The ‘just-in-case’ inventory rebound: Post-pandemic trade-offs between resilience and working capital

2025-09-09T04:55:08+03:00

The COVID-19 pandemic delivered a seismic shock to global supply chains, exposing profound vulnerabilities in highly efficient Just-in-Time (JIT) inventory systems as critical stockouts paralyzed industries worldwide. This crisis triggered an urgent pivot toward resilient Just-in-Case (JIC) buffering strategies—but does this shift represent a lasting transformation or merely a temporary reaction? Through rigorous analysis of panel data spanning 1,200 firms across 10 industries from 2018–2023 and in-depth interviews with 30 supply chain executives, this study examines whether organizations sustain elevated safety stocks post-crisis and at what cost to working capital efficiency. Our findings reveal a complex reality: while 60% of firms increased inventory buffers by 15–40% during the pandemic's peak, only 20% maintained these levels beyond 2022. By 2023, aggregate stocks had reverted halfway to pre-pandemic baselines despite persistent geopolitical and climate risks, demonstrating our pioneering "Resilience Fatigue" thesis—the waning urgency of past disruptions against mounting working capital pressures. JIC adopters incurred 5–12% higher carrying costs, with technology and automotive sectors absorbing the sharpest impacts. Yet semiconductor and pharmaceutical firms institutionalized strategic buffers where catastrophic failure risks outweighed capital efficiency imperatives. By demonstrating how temporal decay reshapes the resilience-capital trade-off, we extend foundational supply chain theory and establish a contingency-based neo-resilience paradigm. This research delivers actionable frameworks for intelligent buffering and charts pathways for AI-driven hybrid models, empowering firms to balance operational robustness against financial vitality in our age of perpetual disruption.

The precision–fragility paradox: How generative AI raises customer lifetime value but increases stockout risks in retail

2025-09-09T04:55:28+03:00

Retailers employing generative AI for hyper-personalization experience a notable increase of 37% in customer lifetime value. They incur hidden operational costs, with a 29% increase in stockouts during disruptions. The Precision-Fragility Paradox emerges when surgical customer segmentation partitions demand streams, thereby constraining supply chain agility and heightening systemic vulnerability. This study examines the tension by proposing an integrated theoretical framework that demonstrates how adaptive service modularity aligns hyper-personalization with operational resilience. The research utilizes a robust methodological triangulation, integrating agent-based modeling of 50 million transactions with a longitudinal field experiment involving multinational retailers. It delineates an existential threshold at which personalization exceeds 18.3% Demand Sensing. Heightened granularity amplifies the risk of fragility by a factor of 2.4, leading to a non-linear increase in stockouts. The research indicates that this fragility is not inevitable: organizations implementing modular architectures improve their reconfiguration capacity by 41% while preserving 92% of revenue gains. AI-driven resilience mechanisms reduce recovery latency by 63% through autonomous supplier rerouting and dynamic inventory adaptation. The findings indicate a service-dynamic capability architecture where real-time personalization governance, liquefiable resource networks, and self-calibrating systems transform volatility from a threat into an advantage. This blueprint allows executives to balance precision and flexibility, utilizing AI's revenue potential while reducing operational externalities. The study redefines competitive resilience, demonstrating that in algorithm-driven commerce, true robustness is derived not from enduring shocks, but from creating systems that adapt amidst disruption.

Frontiers in Research

The ‘just-in-case’ inventory rebound: Post-pandemic trade-offs between resilience and working capital

The precision–fragility paradox: How generative AI raises customer lifetime value but increases stockout risks in retail