The aerospace industry is currently undergoing an unprecedented production ramp-up, placing extreme pressure on multi-echelon supply chains that must simultaneously absorb demand variability, supply disruptions, and capacity constraints across multiple sites and actors. In this context, the ability to plan and coordinate production efficiently and effectively (from day-to-day shop floor execution through to long-term capacity investment decisions) has become a strategic differentiator between manufacturers capable of sustaining growth and those who struggle to meet commitments.
Current planning systems, predominantly based on MRP II logic, were not designed for this level of volatility and interdependence. Their limitations are well-documented in the scientific literature: excessive schedule nervousness when parameters are updated, bullwhip effect amplification across supply chain tiers, and poor reactivity to unforeseen disruptions when lead times are long and variable. Alternative methodologies, notably Demand-Driven MRP (DDMRP), propose a buffer-based, demand-pull logic that decouples segments of the supply chain and replenishes based on actual consumption rather than forecast. While early industrial evidence is encouraging, the performance of DDMRP in multi-site, extended enterprise contexts remains insufficiently characterized, and broader questions around how to design a coherent hierarchical planning system (one that is simultaneously robust, agile, and resilient) across multiple horizons and multiple actors remain largely open.
This project directly addresses these gaps. Its objective is to rethink the configuration of hierarchical planning systems for an aerospace manufacturer and its supply chain partners, covering short, medium, and long-term horizons in an integrated manner. The approach combines a structured AS-IS diagnostic of current planning practices, a systematic literature review of existing methodologies, and discrete-event simulation modelling on the Anylogic platform, enabling rigorous, data-grounded comparison of alternative planning configurations under realistic uncertainty conditions.
The work is organized in three successive phases. The first focuses on short-term planning, comparing MRP II, DDMRP, and alternative flow-control approaches (KANBAN, CONWIP, dynamic scheduling) through calibrated simulation, and producing operational reconfiguration recommendations. The second extends the analysis to medium and long-term horizons, modelling S&OP processes, capacity planning routines, and inter-actor information flows, to ensure cross-horizon decision coherence during the ramp-up. The third phase designs a novel planning methodology explicitly embedding robustness, agility, and resilience criteria, with scenario-building capabilities to support both tactical and strategic decision-making under uncertainty.
Application website:
https://institutminestelecom.recruitee.com/o/phd-proposal-designing-the-future-of-hierarchical-supply-chain-planning-systems
