Penerapan Teknologi AI dan Machine Learning dalam Manajemen Rantai Pasokan

Abstract

Dinamika industri saat ini ditandai dengan fluktuasi permintaan, disrupsi rantai pasok, dan ekspektasi pelanggan yang terus menanjak. Jurnal ini membahas bagaimana Artificial Intelligence (AI) dan machine learning menjadi solusi inovatif untuk mengoptimalkan pengelolaan rantai pasok. AI dan machine learning mampu menghasilkan prediksi permintaan akurat, memungkinkan perusahaan untuk menyeimbangkan persediaan, meminimalisir risiko stockout dan overstock, serta mengalokasikan sumber daya secara efisien. Optimalisasi rute pengiriman turut difasilitasi oleh teknologi ini, menghasilkan penghematan biaya, pengurangan emisi karbon, dan percepatan proses pengiriman. Analisis data sensor menggunakan machine learning memungkinkan prediksi kerusakan peralatan, sehingga tindakan pencegahan proaktif dapat diterapkan dan keandalan operasional ditingkatkan. Selain itu, AI dapat diintegrasikan dalam inspeksi produk untuk menghasilkan peningkatan kualitas, pengurangan pemborosan, dan pada akhirnya meningkatkan kepuasan pelanggan. Meski menawarkan segudang manfaat, implementasi AI dan machine learning memerlukan ketersediaan data akurat dan keahlian dalam pengolahannya. Investasi infrastruktur IT dan pengembangan sumber daya manusia juga menjadi faktor krusial. Secara ringkas, AI dan machine learning memiliki potensi besar untuk merevolusi manajemen rantai pasok melalui peningkatan efisiensi, visibilitas, dan pengambilan keputusan. Penerapan yang tepat berpotensi mendorong keunggulan kompetitif dan memberikan nilai tambah bagi pelanggan.

Current industrial dynamics are characterized by fluctuations in demand, supply chain disruption, and continuously increasing customer expectations. This journal discusses how Artificial Intelligence (AI) and machine learning are innovative solutions for optimizing supply chain management. AI and machine learning are able to produce accurate demand predictions, allowing companies to balance inventory, minimize the risk of stockouts and overstocks, and allocate resources efficiently. Optimization of delivery routes is also facilitated by this technology, resulting in cost savings, reduced carbon emissions and accelerated delivery processes. Analysis of sensor data using machine learning allows equipment failure to be predicted, so that proactive preventive measures can be implemented and operational reliability improved. Additionally, AI can be integrated in product inspection to lead to improved quality, reduced waste, and ultimately increased customer satisfaction. Even though it offers a multitude of benefits, implementing AI and machine learning requires the availability of accurate data and expertise in processing it. IT infrastructure investment and human resource development are also crucial factors. In summary, AI and machine learning have great potential to revolutionize supply chain management through improved efficiency, visibility and decision making. Proper implementation has the potential to drive competitive advantage and provide added value for customers.