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planning process (process model, simulation model) leads to another problem. Each
time a model is slightly modified, any of the other models must also be revised. This
also increases the modeling effort. One of the main problems with the use of
simulation is the availability of valid input data in the right quality, quantity and
granularity [17]. These are an essential precondition for high-value results [18]. In
addition, the used data must be available and visible as fast as possible. Conventional
approaches using data stored in data warehouses and are request-driven [19]. Thereby
a simulation works on retrospective data.
For these problems, the following requirements are derived. The effort for the
development of simulation models must be reduced. Especially as in the planning of
logistics systems several models come to use. These models build upon one another and
have dependencies among each other. A change in a model also leads to changes in
subsequent models. Therefore, the use of simulation techniques has to be integrated in the
planning process [7]. It must be ensured that the created process models within the
planning process, based on a separate description of each logistics service, can be
transferred automatically into a simulation model. However, the different fluctuations (e.g.
fluctuations in demand, sales trend and seasonal fluctuations) of the entire logistics
system, which can potentially arise, should be considered. On the one hand, this
requirement aims to minimize the planning effort of a 4PL. On the other hand, manual
errors in the creation of a simulation model should be avoided. Furthermore, the need for
special training and special experience in simulation model building is reduced. Another
requirement concerns the information acquisition. As a result of the information overload
the investment in simulation projects for information acquisition is almost 50 % of the
total project time. This leads to the need of an efficient approach for gathering information
to support the logistics planner in all planning activities. To gain a robust simulation result
the used data have to describe the current state of all logistics networks.
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Related Work
Simulation:
Simulation approaches are widely used in logistics in order to plan
logistics systems. Ingalls discusses the benefits of simulation as a method to study the
behavior of logistics networks [20]. Additionally, advantages and disadvantages are
presented for analyzing supply chains with the use of simulation models in general. A
concrete simulation approach is not provided. In [21] a commonly applicable
simulation framework for modeling supply chains is presented. Instead of [20] they
focus on a more technical perspective as they show an overview of event-discrete
simulation environments in terms of domains of applicability, types of libraries, input-
output functionalities, animation functionalities, etc. Cimino et al. also show how and
when to use certain programming languages as a viable alternative for such
environments. A modeling approach and a simulation model for supporting supply
chain management is presented by Longo and Mirabelli in [22]. In addition, they
provide a decision making tool for supply chain management and, therefore, develop
a discrete event simulation tool for a supply chain simulation using Plant Simulation
1
including a modeling approach. All these approaches are relevant for developing an
1
(based on eM-Plant)
http://www.siemens.com/plm/PlantSimulation
