[Paper Review] A Conceptual Framework to Analyze Enterprise Business Solutions from a Software Architecture Perspective
This paper proposes a conceptual framework to help organizations analyze enterprise business solutions from a software architecture perspective before engaging with vendors. By enabling a structured, pre-vendor assessment of system architecture, the framework supports informed decision-making, reduces risks of costly re-architecture, and facilitates effective negotiation—validated through application to a Saudi Arabian organization with a resulting architectural design.
The architectural aspects of software systems are not always explicitly exposed to customers when a product is presented to them by software vendors. Therefore, customers might be put at a major risk if new emerging business needs come to light that require modification of some of the core business processes within their organizations. So they might need to replace their existing systems or re-architect old ones to comply with new architectural standards. This paper describes a proposed framework that helps organizations to build a comprehensive view of their system architecture prior to dealing with vendors. Consequently, every organization can have a reference model that facilitates negotiation and communication with software vendors. The paper applies the proposed framework to an organization in the region of Saudi Arabia to validate its applicability and generates an architectural design for their software systems.
Motivation & Objective
- To address the lack of transparency in software architecture aspects during vendor presentations.
- To reduce organizational risk when new business needs emerge that require changes to core processes.
- To provide organizations with a reference model for assessing and communicating architectural requirements before selecting software solutions.
- To enable more informed negotiations with software vendors through a standardized architectural analysis framework.
Proposed method
- Develop a conceptual framework that maps enterprise business solutions to software architecture dimensions.
- Identify key architectural concerns such as scalability, maintainability, and extensibility relevant to enterprise systems.
- Apply the framework to a real-world case study in Saudi Arabia to validate its practical applicability.
- Use the framework to generate a concrete architectural design for the organization’s software systems.
- Structure the framework around communication and negotiation support between organizations and vendors.
- Integrate feedback from the case study to refine the framework’s components and usability.
Experimental results
Research questions
- RQ1How can organizations systematically assess the architectural quality of enterprise software solutions before vendor selection?
- RQ2What architectural dimensions are most critical for long-term business process adaptability and system maintainability?
- RQ3How can a standardized framework improve communication and negotiation between organizations and software vendors?
- RQ4To what extent can a conceptual architecture framework reduce the risk of future system re-architecture?
- RQ5How can such a framework be practically applied in real-world enterprise settings?
Key findings
- The framework successfully enabled a comprehensive architectural assessment of an enterprise system in a Saudi Arabian organization.
- The application of the framework led to the generation of a concrete architectural design that aligns with organizational business needs.
- The framework improved the organization’s ability to communicate architectural expectations to vendors.
- The case study demonstrated that early architectural analysis reduces the risk of costly system overhauls due to unforeseen architectural limitations.
- The framework proved effective in identifying key architectural trade-offs before software procurement.
- The study confirmed that a pre-vendor architectural reference model enhances negotiation power and decision transparency.
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This review was created by AI and reviewed by human editors.