Design for Reducing Waste in Sheet Metal Manufacturing: Industrial Redesign of Hygienic HGN Air-Handler Cabinets

This is the account of a nine-month industry redesign effort in a clean-room air-handling unit (AHU) cabinet production line. The initiative was focused on chronic production inefficiencies, now in early-stage design complexity and variant proliferation, such as extensive material scrappage, reworking, assembly mistakes, and setup time. Five concurrent design measures were taken: (1) remove mirrored variants by placing holes centrally and use redundant-hole strategies to eliminate left/right part dichotomy; (2) standardize chassis cross-section profiles; (3) standardize tolerances to allow cross compatibility; (4) insert parametrized CAD-based automation; and (5) substitute punching for laser cutting for additional flexibility. The result has been dramatic improvement in nesting efficiency, reduction in waste streams, reduction of set-up time, improved assembly accuracy and greater part reuse. One revelation is that decisions made during upstream design have disproportionate influence on manufacturing yield and circularity. Since it incorporates error-proofing, variant minimization and flexible manufacturing into the design stage, the methodology represents a shift from reactive lean waste minimization to proactive design-for-waste prevention. The research also considers the major challenges such as the capital expenditures, the technology integration barriers, the residual human error in the parameter input, and the domain-specific limitations. In the future, efforts need to be turned to creating standardized hole-pattern templates for chassis recycling, embedding sensor-based feedback, and leveraging predictive analytics to inform design decisions. This case delivers an accelerated, high-leverage lesson in waste reduction by design, and can be duplicated in sheet-metal manufacturing and other high-variant production systems. It demonstrates that strategic design innovation can enhance sustainability, flexibility and operational effectiveness concurrently. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.