A data-driven and risk-based prudential approach to validate the DDMRP planning and control system

Daniela Favaretto (), Alessandro Marin () and Marco Tolotti
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Daniela Favaretto: Dept. of Management, Università Ca' Foscari Venice
Alessandro Marin: Dept. of Management, Università Ca' Foscari Venice

No 9, Working Papers from Venice School of Management - Department of Management, Università Ca' Foscari Venezia

Abstract: In this paper, we study the single-item dynamic lot-sizing problem in an environment characterized by stochastic demand and lead times. A recent heuristic called Demand Driven MRP, widely implemented using modern ERP systems, proposes an algorithm that is will e ectively tackle this problem. Our primary goal is to propose a theoretical foundation for such a heuristic approach. To this aim, we develop an optimization model inspired by the main principles behind the heuristic algorithm. Specifically, controls are of the type (s(t); S(t)) with timevarying thresholds that react to short-run real orders; in this respect, control is risk-based and data-driven. We also consider service levels derived as tail risk measures to ensure fulfillment of realized demand with a predetermined probability; in this respect, our approach is pruden- tial. Finally, we use our model as a benchmark to theoretically validate and contextualize the aforementioned heuristic.

Keywords: Inventory management; Manufacturing resource planning; Data-driven demand planning; Tail risk measures; DDMRP (search for similar items in EconPapers)
Pages: 28 pages
Date: 2021-11
New Economics Papers: this item is included in nep-ore
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