Analisis dan Mitigasi Risiko Inbound Logistik pada Rantai Pasok Agroindustri Gula Tebu di PT. XYZ Jawa Barat

Abstract

IDA ROSYIDAH, Analisis dan Mitigasi Risiko Inbound Logistik pada Rantai Pasok Agroindustri Gula Tebu di PG XYZ Jawa Barat. Dibimbing oleh MARIMIN dan MOH. YANI.

Agroindustri gula tebu merupakan sektor strategis yang berperan penting dalam menjaga ketersediaan gula nasional dan mendukung stabilitas rantai pasok komoditas pangan. Aktivitas inbound logistik menjadi fase kritis karena mencakup pengadaan dan pergerakan bahan baku tebu yang bersifat mudah rusak (perishable) dari hulu menuju pabrik gula. Sebagai salah satu pelaku utama agroindustri gula tebu di Provinsi Jawa Barat, PG XYZ menghadapi tantangan operasional yang kompleks yang melibatkan aktor petani, BUMDes/Koperasi, dan manajemen pabrik gula. Penelitian ini bertujuan memetakan struktur jaringan rantai pasok, mengidentifikasi dan menganalisis faktor-faktor risiko yang memengaruhi aktivitas inbound logistik, merumuskan strategi mitigasi yang relevan dan aplikatif, serta menentukan prioritas strategi mitigasi guna meningkatkan kinerja inbound logistik PG XYZ. Penelitian ini menggunakan pendekatan deskriptif-analitis dengan kerangka Food Supply Chain Network (FSCN) untuk memetakan struktur jaringan rantai pasok dan hubungan antaraktor. Identifikasi akar penyebab risiko dilakukan menggunakan Diagram Ishikawa berbasis dimensi 6M (man, machine, material, method, measurement, mother nature). Penilaian risiko dilakukan melalui metode House of Risk (HOR) Fase 1 untuk menentukan prioritas sumber risiko berdasarkan Aggregate Risk Potential (ARP), serta HOR Fase 2 untuk merumuskan tindakan mitigasi risiko. Selanjutnya, metode Analytic Network Process (ANP) digunakan untuk menentukan prioritas strategi mitigasi dengan mempertimbangkan keterkaitan antaraktor dan berbagai faktor pendukung yang memengaruhi sistem inbound logistik. Hasil penelitian menunjukkan bahwa struktur jaringan rantai pasok di PG XYZ melibatkan empat aktor utama yang saling terhubung melalui aliran material, informasi dan finansial. Identifikasi risiko menunjukkan bahwa persoalan operasional utama pada aktivitas inbound logistik meliputi kejadian risiko (risk event): penurunan kualitas tebu akibat keterlambatan, risiko bahan baku terbakar, ketidakpastian jadwal panen, hambatan pasokan yang tidak memenuhi target giling, serta ketersediaan data yang tidak real time. Sebanyak 197 sumber risiko (risk agent) berhasil diidentifikasi, yang terdiri atas 68 sumber risiko pada tingkat petani, 66 pada BUMDes/Koperasi, dan 63 pada manajemen pabrik. Hasil analisis HOR Fase 1, menunjukkan terdapat 29 sumber risiko prioritas berdasarkan nilai ARP tertinggi yang ditetapkan melalui diagram Pareto dan validasi pakar, terdiri atas 10 sumber risiko tingkat petani, 9 pada tingkat BUMDes/Koperasi, dan 10 pada tingkat manajemen pabrik. Dimensi man dan mother nature merupakan sumber risiko yang paling dominan pada sebagian besar aktor rantai pasok. Berdasarkan sumber risiko prioritas tersebut, dirumuskan 16 tindakan mitigasi risiko (preventive action) yang telah divalidasi oleh pakar, terdiri atas 6 tindakan mitigasi pada tingkat petani, 5 pada tingkat BUMDes/Koperasi, dan 5 pada tingkat manajemen pabrik. Hasil analisis HOR Fase 2 menunjukkan bahwa tindakan mitigasi prioritas pada tingkat petani adalah program penguatan tenaga tebang (P-PA1), manajemen konservasi air dan penguatan kesuburan tanah (P-PA4), serta adopsi teknologi panen secara bertahap (P-PA2). Pada tingkat BUMDes/Koperasi, tindakan mitigasi prioritas meliputi program penguatan tenaga angkut (B-PA5), optimalisasi dan fleksibilitas armada angkut (B-PA3), serta sistem alternatif muat-angkut dan mitigasi kondisi ekstrem (B-PA4). Pada tingkat manajemen pabrik, tindakan mitigasi prioritas meliputi penerapan sistem informasi mutu dan tracking tebu terintegrasi (M-PA1) serta kebijakan penerimaan bahan baku berbasis mutu dan SOP seleksi tebu (M-PA2). Penentuan prioritas strategi mitigasi risiko dilakukan menggunakan metode ANP dengan membangun model jaringan yang terdiri atas lima cluster, yaitu Goal, Actors, Enablers, Obstacles, dan Alternatives. Lima program strategi mitigasi yang ditetapkan sebagai Alternatives merupakan hasil integrasi dan penyederhanaan dari delapan tindakan mitigasi prioritas yang diperoleh berdasarkan nilai Effectiveness to Difficulty Ratio (ETD) tertinggi. Kelima program strategi mitigasi tersebut meliputi penguatan tenaga tebang-muat-angkut (TMA) (PA1), manajemen konservasi air dan penguatan kesuburan tanah (PA2), adopsi teknologi panen secara bertahap (PA3), optimalisasi dan fleksibilitas armada angkut (PA4), serta penerapan sistem informasi mutu dan kebijakan penerimaan bahan baku berbasis mutu (PA5). Hasil pembobotan ANP menunjukkan bahwa manajemen pabrik gula (A3) merupakan aktor yang paling berpengaruh dengan bobot sebesar 0,4628. Pada cluster Enablers, manajemen SDM (E1) menjadi faktor pendukung paling penting dengan bobot sebesar 0,3896, sedangkan keterbatasan sistem dan infrastruktur logistik (O2) merupakan faktor penghambat utama yang perlu diantisipasi dengan bobot sebesar 0,3126. Hasil pembobotan pada cluster Alternatives menunjukkan bahwa strategi penguatan tenaga tebang-muat-angkut (TMA) (PA1) memperoleh prioritas tertinggi dengan nilai normalized by cluster sebesar 0,3390, diikuti optimalisasi dan fleksibilitas armada angkut (PA4) sebesar 0,3220, penerapan sistem informasi mutu dan kebijakan penerimaan bahan baku berbasis mutu (PA5) sebesar 0,2059, adopsi teknologi panen secara bertahap (PA3) sebesar 0,0901, dan manajemen konservasi air dan penguatan kesuburan tanah (PA2) sebesar 0,0431. Hasil analisis sensitivitas menunjukkan bahwa urutan prioritas strategi mitigasi relatif stabil (robust) terhadap perubahan bobot elemen dalam jaringan ANP. Temuan ini mengindikasikan bahwa keterbatasan sumber daya manusia dan ketidakselarasan sistem logistik merupakan akar permasalahan yang paling kritis dalam aktivitas inbound logistik. Oleh karena itu, PG XYZ perlu memprioritaskan program penguatan tenaga tebang-muat-angkut (TMA) dan pengembangan sistem manajemen armada berbasis data yang didukung oleh penguatan sistem informasi mutu serta koordinasi antaraktor secara berkelanjutan guna meningkatkan kinerja inbound logistik dan ketahanan rantai pasok agroindustri gula tebu.

IDA ROSYIDAH, Risk Analysis and Mitigation of Inbound Logistics in the Sugarcane Agro-industry Supply Chain at XYZ Sugar Mill, West Java. Supervised by MARIMIN and MOH. YANI.

Sugarcane agro-industry is a strategic sector that plays a crucial role in ensuring national sugar supply and supporting the stability of food commodity supply chain. Inbound logistics activities constitute a critical phase, as they encompass the procurement and movement of perishable sugarcane raw materials from upstream sources to the sugar mill. As one of the major actors in the sugarcane agro-industry in West Java Province, XYZ Sugar Mill faces complex operational challenges involving multiple stakeholders, including farmers, Village-Owned Enterprises (BUMDes/cooperatives), and sugar mill management. This study aims to map the supply chain network structure, identify and analyze risk factors affecting inbound logistics activities, formulate relevant and applicable mitigation strategies, and determine the priority of mitigation strategies to improve the inbound logistics performance of XYZ Sugar Mill. This study employed a descriptive-analytical approach using the Food Supply Chain Network (FSCN) framework to map the supply chain network structure and interrelationships among actors. Root cause identification was carried out using the Ishikawa Diagram based on the 6M dimensions, namely man, machine, material, method, measurement, and mother nature. Risk assessment was performed through the House of Risk (HOR) Phase 1 method to determine priority risk sources based on Aggregate Risk Potential (ARP) values, and HOR Phase 2 was utilized to formulate risk mitigation actions. Furthermore, the Analytic Network Process (ANP) method was applied to prioritize mitigation strategies by considering inter-actor interdependencies and various supporting factors influencing the inbound logistics system. The results indicate that the supply chain network indicate that the supply chain network structure at XYZ Sugar Mill involves four major actors interconnected through material, information, and financial flows. Risk identification reveals that the primary operational issues in inbound logistics activities include risk events such as deterioration of sugarcane quality due to delays, risk of raw material burning, uncertainty in harvest schedules, supply constraints that fail to meet milling targets, and unavailability of real-time data. A total of 197 risk agents were identified, comprising 68 risk agents at the farmer level, 66 at the BUMDes/cooperative level, and 63 at the sugar mill management level. The HOR Phase 1 analysis identified that 29 priority risk agents based on the highest ARP values, determined through Pareto diagram and expert validation, consisting of10 risk agents at the farmer level, 9 at the BUMDes/cooperative level, and 10 at the sugar mill management level. The man and mother nature dimensions were found to be the most dominant sources across the majority of supply chain actors. Based on these priority risk sources, 16 mitigation actions (preventive actions) were formulated and validated by experts, consisting of six mitigation actions at the farmer level, five at the BUMDes/cooperative level, and five at sugar mill management level. The HOR Phase 2 analysis reveals that the priority mitigation actions at the farmer level were the workforce strengthening program for harvesting operations (P-PA1), water conservation and soil fertility enhancement management (P-PA4), and the gradual adoption of harvesting technology (P-PA2). At the BUMDes/cooperative level, the priority mitigation actions include the transportation workforce strengthening program (B-PA5), optimization and flexibility of transport fleets (B-PA3), and an alternative loading-transport system with extreme condition mitigation (B-PA4). At the sugar mill management level, the priority mitigation actions include the implementation of an integrated sugarcane quality information and tracking system (M-PA1), and a quality-based raw material acceptance policy with sugarcane selection standard operating procedures (SOPs) (M-PA2). The prioritization of risk mitigation strategies was performed using the Analytic Network Process (ANP) method by developing a network model comprising five clusters: Goal, Actors, Enablers, Obstacles, and Alternatives. The five mitigation strategy programs designated as Alternatives represent the integration and simplification of eight priority mitigation actions obtained based on the highest Effectiveness to Difficulty (ETD) values. These five-mitigation strategy programs comprised the strengthening of harvesting-loading-transport labor or TMA (PA1); water conservation and soil fertility enhancement management (PA2); gradual adoption of harvesting technology (PA3); optimization and flexibility of transport fleets (PA4); and implementation of a quality information system and quality-based raw material acceptance policy (PA5). The ANP weighting results indicate that sugar mill management (A3) is the most influential actor, with a weight of 0.4628. Within the Enablers cluster, human resource management (E1) is the most critical supporting factor, with a weight of 0.3896, while limitations in logistics systems and infrastructure (O2) represent the primary obstacle to be addressed, with a weight of 0.3126. The weighting results within the Alternatives cluster show that the TMA workforce strengthening strategy (PA1) achieved the highest priority, with a normalized- by-cluster value of 0.3390, followed by optimization and flexibility of transportation fleets (PA4) at 0.3220, implementation of the quality information system and quality-based raw material acceptance policy (PA5) at 0.2059, gradual adoption of harvesting technology (PA3) at 0.0901, and water conservation management and soil fertility enhancement (PA2) at 0.0431. Sensitivity analysis results demonstrated that the priority ranking of mitigation strategies remains relatively stable (robust) against changes in the weighting of elements within the ANP network. These findings suggest that human resource constraints and misalignment of the logistics system represent the most critical root causes in inbound logistics activities. Therefore, PG XYZ should prioritize the TMA workforce strengthening program and the development of data-driven fleet management system, supported by the enhancement of quality information systems and sustained inter-actor coordination, in order to improve inbound logistics performance and strengthen the resilience of the sugarcane agro-industrial supply chain.