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

Author

Listed:
  • Daniela Favaretto

    (Dept. of Management, Università Ca' Foscari Venice)

  • Alessandro Marin

    (Dept. of Management, Università Ca' Foscari Venice)

  • Marco Tolotti

    (Dept. of Management, Università Ca' Foscari Venice)

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.

Suggested Citation

  • Daniela Favaretto & Alessandro Marin & Marco Tolotti, 2021. "A data-driven and risk-based prudential approach to validate the DDMRP planning and control system," Working Papers 09, Department of Management, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpdman:186
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    References listed on IDEAS

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    Keywords

    Inventory management; Manufacturing resource planning; Data-driven demand planning; Tail risk measures; DDMRP;
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