A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse

Lacking timely access to rescue resources is one of the main causes of casualties in tunnel collapse.To provide timely rescue,this study proposed a multi-objective preallocation model of special emergency resources for tunnel collapse based on demand time.Efficiency,multiple coverage,and cost-effectiveness are taken as the key objectives of the model;the demand time service range is used as a coverage decision factor considering the unique nature of tunnel collapse.The weight of potential disaster-affected points and other general factors are also considered in this model in order to thoroughly combine the distribution of disaster points and service areas.Further,we take 15 main tunnel projects under construction in China as an example.When the relative proximity to the ideal point of the selected optimal schemeε_(a)is smaller than 0.5,we will adjust the weight of three objectives and reselect the optimal scheme untilε_(a)>0.5.Compared with the not preallocated case,the number of rescue rigs needed is reduced by 8.3%,the number of covered potential disaster-affected points is increased by 36.36%,the weighted coverage times are increased from 0.853 to 1.383,and the weighted distance is significantly reduced by 99%when the rescue rigs are preallocated,verifying the feasibility and superiority of the proposed model.

A supply chain model for imperfect production system with stochastic lead time demand

This study deals an integrated manufacturer-buyer supply chain system for imperfect production under stochastic lead time demand.Here,defective rate has been followed as a function of production rate.Also,the produced units have been inspected in order to screen the defective units but screening rate is less than the production rate and greater than the demand rate.Buyer purchases the products from the manufacturer.Also,we assume that shortage during the lead time is permitted and demand during the shortage period is fully backordered.The objective is to derive the optimal production rate,ordering quantity and to maximize joint total profit.Basically,two different models for different probability distribution functions of stochastic lead time demand have been developed.Some numerical examples are provided to show the applicability of the proposed models comparing the optimum average profits.Finally,sensitive analysis,conclusion and future researches are presented.

A New Continuous-Production Lot-Size Model with Increasing Time-Varying Demand and Variable Rate of Production Considering Shortages

Here an optimal continuous Production Lot size(PLS) model is developed for deterio rating items with increasing time varying demand over a finite planning horizon.The production rate is dealt with as a decision variable.The deterioration rate is assumed to be a constant fraction of the on hand inventory level.Shortages are permitted in every production cycle and are not backlogged.Some properties for the optimal solution of the proposed model are shown.The solvting procedure is proposed to generate an optimal continuous production schedule. The corresponding models with no deterioration and no shortage are derived as the special cases.Numerical examples for illustrating the solvting procedure are employed for linearly increasing demand.

Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm

Inventory management is a crucial task for any industry.In this paper,we have determined the optimum profit and economical order quantity under variety of assumptions such as the demand per unit time follows either a log-normal or a generalized exponential distribution.Parametric relationship between these two distributions,the proposed models become comparable.For modeling,we consider the expected demand and variable deterioration.Under these probabilistic assumptions,inventory models are developed for situations like no,complete and partial backlogging.Classical methods are unable to solve these situations under these assumptions.Thus genetic algorithm is proposed to solve these models.Economic order quantity is obtained for maximizing the total profit for the respective demand per unit time distributions.A real-world case study of a deteriorated product is presented to illustrate the procedures of the proposed inventory models.