Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

The definition of necessary and sufficient information accumulation level to substantiate a choice of enterprise’s market opportunities directions development

The basic rules of the theory-methodical approaches to definition of a necessary and sufficient information accumulation level for marketing decisions’ on a substantiation of the enterprise’s market opportunities development projects effective acceptance are set forth

Assessing the impact of prices fluctuation on demand distortion within a multi-echelon supply chain

Price fluctuation is a practice commonly used by companies to stimulate demand and a main cause of the Bullwhip effect. Assuming a staggered step demand pattern that responds elastically to retailer’s price fluctuation, and by using a supply chain management dynamic model, we will analyse the impact of these fluctuations on the variability of the orders placed along a traditional multilevel supply chain. Subsequently, the results obtained will serve to propose a forecasting model enabling to calculate the potential variability of orders placed by each echelon on the basis of the price pattern used. Finally, under the hypothesis of an environment of collaboration between the different members of the chain, we propose a predictive model that makes it possible to quantify the distortion of the orders generated by each level.En este artículo se analiza la influencia de la fluctuacion de los precios en la variabilidad de las órdenes generadas a lo largo de una cadena de suministro tradicional multinivel. Para ello, se utiliza un modelo dinámico de gestión de ca- dena de suministro en el que se introduce un patrón de de- manda tipo escalón, que responde elásticamente a la fluc- tuación de los precios ofrecidos por el minorista al cliente final. Posteriormente, utilizando los resultados obtenidos, se propone un modelo de previsión para calcular esa posible variación de las órdenes generadas en cada nivel, a partir del patrón de precios utilizado