Are you struggling to manage product quality across production locations, product portfolios, and various suppliers? Finished product quality management is critical to ensure that your products reach customers in the best condition possible. Protection of finished product quality requires continuous attention and can be a cumbersome and labor-intensive process if you don’t have systems in place to help you manage the variability inherent in food manufacturing.
Visual imaging is one approach that allows for overall quality management through objective measurements. The approach uses cameras and lasers to measure critical finished product criteria in an objective, consistent, and repeatable manner.
Using automated equipment to manage quality has many advantages. The objective measurements require trained staff, calibration, or evaluation time. Since objective measurements are provided, increased reliability, accuracy, and measurements are repeatable over time.
Visual imaging is appropriate for a wide range of food products of all sizes and can adjust to changing criteria as needed for business purposes. Use of imaging equipment results in quick turnaround, allowing for immediate follow-up and corrective actions as needed. Data from this method can be analyzed statistically for significance and trending purposes, helping you manage quality over an extended period of time.
HOW DOES IT WORK?
Automated equipment is an efficient way to collect useful data without the bias of a human judge. Visual imaging equipment incorporates a set of cameras and lasers to collect visual and dimensional data from your finished products. It does this by measuring all your criteria on each sample measured. Measurement criteria and acceptability ranges are determined and inputted into the software prior to measuring the sample products.
Commercially available visual imaging equipment triangulates dimensional measurements using lasers that are carefully aligned to create a 2D or 3D profile of the product. Data is captured as the product passes by the lasers and the measurements are then compared to the quality specifications outlined by those managing product quality. Digital cameras are used to capture and save photographs of each product. These images are extremely helpful in providing feedback to production facilities and suppliers to address quality concerns.
Dimensional data collected by the laser, and digital data collected by the camera, are compared to the acceptability ranges for ideal product and an overall product quality score is generated. This quality management approach is successful due to the many ways it can:
• Provide impartial data across a wide range of products.
• Measure the amount and severity of quality defects based on your criteria which can vary from dimensional characteristics (height, width, depth, symmetry) to product color (external or internal); topping coverage and distribution; and weight, surface, texture, and volume.
Visual imaging also can measure more complex characteristics, such as internal product structure — an attribute that can indicate if appropriate processing parameters were achieved during production.
HOW DOES THIS HELP MY COMPANY?
All companies producing foods are ultimately concerned about pleasing their customers to drive repeat sales and loyalty. Product quality management is key to successful brand protection and reduction of consumer complaints. Visual imaging is especially helpful in gathering product information over time, across multiple suppliers and locations.
Product evaluation using visual imaging can be performed throughout the production and distribution system, allowing a comprehensive understanding of how these supply chain processes are impacting the finished product that your customer ultimately purchases. Tracking supplier performance with visual imaging is an objective way to evaluate performance, as acceptability criteria are clearly outlined, and photos and measurements can be made available to suppliers allowing for quick resolution of quality concerns.
Successful implementation of a visual imaging quality management system must consider multiple factors. Prior to implementation, carefully consider the sample testing frequency, sample size sufficient for statistical data collection and trending, appropriate quality criteria ideally linked to consumer acceptance, rapid corrective action, and occasional review of measurement specifications for efficacy.