Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand

The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy - a case study

Impacts of forecast, inventory policy, and lead time on supply chain inventory—A numerical study

Interactive data analysis: a practical primer, by Donald R. McNeil. Pp xii, 186. £7. 1977. SBN 0 47102631 X (Wiley)

The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand

A basic function in the proper management of repair part inventories is the anticipation of demand. The US Navy maintains a database of univariate demand data for its repair part inventories using a quarterly time interval and a limited number of periods. Historically, the exponential smoothing procedure has been used for demand forecasting. This method is simple and robust, but it does not make use of any characteristics of the entire time series such as trend, cycles, presence of outliers or demand clustering. Sharper information may be available with the use of the Box–Jenkins system. Not all repair parts can capitalize on this and there is a problem in identifying those that do. Moreover the number of parts is quite large and the speed of identification is an issue. This paper addresses this problem. The research begins with the development of several simple, robust and dimensionless time series features. These are used to predict the suitability of Box–Jenkins (ARIMA) modeling. Two predictive models are considered: classical regression and a modern expert-system statistical package, ModelQuest™. Their strengths and weaknesses are compared. The result of either is a computationally simple means for determining which repair parts time series may benefit from the Box–Jenkins methodology for purposes of inventory management.

Identification of demand patterns for selective processing : a case study

The students' perception of overall teaching performance is very strongly aligned with his feeling about the course. Cross-classified data enable the separation of the effects of the teacher, the course, and the individual student groups. All three effects are highly significant and the effect of the course is by far the most prominent. Moreover, the effect of the student group is more important than the effect of the instructor.

Components of Student-Faculty Evaluation Data.

The paper treats the output process of a service center that has a large number of independent exponential channels in parallel Initially all channels are working and there is a fixed backlog of items awaiting service The moments are derived and central limit theorems are developed Problems of computation are discussed and suitable formulae are developed The joint distribution of the output of the center with the center's total busy time and total idle time are derived Normal approximations to these distributions are presented

On the output of parallel exponential service channels

Consider the random sampling of a discrete population. The observations, as they are collected one by one, are enhanced in that the probability mass associated with each observation is also observed. The goal is to estimate the population mean. Without this extra information about probability mass, the best general purpose estimator is the arithmetic average of the observations, XBAR. The issue is whether or not the extra information can be used to improve on XBAR. This paper examines the issues and offers four new estimators, each with its own strengths and liabilities. Some comparative performances of the four with XBAR are made.

The motivating application is a Monte Carlo simulation that proceeds in two stages. The first stage independently samples n characteristics to obtain a “configuration” of some kind, together with a configuration probability p obtained, if desired, as a product of n individual probabilities. A relatively expensive calculation then determines an output X as a function of the configuration. A random sample of X could simply be averaged to estimate the mean output, but there are possibly more efficient estimators on account of the known configuration probabilities.

Robert R. Read

Papers

Identification of demand patterns for selective processing : a case study

Components of Student-Faculty Evaluation Data.

On the output of parallel exponential service channels

Estimating means when sampling gives probabilities as well as values or “Looking a gift horse in the mouth”