Today’s food processors face more complexities than ever in the sanitation process. Labor shortages, water scarcity, changing environmental regulations, and rising energy costs have created no shortage of challenges for food processors all over the globe. Droughts, climate change, the war in Ukraine, and other factors have prompted an urgency around water and utility management that the industry will have to grapple with for the foreseeable future.
It’s no secret that food processing and sanitation require large amounts of energy and water to reduce food safety risks; however, many processing facilities may lose precious resources through inefficiencies in processes and equipment, whether that means incremental loss through steam leakage or unnoticed water usage.
The cost to processors in both scenarios can be high and leaving water and utility management as an afterthought is no longer a viable option; however, runaway resource consumption not only impacts a company’s wallet, but it can also alter the efficiency and adequacy of the sanitization process and have sustainability implications. Food processors who want to cut costs and conserve resources while ensuring food safety need to prioritize total resource management.
Total Resource Management: Taking Advantage of Data
Many industries have capitalized on the wealth of insights that data analysis can provide; however, data analytics is a relatively untapped resource in the food processing industry, particularly in sanitation and resource management. With new software and data experts emerging in the industry, facilities can gain a bigger picture of their water and utilities use and monitor critical data points affecting efficiency, sustainability, compliance, and costs.
It is common for some processors to track basic resource use, such as flow and water and air pressure. But understanding exactly how those resources are consumed empowers food processors to get ahead of potentially significant losses before they even happen. Continued technological innovations allow for tracking detailed utility usage throughout an operation, allowing companies to expose leakages or areas of egregious use that may have gone unnoticed. With accurate insights, plant managers can address loss prevention and forecast their needs more accurately, ultimately conserving resources and optimizing their sanitization process.
Going Beyond Surface-Level Tracking
Improving sanitation and efficiencies in food processing has been limited to periodic audits and automated equipment. Until recently, supervisory control and data acquisition (SCADA) included high costs, so planning and design focused on the production metrics, such as volume and temperature, that would gain the most profitable use of investment.
For example, plant managers could measure the flow of chemicals used in open plant cleaning procedures daily, as seen in Figure 1. This information, however, doesn’t provide insights into where, why, or when usage varied so heavily between the two dates. It shows plant managers that something happened to cause the fluctuation, but without more detailed information, the facility is limited to what it can do to prevent future loss.
Beyond preventing potentially egregious losses such as those in the above scenario, dynamic data is also essential for monitoring fluctuations in resource use, as inconsistencies can lead to incremental losses—of both resources and money. “Small” changes are currently buried in plants using legacy sensors, monitors, and data, meaning that they have a massive opportunity for realizing savings and sustainability goals.
Why More Dynamic Data Matters for Sanitation
While awareness of these factors is important for cost savings and sustainability initiatives, these insights are also invaluable when it comes to chemistries and sanitation. Both overuse and underuse of chemicals can pose problems in the process. Chemical overuse means not only lost chemicals, but also potentially damaged equipment over time. Underuse can be even costlier, posing food safety and product recall risks.