Abstract
When analyzing the basic conditions of modern service and production processes, the intense competition constraints become evident at first. Secondly, more complex customer relations can be observed.
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References
Globalization, in its economic sense, was first mentioned in literature in 1983 [see Levitt 1983].
Especially in the finance sector, information and communication technologies like the Internet were responsible for an intense change in the transaction process [see for example Buhl/ Kundisch 2003, p. 3].
Vertical network partners are companies along the value chain, whereas horizontal network partners are on the same production stage [Friedl 2006, pp. 2].
SCM was first literarily mentioned in 1982 [see Oliver/ Webber 1992].
In a vendor managed inventory, the producer is responsible for the inventory management of the subsupplier. He has access to the inventory data and generates the orders by himself [see Christopher 1998, pp. 195].
Mass customization denotes the production of goods and services corresponding to the individual customer needs combined with an efficient mass production of standardized goods and services [Tseng/ Jiao 2001]. Four types of mass customization can be distinguished: Collaborative customization, adaptive customization, transparent customization and cosmetic customization [Pine 1993].
Demand fulfillment addresses the receipt and processing of customer orders with the two sub-tasks order promising (delivery time promise) and demand-supply matching (material stock allocation) [Fleischmann/ Meyr 2001].
RM (frequently called Yield Management [Weatherford 1997, p. 69]) — as thoroughly explained in the following — is first literarily mentioned in the late 1950’s [see Beckman 1958]. Its role as an integrative approach to price and capacity control began in the late 1970’s, mostly through practical applications [Tscheulin/Lindenmeier 2003]. This role will be the subject matter of this work.
Revenue, as used in this work, means the sum of accounts receivable through the sales of goods and services over a certain time horizon. The term revenue in its broad sense is each event that leads to an increase of the net assets (e.g. including incomes from interests) [Wöhe/ Kußmaul 1996, p. 17].
In this context, initially, targets (e.g. profit enhancement) have to be defined. Per an adequate operationalization, corresponding objective functions on the basis of according objective criteria (e.g. revenue, costs) result. These are denoted in connection to a decision criterion (e.g. building of an extremum like maximization and minimization (or, formulated more abstractly and interchangeable in this work, optimization), satisfication, fixation [see Kosiol 1968]). More objective criteria are combined to an objective system.
Spengler/ Rehkopf/ Volling [2007] formulate a multi-dimensional knapsack problem for the reproduction of the order uniqueness and the capacity demand in make-to-order manufacturing. The acceptance or rejection of the orders is implemented in two bid-price calculation schemes (see Klein [2007] on bid-pricing).
Reservation price, in this context, means the willingness to pay of each single customer. Lies the price above this reservation price, the customer rejects the price offer and no goods and services will be sold to him (with the potential of idle capacities in the case of structurally too high prices). When the price is below the reservation price, the customer gets a consumer surplus and the supplier loses potential revenue. By setting the price equal to the reservation price of each customer, the supplier skims this consumer surplus [see Varian 1999, pp. 236].
As observed in this example, some applications of RM do not only have to be analyzed with respect to the effects on the purchasing behavior of customers [Talluri/ van Ryzin 2004, pp. 614; Campbell 1999; Kahneman/Knetsch/Thaler 1986] but also with respect to moral considerations (especially in the health sector). The same counts for other management approaches (like capacity reductions in SCM leading to dismissals).
See Puppe [1988] for an introduction in expert systems.
The fuzzy set theory was invented by Zadeh [1965] and takes into account the fuzziness of an item’s membership to two different sets (examples are subjective values like “tall”, “high” or “fast”). Biewer [1997] and Zimmermann [1991] give a good introduction to the concept of the fuzzy set theory. Fuzzy approaches are used in many applications in all kinds of industries (e.g. textile industry [Tuma 1994], ecological production control [Tuma/Müller 2000], medical diagnosis [Adlassnig/Kolarz 1982] or weather forecasts and Robotics (see Bothe [1993] for an overview over existing applications)).
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(2008). Introduction. In: Integrated Capacity and Price Control in Revenue Management. Gabler. https://doi.org/10.1007/978-3-8349-9650-3_1
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DOI: https://doi.org/10.1007/978-3-8349-9650-3_1
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