STRUCTURAL EQUATION MODELING OF SUPPLY CHAIN MANAGEMENT, EMPLOYEE INVOLVEMENT, AND EMPLOYEE WORK PERFORMANCE IN THAILAND'S AUTO PARTS INDUSTRY

The objective of this research was to analyse the structural equation modelling (SEM) of supply chain management, employee involvement, and employee work performance in Thailand's auto parts industry. The sample group included 383 employees operating in the aforementioned industry using SEM processing by the AMOS program as the tool. From the research, the latent variable of supply chain management had a direct positive influence on the latency of employee involvement and employee work performance with statistical significance. Simultaneously, the latency of employee involvement had no direct positive influence on the latency of employee work performance. Therefore, the latency of supply chain management did not indirectly influence the latency of employee work performance through the latency of employee involvement.

Redesign of Layout Runner in Rubber Injection Molding for Filling of a Multi-cavity Mold

This study presents a Solidworks� Plastics application in a company in the Automotive Industry for the aftermarket of auto parts manufactured by the injection molding process, the focus is on the redesign of an injection vein plate for achieve uniform filling of a 16 cavity mold with a geometry made up of a mixture of natural rubber and two metal components. This work proves that the use of symmetrical commands is not always the best option. The distances between runners were not taken into account as a source of the future wears problems in the mold. A layout is created with a combination of 2D and 3D sketches by turning the injection chanels 180� in the problem cavities to increase the distances between runners and the filling of the 16 cavities is verified by simulation. It is also demonstrated by simulation that increasing the injection point size is not necessarily always the best option for cavity filling.

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 assessment 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 cofactors 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.