Transforming quality assurance: What role does digitalization play?
Businesses rarely give much priority to status-checking their quality assurance processes. One reason for this may well be that many businesses find it difficult to express the benefits of quality management in numerical, calculable terms.
This changes fundamentally once the company has established a sustainable digital system for handling quality data – true quality data management.
Such a tool is an ideal way to measure and document the costs and benefits of quality management. It makes it easier to advocate for change – top-down, but also bottom-up, because the business units now have access to data that can convince management to undertake investments. Conversely, management can intervene when the data show that the status quo no longer matches the business strategy.
Quality data management offers digital, software-supported quality planning, control and management. All participating segments of your value chain can be connected to it.
Precise, reliable measurement data from production and quality control are available automatically and in digital form for storage, analysis and management. They offer insights for use in decision-making, process optimization and regular quality control.
Detailed and dependable quality data make quality data management a decisive factor for efficiency and quality. This offers manufacturing businesses a wide range of benefits:
- Reliable quality control and better understanding of how quality management promotes business success
- Greater effectiveness in the achievement of quality targets
- Early identification of errors, ensuring lower reject rates and higher product quality
- Digital management of quality data for data integrity and traceability
- Document management offers benefits during audits
Digital solutions make quality management and quality assurance more efficient and effective, and they reduce costs. They also make quality processes more transparent and simplify collaboration – for many businesses, plenty of reasons for fully digitalizing their quality management schemes.
Such a structure also serves as a basis for planning the next milestones in the digital quality ecosystem:
- Leveraging a digital quality twin to find answers to almost all quality dimensions of product creation
- Relying on big data and analytics to exploit large data volumes for the benefit of your strategic goals, from systematic searches for error sources to process optimization to higher product quality
- Profiting from artificial intelligence – in automatic error identification, risk profiling and projections regarding quality problems