How AI-automated Quality Control helps manufacturers become more sustainable

A new white paper sponsored by Fujitsu, Keeping Quality at Pace with Your Business, by analyst firm PAC, shows the significant impact of inadequate quality control on profitability. It cites a study by insurance giant Allianz, for example, which found that the size and number of product recalls is increasing, particularly in automotive and food and beverage, and put the average cost of a major recall at approximately $12m.

Quality issues can impact market capitalization too. Peloton, a massive global corporate success story as customers rushed to buy its home exercise machines and supporting subscription services during lockdowns, was forced to recall more than 125,000 treadmills in the US alone following serious safety incidents and a warning to customers from the US Consumer Product Safety Commission. The recalls cost the company more than $150m in refund and recall costs. Consequently, the company’s valuation fell by more than 80% and this led to a wholesale overhaul of its leadership team.

Peloton’s struggles highlight the difficulties facing manufacturers in balancing quality and speed. This is a massive challenge for many businesses already contending with bottlenecks often caused by an over-dependence on manual intervention.

What’s needed is a complete change of perception of the quality function. Instead of a cost center, it must be regarded as a strategic part of the business — a value generator, where data-driven quality also steers the upstream production process to improve techniques and performance of the manufactured item, while lowering manufacturing costs.

Getting to that point will involve overcoming difficult hurdles. Traditionally, QC has been based on manual processes and visual inspection. However, quality assurance functions face severe resourcing challenges, all the way from entry-level positions to higher-end roles with specific technical and scientific skill sets. And this is at a time when two-thirds of quality leaders in the life sciences sector recently surveyed by the FDA Group identified “sufficient staffing and resourcing” as a primary quality system challenge.

One way to tackle the skills gap in quality assurance is through automation. This can ease the burden on existing team members and remove some of the repetitive, manual processes that deter potential new recruits. But clear progress towards manufacturing production automation generally has not yet been matched on the quality side. Growing industry discussion about “Quality 4.0,” using technology to “shift left” to a more predictive and preventative approach to quality, is not being matched by projects in factories.