A Guideline of Fuel Oil Saving in Manufacturing Sector of Thailand

Abstract

Fuel oil is the primary fuel used in Thailand's manufacturing sector. Currently, the use of fuel oil is steadily increasing, and the world's oil reserves are slowly depleting. Therefore, the Thai manufacturing sector needs to adapt to this situation. The purpose of this research is to investigate possible strategies for fuel oil savings in the manufacturing sector of Thailand by looking at different types of factory sizes. Qualitative research was conducted through in-depth interviews with nine experts to create tools for quantitative research. A focus group discussion of 11 experts was conducted to reach a consensus on the model of this research, and qualitative research methods were used when analyzing five-hundred factories. The results showed that a strategy for fuel oil saving in the manufacturing sector of Thailand aimed to achieve sustainability encompassing four factors. 1) Innovation and Technology (X ̅= 4.65); an essential item was "Using Smart Plant technology to control fuel oil properly. 2) Organization System (X ̅= 4.64); the most important item was "Annual budget allocation for investment expenses for energy conservation and oil energy management. 3) Eco-efficiency (X ̅ = 4.61): The most important item was the installation of a solar rooftop on the factory roof for lighting systems. 4) Mindset (X ̅= 4.60). The most important item was encouraging personnel to participate in the formulation of fuel-oil-saving measures. The hypothesis testing revealed that differences in the number of personnel in the organization between "less than 200 people" and "greater than or equal to 200 people" revealed overall aspects significantly different at the level of 0.05. An analysis of the structural equation model showed that the results passed the assessment criteria using empirical data. The Chi-square Probability Level (CMIN-p) was 0.064, and the relative chi-square (CMIN/DF) was 1.134. The Goodness of Fit Index (GFI) was 0.954. The Root Mean Square Error of Approximation (RMSEA) was 0.016.