Quality and Scrap Reduction

Quality variation is not just a shop floor issue; it is a margin problem. The ASQ estimates that the cost of poor quality can range from 15 to 20 percent of revenue, often due to scrap, rework, and inefficiencies. According to McKinsey, productivity gaps between high and low performers increase by as much as 800 percent as tasks become more complex, highlighting the need to standardize work.

For operations leaders, quality is both a financial lever and a cultural one. Every variation compounds downstream, affecting margins, customer satisfaction, and workforce morale. Traditional approaches like periodic audits, static SOPs, or tribal knowledge rely heavily on experienced operators remembering and correctly interpreting instructions. These methods falter when scaling across multiple lines, products, or geographies. Even top performers deviate under time pressure or when procedures are unclear.

DeepHow enables operational excellence by turning expert procedures into clear, visual, AI-structured digital work instructions. Each step is captured in context, segmented automatically, translated where needed, and made available on any device. Work results can also be validated through an AI agent with photos or videos. This ensures consistent execution across shifts and sites.

This approach builds process discipline at scale, giving leaders traceability and version control for continuous improvement. Patterns of variation become visible, procedural gaps close faster, and the entire operation moves toward a culture of precision.

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‍Book a demo to see how DeepHow helps enforce standard work and protect quality at scale through AI.