Abstract
The design of a concept for SCPC needs, firstly, the specification of the requirements for SCPC as a method for RM in the context of the revenue maximization problem. This includes the description of the addressed planning levels, which have to be considered in the SCPC and the planning tasks that have to be executed. Following the central planning tasks of RM, its underlying application constraints are outlined. The formulation of the objective system of SCPC leads to several problems influencing the operational design of the solution concept. The development of the solution and its first areas of application indicate the appropriate implementation of SCPC.
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References
In literature, different criteria (except from the time scale) can be used to distinguish the different planning levels [see Bronner 1999; Schweitzer 2001].
Concerning the time ranges, there are differing statements existing in the literature [Schweitzer 2001].
Master Planning is used for the design and coordination of the local production program for the single production lines, under the consideration of the available capacity [see Fleischmann/ Meyr/ Wagner 2005].
Forecasting is the prediction of an event, a state or a development over time. Qualitative and quantitative forecasting techniques can be distinguished. In this context, especially quantitative methods are relevant. Examples are exponential smoothing, regressions or various autoregressive and moving-average methods [Lancaster/ Salkauskas 1986; Schlittgen/Streitberg 1987; Bamberg/Baur 1998, pp. 217; Tempelmeier 2005].
Corsten/ Stuhlmann [1998] and especially Fandel/Blaga [2004] discuss the external factor as a difference criterion between the service and production industries.
If the definition of a product mix is not possible (e.g. in make-to-order manufacturing with completely individual products), the implementation of an adequate project management was found to be more reasonable [Kleinaltenkamp/ Plinke 1998; Kimms 2001].
A classical example for the setting of aspiration levels is the conduction of offensive or defensive environmental protection in companies. With the given competing objectives “profit margin maximization” and “pollution minimization”, an offensive protection strategy would be to minimize pollution under the constraint of a minimum profit margin. The defensive alternative is to maximize the profit margin under consideration of maximum pollution levels [see Wagner 1990, p. 14].
A comprehensive overview over capacity and price control methods (and the state-of-the-arts) is given in various contributions to RM (see for example Tscheulin/ Lindenmeier [2003]; Talluri/van Ryzin [2004]; Phillips [2005]).
A further distinction can be made between standard nesting (additional capacity for the more valuable class comes only from the class below) and theft nesting (additional capacity comes from all classes below) [Talluri/ van Ryzin 2004, pp. 30].
Heuristic approaches were already mentioned in chapter 1. Silver [2004] explains the basic understanding of and the reason for heuristics.
In the multiple class case, a dynamic programming approach [Bellman 1957] is basically used to find the same solutions based on the same assumptions.
Simulation is the reproduction of time-related processes in a model, in order to draw conclusions about the behavior of a real system [see VDI 1993]. Conventional simulation methods can be found in Fujimoto [1999].
A knapsack problem is a problem in combinatorial optimization. Given a set of items with a specific cost and value, the problem is to determine the number of each item that is, for instance, allocated as efficient as possible with respect to a limiting factor (e.g. capacity) [see Martello/ Toth 1990].
Thereby, the functional specifics are non-negativity, continuity, differentiability and the downward slope [Phillips 2005, pp. 40].
Certain limitations to price differentiation can be stated: Imperfect segmentation (missing knowledge of the willingness to pay of each customer), cannibalization (customers with high reservation prices trying to get lower prices, e.g. discount prices of airlines) and arbitrage actions (third parties buying at low prices and selling the good at a higher price to people with higher reservation prices) [Phillips 2005, p. 77].
Thereby, for instance, English (open ascending), Dutch (open descending), Sealed-bid first-price and Vickrey (sealed-bid second-price) auctions can be distinguished [Klemperer 2004; Vickrey 1961].
Integer programming is a special case of linear programming with the unknown variables being integer [Schrijver 1998.]
Thereby, a continuum of possible capacity and price adjustments exists depending on the flexibility and the costs of changing [Talluri/ van Ryzin 2004, p. 176].
A central problem in the context of RM is thereby the presence of censored (constrained) data where only the accepted requests of customers are stored in the data basis. Rejected offers (from the supplier or consumer side) are not saved. Thus, not the real demand and transaction structure is reproduced in the data. Several techniques are available for the unconstraining of data [Talluri/ van Ryzin, pp. 473].
The price elasticity of demand ε can be explained as the percent change of demand (in our context the amount of requested capacity units, x) with respect to one percent change in the price (p): ε=†x/x/†p/p. The price elasticity can be the reason for significantly different price levels in different markets despite that the same product is sold. If the demand is price-elastic, even small variations of the price lead to a noticeable change in the demand. A price-inelastic demand does not change with price modifications [Varian 1999, pp. 257].
A data warehouse (DW) is a “subject-oriented, integrated, time-variant, nonvolatile collection of data in support of the management”s decision-making process“ [Inmon/ Hackathorn 1994]. This definition is only one of the various attempts to describe the complex structure of a data warehouse with its typical components of data input (with the important data extraction and transformation process [Bauer/ Günzel 2000]), data management (with the meta-database, the central data base and data marts, as well as the storage system), and the data deployment (use of the data base for the analyses relevant for the operative and communicative CRM) [Devlin 1997].
Data Mining is the automatic knowledge discovery in large databases in order to find complex or complicated patterns with the help of specific data mining techniques (e.g. rule induction, decision trees) [Witten/ Frank 1999; Tan/ Steinbach/Kumar 2005; Hippner et al. 2001].
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(2008). Conception for a simultaneous capacity and price control. In: Integrated Capacity and Price Control in Revenue Management. Gabler. https://doi.org/10.1007/978-3-8349-9650-3_2
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DOI: https://doi.org/10.1007/978-3-8349-9650-3_2
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