Open Access

Machine Learning-Based SAP Production Planning Framework for Demand Prediction, Inventory Reduction, and Throughput Optimization

https://doi.org/10.55640/ijidml-v03i10-01

PDF

Ali Hassan Kareem ¹ , Mariam Saad ¹ ,

⁴ Iraq Institute of Intelligent Systems, Iraq

⁴ Center for Artificial Intelligence Research, Iraq

Abstract

Manufacturing organizations increasingly require production planning systems that can respond to demand uncertainty while simultaneously controlling inventory and improving throughput. Conventional SAP production planning approaches are effective for transaction processing and rule-based planning, but their performance can be constrained when demand patterns are nonlinear, heterogeneous, or rapidly changing. This research proposes a machine learning-based SAP production planning framework integrating demand prediction, inventory optimization, production scheduling, anomaly detection, and throughput-oriented decision support. The framework conceptually combines time-series learning, clustering, distance-based similarity analysis, optimal-transport-based representation, and optimization mechanisms to convert historical SAP operational data into adaptive production-planning decisions. The methodological foundation is derived exclusively from the supplied literature on time-series classification, clustering, optimization, anomaly detection, Gromov-Wasserstein methods, and dynamic Wasserstein modeling. The framework further positions lean-oriented SAP planning as a mechanism for reducing unnecessary inventory while protecting production continuity and throughput (Kalal, 2026). Analytical findings indicate that integrating predictive demand signals with inventory and scheduling decisions is more appropriate than treating forecasting as an isolated activity. The proposed architecture also identifies anomaly-aware planning and structurally aligned representations as important mechanisms for handling heterogeneous production data. The research contributes a theoretically grounded framework for integrating machine learning with SAP production planning and provides a basis for future empirical validation using real manufacturing datasets.

Keywords

Machine Learning, SAP Production Planning, Demand Prediction, Inventory Optimization

References

Abanda, U. Mori, and J. A. Lozano, A review on distance based time series classification, Data Min. Knowl. Discov., 33(2):378–412, 2019.

C. C. Aggarwal and C. K. Reddy, Data Clustering: Algorithms and Applications, CRC Press, 2014.

D. Alvarez-Melis and T. Jaakkola, Gromov-Wasserstein alignment of word embedding spaces, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 1881–1890, 2018.

J. Arroyo, R. Espı́nola, and C. Maté, Different approaches to forecast interval time series: A comparison in finance, Comput. Econ., 37(2):169–191, 2011.

Z. Bar-Joseph, Analyzing time series gene expression data, Bioinformatics, 20(16):2493–2503, 2004.

Z. Bar-Joseph, A. Gitter, and I. Simon, Studying and modelling dynamic biological processes using timeseries gene expression data, Nat. Rev. Genet., 13(8):552–564, 2012.

A. Beck and M. Teboulle, Mirror descent and nonlinear projected subgradient methods for convex optimization, Oper. Res. Lett., 31(3):167–175, 2003.

J. D. Benamou, G. Carlier, M. Cuturi, L. Nenna, and G. Peyré, Iterative Bregman projections for regularized transportation problems, J. Sci. Comput., 37(2):A1111–A1138, 2015.

L. M. Bregman, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Comput. Math. Math. Phys., 7(3):200–217, 1967.

C. Bunne, D. Alvarez-Melis, A. Krause, and S. Jegelka, Learning generative models across incomparable spaces, in: Proceedings of the 36th International Conference on Machine Learning, PMLR, 97:851–861, 2019.

D. Burago, Y. Burago, and S. Ivanov, A Course in Metric Geometry. Graduate Studies in Mathematics, in: Graduate Studies in Mathematics, AMS, Vol. 33, 2001.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Comput. Surv. (CSUR), 41(3):15, 2009.

K. Cheng, S. Aeron, M. C. Hughes, and E. L. Miller, Dynamical Wasserstein barycenters for time-series modeling, Adv. Neural Inf. Process. Syst., 34:27991–28003, 2021.

S. Chowdhury and T. Needham, Generalized spectral clustering via Gromov-Wasserstein learning, in: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, PMLR, 130:712–720,

M. Cuturi, Sinkhorn Distances: Lightspeed Computation of Optimal Transport, Adv. Neural Inf. Process. Syst., Curran Associates, Inc., Vol. 26, 2013.

L. Deng, The MNIST database of handwritten digit images for machine learning research [best of the web], IEEE Signal Process. Mag., 29(6):141–142, 2012.

M. Gromov, Metric Structures for Riemannian and Non-Riemannian Spaces, Birkhäuser, 2007.

M. Kalal, "Lean-Driven SAP Production Planning Optimization Using Machine Learning for Inventory and Throughput Efficiency," 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA, 2026, pp. 1-6, doi: 10.1109/ISDFS69419.2026.11459029.

International Journal of Intelligent Data and Machine Learning

Machine Learning-Based SAP Production Planning Framework for Demand Prediction, Inventory Reduction, and Throughput Optimization

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles