Enhancing Profitability in SAP Supply Chains: Secure AI and ML-Based Dynamic Pricing Framework
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506004Keywords:
SAP Supply Chain, Dynamic Pricing, Artificial Intelligence (AI), Machine Learning (ML), Secure Data Analytics, Cryptography, Predictive Analytics, Prescriptive Insights, Revenue Optimization, Supply Chain ProfitabilityAbstract
Maximizing profitability in SAP-driven supply chains requires intelligent pricing strategies that respond to dynamic market conditions, demand fluctuations, and competitive pressures. This paper presents a secure AI- and ML-based dynamic pricing framework designed to optimize pricing decisions within SAP supply chain environments. The proposed system integrates advanced machine learning models to analyze historical sales data, market trends, inventory levels, and customer behavior to generate predictive and prescriptive pricing recommendations. Cryptographic techniques, including data encryption and secure computation, ensure the confidentiality and integrity of sensitive enterprise and customer data throughout the analytics process. By deploying this framework, organizations can implement adaptive pricing strategies that balance profitability, competitiveness, and customer satisfaction, while mitigating risks associated with data breaches or unauthorized access. Experimental results demonstrate improvements in revenue generation, demand management, and overall supply chain performance. This research underscores the potential of combining AI, machine learning, and secure cloud-based SAP environments to enhance decision-making and drive sustainable profitability in modern supply chains.
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