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Learning from accident means accident is learned to identify the causes and weaknesses of the system so the future accidents be prevented. Although accidents are studied, accidents with similar scenarios still occur at PT X. Therefore, research is conducted which aims to study the process of "learning from accidents" implemented by PT X. The study was conducted using qualitative descriptive method. To maintain the validity of the results, triangulation was carried out using data triangulation, method triangulation, and source triangulation. The learning from accidents that implemented by PT X starts from recognizing events, reporting events, recording and classifying events, collecting data to support the investigation process, finding root causes, making recommendations/corrective actions, communicating learning, monitoring closure of corrective actions, and verifying and validating corrective actions. The study shows that workers have understood events that are categorized as accidents, but a small proportion of workers are still confused in distinguishing between near miss with unsafe conditions or act. Accidents and near miss are reported through e-mail or SharePoint but the reporting of near miss is still relatively lack when compared to the number of accidents. Events are classified based on standard guidelines and recorded in the IT Tool even though the follow-up of reporting event in SharePoint is still lacking. Investigation is begun by gathering the information that grouped into 4Ps, namely people, positions, paper, and parts. Data collection for level 3 investigations tends to review accidents with a similar scenario in the previous cases. The root cause of an accident is determined using the five why or why tree method that starts by determining the top event and progressing to find the root cause of accident. It is found human root causes instead of systemic root causes for the investigation involving motor vehicle crash that is classified as level 1. Recommendations/corrective actions are developed based on SMART criteria i.e., specific, measurable, accountable, relevant, and time limits. However, the sustainability of corrective actions is not emphasized enough. Learning is spread through alerts and bulletins that are distributed to workers through e-mail and other media. However, the making of alerts and bulletins are less consistent and the mechanism of dissemination is less effective. Corrective actions are monitored and completed on time and the sponsor verifies and validates the completion of corrective actions to check the effectiveness. With the advantages and disadvantages that exist, but the learning from accidents that is implemented by PT X was able to reduce the trend of accidents in the period from 2015 to 2019.
Occupational accident cases in the cement industry, especially the packer area, are a serious problem that can have an impact on many things including productivity, safety, and worker welfare. Data shows that occupational accidents in the Packer area of the PT X Cement Industry in 2024 have increased compared to 2023, making it the area with the highest accident frequency in 2024. Occupational accident investigations and corrective actions have been conducted, but accidents continue to recur. This may be due to the absence of human factor analysis during the investigation process. Therefore, this study was conducted to determine contribution of human factors specifically latent conditions and active failures to occupational accidents that occurred in the PT X packer area during 2023-2024. This study was conducted using a descriptive analytical method using the Human Factor Analysis Classification System (HFACS) method. The results of the study showed that latent conditions contributed more to occupational accidents than active failures. The latent condition factors that contributed the most to occupational accidents included organizational climate, organizational process, resource management, and inadequate supervision. Meanwhile, the active failure factor that contributed the most was decision error. PT X needs to improve latent conditions at the organizational level and implement control to mitigate active failures in the packer area.
Industri pertambangan merupakan kegiatan industri yang mempunyai risiko tinggi. Faktor manusia telah diidentifikasi sebagai penyebab paling umum terjadinya kecelakaan besar di industri pertambangan. Oleh karena itu, penelitian ini bertujuan menganalisis data kecelakaan di PT. X dengan menggunakan kerangka analisis faktor manusia dan sistem klasifikasi industri pertambangan (HFACS-MI). Metode penelitian ini melibatkan pengumpulan data kualitatif untuk 322 kasus kecelakaan di PT. X yang terjadi pada tahun 2018-2022 dari basis data Sistem Manajemen Insiden yang dikategorikan sebagai cedera yang dapat dicatat. Faktor penyebab kecelakaan ini diberi kode menggunakan kerangka HFACS-MI. Data kecelakaan dianalisis menggunakan statistik deskriptif. Temuan penelitian menunjukkan bahwa 84% dari seluruh kecelakaan melibatkan pekerja kontraktor dan 16% melibatkan pekerja tetap PT. X. Hasil analisis menggunakan kerangka HFACS-MI menunjukkan bahwa setiap lapisan atau tingkatan memberikan kontribusi terhadap kecelakaan, yaitu faktor luar (44%), pengaruh organisasi (68%), kepemimpinan tidak aman (90%), prasyarat tindakan tidak aman (99%), dan tindakan tidak aman (99,7%). Temuan ini menekankan perlunya fokus pada pengurangan jumlah kesalahan manusia selama operasi penambangan untuk mengurangi tren kecelakaan saat ini. Kerangka kerja HFACS-MI telah terbukti menjadi alat penting untuk analisis kecelakaan yang kuat terhadap faktor manusia di pertambangan.
The mining industry is an industrial activity with high risks. Human factors have been identified as the most common cause of major accidents in the mining industry. Therefore, this research aims to analyze accident data at PT. X using the human factors analysis and classification system-mining industry framework (HFACS-MI). This research collected qualitative data for 322 accident cases at PT. X occurring from 2018 to 2022 from the Incident Management System database categorized as recordable injuries. Factors causing the accidents were coded using HFACS-MI framework. Accident data were analyzed using descriptive statistics. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved the PT. X permanent workers. The results of analysis using the HFACS-MI framework show that each layer or level contributes to accidents, namely outside factors (44%), organizational influences (68%), unsafe leadership (90%), preconditions of unsafe acts (99%), and unsafe acts (99.7%). These findings emphasize the need to focus on reducing the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.
The focus of this research is to analyze all occupational accidents of lifting activities on land rig operations in PT ‘X’ using the Human Factor Analysis and Classifications System (HFACS) method in 2014 - 2018. The type of research methodology is qualitative research with a descriptive design. The final result shows that the unsafe act layer is the most ineffective layer that contributing to almost all occupational accident cases which is 45 of 49 total cases of occupational accidents. Error is the sub-layer of unsafe act which has the highest number of contributions to occupational accident cases with total 39 cases. On the other side, the organizational influences layer is the second layer that has high contribution to accident which is 26 of 49 total cases of occupational accidents. The organizational process is the sub-layer of organizational influences which contributing to 23 cases of occupational accident. The third layer which has contribution to accident is unsafe supervision. The unsafe supervision has contribution to accident which is 16 of 49 total cases of occupational accidents. Inadequate supervision and planned inappropriate operation are the sub-layer of inadequate supervision which contribute to the accident cases for 10 cases equally. The layer of preconditions for unsafe actions is the effective layer which has contribution to occupational accident cases which is 8 of 49 total cases of occupational accidents. Personnel factor is the sub-layer of preconditions for unsafe actions which contribute to 7 cases of occupational accidents. According to the result, researcher recommend that corrective action must be taken at each layer of HFACS as the safety protection system, both latent failures and active failures with the emphasis on improvement, which start from the organizational influences layer, followed by the unsafe supervisions layer, and then unsafe actions layer, while the improvement on the layer of precondition for unsafe actions becomes the last improvement. Improvement to organizational influences layer, unsafe act layer, and unsafe supervisions layer will have a positive influence on the layer of precondition for unsafe actions.
Kata kunci:Kecelakaan, analisis kecelakaan, Human Factors And Classification System, HFACS, Comprehensive List Of Causes, CLC
This thesis assess the accident in PT XYZ 2015 by using Human Factors AndClassification System (HFACS) framework. This research is a semi-quantitativewith design study analytical descriptive. Results from this study are a layer ofHFACS most weakness is unsafe act at 11 from total 11 accidents with theelements of decision error becomes a factor of the number one weakness, thenfollowed with a precondition of unsafe act at 10 with the elements of conditions ofservice to be the factors that most contribute to accidents, followed by unsafesupervision at 7 with inadequate leadership element is the factor that mostcontributed to the accident, and the latter as much as 5 of organizationalinfluences with elements of organizational climate and resource management isthe factor that most contributed to the accident. The analysis of research suggestscorrective actions at each level of HFACS, not only for active failures but alsolatent failures with reinforcing corrective action at the unsafe act layer.
Key words:Accident, accident analysis, Human Factors And Classification System, HFACS,Comprehensive List Of Causes, CLC
Resiko bekerja di perusahaan migas PT X yang berlokasi di offshore Natuna adalah relatif tinggi. Sepanjang tahun 2018 – 2023 terjadi fluktuasi kecelakaan kerja di PT. X. Bahkan setelah dua tahun (tahun 2020 dan 2019) tidak terjadi kecelakaan kerja untuk kategori recordable injury (kasus di atas FAC), di tahun 2021 terjadi lagi 3 kasus (1 RWDC dan 2 MTC) dan di tahun 2022 terjadi 4 kasus (1 LWDC, 1 RWDC, dan 2 MTC). Di tahun 2023 terjadi 1 kasus (1 RWDC). Korban kecelakaan kerja di tahun 2021 didominasi oleh pekerja kontrak dan pekerja tetap sedangkan kecelakaan kerja di tahun 2022 dan 2023 semuanya terjadi pada pekerja kontrak. Sebagian besar kecelakaan yang terjadi penyebab langsungnya adalah unsafe acts. Sampai saat ini belum ada analisis menyeluruh dari data investigasi kecelakaan-kecelakaan yang telah dilakukan PT X untuk mendapatkan faktor-faktor penyebab dasar dari semua kecelakaan tersebut. Dengan demikian, penelitian perlu dilakukan, dan karena berhubungan dengan human factor, maka pada penelitian ini akan dianalisis faktor-faktor yang menyebabkan kecelakaan kerja tersebut dengan metode Human Factor Analysis and Classification System (HFACS). Tujuan: Menganalisis faktor-faktor yang memengaruhi kecelakaan kerja di PT X antara tahun 2018 – 2023 dengan metode HFACS. Metode: Penelitian ini adalah penelitian deskriptif analitik dengan pendekatan kualitatif. Data sekunder yang digunakan berupa rekaman kejadian kecelakaan dan laporan investigasi atas 41 kecelakaan di PT X. Data sekunder tersebut kemudian diklasifikasikan sesuai dengan empat (4) tahapan kegagalan di metode HFACS, yaitu unsafe acts, precondition of unsafe acts, unsafe supervision, dan organizational influence. Pengklasifikasian ini divalidasi oleh dua ahli keselamatan kerja, di mana hasil validasinya relatif tinggi (96%). Hasil: Hasil penelitian menjelaskan bahwa faktor-faktor HFACS yang mempengaruhi kecelakaan terbesar berturut-turut adalah adverse mental state (51,2%), skill-based error (39%), routine violations (34,1%), dan tools/technological dan resource management (masing-masing 31,7%). Kemudian disusul oleh decision error (29,3%), inadequate supervision (22%), failed to correct problem dan organizational process masing-masing (17,1%), lalu supervisory violation dan organizational climate masing-masing (9,8%). Kesimpulan: Faktor-faktor HFACS yang memengaruhi kecelakaan kerja di PT X dapat digunakan sebagai masukan untuk perbaikan program K3 perusahaan guna menurunkan angka kecelakaan dengan memprioritaskannya pada faktor HFACS yang bersifat latent failure baru kemudian pada faktor active failure-nya, karena latent failure - jika diperbaiki- akan menjadi kunci untuk mencegah berulangnya kecelakaan.
The risks of working for the PT X , an oil and gas company located offshore Natuna are relatively high. Throughout 2018 – 2023 there were fluctuations in work accidents at PT. X. Even after two years (2020 and 2019) there was no work accident for the recordable injury category (cases above FAC), in 2021 there were 3 cases (1 RWDC and 2 MTC) and in 2022 there were 4 cases (1 LWDC, 1 RWDC, and 2 MTC). In 2023 there was 1 case (1 RWDC). Work accident victims in 2021 are dominated by contract workers and permanent workers, while work accidents in 2022 and 2023 all occur in contract workers. Most of the accidents that occur are directly caused by unsafe acts. Until now there has been no comprehensive analysis of accident investigation data that has been carried out by PT X to obtain the basic causal factors of all these accidents. Thus, research needs to be carried out, and because it is related to human factors, this research will analyze the factors that cause work accidents using the Human Factor Analysis and Classification System (HFACS) method. Objective: Analyzing the factors that influence work accidents at PT X between 2018 – 2023 using the HFACS method. Method: This research is descriptive analytical research with a qualitative approach. The secondary data used is in the form of recordings of accidents and investigation reports on 41 accidents at PT X . This classification was validated by two occupational safety experts, where the validation results were relatively high (96%). Results: The research results explain that the HFACS factors that influence the biggest accidents are adverse mental state (51.2%), skill-based errors (39%), routine violations (34.1%), and tools/technological and resources, respectively. management (31.7% each). Then followed by decision errors (29.3%), inadequate supervision (22%), failed to correct problems and organizational processes respectively (17.1%), then supervisory violations and organizational climate respectively (9.8%). Conclusion: The HFACS factors that influence work accidents in PT X can be used as input for improving the company's H&S program to reduce the number of accidents by prioritizing the HFACS factors which are latent failures and then the active failure factors, because latent failures - if corrected - will become key to preventing recurrence of accidents.
Introduction: Hydrocarbons are flammable materials can cause major accidents and explosions at offshore platform hydrocarbon processing. Fires and explosions on offshore platforms are relatively rare accidents but can have unforeseen consequences that can have a significant impact on fatality and loss of assets. Methods: Descriptive method with quantitative design from secondary data in 2020 (cross sectional) and literature study without intervention on the research object (non-experimental) using software (PHAST) to evaluate the consequences of fire and explosion models. Frequency analysis with fault tree and event tree analysis methods, to analyse the possibility of overpressure and major accidents events on offshore platforms hydrocarbon processing facilities which are Major Hazard Plants. Result: The highest risk level for the personnel fatality working on the offshore platform is in the ALARP Region level from the largest contributor to the flash fire scenario with the number of fatalities as many as 10 peoples and the frequency value of 3.26E-08/year means 1 out of 30,674,847 flash fire scenario opportunities in 1 year can occur to cause fatality of 10 people, while the risk to assets is in an acceptable risk level from the largest contributor to the jet fire scenario with loss of assets 40,590,800.00 and the highest frequency value is 6.31E-08/year) means that 1 in 15,847,861 opportunities of a jet fire scenario in 1 year can occur to cause asset loss of $ 40,590,800 from fires and explosions in overpressure scenarios that have the potential to occur on the new offshore platform taking into account some of the safety systems that have been defined in the design. Conclusion: There is no need for additional mitigation because the safety system that has been determined in the design is sufficient to prevent major accidents that can occur so that the new offshore platform is declared safe to operate.