不確実性下における炭素考慮型ハイブリッド補充
Carbon-aware hybrid replenishment under uncertainty (原題)
Gábor Nagy, Szabolcs Szentesi, Gergő Zemlényi, Tihomir Opetuk
🤖 gxceed AI 要約
日本語
本研究は、従来の適応的(s,S)在庫モデルに発注遅延の環境影響を組み込んだ炭素考慮型適応補充政策を提案する。モンテカルロシミュレーションにより、安定環境では効果が小さいが不確実性が増すほど有効となることを示した。需要・リードタイム複合不確実性下では、静的ベンチマーク比で物流総コスト3.61%減、緊急輸送28.48%減、輸送排出6.73%減、充足率1.69ポイント改善を達成したが、平均在庫は15.97%増加した。
English
This study proposes a carbon-aware adaptive replenishment policy extending the conventional adaptive (s, S) model by incorporating the environmental consequence of delayed ordering. Monte Carlo simulation shows negligible benefit in stable conditions but growing value under uncertainty. Under combined demand and lead-time uncertainty, it cut total logistics cost by 3.61%, expedited shipments by 28.48%, and transport emissions by 6.73% versus a static benchmark, while raising average inventory by 15.97%.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
Scope 3排出の大部分を占める物流・輸送排出の削減は、SSBJ基準や有報でのサプライチェーン開示を迫られる日本企業にとって実務的示唆が大きい。在庫政策と排出削減のトレードオフを定量化した点は、物流部門の脱炭素計画立案に資する。
In the global GX context
Transport emissions within Scope 3 are a major disclosure item under TCFD/ISSB and CSRD. This paper quantifies the trade-off between inventory policy and emissions, offering a modeling approach for companies integrating carbon into operational replenishment decisions.
👥 読者別の含意
🔬研究者:在庫管理と炭素排出を統合した適応政策のモデル化に関心のある研究者に、シミュレーション設計とトレードオフ分析の枠組みを提供する。
🏢実務担当者:物流・調達部門が緊急輸送の削減と在庫増加のバランスを検討する際の定量的根拠として活用できる。
🏛政策担当者:物流排出削減政策の設計において、企業の在庫・補充行動が排出に与える影響を考慮する必要性を示唆する。
📄 Abstract（原文）
Inventory policies influence not only holding and shortage costs but also the transport emissions associated with replenishment frequency, shipment size, and the use of expedited delivery. Existing carbon-aware inventory models typically represent emissions as a cost term or regulatory constraint, while adaptive inventory approaches mainly respond to changes in demand and lead time. This study proposes a carbon-aware adaptive replenishment policy that extends a conventional adaptive (s, S) model by incorporating the anticipated environmental consequence of delayed ordering. Three policies—a static (s, S) policy, a conventional adaptive policy, and the proposed carbon-aware policy—were evaluated through Monte Carlo simulation under stable conditions, demand uncertainty, and combined demand and lead-time uncertainty. The results show that the additional value of carbon-aware adjustment is negligible in a stable environment but becomes more visible as uncertainty increases. Under combined uncertainty, the proposed policy reduced total logistics cost by 3.61%, expedited shipments by 28.48%, and transport-related emissions by 6.73% relative to the static benchmark, while improving the fill rate by 1.69 percentage points. These benefits were accompanied by a 15.97% increase in average inventory. Compared with the conventional adaptive policy, the additional gains were smaller, indicating that adaptive parameter updating remains the primary source of improvement. Carbon awareness therefore acts as a targeted refinement that is most useful when emergency replenishment is frequent and substantially more emission-intensive than regular supply.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.32971/als.2026.017first seen 2026-10-03 04:58:33
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