Abstract
Effective inventory management is crucial to long-term profitability of organizations. Companies that manage their inventories effectively can substantially improve their operating margins and gain a competitive edge in their industries. We elaborate on inventory management theories under demand uncertainty in this chapter. We first focus on inventory problems that have analytical solutions. One key message of this chapter is that decision-makers can find optimal or near-optimal solutions intuitively by applying marginal analysis without using exhaustive derivations. We also discuss how the assumptions on which analytical results are based are often violated in practice. We conclude the chapter with the Monte Carlo simulation, which has proven to be effective in solving complex supply chain problems when no analytical solution exists.
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Notes
- 1.
The mean demand for the lognormal distribution is given by \(e^{\mu + \sigma ^2/2}\), while the variance is \((e^{\sigma ^2}-1)e^{2\mu + \sigma ^2}\).
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Biçer, I. (2023). Inventory Management Under Demand Uncertainty. In: Supply Chain Analytics. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-30347-0_3
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DOI: https://doi.org/10.1007/978-3-031-30347-0_3
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