The J-Curve Effect describes the initial negative impact followed by longer-term positive outcomes of a supply chain transformation or strategy shift, such as moving to a JIT system or implementing a new inventory platform. Initially, performance may dip due to disruptions or learning curves, but improvements follow as systems stabilize. In inventory optimization, this concept is essential during digital transformations, where AI or automation is introduced. Metrics like service level, turnover ratio, or order fulfillment may initially worsen before improving, causing stakeholders to question the value of change if they don’t understand the curve. Understanding and managing the J-Curve Effect allows supply chain leaders to plan change management more effectively. Monitoring short-term KPIs, setting realistic expectations, and having interim buffers can ease the transition. Platforms like Pull Logic provide phased optimization paths to minimize disruption during the transition.
