Graeme G. Shanks

A model of ERP project implementation

Problems with the implementation of ERP systems are well documented. Although companies spend millions on ERP packages and the implementation process, there is extensive evidence that they experience considerable problems, particularly during the actual implementation project. This paper presents a project phase model (PPM) of ERP implementation projects that is a synthesis of existing ERP implementation process models and focuses on the implementation project. Two case studies of ERP implementation within the same organization, one unsuccessful and a later successful one, are reported and analysed in order to determine which critical success factors (CSFs) are necessary within each phase of the PPM. The CSFs are drawn from an earlier stage of this research and from recent literature. The PPM is used as a ‘lens’ for understanding ERP implementation projects, by highlighting the differences between the two cases. We then offer an explanation for these differences, focusing particularly on the successful case. Firstly, the organizational learning that occurred during the unsuccessful project and the associated early appointment of an experienced ‘champion’ with clearly defined responsibilities were critical to the successful project. Secondly, organizations implementing ERP systems should partition large projects into several smaller, simpler projects identified here as ‘vanilla’ implementations. The PPM, together with associated CSFs, provides guidance for practitioners when planning ERP implementation projects and also provides researchers with a foundation for further empirical research.

Using ontology to validate conceptual models

Theories of ontology lead to improved conceptual models and help ensure they are indeed faithful representations of their focal domains.

A taxonomy of ERP implementation approaches

Enterprise resource planning (ERP) systems are enterprise wide systems which, because of their integration, automate all of a companys business processes. They have rapidly become the de facto industry standard for replacement of legacy systems. Because there is evidence that the overwhelming majority of ERP implementations exceed their budget and their time allocations, researchers have begun to analyse ERP implementation in case studies in order to provide an implementation framework which maximises efficiencies. We argue that the concept of an ERP implementation is not a generic concept, and we present a taxonomy of ERP implementation categories. The evidence for the taxonomy is drawn from previous studies and from a series of structured interviews with practitioners who are expert in ERP implementation. We further argue that understanding the differences between these categories is crucial if researchers are to do case study research of ERP implementation; otherwise, comparisons are being made between ERP implementation projects which are essentially incommensurate. Conclusions based on incommensurate cases are inherently invalid. The taxonomy of implementation categories is also presented as a tool for implementation managers to delineate the scope of an ERP implementation project prior to in depth specification of the project processes.

Improving the quality of data models: empirical validation of a quality management framework

This paper describes the results of a 5-year research programme into evaluating and improving the quality of data models. The theoretical base for this work was a data model quality management framework proposed by Moody and Shanks (In: P. Loucopolous (Ed.), Proceedings of the 13th International Conference on the Entity Relationship Approach, Manchester, England, December 14-17, 1994). A combination of field and laboratory research methods (action research, laboratory experiments and systems development) was used to empirically validate the framework. This paper describes how the framework was used to: (a) quality assure a data model in a large application development project (product quality); (b) reengineer application development processes to build quality into the data analysis process (process quality); (c) investigate differences between data models produced by experts and novices; (d) provide automated support for the evaluation process (the Data Model Quality Advisor). The results of the research have been used to refine and extend the framework, to the point that it is now a stable and mature approach.

Identification of Necessary Factors for Successful Implementation of ERP Systems

The identification of factors which are necessary for successful implementation of enterprise resource planning (ERP) systems is of great importance to many organizations. ERP systems have to be configured and implemented, often by a team of business analysts and consultants over a period of months or years. The process is lengthy and expensive, and may include extensive business process re-engineering. Given that the investment in these systems, including both the package and associated implementation costs, is measured in millions of dollars, failure to meet deadlines and budgets may result in substantial company loss. However, the literature on the ERP implementation process, and the factors which either facilitate or impede its progress, is not extensive. This research reports the first stage of a research program which seeks to understand successful implementation of ERP systems. The objective, of the first phase was to identify what factors are necessary for successful ERP implementation, where success is understood as adherence to time and budgetary constraints. To accomplish this objective the authors studied 42 implementation projects by interviewing 10 senior members of multiple’ ERP implementation teams. Based on these interviews, 10 candidate necessary factors for successful implementation of ERP systems are identified. Of these 10, three are of paramount importance. They are management support of the project team and of the implementation process, a project team which has the appropriate balance of business and technical skills, and commitment to the change by all stakeholders. The next phase of the research will involve in-depth case studies to explore the relationship between these factors and broader contextual and process issues.

How Does Enterprise Architecture Add Value to Organisations

Enterprise architecture (EA) is the definition and representation of a high-level view of an enterprises business processes and IT systems, their interrelationships, and the extent to which these processes and systems are shared by different parts of the enterprise. EA aims to define a suitable operating platform to support an organisations future goals and the roadmap for moving towards this vision. Despite significant practitioner interest in the domain, understanding the value of EA remains a challenge. Although many studies make EA benefit claims, the explanations of why and how EA leads to these benefits are fragmented, incomplete, and not grounded in theory. This article aims to address this knowledge gap by focusing on the question: How does EA lead to organisational benefits? Through a careful review of EA literature, the paper consolidates the fragmented knowledge on EA benefits and presents the EA Benefits Model (EABM). The EABM proposes that EA leads to organisational benefits through its impact on four benefit enablers: Organisational Alignment, Information Availability, Resource Portfolio Optimisation, and Resource Complementarity. The article concludes with a discussion of a number of potential avenues for future research, which could build on the findings of this study.

What Makes a Good Data Model? Evaluating the Quality of Entity Relationship Models

This paper develops a framework for evaluating the quality of data models and choosing between alternative representations of requirements. For any particular set of user requirements there are many possible models, each of which has drastically different implications for database and systems design. In the absence of formally defined and agreed criteria, the choice of an appropriate representation is usually made in an ad hoc way, based on personal opinion. The evaluation framework proposed consists of four major constructs: qualities (desirable properties of a data model), metrics (ways of measuring each quality), weightings (relative importance of each quality) and strategies (ways of improving data models). Using this framework, any two data models may be compared in an objective and comprehensive manner. The evaluation framework also builds commitment to the model by involving all stakeholders in the process: end users, management, the data administrator and application developers.

A semiotic information quality framework : development and comparative analysis

An organization depends on quality information for effective operations and decision-making. However, there is still no agreement as to how quality should be defined in terms of specific quality categories and criteria. Proposed information quality frameworks have limitations with respect to either consistency, resulting from a non-theoretical approach to framework development, or scope, considering only objective but not subjective information quality perspectives. In this paper, we describe a unique research approach to framework development that addresses these problems and compare it to those used previously for other frameworks. Semiotic theory, the philosophical theory of signs, is used to ensure rigor and scope. It provides a theoretical basis for framework structure – quality categories and their criteria – and for integrating objective and subjective quality views. Empirical refinement based on academic, practitioner, and end-user focus groups is then used to ensure relevance.

Representing part-whole relations in conceptual modeling: an empirical evaluation

The part-of or part-whole construct is a fundamental element of many conceptual modeling grammars that is used to associate one thing (a component) with another thing (a composite). Substantive theoretical issues surrounding the part-whole construct remain to be resolved, however. For instance, contrary to widespread claims, the relationship between components and composites is not always transitive. Moreover, how the partwhole construct should be represented in a conceptual schema diagram remains a contentious issue. Some analysts argue composites should be represented as a relationship or association. Others argue they should be represented as an entity. In this paper we use an ontological theory to support our arguments that composites should be represented as entities and not relationships or associations. We also describe an experiment that we undertook to test whether representing composites as relationships or entities enables users to understand a domain better. Our results support our arguments that using entities to represent composites enables users to better understand a domain.

Stakeholder viewpoints in requirements definition: A framework for understanding viewpoint development approaches

Requirements definition is a critical activity within information systems development. It involves many stakeholder groups: managers, various end-users and different systems development professionals. Each group is likely to have its own ‘viewpoint’ representing a particular perspective or set of perceptions of the problem domain. To ensure as far as possible that the system to be implemented meets the needs and expectations of all involved stakeholders, it is necessary to understand their various viewpoints and manage any inconsistencies and conflicts. Viewpoint development during requirements definition is the process of identifying, understanding and representing different viewpoints. This paper proposes a conceptual framework for understanding and investigating viewpoint development approaches. Results of the use of the framework for a comparison of viewpoint development approaches are discussed and some important issues and directions for future research are identified.