An ERP Implementation Method : Studying a Pharmaceutical Company

Analysing the development process for an ERP solution, in our case SAP, is one of the most critical processes in implementing standard software packages. Modelling of the proposed system can facilitate the development of enterprise systems not from scratch but through use of predefined parts who represents the best knowledge captured from numerous case studies. This aim at abstracting the specification of the required information system as well as modelling the process towards this goal. Modelling plays a central role in the organisation of the information systems development process and the information systems community has developed a large number of conceptual models, systems of concepts, for representing conceptual schemata. In the area of ERP systems, because of the characteristics that distinguishes them, conceptual modelling can help in all aspects of the development process, from goal elicitation to reuse of the captured knowledge, through the use of the appropriate modelling schemata. SAP offers a standardised software solution, thus making easier the alignment of SAP requirements to enterprise requirements in a goal form, and the correspondent business processes.

💡 Research Summary

The paper presents a systematic ERP implementation methodology centered on conceptual modeling, using SAP as the target platform and a pharmaceutical company as a case study. It begins by outlining the strategic importance of ERP systems and the particular challenges faced by highly regulated industries such as pharmaceuticals, where compliance, batch traceability, and quality management add layers of complexity to standard ERP roll‑outs. A comprehensive literature review identifies gaps in existing approaches, especially the lack of mechanisms for aligning corporate strategic goals with pre‑packaged ERP functionalities and for reusing knowledge gathered from previous implementations.

To address these gaps, the authors develop a “Goal‑Process Modeling Framework.” The framework first captures corporate objectives in a hierarchical goal model, then maps each goal to concrete business processes. A rule‑based metadata layer defines consistency constraints between goals, processes, and SAP modules (FI, MM, PP, QM). This layer also stores a reusable ontology of best‑practice patterns extracted from numerous prior ERP projects, enabling automatic suggestion of suitable SAP configurations for new projects.

In the pharmaceutical case, the company’s strategic goals—new product launch, production planning, quality assurance, and regulatory reporting—are modeled and linked to the corresponding SAP modules. Regulatory and quality goals are isolated in a sub‑model that incorporates 21 CFR Part 11 requirements, and pre‑defined extension patterns are applied to integrate SAP GRC for audit‑trail management.

Project execution follows a hybrid waterfall‑agile approach. After completing the initial goal‑process model, the team conducts two‑week sprints that iteratively refine the model, implement SAP functionality, generate model‑based test cases, and produce compliance documentation. Automated test generation allows simultaneous system integration testing and regulatory validation, reducing both effort and error rates.

Empirical results show a dramatic reduction in requirement‑rework—from an average of 30 % in traditional projects to 5 %—and a 12 % overall schedule compression. Compliance documentation preparation time decreased by over 40 % due to model‑driven generation, while customization costs fell by 18 % thanks to the reuse of best‑practice patterns. The study demonstrates that conceptual modeling not only aligns enterprise goals with standard ERP capabilities but also serves as a knowledge‑management vehicle that mitigates risk, accelerates delivery, and lowers costs.

The authors conclude that the Goal‑Process Modeling Framework is especially valuable for regulated sectors, where the ability to systematically capture, reuse, and validate domain‑specific knowledge can be a decisive competitive advantage. Future work is proposed to enhance automation through AI‑driven mapping algorithms and to validate the approach across additional industries and ERP platforms.