Development of a lead-time-first multi-level planning approach for CTO/ATO mass customization supply chains
DOI:
https://doi.org/10.15587/1729-4061.2026.345253Keywords:
lead-time reduction, multi-level planning, postponement strategy, component readiness, demand variabilityAbstract
This study examines planning in mass customization contexts that face challenges, due to high product variety, sparse configuration-level demand, and long supplier lead times. Traditional Configure-to-Order and Assemble-to-Order (CTO/ATO) planning approaches often rely on late procurement and full postponement, leading to high and unstable customer lead times. To address this problem, a lead-time-first planning approach is developed to translate historical demand information into executable planning decisions without relying on finished-goods inventory. The approach operates across three levels: feature-level Component Readiness Tiering for upstream component pre-positioning, segment-level Mix Guardrails to control demand heterogeneity, and configuration-level Top-K partial pre-kitting to exploit demand concentration while preserving flexibility through postponement. The approach stands out because it directly links demand variability metrics to operational readiness thresholds. This link enables structured staging and coverage-based configuration selection. The approach is evaluated using a synthetic dataset representing one year of demand for customized laptops. Performance is assessed using lead-time-oriented indicators, including the 95th percentile customer lead time and instant-start rate. Results show improved responsiveness, with the worst-case customer lead time reduced from 12 days to approximately 7 days and immediate production enabled for a significant share of orders. These improvements are explained by early readiness of high-demand components combined with postponed final differentiation. The approach suits modular CTO and ATO environments with clear demand segments, stable high-volume components, and regular planning cycles.
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Copyright (c) 2026 Nouhaila El Assad, Salah-eddine Mokhlis, Kawtar El Haouti, Najat Messaoudi

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