Deep Learning for Enterprise Systems Implementation Lifecycle Challenges: Research Directions

Hossam El-Din Hassanien,Ahmed Elragal

doi:10.3390/informatics8010011

Abstract

Transforming the state-of-the-art definition and anatomy of enterprise systems (ESs) seems to some academics and practitioners as an unavoidable destiny. Value depletion lead by early retirement and/or replacement of ESs solutions has been a constant throughout the past decade. That did drive an enormous amount of research that works on addressing the problems leading to the resource drain. The resource waste had persisted throughout the ESs implementation lifecycle phases and dimensions especially post-live phases; leading to depleting the value of the social and technical dimensions of the lifecycle. Parallel to this research stream, the momentum gained by deep learning (DL) algorithms and platforms has been exponentially growing to fuel the advancements toward artificial intelligence and automated augmentation. Correspondingly, this paper is set out to present five key research directions through which DL would take part as a contributor towards the transformation of the ESs state-of-the-art. The paper reviews the ESs implementation lifecycle challenges and the intersection with DL research conducted on ESs by analyzing and synthesizing key basket journals (list of the Association of Information Systems). The paper also presents results from several experiments showcasing the effectiveness of DL in adding a level of augmentation to ESs by analyzing a large set of data extracted from the Atlassian Jira Software Issue Tracking System across different ecosystems. The paper then concludes by presenting the research directions and discussing socio-technical research courses that work on key frontiers identified within this scholarly work.

Highlights

Rapid developments within the computing industry have resulted in a noteworthy dependency on computing technologies, for a variety of services
To instigate the conceptualization of our research and to establish an understanding of the different enterprise systems (ESs) challenges observed from the literature, we investigate the challenge of diversity across the implementation lifecycle, which in turn constitutes the primary focus of the review
It must be noted that the usual time taken to train the network on CPUs has been significantly time-consuming for which Graphical Processing Units (GPUs) are always advisable given the multi-dimensional parallel computing

Summary

Introduction

Rapid developments within the computing industry have resulted in a noteworthy dependency on computing technologies, for a variety of services. The witnessed advancements of deep learning methods had substantially influenced the aptitude of extracting complex patterns through several natural language processing (NLP) techniques. Research conducted on enterprise systems (ESs), known as enterprise resource planning systems or enterprise resource planning (ERP) systems, had seen significant advancements. These systems aim to produce integrated, modular, off-theshelf systems aiming to control key functional areas within the enterprise such as sales, accounting and finance, material management, inventory control, and human resources [3]

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