In-Firm Planning and Business Processes Management Using Deep Neural Networks

Abstract

Objective - The objective of this paper is to consider using machine learning approaches for in-firm processes prediction and to give an estimation of such values as effective production quantities. Methodology - The research methodology used is a synthesis of a deep-learning model, which is used to predict half of real business data for comparison with the remaining half. The structure of the convolutional neural network (CNN) model is provided, as well as the results of experiments with real orders, procurements, and income data. The key findings in this paper are that convolutional with a long-short-memory approach is better than a single convolutional method of prediction. Findings - This research also considers useof such technologies on business digital platforms. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Novelty - This paper describes the practical usage of 1-dimensional(1D) convolutional neural networks and a mixed approach with convolutional and long-short memory networks for in-firm planning tasks such as income prediction, procurements, and order demand analysis. Type of Paper - Empirical. Keywords: Business; Neural, Networks; CNN; Platform JEL Classification: C45