The proliferation of new information technologies has brought numerous benefits to the food processing industry, including improvements in overall productivity and efficiency. At the same time, the industry continues to experience major lapses in safety and quality, magnified in recent months by several highly publicized product recalls.
Fragmented processes and disconnected systems, each with their own specific data, are problems at many plants. Because these organizations use a paper-based approach, resources are overloaded trying to track safety and quality processes, while inconsistencies and waste abound.
Unconnected data sources and manually tracked quality processes lead to a lack of real-time information. Quality issues are not addressed, and the root causes of a quality deviation are not identified. Additionally, critical production decisions are frequently based on assumptions instead of accurate and reliable information.
These operational deficiencies may be key contributors to recent recalls. They also point to a need for improved management of the entire supply chain, implementation of tighter control and monitoring, and delivery of real-time information on production processes. To achieve optimum performance, manufacturers need timely information about the production process to effectively analyze and detect undesirable trends so that they can take immediate corrective action when needed. Once these best practices are defined, manufacturers can enforce them and, where possible, build quality directly into the solution so that the product is manufactured correctly the first time.
An integrated quality management tool can provide substantial dividends in that type of situation. Despite the natural hesitation of the industry to tinker with a proven, albeit cumbersome, paper-based system of quality management, the underlying benefits of this integrated solution have become too promising to ignore.
Step One: Turn Your Data Into Intelligence
At the core of a robust quality management system are tools that allow users to aggregate information from multiple applications and transform the data into highly visible, actionable, performance-oriented intelligence. Without defined measures and procedures that connect data sources, analyze performance, and enforce processes, the identification and correction of root causes is nothing more than guess work.
A central component in an effective quality management strategy is the ability to gather and correlate information from multiple sources, allowing decision makers to see diverse views and maintain key relationships. Reports, key performance indicators (KPIs), and operational metrics can then be assembled quickly into dashboards so that performance can be measured throughout the facility.
By connecting disparate data sources, quality control staff can access information that provides exception alerts through live information connections, and managers can determine precisely where, when, and why mistakes are occurring. This real-time intelligence will automatically highlight exception conditions, missed targets, and plan deviations.
When these disparate data sources are connected, operators use the intelligence dashboards to make quality improvements. By putting data into context, operators can make process corrections in real time, instead of after the fact, resulting in significant improvements in output, yield, and first-pass quality.
Because dashboards display metrics in rich graphics, operators understand more quickly how to respond to the data. For example, a quick glance at a trend-oriented graphic, as compared to raw numbers, can provide powerful insight into performance history and status. Users can more effectively compare multiple data sources using the dimension of time or a production run.
At one large food processing operation, a manufacturing intelligence application allows the company to aggregate data from all of its control and historical systems. This provides KPI exception reporting and root cause analysis that can be shared in a Web portal. This capability presents one version of the truth for all quality information and provides a basis for better decision making. Operators can easily view, for example, how varying the mixers, ovens, or ingredient suppliers will impact the final product.