The Case for Predictive Quality Management

Food quality has always been a critical factor in the food production process, requiring food manufacturers to abide by stringent quality regulations, inspections and statistical quality control methods.

The impact of poor food quality is severe, with direct bottom-line consequences. Poor product quality reduces production yield, can damage the company’s brand and reputation, and can negatively affect revenues.

Conversely, meeting high-quality standards can reduce manufacturing costs – internally and externally. Internal costs emerge from problems associated with the product before it is delivered, e.g. shortages, waste, and delays. External costs arise post delivery – through recalls, lawsuits and warranty costs – and constitute a major expense for the food industry, resulting in a staggering $7 billion loss annually.

Food manufacturing operations turn to processes and tools to sustain high-quality production in addressing regulatory compliance, risk prevention, and product traceability. While tools such as statistical quality control (SPC) have been used since the early 1920s to identify issues as early in the manufacturing process as possible, they deal with problems that have already occurred.

Industry 4.0 technologies – which include OT and IT data integration, predictive data analytics, and smart data visualization – today provide us the means to predict quality issues before they arise, and to pinpoint the root cause quickly, with a high degree of confidence. Such solutions are referred to as “Predictive Quality Management” or “Predictive Quality Analytics”.

Predictive quality management in food industry

Regulations and standards to control food quality

Food quality is governed by country-specific laws and regulations. Furthermore, there are international regulations which are important regarding globalization and the increasingly complex food supply chains. These laws and regulations assure food safety and a minimum level of quality.

There are many different approaches and guidelines concerning the improvement of food quality that food manufacturers must adhere to, such as the “Good Manufacturing Practice” (GMP). Compliance with such practices is regarded by the WHO (World Health Organization) and the FDA (Food and Drug Administration) as the minimum requirements to assure high-quality products with a focus sanitation conditions and routine inspections.

Another food safety standard is set by the ISO (International Organization for Standardization), called the “ISO 22000:2005, food safety management systems”. Beyond that, the HACCP (Hazard Analysis and Critical Control Points) approach is highly respected as a methodology to prevent biological, chemical and physical hazards in the food production process.


Tracking and Traceability

In food manufacturing, it is critical to trace the food production process and to be able to evaluate the production data afterward. In general, it is important to trace the whole food chain, which starts in the field and ends at the consumer’s home. Doing so, food producers are able to identify the root causes in the food production chain faster, be it, for instance, the ingredients, processing or packaging problems. With tracking and traceability, manufacturers can stop distribution to the consumer and prevent possible recalls.

The importance of tracking and traceability in the food industry is evident from the fact that the FDA (Food and Drug Administration) introduced a new provision in 2011 – the FSMA (Food Modernization Act). By this provision, the food industry must act preemptively, with preventative controls, than to react to recalls.

The Produce Traceability Initiative (TPI) should be also mentioned. This initiative supports factories with the connection between internal and external traceability, for example with barcodes.

These tracking and traceability measures are all aligned in the strive to protect consumers from unacceptable food quality. They impose, however, further challenges for food production facilities.


Industry 4.0 and Predictive Quality Management to the rescue

Fortunately, food factories can overcome many of these quality control hurdles with the use of Industry 4.0 technology, specifically predictive quality management.

Predictive quality management and quality 4.0 provides manufacturers with 3 key capabilities that improve the overall quality metrics of the production floor – OEE, reject ratio, takt time, and others.

  • Real-time visibility
    Predictive quality management provides digital twin visibility into and control over all the production attributes that affect the quality levels of the food manufacturing process. Visibility into quality metrics are provided by lot numbers and can be evaluated per the production recipe for each manufactured product.


  • Predictive quality analytics
    A key component of Predictive Quality Management is predictive quality analytics. This is a statistical means to allow quality teams to anticipate quality issues before they arise.

    Predictive quality analytics relies on aggregated data from production lines (PLCs, data historians, SCADA systems) together with data from information technology systems (ERP, MES, Quality systems), and applies statistical models and machine learning algorithms to identify data correlations and anomalies that may result in an upcoming quality issue.


  • Automated root cause analysis
    Predictive Quality Management provides the QA manager and process engineer with the tools to quickly investigate the root causes of quality issues that have happened or about to happen in the production line.

    Because predictive maintenance and quality take a holistic approach in analyzing the relevant data of the entire production line, including the production flow dependencies in the line, it can provide suspected root causes for quality issues – down to the sensor level in a specific machine – together with a probability level per each root cause suspect. Quality teams are able to quickly address quality issues and prevent them from occurring in the future.

Predictive quality analytics in manufacturing

Predictive Quality Management empowers quality teams to anticipate and proactively address quality problems before they arise. The technology behind Predictive Quality Management – Digital twin visualization, manufacturing quality analytics, and automated root cause analysis – makes sense of complex data patterns to determine areas of greatest quality risk and assign production floor resources before risk becomes reality.

The business gains of Predictive Quality Management are clear and compelling, providing food manufacturers with a competitive edge in an era where food quality can make or break a brand.