A ray method of confidence band construction for multiple linear regression models

Hayter, Anthony J.; Liu, Wei; Ah-Kine, P.

doi:10.1016/j.jspi.2008.04.029

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Two or more independent variables are used in MLR analysis. The selection of these variables should be explained not only by their link to the dependent variable but also by the accessibility of historical data [78]. Equation (2) can be used to illustrate the MLR model:…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Demand Forecasting Application with Regression and IoT Based Inventory Management System: A Case Study of a Semiconductor Manufacturing Company

Jaouhari

Alhilali

Arif

et al. 2022

JERA

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The accuracy of demand forecasting has a significant impact on the supply chain system's performance, which in turn has a major effect on company performance. Accurate forecasting will allow the organization to make the best use of its resources. The synchronization of customer orders to support production is critical for on-time order fulfillment. However, In fact many organizations report that their forecasting method is not working as effectively as they had hoped because orders regularly alter due to client demands. The purpose of this paper is to present an Internet of Things (IoT)-based inventory management system (IMS) that combines a causal method of multiple linear regressions (MLR) with genetic algorithms (GA) to improve the accuracy of demand forecasting in the future period by the customer as closely as feasible and enable smart inventory for Industry 4.0. Based on the data gathered from a semiconductor company that specializes in low-volume, high-mix contract manufacturing equipment and services integration, the suggested IoT-based IMS indicates that inventory productivity and efficiency could be enhanced, and it is resilient to order fluctuation.

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Section: Multiple Linear Regression Modelmentioning

confidence: 99%