2-Level-Service

2-level-service occurs when an inventory system has two or more locations and one location is the source to another location in the network.  The source location receives its stock from a supplier and -- when called upon -- replenishes the stock to another location, here called the 2-level-service location.  This paper shows how to control the inventory at each location and generates table values on inventory levels for a range of scenarios

Demands, Backorders, Service Level, Lost Sales And Effective Service Level

The typical way service level is measured in industry is the demand filled over total demand. Unfilled demand becomes a backorder or lost sales. Lost sales demand is often not known or measured by the management.  This paper shows how to estimate the lost sales demand and also shows how to measure an effective service level.  Tables are provided for easy reference and two examples show how to apply the results of this paper to industry situations

Demands Along The Supply Chain

This paper describes how the monthly demands vary at the locations along the supply chain, coming from the customers to a dealer onto a distribution center and finally to a supplier.  The mean, standard deviation and coefficient of variation are measured for each of the locations.  The results indicate when the demands tend to be normally distributed and when non-normal

Min And Max Triangular Extreme Interval Values And Statistics

This paper concerns the triangular distribution and shows how to find the min and max extreme interval values and related statistics (mean, standard deviation, mode, and median) for a range of observation sizes, n.  The extreme interval value, denoted as , represents a bound where the probability of any value less than is α.  Tables and an application are provided

Min And Max Exponential Extreme Interval Values And Statistics

The extreme interval values and statistics (expected value, median, mode, standard deviation, and coefficient of variation) for the smallest (min) and largest (max) values of exponentially distributed variables with parameter ? = 1 are examined for different observation (sample) sizes. An extreme interval value is defined as a numerical bound where a specified percentage ? of the data is less than . A procedure for finding the extreme interval values when ? > 0, an analysis of the extreme interval values and statistics, and an application of this research are provided

Min And Max Uniform Extreme Interval Values And Statistics

The min and max uniform extreme interval values and statistics; ie expected value, standard deviation, mode, median, and coefficient of variation, are discussed.   An extreme interval value is defined as a numerical bound where a specified percentage α of the data is less than . A numerical example and an analysis of the min and max extreme interval values and statistics are provided.  In addition, a procedure for finding the min and max extreme interval values for different uniform parameter values, and an application of this research are presented

Adjusting An Existing Forecasting Model When Some Future Demands Are Known In Advance: A Bayesian Technique

The purpose of this research is to provide a model which can be used to adjust forecasts that are already available. It analyzes the components of the advanced demand, namely, the number of orders and their corresponding order size. It explores and analyzes the possibility of using the expected number of orders for a future period as the variable to be estimated. The Bayesian estimate of the expected number of orders is used in determining the adjusted forecast. A simulation is applied to calculate a ratio between the adjusting forecasting error and the original forecasting error. Results prove that the adjusted forecast provides greater accuracy for different probable values of getting an order in advance

Min and Max Log-Logistic Extreme Interval Values

The min and max log-logistic extreme interval values are presented. In addition, the paper shows how the log-logistic extreme interval values can be found from the uniform extreme interval values. An application and tables containing some of the min and max log-logistic and uniform extreme interval values are provided

Min And Max Extreme Interval Values

The paper shows how to find the min and max extreme interval values for the exponential and triangular distributions from the min and max uniform extreme interval values. Tables are provided to show the min and max extreme interval values for the uniform, exponential, and triangular distributions for different probabilities and observation sizes

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder