Pimsakul, Sittiporn

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Pimsakul, Sittiporn
Alternative Name
Pimsakul, S.
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Email
sittiporn.pi@kmitl.ac.th

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    Item type:Publication,
    Prioritizing enabling factors of iot adoption for sustainability in supply chain management
    (2021-11-01)
    Pimsakul, Sittiporn
    ;
    Samaranayake, Premaratne
    ;
    Laosirihongthong, Tritos
    Several studies have reported on enabling factors of IoT adoption, emphasizing the importance of key factors for successful IoT adoption. However, only a few studies have investigated enabling factors with consideration of a sustainability perspective and no similar study has focused on manufacturing from an emerging economy perspective. The main purpose is to investigate enabling factors of IoT adoption from a sustainability perspective. This study aims to (i) identify and select key enabling factors from a comprehensive literature review, and (ii) prioritize them using a multiple criteria decision-making approach, validated through industry experts’ opinions. The results showed that system integration and IoT infrastructure are the top enabling factors in increasing the overall success of IoT adoption. Furthermore, enabling factors of IoT adoption are directly connected with organizational resources/technological capabilities that support the resource-based view theory. Supply chain managers can use the findings of this study to guide and prioritize IoT adoption, and develop strategies for going forward with IoT settings, using the relative importance of enabling factors and interdependencies among them from the technological and organizational perspectives. To generalize these findings through benchmarking of enabling factors in manufacturing, a broader range of industries within the manufacturing sector should be considered in future studies.
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    Item type:Publication,
    Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok
    (2022-08-01)
    Jientrakul, Ranon
    ;
    Yuangyai, Chumpol
    ;
    Boonkul, Klongkwan
    ;
    Chaicharoenwut, Pakinai
    ;
    Nilsang, Suriyaphong
    Emergency medical service (EMS) base allocation plays a critical role in emergency medical service systems. Fast arrival of an EMS unit to an incident scene increases the chance of survival and reduces the chance of victim disability. However, recently, the allocation strategy has been performed by experts using past data and experiences. This may lead to ineffective planning due to a lack of consideration of a recent and relevant data, such as disaster events, population density, public transportation stations, and public events. Therefore, we propose an approach of the integration of using spatial risk factors and social media factors to identify EMS bases. These factors are combined into a single domain by using the kernel density estimation technique, resulting in a heatmap. Then, the heatmap is used in a modified maximizing covering location problem with a heatmap (MCLP-Heatmap) to allocate ambulance base. To acquire recent data, social media is then used for collecting road accidents, traffic, flood, and fire incidents. Additionally, another data source, spatial risk information, is collected from Bangkok GIS. These data are analyzed using the kernel density estimation method to construct a heatmap before being sent to the MCLP-heatmap to identify EMS bases in the area of interest. In addition, the proposed integrated approach is applied to the Bangkok area with a smaller number of EMS bases than that of the existing approach. The simulated results indicated that the number of covered EMS requests was increased by 3.6% and the number of ambulance bases in action was reduced by approximately 26%. Additionally, the bases defined by the proposed approach covered more area than those of the existing approach.
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    Item type:Publication,
    Production process improvement using the six sigma DMAIC methodology: A case study of a laser computer mouse production process
    (2013-12-01)
    Pimsakul, S.
    ;
    Somsuk, N.
    ;
    Junboon, W.
    ;
    Laosirihongthong, T.
    This paper aims to improve a production process of a laser computer mouse by using the Six Sigma DMAIC Methodology. This study focuses on the functional test procedure because of its lowest yield. The regression analysis and two-level factorial design of experiments is employed in order to determine the optimal conditions of parameters. By operating under these resulting conditions, yield of the functional test procedure increases from 96.2 to 98.6%. © 2013 Springer-Verlag Berlin Heidelberg.