Classification Models to launch the New Pharmaceutical Product / Drug Using SAP ERP Data
DOI:
https://doi.org/10.58425/ajt.v5i1.474Keywords:
Pharmaceutical launch optimization, machine learning classification, predictive analytics, SAP ERP integration, market access riskAbstract
Aim: This paper examines the potential to improve the early-stage performance of pharmaceutical product launches, particularly within the critical first 3 - 6 months, through the use of supervised machine-learning classification models.
Methods: The study employs supervised machine-learning models, including logistic regression, random forests, and neural networks, implemented on the SAP Business Technology Platform (BTP). These models integrate SAP S/4HANA transactional data with external market indicators to optimize launch strategies by improving prescriber targeting, detecting market-access risks, segmenting patient and payer profiles, and enabling real-time decision-making.
Results: The findings indicate that the models decrease forecast variance, improve prescriber reach, and enable earlier corrective interventions during the early launch stage. The models support managerial decision-making by scoring prescriber adoption propensity and categorizing market-access risks to streamline payer strategies.
Conclusion: The study demonstrates that integrating predictive classification models into ERP-based pharmaceutical workflows enhances agility and supports more evidence-based, accurate, and timely decision-making, thereby improving overall launch performance.
Recommendation: The study highlights the need to integrate supervised machine-learning models into pharmaceutical launch workflows while emphasizing the importance of governance systems, privacy, equity, and regulatory considerations to enable proactive changes, improve market accessibility, and optimize resource allocation.
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