Based on innovation and competitive market for food industry, there are several food products which have been designed to attract customer. Since there is USD 39,431 millions of chocolate sales in 2018, USA [1], chocolate product shapes have been developed based on manufacturing process. This paper presents a review process of a machine design of chocolate extrusion based co-rotating twin screw extruder. A property of suitable chocolate for extruder was established. The pros and cons of machine extruder for food processing including, a screw extruder design were exposed. Since there were problems in the chocolate extruder, the process parameters such as barrel temperatures, feed rate, screw speed, motor load and melt pressure were established. These parameters would be applied to design screw extruder for chocolate processing.

This paper focuses on developing a bulk material volume estimation system employing automatic moving rail optical distance measuring technology. The research objective is to devise a system capable of estimating warehouse bulk material volumes utilizing point cloud data. Additionally, the research proposes guidelines for enhancing efficiency in bulk material volume estimation processes. A prototype system was developed and tested using dry rice husk with a humidity level of 15% as the sample material. The testing environment comprised a laboratory with dimensions of 36 square meters and a height of 3 meters, wherein the sample material was arranged in a cone shape with a volume of 1 cubic meter. The system was designed to test with movement speed range from 2 to 30 centimeters per second, and the scanning angles of 0, 45, and 90 degrees. Statistical principles were applied to analyze the collected data, determining averages, and comparing results with actual data to assess accuracy and precision in volumetric measurements. Furthermore, the research evaluated the advantages and disadvantages of alternative tools for bulk material volume estimation in comparison to the developed system, considering factors such as data collection duration, operational costs, and safety measures. Experimental results revealed that at a scanning angle of 90 degrees and a moving speed of 20 centimeters per second achieve a volumetric data accuracy of 97%.

A recent study on the UAV industry in Thailand highlights the challenges and opportunities facing entrepreneurs in this sector. The research, based on surveys and interviews with industry leaders, sheds light on the impact of import policies on local production costs. The results showed that the majority of UAV companies imported UAVs for specific missions such as agriculture, surveying, and individual entertainment. A small portion of UAV companies imported components such as batteries, motors, and propellers for assembly in Thailand. However, companies importing UAV components face higher import taxes compared to those importing finished UAVs due to the current promotion policies of the Board of Investment of Thailand (BOI). This increases production costs for entrepreneurs in Thailand. To address this issue, tax rates should be reconsidered to allow operators to compete equally in the market, benefiting both entrepreneurs and consumers in Thailand.

Small- and medium-sized enterprises (SMEs) in the food and beverage industry play an important role in advancing business sustainability in emerging economies. However, many resource-endowed SMEs remain unable to translate their potential into sustainable business outcomes. This study examines the factors influencing the business sustainability of food and beverage SMEs in Northeastern Thailand and explains how organizational capabilities are hierarchically structured to create sustainable competitive advantage. A mixed-methods design was employed. Qualitative in-depth interviews with entrepreneurship-incubation experts and entrepreneurs revealed that SME sustainability problems were closely associated with weak planning practices, limited market knowledge, informal financial systems, and insufficient product development capability. These insights informed the development of the research instrument. Quantitative data were then collected from 401 entrepreneurs in the three highest-GPP provinces: Nakhon Ratchasima, Khon Kaen, and Ubon Ratchathani. Structural equation modelling was used to test causal relationships among strategic planning, finance and accounting, leadership, business knowledge, product development, external factors, and business sustainability. The model demonstrated acceptable fit and explained 92% of the variance in business sustainability. Strategic planning emerged as the strongest direct driver, supported by business knowledge and product development as complementary capabilities. Finance, leadership, and external factors showed no significant direct effects. The study proposes a Planning-Centric Sustainability Model and contributes to Resource-Based View, Competency-Based View, and Dynamic Capabilities perspectives in emerging economies.

The electronics industry in Thailand heavily relies on machinery for large-scale production to enhance efficiency and reduce costs. As a result, a dedicated maintenance department is crucial to ensure proper machine upkeep, which includes maintaining a record of maintenance history. A study conducted at an electronics printer production unit, part of the plastic parts manufacturing department at an industrial plant in Nakhon Ratchasima, Thailand, identified inefficiencies in managing maintenance data for 21 types of machinery, totaling 833 machines. The maintenance data recording process was found to be time-consuming and lacked a systematic management approach, resulting in operational inefficiencies. This paper aims to improve the process and method of data entry to enhance efficiency and reduce recording time. A Fishbone Diagram was used to identify the root causes of these inefficiencies. Improvements were implemented based on the principles of Eliminate, Combine, Rearrange, and Simplify (ECRS). Microsoft Access was chosen to replace Microsoft Excel for maintenance data recording, as it is better suited for managing complex datasets. After implementation, the average data recording time per session decreased from 66 minutes to 30 minutes, representing a 54.55% reduction. Additionally, the time to record individual machine maintenance data was reduced from 15.1 minutes to 7.2 minutes per session, a reduction of 52.32%. These results demonstrate the effectiveness of the proposed approach in optimizing maintenance data management within the electronics industry.