The Use of Switching Point and Protection Levels to Improve Revenue Performance in Order‐Driven Production Systems

In a multiproduct order‐driven production system, an organization has to decide how to selectively accept orders and allocate capacity to these orders so as to maximize total profit (TP). In this article, we incorporate the novel concept of switching point in developing three capacity‐allocation with switching point heuristics (CASPa‐c). Our analysis indicates that all three CASP heuristics outperform the first‐come‐first‐served model and Barut and Sridharan's dynamic capacity‐allocation process (DCAP) model. The best model, CASPb, has an 8% and 6% average TP improvement over DCAP using the split lot and whole lot policies, respectively. In addition, CASPb performs particularly well under operating conditions of tight capacity and large price differences between product classes. The introduction of a switching point, which has not been found in previous capacity‐allocation heuristics, provides for a better balance between forward and backward allocation of available capacity and plays a significant role in improving TP.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112181/1/j.1540-5915.2011.00320.x.pd

Data-Mining für die Analyse von Nachfrage und Angebot im Revenue Management am Beispiel von Fluggesellschaften

Nachfrageprognose und Angebotsteuerung werden im Revenue Management zunehmend auf die Betrachtung komplementäre und substituierbare Produkte ausgeweitet. Im Revenue Management bedeutet dies die Prognose und Optimierung auf der Ebene von Reisewegen, Verkaufsstandorten, Buchungsklassen und Buchungsklassengruppen. Diese detaillierte Betrachtungsweise ermöglicht eine gezielte Optimierung der Angebotssteuerung, doch sie erhöht auch die Komplexität des Modells. Dieses Paper präsentiert eine Möglichkeit zur Analyse und Klassifikation von Nachfrage und Angebot durch die Anwendung von Data-Mining. Eine entsprechende Klassifikation bildet eine Möglichkeit, das Problem kleiner Zahlen und den Aufwand komplexer Einflüsse zu reduzieren

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment

Mitigating demand risk of durable goods in online retailing

Purpose An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost. Design/methodology/approach Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer. Findings Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction. Research limitations/implications Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss. Practical implications The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner. Originality/value The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information

A Development of a Game-Theoretic Artificially Intelligent Neural Network Revenue Management Forecasting Model

The aim of this dissertation is to create and test a risk induced game-theoretic price forecasting model. The models were tested with datasets from 3 Upper Midscale hotels in 3 locations (urban, interstate and suburb), one hotel from each location. The data was obtained from STR, a leading hospitality marketing company which consolidates all of the daily hotel data from hotels in the United States. Multiple error measures were used to compare the accuracy of models. Three LSTM models were proposed and tested; LSTM model 1 that relied on ADR to forecast ADR, LSTM model 2 that used ADR, supply, demand, and day of the week to generate the forecast, and finally LSTM model 3 that used all the predictors of LSTM model 2 plus ADR of 4 competitors of the same size and scale to predict ADR values. The LSTM models were tested against traditional forecasting methods. The findings showed that LSTM model 2 was the most accurate of all the models tested. Moreover, LSTM model 1 and 3 showed higher accuracy than traditional models in some cases. In particular, all the LSTM models outperformed the traditional methods in the most volatile property (property C). Overall, the results indicated the higher accuracy of LSTM models for times of uncertainty. Finally, estimation of Value at Risk was introduced into the LSTM models, however the accuracy of the models did not change significantly

Contingency theories of order management, capacity planning, and exception processing in complex manufacturing environments

Technological development and market diversification increase the complexity of modern manufacturing environments. Although the popular literature on lean management practices and quality improvement programs describe numerous ways of decreasing the complexity of manufacturing processes, the complete elimination of complexity is seldom possible. Thus, one needs to understand how to mitigate the performance effects of complexity with appropriate management practices. The research questions of this dissertation ask first, what do we already know about operations management under complexity, and second, how the applicability of day-to-day operations management practices depends upon the different dimensions of complexity. The research question on the existing knowledge about operations management under complexity is answered in two steps. First, I present a comprehensive review of organization-theoretical literature on the concept of complexity. This review results in a number of propositions on different ways of managing complexity. Second, I analyze the evidence for those propositions in a systematic literature review of recent operations management research. The results of that review point to a number of contribution opportunities, which guide the empirical studies that address my second research question. The research question on the applicability of operations management practices under different kinds of complexity is addressed with three studies within the same focused sample of 163 machinery manufacturing processes. The first study examines how the applicability of different order management practices depends upon the complexity arising from product customization. The second study examines the effects of process complexity on the applicability of different capacity planning methods. The third study examines the effects of different kinds of uncertainties on the applicability of different exception processing routines. As the studied practices begin from the acquisition of orders and end in the delivery of products, they constitute a holistic view of day-to-day operations management in manufacturing firms. The empirical analyses result in three contingency-theoretical propositions. First, I argue that product configurator tools, available-to-promise verifications, and configuration management practices are only applicable with specific levels of customization in products' configurations and components. Second, I argue that rough-cut capacity planning methods are only applicable with job-shop processes, capacity requirement planning is only applicable with batch-shop processes, and finite loading methods are only applicable with bottleneck-controlled batch shops and assembly lines. Third, I argue that only formal automated exception reporting channels are applicable when urgent glitches are being resolved in production processes. Meanwhile, only formal interpersonal exception reporting channels are applicable when equivocal glitches are being resolved. The theses have immediate practical implications for managers who are responsible for production processes in complex task environments. The studies show that none of this dissertation's theses are commonly known by practitioners nor discussed in the literature. In addition to the immediate implications for the studied environments, the theses can be theoretically generalized to other environments that satisfy certain boundary conditions. Examples can be found in service production, healthcare operations, and software development. The resulting middle-range theories of operations management in complex task environments can be tested in future studies with random samples of processes from other operations management contexts