Abstract
In this chapter, we introduce two important topics that mainly deal with the storage and handling of physical goods and materials in supply chains. First, the concept of warehouse management, associated activities, and warehouse management system are discussed, followed by warehouse performance measurement. Then, we move onto inventory management, focusing on addressing two essential questions for inventory managers, i.e., ‘how much to order?’ and ‘when to order?’. In latter sections of the chapter, we introduce warehouse optimization using linear programming and classification algorithms including logistics regression and boosting methods, to solve practical warehousing and inventory stockout problems in Python.
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Paul Clarke, CTO, Ocado, 2018, “How Online Grocer Ocado Is Automating Warehouses Using Swarms of Robots”, Harvard Business Review.
References
Breiman, L. 1997. “Arcing The Edge”. Technical Report 486. Statistics Department, University of California, Berkeley.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002. “SMOTE: synthetic minority over-sampling technique”. Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357.
Freund, Y. & Schapire, R.E. 1997. “A decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences, Vol. 55(1), pp. 119-139.
Friedman, J. H. 1999a. “Greedy Function Approximation: A Gradient Boosting Machine”. Department of Statistics and Stanford Linear Accelerator Center, Stanford University, Stanford.
Friedman, J. H. 1999b. “Stochastic Gradient Boosting”. Department of Statistics and Stanford Linear Accelerator Center, Stanford University, Stanford.
King, P.L. 2011. “Understanding safety stock and mastering its equations”. APICS magazine.
Lemaître, G., Nogueira, F. & Aridas, C.K. 2017. “Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning”. Journal of Machine Learning Research, Vol. 18(17), pp. 1−5.
Zhu, J., Zou, H., Rosset, S. & Hastie, T. 2009. “Multi-class AdaBoost”. Statistics and Its Interface, Vol. 2, pp. 349-360.
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Liu, K.Y. (2022). Warehouse and Inventory Management. In: Supply Chain Analytics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-92224-5_7
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DOI: https://doi.org/10.1007/978-3-030-92224-5_7
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