This article aims to address the impacts that companies can have with the application of machine learning to carry out their demand forecasts, knowing that a more accurate demand forecast improves the performance of companies, making them more competitive. The methodology used was a literature review through descriptive, qualitative and with bibliographical surveys in International Journal from 2010 – 2022 by different authors. Findings show that the references prove that demand forecasting with the use of machine learning brings many benefits to organizations, for example, since the results are more accurate, there is better inventory management, consequently customer satisfaction for having the product at the right time and place. Further, this article concludes and suggests that the use of machine learning is able to identify variables that affect the demands, with this it makes a forecast closer to reality and helps managers to make more accurate decisions, improving strategic planning and supply chain management. of company supplies.

Dalam kurun waktu beberapa tahun terakhir faktor lingkungan merupakan hal yang penting untuk diperhatikan untuk mewujudkan industri yang berkelanjutan. Green Supply Chain Management merupakan model yang mempertimbangkan faktor lingkungan dalam konteks manajemen rantai pasok pasok mulai dari pemasok, perusahaan manufaktur, konsumen. Dalam makalah ini, literatur berkaitan tentang penerapan manajemen rantai pasok hijau ditinjau secara kritis untuk mengidentifikasi faktor penghambat dan pendorong dalam penerapannya.   In the past few years environmental factors have been important to note in order to create a sustainable industry. Green Supply Chain Management is a model that considers environmental factors in the context of supply chain management starting from suppliers, manufacturing companies, consumers. In this paper, the related literature on implementing green supply chain management is critically reviewed to identify the inhibiting factors and drivers in its application.

PT. XYZ merupakan sebuah perusahaan memproduksi berbagai macam produk dengan bahan baku plastik. Proses distribusi pada PT. XYZ memiliki beberapa permasalahan yang terjadi pada masing-masing Distribution Centre (DC).Beberapa permasalahan terjadi diperusahaan berupa keterlambatan dalam pengiriman barang kepada konsumen. Dalam memecahkan permsalahan tersebut penelitian menerapkan metode Distribution Resources Planning. Input metode DRP berupa data historis didalam perusahaan selama setahun. Pengolahan diolah melalui jumlah frekuensi pemesanan, order quantitiy dan safety stock. Sistem DRP memberikan aliran produk dari Central Supply Facility secara terstruktur. DRP memberikan kemudahan dalam memenuhi kebutuhan permintaan tanpa mengalami kekurangan stok (Stock Out). Perusahaan dapat menignkatkan tingkat pelayanan dalam memenuhi kebutuhan pelanggan yang akan datang.   PT. XYZ is a company which engaged in injection molding producing a wide range of products with plastic as the raw material. Constraints in distribution process at PT. XYZ is carried out in order to fulfill products demand on every Distribution Centre. One of those contraints indicated that their company are having a bit problem like stocked out on every Distribution Centre that effected to their company performance’s tardiness and products fulfillment. The solution for these problems for a better distribution system is apllying one of the distribution planning by using Distribution Resources Planning Method. Forecasting from the historic demands of the company will be input in the Distribution Resources Planning Method and will be used as input in calculating the ordering frequency, order quantity and safety stock. Distribution Resources Planning system could give a specific products flow from Central Supply Facility to every Distribution Centre in integrated numbers and periods, as if the smoothness of the distribution activity isn’t disturbed and minimize stocked out in every Distribution Centre and optimize the level of service through distribution planning thay could planned the future needs.