Review on Trends in Machine Learning Applied to Demand & Sales Forecasting

Abstract

Supply Chain Management (SCM) is one of the new concepts put into practice in the commercial sector. At the beginning, Multinational Companies (MNCs) incorporated the supply chain into their structures, then other private conglomerates and local people defended these concepts. From the beginning, the main functions of SCM were the management of purchases and purchases, but subsequently SCM took the integrated form i.e. consists of sourcing, materials management, production support and sales management. Given the highly competitive market scenario, supply chain management is becoming the most important functional area of the business. Demand forecasting is affecting the success of Supply Chain Management (SCM), and the organizations which support them and are in the early stage of a digital transformation. In a near future it could represent the most significant change in the integrated SCM era in today’s complex, dynamic, and uncertain environment. The ability to adequately predict demand by the customers in an SCM is vital to the survival of any business. In this paper a review is presented in which this problem is tried to solved by using various demand forecasting models to predict product demand for grocery items with machine learning techniques.

Penerapan Ensemble Stacking untuk Peramalan Laba Bersih Bank Syariah Indonesia (BSI)

“…Hasil dari peramalan level ini akan digunakan untuk penjadwalan. Tingkatan terakhir adalah peramalan immediate, yang digunakan untuk menentukan kinerja sehari-hari dari aktivitas yang sedang berlangsung [4]. Beberapa penelitian tentang peramalan tersebut misalnya peramalan dalam hal energi kelistrikan [5][6] [7], tentang kesehatan [8] [9], penjualan [4] [10], meteorologi [11] dan masih banyak bidang lainnya.…”