Optimization of warehouse logistics in household woods

Objective: Household‑wood warehouses grapple with bulky SKUs, moisture risk and same‑day fulfilment, evidence on optimisation is scattered. This review quantifies operational and sustainability gains attainable through contemporary optimisation techniques. Methods: A systematic search of Scopus, Web of Science and IEEE Xplore (2017‑2025) retrieved 326 records. After PRISMA screening, fifteen empirical papers met inclusion criteria. Key performance indicators-travel time, inventory cost and cradle‑to‑gate CO₂ per tonne-were harmonised. Random‑effects meta‑analysis computed pooled Hedges g values, subgroup tests contrasted AI‑driven scheduling with heuristic approaches. Results: Combined data (n = 112 warehouse observations) reveal optimisation trims travel time by 18 % (95 % CI 14‑22 %) and inventory cost by 15 % (CI 11‑19 %). CO₂ intensity falls 20 % when digital picking aids shorten forklift routes. AI outperforms heuristics by eight percentage points in sites below 10 000 m², yet the edge shrinks in high‑bay operations. Heterogeneity remains high (I² = 71 %), but sensitivity checks uphold central tendencies. Conclusions: Even amid methodological noise, optimisation tools deliver repeatable gains. Practising managers should start with low‑code analytics, then escalate to full automation as data maturity improves. Future researchers must capture live sensor streams to refine effect estimates and link efficiency targets with carbon agendas.