How Production Data Reduces Food Processing Losses

Food processing manufacturers collect data on equipment performance, downtime, output, yield, package weight and product quality every day. But much of this information is still used mainly for record-keeping. When small deviations continue over time, they can become significant production losses.

By connecting food processing data and analyzing production trends, manufacturers can identify hidden losses such as micro-stops, yield loss, overfilling and declining equipment performance.

Where Production Losses Hide

Not every production loss comes from equipment failure. A machine may keep running while experiencing frequent short stops. A production line may meet its output target while its yield falls below expectations. Packaging may also appear normal while consistently using more product than required.

These problems are often recorded in separate systems, making it difficult to identify where losses originate. Connecting equipment, quality and production data provides a clearer view of the process and helps manufacturers identify recurring problems.

Micro-Stops Reduce Equipment Efficiency

Major downtime is easy to notice, but frequent stops lasting only seconds or minutes can also reduce production capacity. Cleaning, changeovers, material supply and equipment adjustments may all create micro-stops.

Production data analysis can show when these stops occur, how often they happen and which processes are affected. Food Engineering has reported that food, dairy and pet food manufacturers are using real-time production data and data modeling to identify micro-stops and improve production performance.

Yield Loss Can Signal Equipment Problems

Yield loss does not always result from raw materials or processing conditions. Changes in equipment performance can also reduce product recovery.

In one case reported by Food Engineering, Lincoln Premium Poultry analyzed historical deboning data and found that increasing residual meat was linked to equipment performance. After restoring the equipment condition, the company reportedly recovered more than $1 million in annual losses.

This shows why equipment performance and quality data should be analyzed together rather than separately.

Overfilling Creates Hidden Costs

Packaging is another source of production loss. A small amount of overfilling per package may seem insignificant, but at high production volumes, it can create substantial product giveaway.

Food Engineering reported that Death Wish Coffee used package-weight data to identify persistent overfilling. After adjusting specifications and calibration practices, the company reportedly reduced annual product losses by about $5.4 million.

Data Supports Better Equipment Decisions

Production data can do more than generate OEE reports. It can help manufacturers determine which equipment is causing losses, where problems occur and whether maintenance, adjustment or replacement is the better solution.

Manufacturers do not necessarily need a complete digital transformation to begin. Data from PLCs, SCADA, MES, quality systems and production records may already provide useful insights. The key is to connect these sources and turn production data into actionable decisions.For food manufacturers, the goal is not simply to collect more data. It is to use existing data to identify hidden losses, improve equipment efficiency, increase yield and reduce production costs.

Disclaimer: This article is based on publicly available information and assisted by AI-generated content and optimization. It is provided for industry information sharing and reference only.