Information Technology Reference
In-Depth Information
introduction
Planning and implementing corporate strategy very often requires substantial efforts in gathering rel-
evant data and information underlying the decisions to be met. Hence, the decision makers face at least
two elementary issues: First, the planner has to be supplied with appropriate data about the underlying
relevant key figures and business drivers as well as environmental information related to the market
or competitors. This first function of data support as outlined before is the main focus of so-called
management information systems (MIS). These tools usually employ powerful techniques to gather the
necessary figures as a basis for strategic planning efforts.
Second, this raw data has to be arranged within decision models in order to reduce the variety and
complexity coming with it: One characteristic of a complex strategic decision is that it is influenced by
an immense set of business variables which have to be analyzed in this context. As a consequence data
supporting tools do not provide appropriate aids for this type of entrepreneurial function: It is to reduce
the complexity emerging from this amount of data which becomes the principal task of decision support
systems (DSS). Hence it can be observed that the architecture of any arbitrary DSS is highly dependent
of the managerial approach it is designed to support. It necessarily incorporates the notion of a mental
model underlying the respective decision theory as well as techniques to derive decisions from these
assumptions. Sprague & Carlson (1982) specify these two core components of a DSS as model base
and method base, respectively. The former defines the structure of the decision model which arranges
the raw data provided by a data support component, whereas the latter encompasses decision theoretic
methods specifically designed to operate on the given decision model. According to the type of the model
base, analytic techniques like optimization as well as statistical methods or stochastic approaches like
simulation are used to draw decisions from the raw data organized in the decision model.
The rest of the chapter is organized as follows: The following section provides review of the appro-
priate literature within the field of causal strategy planning techniques as well as of causality concepts.
Consequently, specific causality criteria are defined on this basis. This definition is employed in the
subsequent section in order to establish an approach for the automated proof of nomothetic cause-and-
effect hypotheses. Since every single of these proven causal relations are characterized by an arbitrary
unknown cause-and-effect function, this function has to be approximated in order to build a quantitative
model base for DSSs. Therefore this chapter discusses appropriate approximation techniques and pro-
poses a nonparametric approach for the universal approximation of arbitrary cause-and-effect functions
by the means of ANNs. This chapter is concluded by the presentation of experimental results.
LITer ATure r eVIeW
Causal Strategy Planning Approaches
A considerable number of recent approaches within the domain of strategic decision making proposes to
organize business indicators in the form of causal models. The main task of these models is to visualize
the cause-and-effect relations which the decision maker assumes to exist between the given variables
and/or goals (Hillbrand & Karagiannis, 2002a).
One well-known example for this type of strategic decision methodologies is the Balanced Score-
card approach (Kaplan & Norton, 1992): The main idea behind this concept is that short term goals
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