Randers, Jørgen,"Prediction of Pulp Prices – A Review Two Years After", 1984

238

PREDICTION OF PULP PRICES -
A REVIEW TWO YEARS AFTER

PREDICTION OF PULP PRICES -
A REVIEW TWO YEARS AFTER

Jgrgen Randers
Norwegian School of Management
Bekkestua, (Oslo), Norway

ABSTRACT
1. INTRODUCTION
During the summer of 1982 the author made predictions of wood pulp Inventory Cycle Theory
prices for the period 1982 to 1986. The predictions were part of
a sion on whether to sell a large pulping plant in Norway. Purpose of this Paper
This paper presents the predictions and the basis on which
they were made. Next, the predictions are compared with actual
data for the period 1982 to 1984. 2. THEORETICAL EXTENSION
The price predictions were based on the assumption that the inter- peels: Neciantene
national market for wood pulp is characterized by inventory
cycle theory. According to this theory price oscillates with a Elaboration
3-6 year wavelength primarily caused by the “production rate -->
inventory --> production rate” loop.
+ MEASUREMENT OF PAST DEVELOPMENTS
This paper reviews the existing literature on inventory cycles, 3+ MEASUREMENT OF PAST DEVELOPMENTS
and extends the theoretical work to cover inventory oscillations pEesealire
at the industry level in some detail.
Discussion
The resulting theory turns out to be supported by time series of
quarterly data for international pulp production, pulp inventories
and pulp prices over the 25 years prior to 1982. 4. PREDICTION OF FUTURE DEVELOPMENTS
The time series for pulp price, once corrected for inflation, Procedure:
demonstrate a clear 5-year fluctuation around an exponentially
declining trend. The declining trend is probably due to technical Discussion
advance in the production of wood pulp. The 5-year cycle is even
more conspicuous in inventory statistics.
5. COMPARISON OF PREDICTED AND ACTUAL DEVELOPMENT
The price predictions were made by assuming: —=—_—_—
Results
4) continued technical advance - i.e. a continuation
of the exponentially declining trend in prices biseiswied
ii) continued inventory fluctuation - i.e. a continuation “
of the 5-year oscillation in prices around the trend. 6. ‘CONCLUSION

Comparison with actual data demonstrates that this (simple)
method of prediction yields good results. Interestingly,

the unavoidable noise (due to random, unpredictable events in the
market) appears too weak to hide the systematic features in the
development of prices on which this method depend.

CONTENTS

239

PREDICTION OF WOOD PULP PRICES -
A REVIEW TWO YEARS AFTER

INTRODUCTION

INVENTORY CYCLE THEORY

Inventory cycles is an old topic in the system dynamics

literature. Periodical fluctuations in production, inventories
and price are frequently observed in the economy, and have attracted
er's original

the inte:

jt of system dynamicists since Forr
work on 3 to 6 year long cycles at the company level (Forrester, 1961).
Cycles of a 3 to 6 year wavelength can be observed both at
the company, industry and economy level. In order to observe the
cycles, one does need a longer time horizon than is common in
economic affairs. To observe a pattern of waves where each wave
is 3 to 6 years one must have knowledge of, or time series for, a
period of at least 10 to 15 years. Further, yearly data is
insufficient, - because they only give 3 to’6 observations per wave:
Quarterly data, at the least, are necessary. Because of these
reasons, inventory cycles are hard to observe and hence much less
recognized than should be expected, given their prevalence.
Still, inventory cycles have been given extensive treatment

in the system dynamics literature. Common to these treatments is

the belief that inventory cycles are endogenously generated; they
are not seen as the result of some exogenous rhytmic driving force
(like sun spots, wars, or parliamentary decisions). In hie original
work in Industrial Dynamics (Forrester, 1961) demonstrated how
decision rules (policies) in the company cause regular fluctuations

in the level of activity with a wavelength of some year: The

interesting point is that fluctuations arise from policies that are
common to most manufacturing businesses. Forreater shows that
the common tendency to fill orders in proportion to the size of

the utilization

the order backlog - or equivalently to incre:
of installed capacity when there is a small inventory of finished
goods ~ is sufficient to generate cyclicality when the company
operates in an environment characterized by noise (e.g. random
variations in demand, delivery delays, input prices or the like).

The central role of order bakclogs - or product inventories -

in this process is the basis for the name “inventory cycle:

The theme of inventory cycles was picked up by Meadows
(Meadows,.1970) at the industry level in hie etudy of the U.s.
Pork industry. Of course the cyclicality of the hog industry had

been known for decades (see (Ezekiel, 1938) for the classic expositon)

and commonly been used as an example of the cobweb theorem in
textbook micro-economics. However, Meadows did describe

the detailed decision process of the farmer. He demonstrated

how "rational" decisions by individual farmers concerning breeding
and slaughtering of pigs, add up to strong and unintended
fluctuations in the industry, with periods of boom and depression
following each other in endless succession.

Meadows' work was extended by Naill et al. (Naill,1973) who made
3 “ 240

the ties to the real world of the hog industry more obvious by
by incorporating exogenous effects - like seasonality in the final
demand for pork.

A weakness of all studies this far was a lack of statistical

evidence of the inventory cycle. The studies were based on scant,

if any, time series illustrating cycles in past developments. High
quality time series were not published until 1979 (H¢steland,
1979); not for the hog industry, but for the Norwegian sulphite
pulp industry. Hpsteland gave a lucid illustration of the reality

of inventory cyel:

(wee figure 1 a) showing waves in production,
sales, inventory and price over a 20 year period. The variables did

interact in a pattern that was easy to understand in terms of the
mechanisms described by earlier authors: Prices are slowly reduced

when inventories are large. Production does respond to prices,

and ultimately production ra’

are reduced below sales. Inventories
start to contract. Production is not increased again, however,
untill inventori

fall so low that prices pushed upwards by worried
Customers - signal a new period of and boom in the industry.
Hgateland streesed ~ as did Majani and Makridakis (Majani

and Makridakis, 1977) in an independent analysis outside the system
dynamics tradition - the need to smooth the w kly or monthly data
that is commonly used in the industry. Noise in the data obscures
cyclical trends in production and sales and may explain the very slow
adjustment towards market equilibrium. Production and sales
statistics are so noisy that industry decision makers hardly can be
expected to discover new market trends untill much time has passed.

At the same time, Randers (Randers, 1978) published several

other indications of the 3 to 6 year cycl. He distinguished the

short inventory cycle from other periodicities that can be
observed. He argued that the 3 to 6 year cycles in various
industries are sufficiently strongly coupled to move in phase, and
add up to “the business cycle” at the national level.

Another excellent illustration of the 3 to 6 year cycle at
the industry level was assembled by Antun (Antun, 1981) in his
study of the ferro alloy industry of Norway (see figure 1 b).

Outside the system dynamics tradition, the 3 to 6 year cycle
at the level of the economy is probably best known as “the
business cycle". The business cycle is rarely viewed as an
endogenous phenomenom. This is, however, the perspective of the

first system dynamics study of the 3 to 6 year cycle at the level

of the economy by Mass (Mass, 1975). Mass’ study can be seen as
a further generalization of the inventory cycle studies at the
company and industry levels. Mass defends the view that the 3
to 6 year fluctuation in the economy is rooted in adjustments in
the use of labor (and not, as is more often hypothesized, in the

that the adjustment of production

use of capital). He argu
rates to sales in the short run takes place primarily through
adjustments in the utilization of existing capital, by alteration
of such factors as the number of shifts, the average length of
the work week, the extent of temporary layoffs etc. Furthermore,
he argues that these adjustments are the primary cause of the
short term business cycle. .

Finally, it is worth mentioning the work of Low (Low, 1980),
where he tries to bridge the gap between the system dynamice
perepective on the short term business cycle and the more conventional

macroeconomic perspective on economic dynamics. Starting with a
241

simple Keynesian multiplier-accelerator model Low makes the fewest
possible extensions to make the model dynamically complete. He
shows that 3 to 6 year cycles do arise in the extended model,
caused by variations in the utilization of the existing capital
stock. Forrester (Forrester, 1962), using advanced mathematical
techniques, has extended Low's work, adding much support to the
hypoth:
variations in capital investment rates, is the prime force behind

© that adjustments in capital utilization, rather than

the short term business cycle.

PURPOSE OF THIS PAPER

One purpose of this paper is to:present a detailed theory of
inventory cycles at the industry level, using sulphate pulp as
the example.

Another purpose is to fill in a missing piece in the line of
work that was discussed above: Statistical work supporting the
existence of the inventory cycle at the industry level - in an
international perspective. This far, statistical work on inventory
cycles has been restricted to one nation, in spite of the fact
that available inventories probably impact ‘on the level of prices,

and on industry optimism and production rates, regardless of where the

stocks are held. In this paper the sulphate pulp industry is seen in
an international perspective. The sulphate pulp industry is

ideally suited for the study of inventory cycles, because sulphate
pulp ie a stable, homogenous commodity that has been produced and
traded internationally for several decades. ‘The industry is dominated

by Canada, USA, Sweden, Finland and Norway. statistical tine
series are gathered for these 5 nations covering the last 25
years.

A third purpose of the paper is to try and exploit the
opportunity to use inventory cycles for the purpose of predicting
future developments. one such prediction of future price was
made in 1962. The method of prediction is described and the result
compared with actual developments over the ensuing
two years (i.e. the period 1982 to 1984).
2. THEORETICAL EXT! On 242

BASIC MECHANISMS
Based on the work of the various authors described in section 1,

it ie possible to propose the following view of inventory cycles
+ This view - or

at the industry level, and their underlying cau
theory - forme the fundamental hypothesis which the present paper
ke to test. The sulphate pulp industry is used as an example.

The prime characteristic of the 3 to 6 year cycle is the
following: in periods when inventories are larger than normal,
prices tend to fall. When inventories are lower than normal,
prices tend to increase.

This relation between inventories and price is easily
understandable. Both buyers and sellers know when there is much pulp
in the market, and buyers understandably use the opportunity to
negotiate prices. Oppositely, when inventories are depleted,
containing enough pulp only to cover demand for a whort period of
time, producers see a possibility to increase prices. And buyers
have to follow suit, for fear of not being able to feed a
continuous stream of pulp to their paper machines.

But depleted inventories and high prices furthermore tempt
pulp producers to increase production rates. Ultimately production
rates exceed sales, and inventories once more begin to accumulate.
Inversely, production rates have to be lowered when inventories
excess storage capacity, or when prices fall below production costs.

The link between inventory. size and production rate closes the

negative feedback loop portrayed in figure 2. This loop is hypo-
thesized as the basic mechanism behind the 3 to 6 year inventory
cycle at the industry level.

To some observers the wavelength of the inventory cycle
appears surprisingly long. Many find it hard to believe that
the “inventory-production rate"-loop is capable of generating
waves measuring 3 to 6 years between peaks. The explanation
rests with the many delays in the pattern of behavior over one
cycle. When inventories are swelling and prices start to fall,
producers start to reduce production rates. The process, however,
takes time. First of all inventory statistics have to be gathered,
not only nationally, but internationally. Statistical evidence and
industry gossip have to convince management that the ongoing
inventory buildup is not only temporary. Palling prices in the
spot market adds weight to gossip, but even faced with falling
prices management often hesitate, hoping for a imminent reversal
of negative trends. But as inventories continue to accumulate, there
is finally no way around the bitter decision to change production
plans, reduce the labor force (or at last the hours worked), - i.e.
to reduce utilization of installed capacity.

This lengthy process of adjustment takes place in all pulp
Producing companies more or less simultaneously, given that
they all respond to the same signal: international price and
inventory levels. As a result, aggregate production of pulp finally
falls below aggregate consumption of pulp, and inventories of finished
Pulp start to contract.

With time inventori

decrease so much that pulp buyers no

longer feel certain that there will continue to be a surplue of
243
readily available pulp. They increase their orders, securing raw

materials for. additional days of production - “just in case".
As a consequence there appears a stronger tendency in the market,
and prices stabilize. Given the higher demand for pulp, existing
inventories appear smaller (inventory “size” is beset expressed
as the number of weeks of sales that can be supported by the
inventory). As inventory coverage declines, buyers become increasingly
worried about insufficient availability. Orders are increased,
and with time buyers also become willing to pay higher prices to
secure deliveries.

It takes more time, however, before increasing sales force

+ After some months,

pulp producers to up their production rat.
however, producers’ inventories are sufficiently depleted that plans

must be made to incr: production rates. Implementing the plans also

requires time, however, and before production actually exceeds sali
pulp inventories have become miniscule, the market hectic, and
prices are forced upwards. The “good times" lure the pulp companies
to maintain high production rates, and as a consequence, inventories
once more start to accumulate. Gradually inventories build to
“normal” levels, prices stagnate, and the industry is back to

where it started some years earlier. It takes time, however,

before growing inventories actually cause a new decline in prices.

But finally price drops occur, first in the real price adjusted
for inflation and later even in the nominal price per ton. This
closes the cycle, and the pattern of behavior repeats itself.

Experience shows that one full inventory cycle last between 3
and 6 years. Roughly speaking, it takes a first year of low

production rates to reduce inventories from their peak to “normal”

10

levels. Then it takes a second year while inventories continue to

decline, before prices incre

sufficiently to call forth pro-
duction rates that exceed sales. Next, it takes a third year of
high production rates to move inventories back up to “normal”

levels. And, finally, a fourth year before steadily increasing

inventories do result in price declines which in turn force

Producers to reduce their capacity utilization, thereby halting
the accumulation of inventories.

The reference mode is summarized in figure 3.

ELABORATION

The theory of the inventory cycle at the industry level

described above, can be supplemented with the following hypoth

1. he price of pulp will be influenced by the total, international

inventory of pulp. Pulp is reasonably simple to transport. Hence,
pricea will not increase much if, say, Norwegian inventories are
depleted, as long as foreign producers (or consumers) have bulging

inventories.

2. Producers will tend to give discounts to get_rid of pulp when
their inventories are swelling. Consequently, achieved price

is not the same as listed price. The price actually received
by producers is lower than listed price (the formally
agreed price quotations) when the market is falling. Conversely,

when pulp is scarce, suppliers may well charge above the listed
3.

nv

244

price of the industry.

‘The minimum level of prices in the 3 to 6 year cycle will be
related to variable costs of production in those plants that still

operate at the bottom of the price cycle. In fact, the great

majority of pulping plants are still operating when prices
are at their lowest. The pulp industry is a “cyclical” industry.
Still, it is primarily inventories and, hence, prices, that
fluctuate significantly during the cycle. Production rates
tend to be more stable, moving perhaps 15% above and below
the long term average. In other words, capacity utilization
varies between approximately 70 and 100%. As a consequence, the
lowest price in the price cycle must be related to the lowest
price for which a majority of producers are still willing to
produce. This minimum price is probably linked to an average

of variable production costs in the majority of existing plants.

Plants that have economic difficulties during price troughs
include the oldest plants (having high variable costs due to
oldfashioned technological standard) and the newest plants
(having high capital coats because of high debt loads compared
with industry average).

The price peaks have no relation to production costs. The
maximum price reached during the cycle is determined by the

scarcity of pulp when inventories are low. The extent to which

12

pulp becomes a scarce - and hence highly valued - commodity

during inventory lows depends on unpredictable, random events

like transient production problems, atrikes, shortages of

raw materials or other inputs, transportation bottlenecks, temporary
surges in demand and so on. The occurence of euch events
are impossible to predict, as ia the maximum price level
they will generate. However, random events like those mentioned

have strong effects when inventori

are low, even though they

would not have been noticeable if inventories were large.

Busin cycling in the sectors that consume pulp - primarily
Paper, newsprint and cardboard production - tends to amplify

the inventory cycle. The bysiness cycle produces an exogenous
3 to 6 year wavelength in the consumption of pulp. This
exogenous driving force will tend to resonate with the
“inventory --> production rate"-loop, to generate stronger

cycles.
” 245

MEASUREMENT OF PAST DEVELOPMENTS

2: MEASUREMENT OF A eee

PROCEDURE

In order to test the theory and the various hypoth
presented in section 2, time series data were collected
for the bleached sulphate pulp industry, covering the period
1957 to 1984. 1) quarterly data were collected for production,
inventories and listed price, covering Canada, United States,
Sweden, Finland and Norway. These countries are the major producers
of bleached sulphate pulp in the Western world and completely
dominate the international trade in this commodity. Their relative
size is indicated by the following production figures. for 1980.

Production
(mi11.tons/year)

Market share

Canada 6.0 44

USA 4.0 29
Sweden 2.1 15
Finland 1.5 il
Norway Oud =
Sum "HORSCAN" 13.7 100

Over the last 25 years the ranking has remained as in the table,
but the relative importance of Sweden and Norway has decreased.

1) pata were collected in 1982 for the period 1957 to 1982,
and updated in 1984.

14

Canada, Sweden and Finland are the large exporters: Canada covers
the US deficit; and Sweden and Finland sell to the European markets.
During market down turns, North America typically tries to sell
excess pulp in European markets.

To arrive at time series for total production rate in the
“NORSCAN" countries (see figure 4), monthly production data for
the five producers were added up to quarterly production figures.
These in turn were multiplied by 4 to arrive at production rates
expressed in million tons per year. As can be seen from figure 4,
production grew fast until 1970, and somewhat slower in the
following decade. Fluctuations around the long term trend were
severe, particularly during the period of slower growth.

The time series for total inventories (see figure 5) include
bleached sulphate pulp held by producers. The plot shows the
sum of inventories in the five producer countries at the end of each
quarter. No smoothing has been applied to the data. As can be seen
from figure 5, inventories have fluctuated strongly over the last
25 years ~ with a wavelength of some 5 years. This regularity is
more obvious when inventories are expreesed as a number of days
(i.e. measured as the number of days it would take to produce
the amount of pulp held in inventory). To arrive at time series
for this variable (see figure 6), total inventory at the end of
each quarter (from figure 5) was divided by the production rate
of that quarter (from figure 4), and the result multiplied by
365 days/year.

Finally, time series for the price of bleached sulphate pulp

First the quarterly listed price

were gathered (see figure 7).

(in British £ per ton from 1950 through 1969, and in US $ per ton from
15
246

1970 on) was converted into Norwegian kroner per ton using quarterly
averages for the exchange rate between £, $ and kroner. Then the
Norwegian consumer price index at the end of each quarter was used
to convert the series into constant 1979-kroner per ton of pulp.
As can be seen from figure 7, the price of pulp has fallen
gradually since 1950, with prices fluctuating around the long
term trend. Figure 7 represents “international” prices as they
appear from the point of view of an Norwegian exporter of pulp.

The starting point for figures 4 through 7 were industrial

branch statistics obtained from the forest products industry.

DISCUSSION

In general figures 4 through 7 support the theoretical
Perspective and many of the hypotheses presented in section 2.

Starting with the general theory of the inventory cycle, over
the last 25 years the sulphate pulp industry has been strongly cyclical.
The cycle can be most clearly seen in inventory statistice, and can
also be traced in price devileopments. ‘The cycle is much harder to
The phi

are as expected: Prices increase when

see in time series for the production rate. relationship

between the variabl

inventories are below “normal” levels, and prices fall when
inventories are above “normal”. “Normal appears to be between
3 and 4 weeks of coverage. ‘The phase relations of the production
rate is more complex, and is discussed below.

Although these conclusions follow easily from figures 4 to 7,

they are not at all apparent in most time series 4 jcribing the

16

industry. In order to display the desired results, time series

must be sufficiently long to cover several wavelengths of the

inventory cycle. Less than 12 years of data will not suffic
Secondly, yearly data are too coarse to portray a 3 to 6 year cycle:
at least quarterly data are necessary. On the other hand, —
excessive resolution in time also disguises the inventory cycle.
If monthly or, worse, weekly data are used, the 3 to 6 year periodicity
is hard to see because of seasonal effects and noise. Thirdly,
current prices can not be used because general price inflation hides
both the downward trend and the 3 to 6 year cycle in prices. Fourthly,
the data must cover a sufficiently large fraction of the total
market. Individual country (or producer) inventories often move
out of phace with the total, and hence may not be a good indicator
of total inventories.

Next, figures 4 through 7 seem to support the following
conclusions:

1. There is a clear _invento1 ‘cle in the bleached sulphate BS

industry.
total inventories, but can be perceived in

The cycle is most easily observable in time series for

ries for real price

as well. The best indicator seems to be total inventory

expressed in days (see figure 6).

2. I£ one number has to be picked, the inventory cycle in bleached —
sulphate pulp should be described as a "5" year cycle. The
wavelength of the cycle apppears to have increased somewhat
over the 25 years since 1957. Inventories have fluctuated

with a clear 5 year wavelength since 1968. Prices have
7

followed the same cycle from 1958 or, possibly, 1953. 247

Inventories appear to cycle around a “normal” value of some 3

to 4 weeks of production. The total inventory held by producers

Maximum values of 5 weeks are common,

rarely falls below 2 weeks.
and peaks of 10 weeks have been observed. Since 1970 inventories
have fluctuated around a "normal" value of some 3 to 4 weeks,

from troughs of 2 weeks to peaks of 7 weeks.

Inventory trough values vary 2 than inventory peak values.

The decline in inventories are halted when the producers find
it necessary to boost production rates above sales. This
decision appears to be triggered simultaneously in all producing

companies once inventory coverage falls towards two weeks.

Inventory peak values, on the other hand, are determined by a
The extent to which produc-

large number of independent factors.
ers are willing to hold large inventories is influenced by expect-
ations about future sales, by interest rates, by current social

towards lay-offs, by available storage space and so

predictable was the state subsidy “for extraordinary

inventory accumulation” that was offered in the three Scandinavian
countries during the middle 1970's (in order to avoid lay-offs

in the pulp industry). ‘The subsidy covered much of the interest

co
cost of inventori

and probably contributed significantly
to the large inventory peak in 1975-77.
continued in 1977.

The subsidy was dis-

18

The ong ter#itrend in the price of bleached sulphate pulp in
constant dollars per ton) appears to be falling at a rate of

2% per year. During the last decades, the long term trend
in the price of pulp has sunk at a rate of approximately 2%
per year. This is probably because of increased plant size,
cheaper raw material and general increases in productivity.
As a consequence, the minimum price in one cycle may be up to
10% below the minimum price of the preceding cycle. The
rate of decline in the long term trend appears to be slower
during periods of low growth in capacity, i.e. when few new

plants come on stream.

There is a “5"-year cycle in the real price of pulp around
ite long term trend. As for inventories the wavelength varies
somewhat over time, but 5 years is a fair approximation if
one number has to picked. Price troughs have been 3 - 16% below

the long term trend, with an average’ of 11%.

The peaks in pulp prices lie 3 ~ 43% above the minimum price

of the preceding cycle, with an average of 19%. The exception
was the boom of 1951, when the real price of pulp peaked more

than 100% above the preceding minimum.

The price of pulp moves 90° ahead of inventory in the cycle.

The time series show that prices increase when inventories are

higher than normal. More precisely, inventories have to fall
lo.

19

below 3 to 4 weeks of coverage before the industry increases
the listed price (in real terms). And conversely,
inventories have to grow. beyond 3 to 4 weeks before the

listed real price actually is reduced in real terms. The

result is that the price wave lies 90° (or 1.25 yei ) ahead

of the inventory wave.

The listed price appears to lag behind the achieved price.
Even though it takes time before the listed price is
reduced, spot price and company income may well decline
rapidly when inventories start to accumulate. Most likely
Producers are willing to give discounts (in order to get
rid of excess pulp) long before the listed price is lowered
through formal agreements. Conversely, spot prices and
company income may well increase faster than listed price,

when inventories are depleted and pulp is getting scarce.

This conclusion does not follow directly from figures 4 through
7, since we have no time series for achieved price, only for
listed price. The conclusion is supported, however, by common

sense and by Hgsteland's data (see figure 1). His data indicate

that the

hieved price responds rapidly to inventory changes.
Listed price may lag half a year behind achieved price.

redictable than the level

The level of price

of price troughs. The peak value of prices is determined by

the pressure in the market at the time of minimum inventori

Accidental shortages may send pric

soaring - at least the

248

aks

20

the pressure in the market at the time of minimum inventories.

t the

Accidental shortages may send prices soaring - at 1
spot price, but even the listed price if supply problems
endure. The price minima develop in a more predictable fashion,
Probably due to their relation to the slowly decreasing real
costs of production in the industry. Achieved prices (spot
prices corrected for discounts and expressed in local currency)
probably reflect actual production costs when inventories
bulge. In periods of excess inventories, pulp buyers find it
easier to press prices towards the producer's variable costs.
Producer costs are typically expenditures in local currency.
Thug a Norwegian seller may well have to accept a $ price below
the listed § price in times when, high exchange rates give him

a larger amount of kroner per $.

Pulp eales appear to move 180° behind inventories in the cycle.

In other words, pulp sales are large when inventories are small
and vice versa. This conclusion is derived by combining the
inventory statistics (figure 5) and the production figures

This indirect method is us

(figure 4). |, since data for

pulp sales are not available. From pure logics it follows that

if inventories grow (fall),

les must be lower (higher) than
the production rate. Applying this algorithm, one obtains the
rough outline of historial sales shown in figure 8. As can
be seen by comparing figures 6 and 8, low (high) sales coincide
with high (low) inventories. ‘This. conclusion is also corro-

borated by Hgstejand's data (figure 1).
2

But why should sales be large when producers’ inventories
are small? And vice versa? The likely explanation is that
both producers' and consumers’ inventories are large at the
same time. Throughout the period of excessive production,
not only producers’, but also consumers’ inventories gradu-
ally fill. The result is a non-causal, but strong,
correlation between large producers’ inventories and

small sale: Because large inventories also coincide with

falling prices, one has the surprising conclusion (at least in
theoretical micro economics) that sales are small when prices
are falling. And conversely, booming sales coexist with
increasing prices at other points, in the inventory cycle.
Thus consumers will need less pulp (because their inventories
are full) just when the producers would want to sell more pulp

(because their inventories are full).

The pulp production rate can best be viewed as a lagged version
of pulp sales. There is no simple phase relation between the

production rate on the one hand and inventory or price on the
other. Production rates tend to be high when inventories are
below normal and when prices are above the trend. But neither

relationship is unambiguou:

It is better to view pulp production as a lagged version of
pulp sales, with a lag of some 6 to 12 months. ‘Thie conclusion
is aleo supported by Hgsteland's data (figure 1). This relation
of sales to production is caused by the coupling through

producers’ inventory, which is kept within rather strict limits.

22

13. The Norwegian sulphite pulp industry and the international
sulphate pulp industry move in phase. There is an extra~

ordinary degree of correlation between Norwegian sulphite

pulp inventori (in figure 1) and NORSCAN sulphate pulp

inventories (in figure 5). The degree of covariation is
all the more surprising given the vast differences between
the two products, the two producer groups and the tonnages

involved. The covariation in pric jive.

is not less impr

This is but another example of the fact that the 3 to 6

year inventory cycle in most industries are in phase,
Presumably because increases in production rates in one sector
require output ‘from other sectors. Bullish tendencies tend
to spread throughout the economy, as do bearish (Randers,

1978).

The foregoing results and conclusions concerning past develop-
ments in the sulphate pulp industry are summarized in figures 9 and
10.

Figure 9 portrays the phase relationships that appear to exist
between central variables in the inventory cycle. The figure
should be viewed as an extension of figure 3. ‘Two important

perspectives follow from figure 9:

1) Production can be seen as a lagged version of sales.
When sales fall, production is pulled after - because the

two are couped through inventori. Inventories would

grow out of hand if prodyction were not stopped. When

sales increase, production must follow, lest inventories
23

250

shall be depleted. In short: production rat«

primarily governed by inventory size.
Higher prices

4i) Price does have some effect, however.

cause production to increase, but only relative to

If are declining of other reasons, production may

fall even when prices are growing.

Figure 10 portrays the system structure that appears to underlie

the inventory cycle at the industry level. The structure deviates

lier authors (Meadows, Hgsteland,

from the structures proposed by
Antun) in that production and sales depend more on inventory size and
less on price. The conclusion is derived primarily from the phase
relationships in the time series data reviewed (sulphite pulp,

phase relationships are not

ferro silicon, sulphate pulp). ‘Th
easily explained by the traditional structures. The proposed

structure does, however, require for the study.

24

PREDICTION OF FUTURE DEVELOPMENTS
4+_EREDICTION OF FUTURE DEVELOPMENTS

PROCEDURE

Prediction of future price is of great interest to decision
makers in the pulp industry. Production Plans, sales strategy,
and investment policy - all depend on opinions about the future
price of pulp.

Scientifically speaking, price prediction in socio-economic
systems is often impossible, except with wide margins of uncertainty.

The system structure and the amount of noise often cause enough

uncertainty in the forecast to make it useless for practical pur-
Poses. (This of course does not exclude the possibility of doing
crystal ball guesswork - without indication of the margin of
uncertainty).

The preceding sections reveal that the sulphate pulp induetry
is characterized by a system structure and a level of noise which
make useful price predictions possible on a 1 - 5 year time
horizon. The system structure creates a stable inventory cycle
with a 5 year wavelength in inventories and price. The noise
characteristics are such that quarterly data are well behaved and
smooth.

It is important to add that this conclusion does not.
necessarily hold for price predictions on a shorter or on a longer
time horizon. In both cases price developments are determined by
other factors than those responsible for the inventory cycle.

These factors constitute systems with different stability and noise
26
2s

to this tii des.
characteristics, which may well make useful (i.e. high certainty) 251 ‘S time series
predictions impossible.

vi) The extension of the sinusoid is added to the extension
The recommended procedure for 1-5 year prediction of pulp

of the exponential, giving the most likely prediction

prices, based on inventory cycle theory, is as follows: merre
‘ of price | Ae™*(#, (1 + Cin (2m8, 10- ™)).

1) An exponentially declining, long term trend

et, di) oA ‘
Jae? *)] de Hited teitea Tengeae RvmLUaEIS vii) margin of uncertainty is added, based on the standard

deviati c :
aoarteriy:dime series ci the reaipeice of puip- jeviations in the estimated values of the rate of
decline, the wavelength and the amplitude.
44) The exponentially declining trend is extended into the
future using the same rate of decline [«] as during the

ISCUSSION
preceding decades. DISCcUSSI

The fitting of the exponential and the sinusoid to historical

144) A sinusoia [Be +6, sin(1et fs fitted to the quarterly

data can be done quantitatively, using standard regression

time series of the total inventory of pulp, expressed

techniques, or manually, by hand in graphical plote. The la

in days of production. The sinusoid is used to determine

ehie Waverenger fr] 4a the Belew aeven " procedure does not necessarily give inferior results; but it

is less reproducible. ‘The manual procedure does have the
iv) Furthermore, the sinusoid is used to locate the position advantage of being simple, fast and transparent, even for

of future troughs and peaks in the price of pulp by non-mathematicians.

ming that price moves 90° ahéad of inventories in the ‘Theerueial point inthe: recommended procedure ie to use inventory

cycle, i.e. that price is proportional to sin(ox8, +p-¥). series to determine the wavelength and to locate the position of

troughs and peaks. The inventory fluctuations are much more
v) ‘The amplitude of the price wave is determines froma tine accentuated - and easier to observe - than fluctuation in other

ries of the ratio between the original price and the variables like price, production and sales. Furthermore, inventory

~ale-te) ) statistics are easily available, and they show little seasonal

exponential trend (i.e. ratio = pricee/ Ae

aie anpiituce [c} ie derived by fitting @ sinusoid with fluctuations given that they are true levels or accumulations.

the “correct” phase [i+Can(amg +6-3)] The position of price peake and price troughs are found by shifting
27
252
the inventory wave one quarter wavelength ahead. Rather than
relying on thie phase relationship, one may choose to fit a
sinusoid with the right wavelenght to the price series

in order to determine the phi relation between price and

inventories.
Even if inventories play a crucial role in the recommended method
of prediction, one should of course use all other available time

ries to check consistency and to remove ambiguities in the

location of troughs and peaks.

28

5. COMPARISON OF PREDICTED AND ACTUAL DEVELOPMENT

RESULTS

Figure 11 shows the result of applying the manual method of
prediction, based on time series for the period 1952 to 1982.

The graph is copied from an consulting report by the author dated
September 27, 1982.

Figure 11 also shows how prices actually developed over the
first two years of the prediction period. Figures 4 through 7 did
display that actual developments over these two years were in
accordance with inventory cycle theory. The inventory cycle
has continued in the sulphate pulp industry in the 2 years following
1982, as it did in the 25 years preceding 1982. In retrospect it is

yy to see that the price prediction was sufficiently precise for

ite intended purpose: to determine whether pulp prices would start
to increase within the next few years, creating a more favourable
climate for the planned sale of a large pulping plant. The con-
sulting report concluded that pulp prices would halt their decline
in the 4th quarter of 1983 (£2 quarters) and reach a new peak in
the lst quarter of 1986 (42 quarters). Hence the sale of the
pulping plant ought to be postponed to 1984/85.

In fact, prices did reach a minimum in the let quarter of 1983.
Afterwards prices have inched upwards, while inventories have
declined. The most recent available statistics (May 1984) show
inventories in the process of falling towards the magical limit
of some 3 to 4 weeks of coverage which formerly has signalled

significant price increases. The spirit of the sulphate pulp
29

253
industry is once more rising. One has come a long way from the
1982. The price of pulphing plants are substantially higher -

and increasing.

DISCUSSION
In principle the total, global inventories of pulp influence
the price of pulp. All finished pulp - resting with the producer,

in transit to consumers, in consumers' inventories and elsewhere -

is in principle available and thus acts to press price. The current
work seems to indicate, however, that the producers’ inventories
give a good indication of market conditions. This is advantageous

from a practical point of view, since producers’ inventori

are
the most readily available inventory statistics.

The individual supplier of pulp is most interested in the price
the discounts he has to give

he can achie i.e. listed price 1

in order to sell his pulp. The achieved price is closer to spot
price than to listed price, But time series for spot prices are
hard to define as are time series for achieved price. Hence it

appears more practical to base price predictions on time series of

listed price, keeping in mind that achieved price may well deviate
from the listed price - increasing before it does and declining

ahead of it. Finally, using listed price mak
amooth the price series, since the listed price is itself a
“running average” of spot prices. The listed price is quoted

roughly once every quarter and is representative of the current

going ra‘
Even simpler than the manual method of prediction, is to

* 30

establish a long, quarterly time series of inventories and use thie

curve as a rough indicator of one's present position in the

inventory cycle. Inventory figures in tons, from one major

supplier of pulp, may suffice for this purpo But quarterly
data measured in days of production covering a significant
fraction of the international market, gives a much better indication

of the state of affairs.
32

31° 254

REFERENCES

6. CONCLUSION

This paper has elaborated on the inventory cycle theory and
proposed a detailed theory for inventory cycles at the industry Antun, F.,

level. The detailed theory is supported by time series obtained

for the sulphate pulp industry, covering some 25 years. Bekkeetua (oslo). 1981

A method for prediction of pulp prices, based on the detailed Ezekiel, M., “The Cobweb Theorem", Quarterly Journal of Economics,
Vol 52, p. 255, 1938

theory, was proposed. The method was used in 1982 to forecast the
price of sulphate pulp. rison with actual devel sits cover: Forrester, J.W., Industrial Dynamics, MIT Press, Cambridge, Mass.,

the two ensuing years showed that the method gave good results. Forrester, N.B.,

The method of prediction is most likely applicable in other aDs tation, A.P. Sloan School of na timariey
M.I.T., 1982

industries producing commodities.

Hgsteland, J., “stock Fluctuations in the Pulp Industry" in
Lgnnetedt, L., and Randers, J., (editors) Wood

Resource pinenies in the Scandinavian Forestry
Sector, Studia Forestalfa Suecica, Nr. 152, 1579
Low, G., “The Multiplier-Accelerator Model of Business
Cycles Interpreted from a System Dynamics Perspective"

in Randers, J., (ed.) Elements of the System Dynamics
Method, MIT Press, Cambridge, Mass., 1580

Majani,B. and "Can Recessions be Predicted?", in Longe Range Planning
Makridakis, S., Vol 10, April 1977

Mass, N.J.,

Meadows, D.L., mamics of Commodity Production Cycles, Wright~Allen
Pieans Ganbrlage: Mats ge, Messe 1870

Naill, R.F., Mi » “Dynamic Modelling as a Tool for Managerial
N.J., Randers J., Planning: A Case Study of the US Hog Industry”,

Simpon M.K., Proceedings of the 1973 Summer Computer Simulation
Conference, Montreal, 1973

Randers, J., “om den gkonomisk utvikling pA 10-15 dre sikt”
("Economic Development in a 10 - 15 year Perspective"),

Bergen Bank Kvartalsskrift, No 4, 1978

Economic Cycles: An Analysis of Underlying Causes,

255
ial SALES
RATE
+ a
INVENTORY
PRODUCTION .
RATE ¢-) J] \
+ - PRICE
CAPACITY

UTILIZATION

Figure 1a. The inventory cycle in the Norwegian sulphite pulp
industry. (From Hgsteland, 1979. 12 month running averages of

Pi 2. The basi hand: behind the inventory cycle.
monthly data. Quarterly data for "achieved" price.) sure e baste mechantem Pe ey!

1000tons , 1000 tens /nenth Nkr/ton puce
400
too INVENTORY’
(tens)
fo eee
bo
2000
4o
Pup
a {000 PRICE
(1979-kee/ton)
3 io me ito ms "80
Figure 1b. The inventory cycle in the Norwegian ferré silicon
industry. (From Antun, 1981. Exports from the A/S FESIL & Co.
group of companies. th order running averages of monthly data.

Quarterly data for “achieved"price in constant 1977-kroner.)

Figure 3. The reference mode of the inventory cycle theory.
Cin thousand tons per year )

— T T —
hove : . 4
Woe0 4
foe 4
fp000 4

H

000 | 4
000 J
2000

oo Mo is aT a 80

Figure 4. Production rate.
Quarterly data for the NORSCAN countries - production of bleached sulphate pulp.

ase Mo ms me ms ) on

Figure 5. Inventories in tons.
Quarterly data for the NORSCAN countries ~ producers’ inventories of bleached sulphate pulp.

9SZ

ge

SE
Cin days)

Figure 7. Price.

od 4
yo
6o
so
e
~
Yo
30
20
Jo 4
i) HS ito ae 1980 (384
Figure 6. Inventories in days.
, hate pulp
NORSCAN countries - producers’ inventories of bleached sulp!
Quarterly data for the NORSEM! red in days Of production «
nN
uw
~
Cin 1979 NOK per ten)
an was wd
1000 4
fr ener Fi : pecans
ee cd ry cr ite ir ae 7

w
Quarterly data for the “listed price” of bleached sulphate pulp expressed in constant kr/ton. *

258

see)

cycle, inventory and price
Large inventories (+

cause falling price(—-),
and vice versa.

fluctuations.

behind achieved price(+++«).

The sales rate(——) oacil-
lates due to the business
Listed price(—~) lags

40

Figure 9. Sumary of phase relationships in the inventory cycle.

*seanbyz uoTzIMpozd pue Azoqueauy butuTquoo Aq peaTzep /STIZUNOS NVOSHON 9842 WoOIz SaTeS JO YOIEXS
‘eqez seTes peated -g eanbTa

nob oti! St Ott sm om osu

39

L ; ; oy

(awok ood suo ment “)
PRICE OF BLEACHED SULPHATE PULP IN 1982-kroner/ton

Figure 10. Summary of the system structure
behind the inventory cycle.

PREDICTED PRICE

Figure 11. Prediction of the real price of bleached sulphate pulp made in
September 1982 compared with actual development in price.
(2. quarter 1984 provisonal.)

tw

6S2

rs
S