Improved taxonomy along with a tool of challenges for cloud ERP systems implementation

Abstract

The ever-growing adoption of cloud computing has significantly impacted the landscape of Enterprise Resource Planning (ERP) systems. Cloud-based ERPs offer organizations several advantages compared to traditional on-premise solutions. However, the implementation process for cloud-based ERPs presents unique challenges that can impede successful deployment. Acknowledging and addressing implementation challenges is key to a successful cloud ERP adoption. The goal of this thesis is to enhance the current understanding of the challenges that arise in the implementation of cloud-based Enterprise Resource Planning (ERP) systems. The research examines the state of the art in cloud ERP implementation and proposes an updated and more comprehensive taxonomy of challenges. The improved classification takes into account recent advancements and trends in the field and provides a clear and structured understanding of the challenges faced by organizations during the implementation process. The proposed taxonomy categorizes cloud-ERP implementation challenges across three key dimensions: Types of Challenges: This dimension classifies challenges based on the specific area they impact. It encompasses strategic challenges which are related to the organization's overall vision and goals for implementing cloud-ERP systems, organizational challenges which are pertaining to operations that can affect the system, and technical challenges which are related to the technical aspects of cloud-ERP implementation and integration. Locus of Challenges: This dimension distinguishes between challenges originating internally and externally relevant to the organization. Organization Size: This dimension acknowledges that challenges can differ based on size and structure of the organization such as small, medium, and large. Moreover this thesis proposes a tool where users will be able to input details such as their organization size and specific industry. The tool implements a machine learning model to predict challenges most likely to be encountered during the cloud-ERP implementation process by organizations based on these inputs. Both this taxonomy and tool can serve as useful references for practitioners, researchers, and decision-makers to better understand the challenges and complexities associated with implementing cloud-based ERP systems via proactive identification and prioritization of anticipated challenges based on their specific context and allows for the development of mitigation strategies, and benchmark implementation strategies. The research presented in this thesis contributes significantly to the existing knowledge on cloud-ERP implementation challenges. By proposing a comprehensive and user-centric approach, this research equips organizations with the necessary tools for navigating the complexities of cloud-ERP implementation and ultimately achieving successful system deployment.