The future of quality

Rethinking your quality tactics in a changing world

As global sourcing continues to shift driven by geopolitical shifts, rising costs, environmental considerations, and increasing consumer expectations - quality has emerged as a strategic pillar for brands and retailers. With new technologies redefining traditional quality control (QC) models, it's time to revisit how we evaluate effort, investment, and return when it comes to product quality.

We’ve compiled a guideline to help navigate the evolving landscape, supported by practical tools and reflective questions. Below is a structured checklist and set of themes that aim to inspire smarter decisions and future-proof strategies.

QC Mix - Defining the right balance

The QC mix is no longer a binary choice between internal inspectors and third-party audits. Instead, brands are adopting a hybrid model tailored by risk level, supplier maturity, and product complexity.

Key questions to consider

• Are we applying the same level of QC effort across all suppliers, regardless of risk?
• Do we understand the ROI of our current inspection model?
• Where could we empower suppliers more directly while lowing the cost and building more supplier acountability?

Put it into action

• Use a risk-based segmentation model: allocate higher QC scrutiny where risk is high (e.g., new suppliers, product complexity, QTY, critical SKUs).
• Introduce supplier self-inspections for low-risk vendors, but ensure it's not just a checkbox exercise.
• Equip suppliers with digital tools that build accountability through photo - and video proofing, automatic GPS- and timestamping.
• Set clear KPIs and monitor performance using inspection pass rates, defect trends, and claim history.

The future of checklists – From paper to prediction

Static, checklist-based QC is giving way to dynamic tools that leverage data, memory, and habits. AI-driven checklists are not only smarter but also personalized and context-aware.

Key questions to consider

• Are we still using the same checklist for every inspection?
• Do inspectors waste time checking items that are rarely defective?
• Are we capturing learnings from previous inspections and claims?

Put it into action

• Implement AI-enhanced checklists that learn from past findings and focus on high-risk areas.
• Introduce habit-building safeguard mechanism in the interface for inspectors that help validate input and promote consistency without fatigue.
• Create memory-based prompts that adapt based on product and factory history.

Leveraging data & tech - From reactive to predictive

Traditional QC systems often act as post-mortem tools - flagging issues after the fact. Today’s quality method, processesand tools need to serve as early warning systems, embedding real-time data and insights into daily workflows. Real quality doesn’t live in the QC department alone. Catching defects is reactive - the goal is to prevent them altogether.

Key questions to consider

• Are we using inspection data to influence sourcing and buying decisions?
• Is our BI dashboard actionable - or just informative?
• Can we correlate inspection data with claims, returns, and customer reviews?

Put it into action

• Combine KPI dashboards, BI tools, and AI forecasting to spot trends early.
• Enable visual analytics that compare factories, SKUs, and suppliers.
• Use embedded data capture at every inspection point (e.g., location, user, conditions).
• Integrate with ERP/PLM systems or other data sources to connect quality with operations and sourcing.

AI vs. EYE - The role of human judgment

As AI continues to evolve, it brings incredible speed and scale to quality control spotting patterns, surfacing risks, and learning from data. But quality isn’t only a data problem. It's also about context, experience, and gut instinct - elements that still reside firmly with the inspector and human EYE. The future of QC isn't AI or human judgment; it’s both -working in tandem

Key questions to consider

• How can AI help spot trends and focus our human effort?
• Where is human judgment irreplaceable?
• Are we designing processes that augment rather than replace?
  

Put it into action

• Use AI to triage issues, recommend actions, and prioritize risk.
• Preserve manual photo review and contextual judgment where visual ambiguity is high.
• Blend AI-powered decision support with human-led validation in final assessments.
• Emphasize training over replacing your best inspectors.

Data-driven quality - Removing the overhead

More data doesn’t automatically mean better quality. It must be digestible, visual, and linked to action. Quality should be embedded, not isolated.

Key questions to consider

• Are teams drowning in data or acting on insights?
• Is inspection reporting adding unnecessary manual work?
• Can data tell a story we can act on?

Put it into action

• Automate reporting and feedback loops with plug-and-play dashboards.
• Apply data validation rules to reduce duplication and human error.
• Centralize defect libraries and sync across mobile QC apps and desktop portals.
• Remove friction between departments by enabling shared views of product quality.

Ready to rethink your quality tactics?‍

Discover how Qarma helps brands, importers and retailers move from reactive checks to proactive QC. Book a demo here to see Qarma in action.