SIMULATION OF INTERACTIVE BUSINESS STRATEGIES
AND OPERATIONS
Dr. U. La Roche, Georg Fischer AG, Schaffhausen, Switzerland
There is growing application of simulation to practical training
in management of business on strategic and operational level. In
use are simple models where a business is immersed in a much
bigger market which sets the context, and others where the
context is set dynamically by the actions of the competitors (3),
(4).
The simulation exercises reported are centered on the question of
how to appreciate the impact of a reactive context in managing a
business (1). The Implementation of Simulation with continuous
simulation (Dynamo, etc.) gives easy appreciation of the impact
of operational dynamics in a reactive strategic context.
INTRODUCTION
With earlier work (1), presented at the 1989 S.D.S. conference,
it was shown, how based on strict definitions of markets,
business segments and company portfolio strategies, a reactive
strategy context could be simulated. The work reported covered a
first simulation experiment with a one company ~- one segment
model extended to a two competitor situation.
In this paper we report further work towards a general approach
which is starting to be used for verifications of robust business
strategies in specific and real competitive contexts.
An interesting key question for which we report some simulation
results is centered on the paradigm that not the bigger but the
faster competitors will succeed. This story of the hare’s and the
turtle’s race is translated into a model comprising two
competitors, each with two businesses immersed in two separate
markets.
The simulation results do not confirm that the best way to win is
maximum operational flexibility. Much better success goes with a
longer strategy-setting time span and reasonable operational
flexibility adapted to the market. A strategy setting time
horizon serves to alleviate the need for operational flexibility
in confronting competitors. ,
S fe) NG_STRU.
In this chapter we will step by step put the concepts together
that are the building blocks of our modelling approach to the
real world of competitive, interactive business.
636 System Dynamics '90
The very basis is the definition of the business segment as the
specific arena, where competition meets and market shares are
measured. A business segment is defined by a client or clients
problem served by a product (7). This is understood to be a
market context outside the company. All a company can do is to
take part in business segments on the market or to leave them.
Business strategy will be based on the relative position a
company achieves in a certain business segment. There exists a
natural strategy which starts a business in a specific business
segment according to its so-called life-cycle. From the two
prominent strategy-classifications, namely (5)
- market-/company attractiveness (Boston Consulting Group)
- product life-cycle (Arthur D. Little Inc.)
we compiled a simplified combination, that uses relative market
share and product life cycle as parameters to define the four
typical strategic cases such as dog, question-mark, star, cow.
The necessary backbone of a strategy simulation in an interactive
context however is a reasonable realistic simulation of the every
day operational business transactions within each of the
competitors (2). For the reported simulation exercise we have
restricted ourselves to simplified models, where for each
business segment, even within the same company, we have separate
business operations. This could of course be changed and adapted
to any relevant case.
The causal-loop structure of adjusting demand and supply in a
business is summarized in Fig. 1, adapted from (3). There are
four major parameters:
- BL, backlog of incoming orders, which defines delivery delay,
capacity increases and delivery rate
- FI, stock of finished goods, which together with production
capacity defines production rate, delivery rate and selling
price preference
- PC, production capacity, which defines production rate and
fixed manufacturing cost
~- PRICE, price preferences to customer, which is used to control
the stock of finished goods
Of course this simple structure is complemented by an accounting
sector and a strategy sector, treated below.
The accounting sector in its illustrative and minimum
implementation just represents the calculation of the net product
contribution after marketing and after deduction of the fixed
manufacturing cost (financing and managing cost, not initial
investments etc.).
Included are the effects of the learning curve in production
capacity and in the unit-variable-cost over time. Total marketing
System Dynamics '90 637
expense also includes financing of stocks of finished goods. A
model for this simplified accounting is demonstrated in (3).
Based on accumulated accounting parameters and market-shares a
strategy-setting sector within operations sets the strategy
limits for the strategic business parameters. In the minimum
implementation reported these were chosen to be, see also (3):
- price
- capacity for production
- sales expense
Policy setting, e.g. defining the actual strategy case is
implemented using the parameters from the interplay of operations
and accounting. Strategy setting and policy setting as input to
strategy results in the overall structure shown in Fig. 2. The
model can of course be adapted so that policy setting does not
follow the interactive market development but instead follows a
given program over time. Such a feature allows to test
preprogrammed business plans.
Modules for coupling the company to competition
The market allocation module calculates for each moment in time
what will be the incremental gain or loss of market share
depending on how the relative product attractiveness compares to
market average.
By integration over time we get the time dependent market~share
of the company in the business-segment considered. Multiplication
of market-share and the also time dependent market-size
calculated in the competition-data module gives the flow of
orders booked as input to the business operations. The
implementation presented is summarized in Fig. 3.
In order to couple a number of competitors to a business segment
we use, as shown in Fig. 3, a module competition data called
"gendat" that calculates the mean values of product
attractiveness parameters needed within the market allocation.
For each separate company the budget module finally serves to
calculate the allowable sales effort expenses based on fixed
manufacturing cost and net product contribution sum.
Implemented is a priority-setting algorithm that concentrates
allowable expense on question-mark/star strategies. If a business
is terminated the remaining assets (here simply the stock of
finished goods) are liquidated and the result transferred to the
net contribution sum of the company.
Strategy setting and operational day to day adjustment
In order to simulate business strategies as compared to
operational business behaviour a strategy setting time period is
used. The relevant strategy paramenters (price, capacity, sales-
effort) are fixed for a strategy planning period, which like most
638 System Dynamics '90
other parameters of the model can be changed for each simulation
run.
This allows to verify the size of optimum strategy-revision
periods. If the period is set equal the time step for integration
of the model equations we would have a purely operational
oriented business adjustment from day to day.
TION OF MEST. OMPET. V) ITUATIONS
In order to illustrate what kind of investigation is possible
with simulation of competitve situations we will go through two
increasingly complex cases. They are:
A a single company, single business segment immersed in a
stable market context, Fig. 4
B two companies, each with two businesses immersed in the two
separate markets, Fig. 5
With this increasing complexity we will illustrate the procedure
to simulate any given competitive context starting from the
building blocks
- operational module
- gendat. or competitive data module
- budget module per company involved
RUN_A_- The single company, single business, immersed in a stable
Market context, Fig. 4
is the minimum configuration of our simulation. From the figure
we identify the basic modules described above:
- the competitive data/gendat. module, that gets as input data
the stable parameters of the immersion part of the market and
those of our company business present
- the operations module (one per business and market) is shown as
aggregation of the submodules:
+ market allocation
. business adjustment loops supply/demand
» accounting
. strategy setting/policy setting
- the budget module where the means for sales effort are
allocated
The simulation runs demonstrate the behaviour found for this
minimum configuration with an aging product starting from a
question-mark position.
Run A used starting parameters that let the business complete a
full cycle from question-mark to star to cow and end with dog.
This was assured by
System Dynamics '90 639
- low starting price
- low starting market share
~ size of immersed market small
RUN_B_- Two companies, ea with two businesses immersed in two
separate markets
We do have two markets:
a. with X-business and U-business
b. with Y-business and V-business
The two companies are the XY-company and the UV-company. Just to
get a first impression of the behaviour of this competitive
structure we did introduce some differences into the parameter
settings as summarized below:
- strategy setting periods for UV-company much longer
- B-market immersed size smaller
- B-market price~sensitivity higher
As a consequence the company UV does appreciably better mainly
because of a better suited strategy setting time against more or
less opportunistic operations management of its opponent, the XY-
company. This can be inferred from the printouts of
- UBDUGET / XBUDGET
- net product contributions. NETPC for XY/UV
RUN _C
In the next setup the only difference is a faster operational
response of XY-company. This results in XY-company doing better
(fewer lost sales and lower inventory).
Further experiments not shown in the paper confirm, that if a
long enough strategy period is used, fast operational flexibility
does only improve the results, as long as required by the market.
GENERAL COMPETITIVE STRUCTURES/APPLICATIONS AND FURTHER
INVESTIGATIONS
With the very much simplified simulation exercises we just wanted
to illustrate the feasibility of strategic business simulation
with a tool, the Dynamo Simulation Language, and in a format that
is comparatively easy to adapt to real world circumstances.
In a general competitive situation with several competitors
serving different markets we would arrive at a structure like
e.g. Fig. 6. Using the model building blocks introduced, the
general relationship on which to build a simulation model for
strategic planning analysis would be:
640 System Dynamics ‘90
- number of different markets served = number of competitive
data modules "gendat."
- number of competing companies = number of budgeting
modules
- number of business per market = fan-out of competitive
data module concerned
fan-out of budgeting
module concerned
- number of business per company
In case of modelling a real company with known competitors one
would of course not simply take identical modules for the
respective business operations but rather modules adapted to
represent the operations in question. As shown in his early book
Industrial Dynamics (9) J. Forrester has shown how to do this and
has demonstrated the relative ease to accomplish a realistic
model for a specific case.
In our view using the identical simulation approach for strategy
and operations planning helps very much to shorten the path for
optimising in parallel the dynamics of the business operations
themselves, which somehow appears to have been the original idea
also of Industrial Dynamics, but which today in the context of
computer~integrated-manufacturing is a much clearer necessity
(8).
CONCLUSIONS
The straigth-forward approach to image the two competitive
processes of market clearing of products and company clearing of
businesses is validated further.
A very much simplified exercize on the question of the impact of
different strategy planning periods on the one hand and
operational flexibility on the other hand yields some
clarifications on the effect of fast reaction flexibility in
competition. Fast operational flexibility only helps win if your
competitor does not use a strategy planning for a longer interval
than you.
The work reported represents in the authors view a useful
demonstration how to implement a strategy validation based on
simulation of the behaviour of a reactive competitors context.
The separation of the competition into a market clearing process
and a company portfolio clearing process helps to generalize and
adapt the approach to any real competitive context, e.g. Fig. 6.
References:
(1) U. La Roche; modelling business strategies for verification
of planning, computer-based management at complex systems,
proc. 1989 SDC, p. 128
System Dynamics '90 641
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
James M. Lyneis; Corporate planning and policy design MIT
Press 1980 ISBN 0-262~12083-6
J.C. Larréché; Markops, The simulation for marketing
training STRAT*X 1988 ISBN 2-906584-03-7
Peter P. Merten, Rainer Léffler and Klaus-Peter Wiedmann;
Portfolio simulation: a tool to support strategic
management.
System Dynamics review, vol. 3, No. 2 1987, p. 81
A.C. Hax and N.S. Mailuf; Strategic management 1984
Prentice-
Hall ISBN 0~-13-851270-1
Pugh-Roberts Associates, Inc.: Professional Dynamo program
series
U. La Roche; Von der Kunst, mit der richtigen Produkt-
/Markt-Strategie-Methode zu planen, IO Management
Zeitschrift 56 (1987) Nr. 3, DK 658.624.012.2
Alan K. Graham; Generic models as a basis for computer-
based case studies. Proc. Int. Conference of the System
Dynamic Society 1988 La Jolla, P. 133
g.W. Forrester; Industrial Dynamics, MIT Press, first ed.
1961
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System Dynamics ’S0 645
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Al The strategy transition from quest to star to cow and dog before divesting is
visible by following market-share XMAR and price XPRICE. Around time = 32 the
business is liquidated, the proceeds get the remaining budget above zero.
XNETPC the net contribution of the product and XPRICE reflect the strategy changing.
XLIQ is the eroding business liquidation proceeds available after divesting.
Portfolio-Parameter ENTRY signals, if conditions would be right to enter depending
on competitor market-share.
crayad.rsl - Fane t PORTFOLIO-PARAMETERS ONE SEGMENT
TIME oO 5. 10, 15. 20. 25). 30,
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System Dynamics ‘90
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Please remember the market and company settings:
companies are: xy-company with x-budget markets are: xu-business in A-market
uv-company with u-budget yv-business in B-market
B-market is reduced immersed size, highly price sensitive and short market-share
reaction. The uv-businesses use a strategy setting period which is very mich
longer than xy-businesses.
BI On the same scale we see the dramatically bigger income generated in the longer
term for uv-company with longer strategy-setting period, compare u-budget/x-budget.
ULIFDEL
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B2 Operational flexibility is only in the slow market A enough to gain market share
after start-up also.
System Dynamics '90 647
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System Dynamics '90
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‘The strategy setting periods of the competitors are now equal but the xy-company
has faster operational reactions. So xy-company does now better, compare
Cl UBUDGET vs. XBUDGET as the respective totals of product contributions.
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System Dynamics '90
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C3 Delivery delay problems (with loss of business) build with uv-company only.
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