- The paper introduces a structured, ontology-based decision-support framework that clarifies trade-offs by mapping strategic, cost, and risk factors.
- It employs deterministic reasoning based on reference-level matching and expert rules to integrate partial inputs and ensure auditable recommendations.
- The empirical evaluation in a finance case study demonstrates its ability to surface latent trade-offs and inform dynamic, iterative sourcing strategies.
Structured Support for Build-vs-Buy Decisions in Enterprise Software
Background and Motivation
The perennial build-versus-buy dilemma in enterprise software engineering is marked by multifaceted trade-offs across strategic, technical, economic, and risk dimensions. With the proliferation of commercial and open-source solutions, as well as emergent possibilities for efficient custom development—enabled by cloud-native, API-driven, and low-code/no-code platforms—the complexity of these sourcing decisions has intensified. Existing decision processes in industry settings are typically ad hoc, fragmented, and reliant on informal heuristics and partial organizational memory. This lack of structure impedes systematic factor analysis, transparency, and the ability to revisit decisions as context evolves.
Limitations of Prior Approaches
Previous research in software sourcing has identified the multi-criteria, context-dependent nature of these decisions. Formal optimization-based methods, such as those found in architectural analysis and component selection, offer structured evaluation but presuppose fully specified input spaces and extensive historical data. These conditions rarely hold in realistic enterprise environments, especially under cold-start scenarios where analogous decision precedents are minimal or absent. Recent works employing knowledge management and LLM-driven ADR generation focus on capturing architectural reasoning but fall short in providing comprehensive, cross-cutting trade-off analysis or supporting partial-information contexts.
Ontology-Driven Decision Support Artifact
This work introduces a structured decision-support approach—termed the Build vs. Buy Advisor (BBA)—designed specifically for enterprise cold-start conditions. Central to the artifact is a practical ontology organizing decision factors into four primary categories: strategic factors (e.g., competitive differentiation, standards compliance, IP protection), application characteristics (e.g., domain specificity, complexity, non-functional requirements), cost and budget (development, licensing, maintenance), and risk factors (including both technical and vendor-related risk dimensions). This ontology operationalizes a comprehensive scaffold, ensuring systematic consideration and alignment across stakeholders by making both assumptions and omissions transparent.
Reasoning Mechanisms: Reference-Level Matching and Expert Rules
The BBA eschews probabilistic or optimization-based logic in favor of two complementary deterministic reasoning mechanisms. First, reference-level matching aligns user-supplied factor values against indicative benchmarks known to favor either build or buy options in isolation. This yields a structured map of factor alignments, surfacing trade-off concentration points and ambiguous or underspecified dimensions. Second, encoded expert rules leverage entrenched organizational heuristics to decisively resolve common patterns—such as mandated regulatory compliance favoring third-party acquisitions or absence of market-fit solutions mandating custom builds. These rules provide explainable recommendations and can override the reference-level analysis when decisive, policy-driven knowledge exists.
Explanatory, Auditable Recommendations
A defining feature of the approach is its emphasis on explicable and auditable recommendations. Rather than generating opaque verdicts, the BBA details which factors and rules contributed to each recommendation, clarifying the provenance of its reasoning. This supports not just acceptance or rejection of recommendations but also their justification in multi-stakeholder settings and longitudinal audit as circumstances (e.g., internal capability, vendor landscape) evolve.
Prototype and Empirical Use in Finance Domain
A lightweight, web-based prototype implements the BBA, supporting form-based and spreadsheet import of factor values, operating effectively with incomplete information by treating missing values as neutral. Outputs are multi-level: (1) category-wise analysis clarifies the distribution of factor support, and (2) a global build-vs-buy recommendation is expressed both qualitatively and with a percent-score indicating the relative weighting of supporting factors for each alternative.
The artifact was evaluated through a case application to a Cross-Border Payments System for a monetary authority. In this scenario, both custom development and third-party adoption were feasible, with neither option dominating a priori. The BBA facilitated a structured review that surfaced latent trade-offs—such as long-term maintenance implications, vendor dependence, and compliance sensitivities—while distinguishing between strongly aligned and neutral factors. Notably, the final recommendation favored 'buy', but the primary contribution lay in clarifying the decision rationale, surfacing conditions that could shift the decision in future iterations, and revealing that certain expected determinants (e.g., security requirements) were not differentiators given the regulatory context.
Practical and Theoretical Implications
The explicit, ontology-driven structuring of decision variables in the BBA broadens and deepens the decision-making conversation, reducing blind spots inherent in informal, unstructured processes. The artifact's support for partial information ensures productive analysis at early stages, aligning with the incremental information flow typical of enterprise environments. By surfacing factor-to-recommendation mappings, the tool fosters both reflection and forward planning: decision-makers can evaluate "switch conditions" for later reassessment, thus directly supporting dynamic, iterative governance.
From a theoretical standpoint, the approach demonstrates that rule-based, ontology-backed decision-support artifacts can deliver practical value even in the absence of extensive organizational memory or formal data, contrasting with the assumptions baked into many prior optimization or ML-driven frameworks.
Limitations and Future Directions
Several important limitations constrain the current findings. The empirical evaluation is limited to a single, albeit representative, case and several exploratory deployments, precluding broad claims of effectiveness or generalizability. No comparative baseline is established against alternative support tools or purely informal methods, so the work focuses on feasibility, not superiority. Critically, the quality and reliability of the BBA's recommendations are dependent on user input fidelity, echoing a perennial challenge in all knowledge-based support systems.
The ontology and expert rules require further validation and adaptation across diverse domains and regulatory environments. Future research directions highlighted include ontology extension, richer modeling of factor interdependencies, and longitudinal study of build-vs-buy decision evolution across industries and portfolio contexts.
Conclusion
This work contributes a structured, ontology-based decision-support framework for build-vs-buy decisions in enterprise software under cold-start and partial-information conditions. By making the decision criteria, trade-offs, and supporting rationale explicit, the approach enhances transparency, auditability, and the ability to systematically revisit decisions. Broader deployment and iterative refinement are necessary to validate adaptability and long-term organizational impact, but the structured methodology lays a pragmatic foundation for disciplined sourcing decisions in complex, data-poor enterprise settings.