Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions.

Perishable food supply chains require tight coordination to balance service, cost, and waste. Milk supply chains, in particular, are vulnerable to information delays and handling errors that accelerate expiry and stockouts. This study develops a case-calibrated system dynamics-based simulation of an Irish three-echelon dairy chain to examine whether Radio Frequency Identification (RFID)-enabled traceability actually converts visibility into managerial, operational, and sustainability gains. We compared a periodic-review baseline (N-RFID) and an RFID-enabled scenario (E-RFID) with near-real-time inventory visibility, First-Expired-First-Out (FEFO) rotation, and continuous review, while holding physical lead times constant. A Taguchi L36 design explores—in a structured experimental framework—robustness across lead times, ordering costs, demand and forecast uncertainty, safety-stock policies, and price levels. Outcomes include service performance, demand amplification, average inventories, expiry waste, and net present value. Simulation results show that E-RFID improves retail fill rate from about 95 to 99.9%, cuts expired waste by over 50%, and reduces inventories by up to 30%, while raising supply chain NPV by 5–10%. Benefits are most pronounced under high uncertainty and low-margin conditions, highlighting that RFID complements, rather than replaces, logistics efficiency. We also provide a compact analytical threshold analysis to position economic feasibility and discuss decentralised adoption via cost- and benefit-sharing mechanisms. The modeling framework translates RFID-enabled visibility into measurable operational and economic impacts, identifying robust policy configurations through Taguchi analysis and deriving feasibility thresholds that inform implementation strategies and equitable cost-sharing in decentralised supply chains. Graphical abstract The alternative text for this image may have been generated using AI.