking in the post recession period. In the same row of
Exhibit 7 we observe that 19 percent (8/42) of the companies that had
the highest receivable performance rank in the recession had declined
to the fourth ranked cell 3, where the trend of sales and receivables
were increasing at the same rate. Finally, in the same row we observe
that receivable performance declined for two companies (2/42=4.3%)

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froni the highest to the lowest level hetween the recession and the
post recession period. Using the principle developed in the above
examples, it is possible to determine the probability of a company
changing its receivable performance between a recession and a post
recession period. For example, using cell 4, there was a 27.5 percent
probability of a company having below average performance in the
recession, but improving to the highest rank, cell 7, in the post
recession period. Or there was a 40 percent chance that the
receivable performance of a company starting in cell 4 would remain
unchanged in the post recession period.

There are several significant observations related to Exhibit 7,
the transition matrix. There is not a clustering of the companies in
the highest performance rankings. By inspection one can observe that
cells 7, 3, 4, and 6 are most widely pursued strategies in the reces-
sion. That is 42 companies started in cell 7, 20 in cell 3, 40 in
cell 4 and 12 in cell 6, which represents over 95 percent (114/119) of
the sample companies. For the post recession period five strategies
were most widely pursued. That is 38 of the companies were in cell 7,
six in cell 5, 21 in cell 3, 47 in cell 4, and five in cell 6.

Exhibit 8 summarizes the number of companies that experienced
either an improvement or a decline in their receivable performance
between the two periods. There were 41 companies that had an improve-
ment in performance and 41 companies that experienced a decline.
There were 37 companies that experienced no change in their performance
between the two periods. This equal distribution among the three per-
formance nodes suggests a rather random performance pattern for the

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119 companies In the sample. Exhibit 8 also shows the number of
levels that the performance rank either improved or declined. For
example, five companies improved eight levels, that is from the worst
to the best, and 11 companies improved six levels, i.e., from cell 4
to 7. Exhibits 7 and 8 show there were five companies that started in
the worst performing cell, 6, and ended up in cell 7, the best per-
forming cell. In summary, the information in Exhibit 8 is taken from
Exhibit 7.

The mean and standard deviation of the forecasted trends of sales
and receivables for the major performance cells are presented in
Exhibit 9. The summarized information is subdivided into the reces-
sion and post recession period. These summary data provide an over-
view of the trends for each performance ranking.

Inventories

Assuming the objective of management is to create shareholder
wealth, the best possible inventory management strategy is to reduce
the level of inventories and simultaneously avoid a shortage or
excessive handling or ordering costs. However, in the presence of
powerful economic and industry influences, this is at best a difficult
assignment. If management is successful in implementing an inventory
strategy that achieves this task, inventory performance would be
expected to be located in the higher ranking cells in Exhibits 1 and
4. As in the case of receivables performance, the top ranked cells
are in the bottom row of Exhibits 1 and 4 where the projected trend of
inventories is always lower than its production cost trend. That is
when production costs are increasing, the best strategy is for the

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trend in inventories to be below the growth of production costs, which
is cell 7 in Exhibits 1 and U. If production costs are flat or
declining, cells 2' and 5, respectively, reflect the best strategy.
The estimated trends of production costs and inventories are reported
in Exhibit 10 for all 119 companies in the sample.

The transition matrix for evaluating the performance ranking of
the 119 companies in a recession and in a post recession period is
presented in Exhibit 11. One of the most important observations in
Exhibit 11 is found in the northwest corner, in cell 7, the highest
ranking inventory performance cell. The data show 55.6 percent
(35/63) of the companies that achieved a highest inventory performance
ranking in a recession, repeated this highest ranking in a non
recession period. Additionally, Exhibit 11 shows 17.5 percent (11/63)
of the companies that achieved the highest performing inventory
management rank in the recession experienced a below average per-
formance in the post recession period. Also, 14.3 percent (9/63) of
the companies that achieved the highest ranking the recession declined
to an above average performance in cell 3.

Exhibit 11 also shows that 39.5% (15/38) of the companies that
achieved a below average inventory performance rank, a 7, in a reces-
sion period experienced a significant change in accomplishing the
highest performance ranking in the post recession period. Further-
more, approximately 29% (11/38) of the companies that ranked in the
seventh level of inventory performance in the recession, repeated
this performance in the post recession period.

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In the recession period, cells 7, 4 and 3 accounted for approxi-
mately 91 percent of the inventory performance results. The same
cells accounted for 84 percent of the inventory performance results
in the post recession period. In all three of these cells, produc-
tion costs were increasing.

Exhibit 12 shows there were 38 companies that improved their in-
ventory performance one or more levels between the recession and the
post recession period. In contrast, 35 companies experienced a
decline in inventory performance of one or more levels between the
recession and post recession periods. There were 46 companies whose
performance was unchanged, and 35 had the highest performance rank,
cell 7, and 11 were in cell 4.

In conclusion, the probabilities in the transition matrix show
that achieving high inventory management performance in a recession
does not assure the firm of a similar performance in a post recession
period, or vice versa. Also the empirical evidence shows the pre-
ponderance of the companies experienced increasing production costs,
but their ability to control the growth of inventory varied sig-
nificantly. Finally, approximately 30 percent (36/119) of the
companies managed to be in the top three performance cells in both a
recession and a post recession period, which highlights the difficulty
of consistently achieving the highest level of performance.

Combined

An analysis of the combined performance of receivable and inven-
tory management provides unique insight into the chances of having
consistent performance in both a recession and a post recession

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period. A frequency distribution of the various performance combina-
tions is presented in Exhibit 13. The most significant observation
related to Exhibit 13 is that there is nearly a random plotting of the
performance path followed by the 119 companies. There were 78
separate performance paths taken by the 119 companies. The most
optimal path would be cell 7 for both receivables and inventory manage-
ment. There were four companies that achieved the highest level of
performance in both receivable and inventory management on both time
periods studied. Further analysis shows that only 6 percent (7/119)
of the companies were in the top performing cells, 7, 2' and 5, for
receivable and inventory management in both time periods. These
observations highlight the extreme difficulty of achieving top current
asset management performance under varying economic conditions.

Analyses of each company's changes in performance within its
respective industry also provides additional insight. There are 59
separate industry classifications based on the four digit SIC codes.
Because most of the industries have only one or two companies, it is
difficult to assess performance results within an industry. There-
fore, industries with four or more co«panies were selected to
illustrate performance results. The performance change in receivable
or inventory management is shown in Exhibit 14. The companies are
ranked according to the number of cells receivable performance
improved, declined or remained constant according to the ranking
system in Exhibit 5. The change in inventory performance is also
shown for each company. For example in the paper and allied products
industry, Fort Howard's receivable performance declined by six cells

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between the recession and post recession periods, while the inventory
performance was unchanged. Likewise, International Paper's
receivables and inventory performance improved the maximum of eight
cells, i.e., it went from the worst to best performance between the
two periods.

A casual study of the changes in receivables and inventory perfor-
mance within an industry shows the results vary widely among the
several companies. The joint performance of receivable and inventory
management for companies within an industry is mixed. There are no
performance patterns that arise from this small sample of companies
within the five industries.

V. CONCLUSIONS
A methodology was presented that ranked the performance of
receivable and inventory management. The receivable strategies pur-
sued by a company can be evaluated on the basis of the relative trends
of sales and receivables. Likewise, the trends of production costs
and inventories provide the information needed to evaluate the inven-
tory strategies followed by a company. The methodology makes it
possible to determine the probability of a firm changing its re-
ceivables of inventory performance between two comparative periods.
Also it shows the stability of receivable or inventory strategies
among firms and/or across industries. The contribution of the
methodology is that it provides management, analysts and academic
researchers a tool for better evaluating the contribution of re-
ceivable and inventory management to the value of the firm.

-21-

FOOTNOTES

The authors are grateful to the research assistance of Michael J.
Gallicho and Chau Chen Yang.

2
The Financial Accounting Standards Board offers firms flexibility

in measuring inventory levels which can affect performance measures

during periods of inflation. During a period of inflation firms

maintaining constant inventory levels in unit terms and utilizing

the LIFO method, can experience rising inventory levels in dollar

terras. Firms maintaining constant inventory levels in unit ternls that

utilized the FIFO method experienced decreasing inventory in dollar

terras. This observation highlights the need to determine the

measurement method(s) utilized in accounting for inventory value,

when comparing inventory and production performance among firms or

industries.

3
Sales, receivable and inventory data were readily generated from

the Corapustat files. Quarterly production costs were derived from

the Corapustat file and were based on the following equation:

Pfc - INV + CGSt - INVt_j

where Pt is the production costs in period t, CGSt i-s tne cost of
goods sold in period t, INV^ is the ending inventory in period t and
INVt_i is the beginning inventory for period t.

4
There were 46 observations for production costs. One observation

was lost because beginning and ending inventory were used in the

calculation of production costs, as shown in footnote 2.

When sales, receivables, production and inventories have a trend
of less than one percent on either side of zero, the company is
classified as a 1 in Exhibit 1, which is zero growth of sales and
receivables. For all remaining cases, if the difference between the
trend of sales and receivables or production and inventories is less
than one percent, it is assumed they are changing at the same rate and
would be in cells 3 or 3'. If the trend of sales or production is
flat and the trend of receivables or inventories is greater than one
percent, the company will be classified as a 2. If the receivable
or inventory trend is greater than a negative one percent and sales
or production are flat, the company is classified as a 2'. The
remaining companies are appropriately classified in cells 7, 5, 4
or 6.

-22-

REFERENCES

1. W. Beranek, Analysis for Financial Decisions, Homewood, IL,
Richard D. Irwin, 1963.

2. George E. P. Box and Gwilym M. Jenkins, Time Series Analysis, Fore-
casting and Control, San Francisco: Holden Day, 1970, revised ed.
1976.

3. George E. P. Box, David A. Pierce and Paul Newbold, "Estimating
Trend and Growth Rates in Seasonal Time Series," Journal of the
American Statistical Association, Vol. 82 (March 1987), pp. 276-282.

4. Michael D. Carpenter and Jack E. Miller, "A Reliable Framework for
Monitoring Accounts Receivable," Financial Management, Vol. 9 (Winter
1979), pp. 37-40.

5. R. M. Cyert, H. J. Davidson, and G. L. Thompson, "Estimation of
Allowance for Doubtful Accounts by Markov Chains," Management
Science (April 1962), pp. 287-303.

6. R. M. Cyert and G. L. Thompson, "Selecting a Portfolio of Credit
Risks by Markov Chains," Journal of Business (January 1968), pp.
39-46.

7. L. P. Freitas, "Monitoring Accounts Receivable," Management
Accounting (September 1973), pp. 18-21.

8. G. W. Gallinger and P. B. Healey, Liquidity Analysis and
Management , Reading, MA, Addison-Wesley Publishing Company, 1987.

9. George Gallinger and James Ifflander, "Monitoring Accounts Receiv-
able Using Variance Analysis," Financial Management, Vol. 15 (Winter
1986), pp. 69-76.

10. James A. Gentry and Jesus M. De La Garza, "A Generalized Model for
Monitoring Accounts Receivable," Financial Management, Vol. 14
(Winter 1985), pp. 28-38.

11. , "Monitoring Payables and Receivables," Working Paper,

January 1988, 32 pages.

12. , "Monitoring Payables and Receivables," Faculty Working

Paper No. 1358, College of Commerce and Business Administration,

Bureau of Economic and Business Research, University of Illinois,

May 1987, Revised October 1987.

13. J. J. Hampton and C. L. Wagner, Working Capital Management, New
York, John Wiley & Sons, 1989.

-23-

14. N. C. Hill and K. D. Riener, "Determining the Cash Discount in the
Firm's Credit Policy," Financial Management (Spring 1979), pp.
68-73.

15. N. C. Hill and W. L. Sartoris, Short-Term Financial Management,
New York, Macmillan Publishing Company, 1988.

16. H. C. Hunt, "Potential Determinants of Corporate Inventory
Accounting Decisions," Journal of Accounting Research (Autumn
1985), pp. 448-467.

17. J. D. Kallberg and A. Saunders, "Markov Chain Approaches to
Analysis of Payment Behavior of Retail Credit Customers,"
Financial Management (Summer 1983), pp. 5-14.

18. Y. H. Kim (editor) and V. Srinivasan (collaborator), Advances in
Working Capital Management, Volum 1, Greenwich, CT , JAI Press
Inc. 1988.

19. G. H. Lawson, "The Mechanics, Determinants and Management of
Working Capital," Managerial Finance (No. 3/4 1984), pp. 12-25.

20. C. J. Lee and D. A. Hsieh, "Choice of Inventory Accounting
Methods: Comparative Analyses of Alternate Hypotheses," Journal
of Accounting Research (Autumn 1985), pp. 468-485.

21. W. D. Lewellen and R. W. Johnson, "Better Way to Monitor Accounts
Receivables," Harvard Business Review (May-June 1972), pp. 101-109.

22. W. D. Lewellen and R. 0. Edmister, "A General Model for Accounts
Receivable Analysis and Control," Journal of Financial and
Quantitative Analysis (March 1973), pp. 195-206.

23. M. E. Porter, Competitive Strategy: Techniques for Analyzing
Industries and Competitors, New York, The Free Press, 1980.

24. , Competitive Advantage, New York, The Fress Press, 1985.

25. Alfred Rappaport, Creating Shareholder Value, New York, The Free
Press, 1987.

26. William Sartoris and Ned C. Hill, "A Generalized Cash Flow Approach
to Short-Term Financial Decisions," Journal of Finance, Vol. 38
(May 1983), pp. 349-360.

27. B. K. Stone, "The Payment Pattern Approach to Forecasting and
Control of Accounts Receivable," Financial Management (Autumn
1976), pp. 65-82.

D/496

Exhibit 1

Sets of Conditions Responsible for
Changes in Inventories and Receivables

Inventory

Control
Experience
(IC)

or

Collection
Experience
(CE)

Deteriorate (+ )

No Change (NC)

Improve (+ )

Production Cost Patterns (P) or
Sales Patterns (S)

Up (+)

No Change
(NC)

Down (+)

4

2

6

3

1

3*

7

V

5

t Inventories or Receivables Increase
+ Inventories or Receivables Decrease

Exhibit 2

Algorithms for Measuring the Pattern Effects
That Cause a Change in Inventories

Condition Description
1 NC in IC or PC

2 & 2' t or + in IC and

NC in PC

( PC . = PC . )

J i

3 & 3' t or 4 in PC and

NC in IU

4 t in IC and 4- in PC

4 in IC and t in PC

t in IC and 4 in PC

4 in IC and t in PC

Pattern

Effects

Algorithm

None

ICE

AIC x PC. '

l

PCPE

PCPE

ICE

JE

PCPE

ICE

JE

PCPE

ICE

PCPE

ICE

A PC

X

IC.

1

A PC

X

IC.
i

AIC

X

PE.

l

A PC

X

AIC

A PC

X

AIC

AIC

X

PC.
J

-APC

X

AIC

APC

X

AIC

AIC

X

PC.
J

APC

X

IU.
J

AIC

X

PC.

Legend

PC = production cost patterns
IC = inventory control patterns
NC = no change

t or 4 = see Exhibit I
i = oldest month

j = current month

PCPE = production cost pattern
effect

ICE = inventory control effect

JE = joint effect

Exhibit 3

Examples of Relationships that Cause
Changes in Receivables

$

Up(|)
(Best)

4

1?

$

Sales Patterns

No Change
(Neutral)

2

Deteriorating ( | )
(Worst)

Down (|)
(Worst)

6

Collection

Experience

Patterns

No Change
(Neutral)

Improving ( \ )
(Best)

Slope of sales in period t
Slope of receivables in period t

Exhibit 4

Examples of Relationships that Cause
Changes in Inventories

Production Cost Patterns

$

Up(|)
(Best)

4

$

No Change
(Neutral)

2

Deteriorating ( f )
(Worst)

^~~~~~

Down (f )
(Worst)

6

nventory

Control

xperience

No Change
(Neutral)

Improving ( | )
(Best)

Slope of cost in period t
Slope of inventories in period t

Exhibit 5
Ranking the Performance of Receivable and Inventory Management

Receivables

Inventory

Cell in Production Control Cell in Sales Collection
Rank Exhibit 4 Performance Performance Exhibit 3 Performance Performance

1

7

best

best

7

best

best

2

V

neutral

best

2'

neutral

best

3

5

worst

best

5

worst

best

4

3

best

neutral

3

best

neutral

5

1

neutral

neutral

1

neutral

neutral

6

3'

worst

neutral

3'

worst

neutral

7

4

best

worst

4

best

worst

8

2

neutral

worst

2

neutral

worst

9

6

worst

worst

&

worst

worst

Exhibit 6

Trend of Sales and Receivables for a Recession
and a Post Recession Period, and Cell Location
of Performance in Exhibit 1
(in percent)

RECESSION PERIOD

POST RECESSION PERIOD

CELL LOCA-

CELL LOCA-

TION IN

TION IN

COMPANY

SALES

A/R

EXHIBIT 1

SALES

A/R

EXHIBIT 1

TOOTS IE ROLL

2.59

9.69

4

1.11

7.77

4

BELDING HEMINGWAY

0.23

-0.81

7

4.63

3.05

7

FIELDCREST

8.01

1.83

7

28.60

33.22

4

SPRINGS IND.

5.64

5.97

3

12.84

9.17

7

ADAMS MILLS

3.92

1.50

7

9.75

21.78

4

ALBA WALDENSIAN

9.13

10.02

3

6.17

11.08

4

RUSSELL CORP.

10.61

20.27

4

7.36

8.98

4

HAMPTON INDUSTRIES

8.2

7.89

3

6.81

7.24

3

LOUISIANA PACIFIC

-22.84

-20.74

6

12.45

8.15

7

WEYERHAUEUSER

2.67

14.48

4

8.33

9.82

4

BARRY WRIGHT

19.98

22.89

4

8.94

9.63

3

GF CORP.

10.77

3.19

7

-2.18

-2.35

3'

BOISE CASCADE

-0.35

2.37

6

0.97

5.60

4

FEDERAL PAPER BOARD

7.45

17.93

4

11.99

12.17

4

FORT HOWARD

13.94

7.13

7

19.63

22.40

4

GREAT NORTHERN NEKOOSKA

11.71

-4.00

7

9.47

8.91

3

INTERNATIONAL PAPER

-45.14

29.11

6

16.54

8.07

7

KIMBERLY-CLARK

10.96

2.57

7

10.08

7.52

7

LYDALL INC.

6.47

17.17

4

8.71

13.23

4

MACMILLAN BLOEDEL LTD.

-0.77

-0.47

1

3.51

4.60

4

POTLATCH CORP.

6.54

10.74

4

4.62

2.57

7

STONE CONTAINER

8.17

12.13

4

30.76

23.42

7

UNION CAMP

-4.14

7.54

6

9.00

9.15

3

MMM

3.30

6.90

4

7.85

7.09

3

DOW JONES

16.64

21.92

4

8.89

11.75

4

GANNET CO.

21.83

22.41

3

16.60

18.47

4

KNIGHT-RIDDER

11.83

12.20

3

8.83

11.41

4

MEDIA GENERAL

12.45

14.71

4

11.95

14.74

4

TIMES MIRROR

14.26

17.16

4

2.90

8.27

4

DU PONT

66.37

62.15

7

9.26

9.33

3

PPG INDUSTRIES

6.96

2.46

7

7.74

9.84

4

ROHM & HAAS

1.96

2.02

3

4.90

4.92

3

UNION CARBIDE

4.10

6.32

4

-1.76

-3.85

5

ESSEX CHEMICAL

12.99

11.00

7

2.70

-0.78

7

MERCK & CO.

11.37

15.63

4

15.40

9.42

7

ABBOTT LABS

15.26

12.04

7

11.13

12.60

4

Exhibit 6 (continued)

RECESSION PERIOD

POST RECESSION PERIOD

CELL LOCA-

CELL LOCA-

TION IN

TION IN

COMPANY

SALES

A/R

EXHIBIT 1

SALES

A/R

EXHIBIT 1

BRISTOL-MYERS

10.76

13.35

4

8.87

9.00

3

SMITHK.LINE BECKMAN

30.67

24.15

7

12.21

16.73

4

LAMAUR INC.

31.63

24.09

7

11.47

14.68

4

GUARDSMAN PRODUCTS

7.15

7.32

3

11.64

9.83

7

PRATT & LAMBERT

21.46

14.85

7

9.55

8.65

3

CROMPTON & KNOWLES

-1.51

-10.28

5

4.59

21.01

7

FAIRMOUNT CHEMICAL

8.01

9.51

4

-1.58

-4.36

5

DEXTER CORP.

-4.44

2.95

6

16.67

8.88

7

FERRO CORP.

-2.05

10.69

6

11.31

7.46

7

LUBRIZOL CORP.

11.00

2.70

7

2.18

4.13

4

NALCO CHEMICAL

9.4

14.4

4

6.63

6.49

3

AMERICAN PETROFINA

10.42

10.57

3

7.15

8.36

4

AMOCO CORP.

17.14

15.89

7

4.45

2.13

7

ATLANTIC RICHFIELD

21.4

11.63

4

-2.24

-4.05

5

CHEVRON

17.07

7.52

7

2.77

0.53

7

IMPERIAL OIL

8.7

-23.69

7

-1.15

-0.81

6

KERR-MCGEE

15.94

-4.52

7

-1.47

-4.48

5

MURPHY OIL

20.46

12.43

7

-0.42

-0.55

1

TEXACO

9.54

-7.53

7

2.12

0.85

7

TOSCO CORP.

31.91

31.29

3

-4.60

-15.26

5

UNOCAL

12.37

-15.74

7

3.60

0.92

7

BANDAG INC.

10.01

10.28

3

7.25

7.63

3

CARLISLE

14.94

21.8

4

7.00

7.29

3

COOPER TIRE & RUBBER

12.07

6.78

7

10.98

9.97

7

PANTASOTE

3.83

-2.95

7

2.50

23.94

4

VOPLEX

14.64

14.77

3

9.19

13.88

4

WOLVERINE WORLD WIDE

-10.11

20.46

6

-6.05

-2.34

6

BROCKWAY INC.

11.83

9.68

7

6.84

5.23

7

IDEAL BASIC IND.

4.73

10.10

4

-11.37

-23.05

5

LONE STAR IND.

4.89

6.55

4

0.27

-8.55

7

USG CORP.

-1.69

-0.93

3'

10.11

3.44

7

NORTON CO.

13.72

2.12

7

3.36

2.91

3

LUKENS INC.

10.27

7.89

7

7.32

7.28

3

ALCAN ALUMINUM

2.71

-3.09

7

6.46

6.36

3

ALUMINUM CO. OF

AMERICA

-24.57

-6.53

6

3.59

6.13

4

ARMANDA CORP.

6.44

16.94

7

4.78

2.80

7

VAN DORN CO.

9.70

12.77

4

6.25

7.45

4

SNAPON TOOLS

7.44

12.71

4

7.88

13.80

4

GENERAL HOUSEWARES

11.52

17.27

4

-0.54

1.89

6

HEXCEL CORP.

15.39

13.92

7

16.67

18.54

4

CRANE CO.

5.16

6.12

3

2.59

1.67

3

CUMMINS ENGINE

12.06

13.19

4

9.89

10.26

3

CATERPILLAR

11.51

12.64

4

3.54

8.75

4

SAFE GUARD SCIEN.

-17.74

-4.82

6

2.41

3.76

4

Exhibit 6 (continued)

RECESSION PERIOD

POST RECESSION PERIOD

CELL LOCA-

CELL LOCA-

TION IN

TION IN

COMPANY

SALES

A/R

EXHIBIT 1

SALES

A/R

EXHIBIT 1

SALEM CORP.

-6.21

6.31

6

3.18

-0.62

7

PITNEY BOWES

18.44

18.22

3

11.91

12.90

4

LSB INDUSTRIES

18.77

18.15

3

14.73

13.20

7

VENDO CO.

0.8

-9.67

7

7.37

1.42

7

GENERAL ELECTRIC

11.45

12.43

4

19.53

8.16

7

AMETEK INC.

10.83

10.96

3

7.99

9.41 '

4

BALDOR ELECTRIC

11.16

6.45

7

7.28

6.39

3

WHIRLPOOL

7.77

6.85

3

9.16

8.84

3

THOMAS INDUSTRIES

6.00

8.46

4

6.19

7.55

7

ZENITH

6.96

7.78

3

9.62

25.68

7

ANDREA RADIO

21.00

-8.89

7

-2.20

6.77

6

TRW

-2.13

13.11

6

6.97

9.73

4

E-SYSTEMS INC.

14.23

24.18

4

12.16

13.74

4

EDO CORP.

11.86

22.82

4

6.12

8.13

4

WATKINS-JOHNSON

-2.15

6.92

6

6.98

10.01

4

AMP

5.88

8.98

4

12.89

13.35

3

KOLLMORGEN

20.54

20.06

3

0.47

9.57

4

IBM

12.93

1.43

7

4.71

13.52

4

NCR

7.2

11.97

4

8.36

4.40

7

DIEBOLD

15.05

-13.90

7

2.40

-1.30

7

STORAGE TECHNOLOGY

37.71

52.37

4

-1.26

6.96

6

CHAMPION SPARK PLUG

5.64

-5.77

7

8.33

13.10

4

FORD OF CANADA

4.16

2.43

7

9.09

-9.55

7

FORD

2.33

-10.14

7

8.56

13.45

4

ARVIN INDUSTRIES

-0.44

0.54

1

11.99

26.80

4

ILLINOIS TOOL WORKS

12.32

15.55

4

48.43

19.25

7

SUPERIOR INDUSTRIES

11.33

-10.90

7

12.36

5.86

7

SUNDSTRAND

12.01

17.28

4

0.42

-0.94

7

TELEDYNE INC.

9.78

10.38

3

4.80

0.11

7

MCDONNELL DOUGLAS

15.87

23.95

4

12.98

20.11

4

NORTHROP CORP.

11.46

6.17

7

10.34

21.98

4

FISCHER & PORTER

7.02

5.64

7

4.14

2.85

7

BIO-RAD LABORATORIES

23.32

26.35

4

21.50

24.66

4

EASTMAN KODAK

8.44

8.87

3

13.09

15.06

4

COLECO INDS.

21.04

47.09

4

5.21

12.26

4

HASBRO INC.

0.20

0.88

1

24.43

13.56

7

TONKA CORP.

0.82

6.61

4

10.95

14.44

4

CROSS (A.T. )

16.37

18.10

4

9.41

3.49

7

BIC CORP.

11.96

6.27

7

5.37

6.22

3

Rank1/Cell2

1/7

2/2'

3/5

4/3

5/1

eginning

6/3'

eriod -

ecession

7/4

8/2

9/6

%

Exhibit 7

Receivable Performance Matrix of 119 Companies

During a Recession and Post Recession Period

(in percent)

Ending Period - Post Recession

T0TA1
1/7 2/2' 3/5 4/3 5/1 6/3 7/4 8/2 9/6 %

35.7 — 2.4 19.0 2.4 2.4 33.3 — 4.8 100.0

100.0 -- — — — — — — -- 100.0

20.0 — 5.0 25.0 — — 50.0 — — 100.0

33.3 — — — ~ — 56.7 — ~ 100.0

100.0 — -- — — — — — — 100.0

27.5 — 10.0 17.5 — -- 40.0 — 5.0 100.0

41.7 — — 8.3 — — 41.7 — 3.3 100.0

31.9 — 5.0 17.6 .84 .84 39.5 — 4.2 100.0

38 — 6 21 1 1 47 — 5

Performance rank, from Exhibit 5.
Cell location from Exhibit 1.

Exhibit 8

Distribution of Companies Whose Receivable Performance

Improved, Declined or was Unchanged

Between A Recession and A Post Recession Period

Performance Improved

Performance Declined

Number of

Companies

•

in the

Number of

Number of

Sample that

Number of

Companies in

Levels in

Experienced

Levels in

the Sample that

in Exhibit 5

a Decline

Exhibit 5

Improved their

that the

in their

that the

Receivable

Performance

Receivable

Performance

Performance

Improved

Performance

Declined

1

1

—

1

7

2

5

2

11

3

18

3

5

4

1

4

2

5

1

5

11

6

14

6

—

7

—

7

5

8

2

8

TOTAL 41

TOTAL 41

Number of
Sample Companies
that Experienced
No Change in
Receivable
Performance

Location of
the Performance
Cell in Exhibit 1

16

15

5

1

37

Exhibit 9

A Statistical Summary of the Forecasted
Trends for Sales and Receivables in a
Recession and Post Recession Period
(in percent)

Post Recession
RANK CELL STAT SALES A.R. SALES A.R.

Recession

STAT

SALES

A.R.

MEAN

13.05

4.04

STD

10.81

13.76

MIN

0.23

-23.69

MAX

66.37

62.15

N

42

42

MEAN

-1.51

-10.28

STD

MIN

MAX

N

1

1

MEAN

15.02

15.16

STD

7.12

6.90

MIN

1.96

2.02

MAX

31.90

31.29

N

20

20

MEAN

11.23

16.50

STD

6.91

9.38

MIN

0.82

6.32

MAX

37.71

52.37

N

40

40

MEAN

-11.82

5.61

STD

13.46

12.89

MIN

-45.14

-20.74

MAX

-0.35

29.11

N

12

12

10.

12

9.

17

0.

27

48.

43

38

-3.

84

3.

87

■11.

.37

-1.

47

6

7,

,61

2.

,35

2.

59

12.

,89

21

8.

,92

5.

,53

0.

,47

28.

,60

47

-2.

,24

2,

,21

-6.

,05

-0,

,54

5

6.

01

7.

60

-9.

55

25.

68

38

-9.

18

8.

12

23.

05

-3.

85

6

7.

,55

2.

,52

1.

,67

13.

35

21

13.

,44

6.

,31

3.

,76

33.

,22

47

2.

,49

4.

,27

-2.

,34

6,

.96

5

Exhibit 10

Trend of Production and Inventories for a Recession
and a Post Recession Period, and Cell
Location of Performance in Exhibit 1
(in percent)

RECESSION PERIOD

POST RECESSION PERIOD

CELL LOCA-

CELL LOCA-

PRODUC-

INVEN-

TION IN

PRODUC-

INVEN-

TION IN

COMPANY

TION

TORY

EXHIBIT 1

TION

TORY'

EXHIBIT 1

TOOTS IE ROLL

7.27

7.31

3

3.01

5.72

4

BELDING HEMINGWAY

0.53

-0.47

1

0.71

2.34

2

FIELDCREST

1.67

-0.63

7

20.21

19.38

3

SPRINGS IND.

6.99

3.36

7

8.75

6.18

7

ADAMS MILLS

5.22

2.56

7

8.80

13.70

4

ALBA WALDENSIAN

14.68

13.02

7

7.06

9.49

4

RUSSELL CORP.

12.23

9.30

7

10.85

7.80

7

HAMPTON INDUSTRIES

22.88

10.77

7

5.33

7.97

4

LOUISIANA PACIFIC

-20.31

-7.11

6

13.94

5.81

7

WEYERHAUEUSER

9.10

6.20

7

8.78

5.53

7

BARRY WRIGHT

20.50

16.65

7

10.50

5.78

7

GF CORP.

5.25

8.52

4

-1.81

1.66

6

BOISE CASCADE

4.03

7.51

4

1.93

0.25

7

FEDERAL PAPER BOARD

9.81

4.14

7

10.51

6.02

7

FORT HOWARD

12.86

10.64

7

19.46

16.83

7

GREAT NORTHERN NEKOOSKA

12.32

14.14

4

8.46

4.42

7

INTERNATIONAL PAPER

-67.66

-14.69

6

8.08

6.4 5

7

KIMBERLY-CLARK

L. 11

8.00

4

9.32

2.76

7

LYDALL INC.

4.32

19.01

4

8.09

9.97

4

MACMILLAN BLOEDEL LTD.

-0.65

8.07

2

2.47

2.94

3

POTLATCH CORP.

5.73

8.43

4

-0.33

1.87

2

STONE CONTAINER

12.43

12.27

3

23.47

26.26

4

UNION CAMP

6.28

7.58

4

6.92

8.10

4

MMM

12.51

10.83

7

7.70

3.40

7

DOW JONES

11.56

20.59

4

9.11

1.38

7

GANNET CO.

19.49

23.08

4

17.10

16.79

3

KNIGHT-RIDDER

12.01

19.42

4

7.87

4.89

7

MEDIA GENERAL

12.21

20.94

4

11.21

15.23

4

TIMES MIRROR

12.33

12.26

3

0.46

-4.86

2'

DU PONT

62.69

40.60

7

11.04

11.04

3

PPG INDUSTRIES

7.55

5.01

7

6.84

7.87

4

ROHM & HAAS

10.49

11.18

3

1.86

0.24

7

UNION CARBIDE

-1.24

8.33

6

-2.32

-17.40

5

ESSEX CHEMICAL

15.52

28.82

4

5.13

7.23

4

MERCK & CO.

15.81

7.56

7

9.24

3.40

7

ABBOTT LABS

15.56

11.04

7

10.62

7.63

7

Exhibit 10 (continued)

RECESSION

PERIOD

CELL LOCA-

POST

RECESSION PERIOD

CELL LOCA-

PRODUC-

INVEN-

TION IN

PRODUC-

INVEN-

TION IN

COMPANY

TION

TORY

EXHIBIT 1

TION

TORY

EXHIBIT 1

BRISTOL-MYERS

10.99

9.73

7

4.73

3.39

7

SMITHKLINE BECKMAN

40.21

56.92

4

14.13

14.66

3

LAMAUR INC.

14.74

20.61

4

17.78

17.39

3

GUARDSMAN PRODUCTS

5.91

6.24

3

11.44

7.99

7

PRATT & LAMBERT

22.93

21.28

7

8.60

7.91

3

CROMPTON & KNOWLES

-3.84

5.08

6

5.33

2.62

7

FAIRMOUNT CHEMICAL

6.74

10.61

4

-0.49

0.73

I

DEXTER CORP.

1.03

-8.03

7

8.94

11.65

4

FERRO CORP.

10.65

3.36

7

8.16

3.63

7

LUBRIZOL CORP.

8.81

10.49

4

1.89

7.34

4

NALCO CHEMICAL

6.01

3.26

7

5.37

2.81

7

AMERICAN PETROFINA

7.55

7.44

3

6.00

8.37

4

AMOCO CORP.

13.20

8.38

7

0.00

-4.08

5

ATLANTIC RICHFIELD

24.30

17.56

7

-6.77

-10.66

5

CHEVRON

14.61

8.72

7

-0.64

8. LI

2

IMPERIAL OIL

7.66

34.85

4

0.30

-0.66

1

KERR-MCGEE

15. 14

22.09

4

-4.46

-8.37

5

MURPHY OIL

15.96

12.33

7

-8.35

-9.76

5

TEXACO

13.34

-3.63

7

2.47

0.94

7

TOSCO CORP.

24.46

-24.00

7

-2.03

7.17

6

UNOCAL

11.11

14. 12

4

3.01

1.85

7

BANDAG INC.

9.28

-1.39

7

4.68

2.03

7

CARLISLE

12.82

15.94

4

5.80

10.20

4

COOPER TIRE & RUBBER

11.15

6.75

7

7.74

4.62

7

PANTASOTE

7.04

8.25

4

1.76

5.81

4

VOPLEX

24.95

19.66

7

15.29

6.19

7

WOLVERINE WORLD WIDE

12.93

17.79

4

-0.62

-24.07

2*

BROCKWAY INC.

12.38

8.54

7

8.21

6.21

7

IDEAL BASIC IND.

11.31

4.56

7

-12.61

-4.01

6

LONE STAR IND.

4.86

2.31

7

1.09

-4.36

7

USG CORP.

2.09

-2.24

7

9.20

4.49

7

NORTON CO.

6.45

9.20

7

2.61

2.63

3

LUKENS INC.

10.57

5.72

7

4.81

8.97

4

ALCAN ALUMINUM

12.96

16.30

4

6.87

1.54

7

ALUMINUM CO. OF

AMERICA

9.25

5.51

7

4.81

-2.68

7

ARMANDA CORP.

3.32

23.75

4

2.61

6.27

4

VAN DORN CO.

10.71

6.36

7

8.02

5.42

7

SNAPON TOOLS

11.92

' 1.62

3

9.74

1.55

7

GENERAL HOUSEWARES

5.98

22.39

4

-0.41

-2.02

2'

HEXCEL CORP.

14.25

19.22

4

15.60

15.12

3

CRANE CO.

6.47

0.82

7

1.07

0.59

3

CUMMINS ENGINE

10.01

15.44

4

10.06

8. 17

7

CATERPILLAR

13.03

17.70

4

3.46

-3.63

7

SAFE GUARD SCIEN.

-15.38

-11.01

6

-0.97

3. 10

2

Exhibit 10 (continued)

RECESSION PERIOD

POST RECESSION PERIOD

CELL LOCA-

CELL LOCA-

PRODUC-

INVEN-

TION IN

PRODUC-

INVEN-

TION IN

COMPANY

TION

TORY

EXHIBIT 1

TION

TORY—

EXHIBIT 1

SALEM CORP.

4.34

-10.40

7

1.98

-1.23

7

PITNEY BOWES

20.79

13.68

7

11.64

9.36

7

LSB INDUSTRIES

27.63

84.20

4

13.56

5.58

7

VENDO CO.

15.81

-44.54

7

6.98

-1.75

7

GENERAL ELECTRIC

10.55

6.87

7

24.95

30.99

4

AMETEK INC.

12.08

7.07

7

7.56

2.14

7

BALDOR ELECTRIC

15.79

4.79

7

9.39

3.53

7

WHIRLPOOL

7.29

-0.76

7

8.21

10.15

4

THOMAS INDUSTRIES

9.96

4.42

7

7.22

6.68

3

ZENITH

7.78

15.04

4

13.75

14.17

3

ANDREA RADIO

18.71

28.00

4

3.41

5.63

4

TRW

7.58

-3.48

7

5.61

-3.04

7

E-SYSTEMS INC.

16.85

-5.93

7

12.29

-10.77

7

EDO CORP.

-15.81

-8.81

6

2.95

10.27

4

WATKINS- JOHNSON

9.22

5.57

7

10.71

10.59

3

AMP

18.80

14.49

7

14.53

10.42

7

KOLLMORGEN

15.53

12.22

7

-4.37

2.85

6

IBM

14.62

20.21

4

12.55

-8.06

7

NCR

-2.15

5.85

6

6.78

1.00

7

DIEBOLD

12.44

-23.25

7

5.13

6.35

4

STORAGE TECHNOLOGY

34.40

32.28

7

6.02

-4.59

7

CHAMPION SPARK PLUG

12.43

9.04

7

7.70

1.54

7

FORD OF CANADA

3.46

5.35

4

8.71

-0.35

7

FORD

1.88

-8.71

7

7.57

12.09

4

ARVIN INDUSTRIES

4.53

1.11

7

16.80

17.17

3

ILLINOIS TOOL WORKS

11.51

14.07

4

37.45

70.18

4

SUPERIOR INDUSTRIES

8.27

-6.46

7

11.48

19.69

4

SUNDSTRAND

11.11

8.63

7

6.49

6.93

3

TELE DYNE INC.

9.31

0.19

7

5.24

3.39

7

MCDONNELL DOUGLAS

14.87

9.67

7

12.82

7.76

7

NORTHROP CORP.

9.01

13.40

4

16.28

11.54

7

FISCHER & PORTER

8.33

6.67

7

4.09

2.12

7

BIO-RAD LABORATORIES

28.58

30.47

4

22.45

24.40

4

EASTMAN KODAK

14.18

11.87

7

0.79

6.42

2

COLECO INDS.

11.71

17.00

4

0.54

6.04

7

HASBRO INC.

-5.56

-16.07

5

25.49

45.69

4

TONKA CORP.

-1.45

-3.48

5

7.87

1.40

7

CROSS (A.T.)

17.07

22.47

4

7.43

2.58

7

BIC CORP.

10.71

3.60

7

5.23

2.58

7

Exhibit 11

Inventory Performance of 119 Companies During

a Recession and a Post Recession Period

(in percent)

Ending Period - Post Recession

tota:

Rank1 /Cell2 1/7 2/2' 3/5 4/3 5/1 6/3' 7/4 8/2 9/6 %

1/7 55.6 — 4.8 14.3 — — 17.5 3.2 4.8 100.0

3/5 50.0 — — -- — — 50.0 — — 100.0

4/3 42.9 14.3 — -- — — 42.9 -- — 100.0

5/1 — — — — — — ~ 100.0 — 100.0

beginning 6/3' — — — — —

'eriod -

lecession 7/4 39.5 5.3 2.6 13.2 5.3 — 28.9 2.6 2.6 100.0

8/2 — — -- 100.0 — — — — — 100.0

9/6 57.1 -- 14.3 — -- — 14.3 14.3 — 100.0

%

n 58 3 5 15 2 0 27 5 4

"Performance rank from Exhibit 5.
"Cell location from Exhibit 1.

Exhibit 12

Distribution of Companies Whose Inventory Performance

Improved, Declined or Was Unchanged Between

a Recession and a Post Recession Period

Performance Improved

Performance Declined

Number of

Number of

Companies in

Leve

Is in

the Sample that

in Exhibit 5

Improved their

that

the

Inventory

Perfi

ormance

Performance

Improved

1

1

5

2

8

3

2

4

2

5

16

6

0

7

4

8

Number of
Companies
in the
Sample that
Experienced
a Decline
in their
Inventory
Performance

1

4
13

1

0
11

2

3

Number of
Levels in
Exhibit 5
that the
Performance
Declined

1
2
3

4
5
6

7
8

TOTAL 38

TOTAL 35

Number of
Sample Companies
that Experienced
No Change in
Inventory
Performance

Location of
the Performance
Cell in Exhibit 1

35

11

46

Exhibit 13

Frequency Distribution of Combined Receivable
and Inventory Performance Results in a
Recession and Post Recession Period

Recession Period

Post Recession Period

Receivable

Inventory

Cell #

Cell #

1

1

1

2

1

2

3

1

5

4

2'

5

5

2'

7

6

1

7

7

2

4

8

3

3

9

3

3

10

3

3

11

3

4

12

3

4

13

3

4

14

3

7

15

3

7

16

3

7

17

3

7

18

3

7

19

3

7

20

3

7

21

3

7

22

3

7

23

3

7

24

3'

7

25

4

3

26

4

3

27

4

3

28

4

3

29

4

4

30

4

4

31

4

4

32

4

4

33

4

4

34

4

4

35

4

4

36

4

4

Receivable

Inventory

Total

Cell

#

Cell #
2

Frequency

7

4

3

7

4

4

7

7

7

4

3

2

7

3

7

4

4

7

7

4

3

4

7

7

7

2

6

3

3

3

4

3

7

4

2

4

4

4

7

5

6

7

4

7

7

7

7

4

2'

4

4

4

7

7

4

2

2'

3

4

3

7

4

4

4

7

3

5

1

7

2

7

4

2

Exhibit 13 (continued)

Recession Period

Post Recession Period

Receivable

Inventory

Receivable

Inventory

Total

Cell #

Cell //
4

Cell #

Cell #

7

Frequency

37

4

7

1

38

4

6

4

4

1

39

4

6

5

5

1

40

4

6

7

7

1

41

4

7

3

7

6

42

4

7

4

3

1

43

4

7

4

7

5

44

4

7

5

6

1

45

4

7

6

7

1

46

4

7

7

7

2

47

5

6

4

7

1

48

6

4

3

4

1

49

6

4

6

2'

1

50

6

6

4

2

1

51

6

6

7

7

2

52

6

7

4

3

1

53

6

7

4

7

2

54

6

7

7

4

1

55

6

7

7

7

2

56

7

4

3

7

2

57

7

4

3'

4

1

58

7

4

3'

6

1

59

7

4

4

3

3

60

7

4

4

4

2

61

7

4

4

7

2

62

7

4

5

5

1

63

7

4

6

4

1

64

7

4

7

4

1

65

7

4

7

7

3

66

7

7

1

3

1

67

7

7

1

5

1

68

7

7

3

3

3

69

7

7

3

4

1

70

7

7

3

7

2

71

7

7

4

3

1

72

7

7

4

4

3

73

7

7

4

7

3

74

7

7

5

5

1

75

7

7

7

2

1

76

7

7

7

4

2

77

7

7

7

5

1

78

7

7

7

7

4

TOTAL

119

Exhibit 14

Change in Receivable and Inventory Performance
Between a Recession and a Post Recession Period

Paper and Allied Product

International Paper
Stone Container
Potlatch Corp.
Union Camp
Boise Cascade
Lydall Inc.
Federal Paper Board
Kimberly Clark
Macraillan Bloedel Ltd.
Great Northern Nekooska
Fort Howard

Performance

of

• • •

Receiva

bles

Inventories

8

8

6

-3

6

-1

5

0

2

6

0

0

0

0

0

6

-2

4

-3

6

-6

0

Petroleum Refining

Atlantic Richfield

Tosco Corp.

Unocal

Amoco Corp.

Chevron

Texaco

Kerr-McGee

American Petrofina

Murphy Oil

Imperial Oil

Newspaper Publishing and Printing

Times Mirror
Media General
Dow Jones
Gannet
Knight Rider

Chemical and Allied Products

Union Carbide

Rohm & Haas

DuPont

PPG Industries

Misc. Chemical Products

Ferro Corporation
Dexter Corporation
Nalco Chemical
Lubrlzol Corporation

8

8

-3

-6

0

-6

0

0

'+ Number of cells performance Increased between periods
- Number of cells performance declined between periods.
0 No changes In performance between periods.

HECKMAN

BINDERY inc.

JUN95

'^-To-Pf,^ N. MANCHESTFR I