Due to consumer pressure and changing preferences, the food industry has morphed from one that operated in a relatively stable setting to one that operates in a very fast-paced manner where flexibility and cost are key. Consumers prefer to have many different choices (products) to satisfy their individual needs. This high variation in product mix, though desired by the consumers, can create quality and safety challenges—both in microbial contamination and cross contact of allergens. Additionally, it requires the production of a large number of stock keeping units and increases complexity processing/manufacturing and their supply chains. All of these factors create a dynamic environment to which engineers of modern food safety systems must adapt.
Current food safety systems comprise two parts: A prescriptive part (written plan) in which a set of documents is generated and followed, and a descriptive part in which the data generated is used to determine the existence of a deviation. The system outcomes can be treated as binary where food is categorized as either safe or unsafe. The risk-based approach that the Food and Drug Administration (FDA) requires for FSMA will, in time, require that companies supply more data and validate food safety systems as a way to deal with corrections. Fortunately, the use of electronic controls and data logging systems generate a data-rich environment that can be used to create a resilient and adaptive food safety system.
The food safety plan must be adaptive and resilient, which means that engineers of the food safety system must self-organize and correct when any deviation is detected. When this occurs, a correction or corrective action must be applied, and the system may need to be modified. This not only necessitates proper equipment and facility design, but it also requires the implementation of appropriate engineering measures, such as control and feedback loops within the equipment and the main control that generate data and adapt to new circumstances. While this is similar to the current way processes have been adjusted when deviation set points are detected, the food safety system utilizes data on a much larger scale and includes information on personnel, training, supply chain and external stimuli.
Engineering such an adaptive food safety system is a challenging task. Aside from good engineering and design knowledge, it also requires using sophisticated tools and techniques like the ones provided by process systems engineering (PSE), a branch of engineering that allows for systematic exploration of and improved decision making for the design and control of processes. These decisions can be made with the support of mathematical modeling, data mining techniques, process modeling and simulations.
Engineers developing food safety systems must adopt approaches that use the data-rich environment created by the electronic sensors and controls in the entire operation. This data must be analyzed to find discrete trends/patterns, identify root cause(s) for deviations, rank or prioritize risk factors, develop a plan to mitigate risk and recommend corrective/preventive measures. (It should be noted that corrective/preventive measures may require further engineering, equipment or design changes to be made.) The use of data analysis must be extended to suppliers and customers—not only for traceability, but also to identify conditions that induce risks before products are processed, shipped and consumed.
To improve food safety and truly implement FSMA, it is very important to have the right experts who can help make informed decisions using a data-driven, risk-based approach. It is important to satisfy FDA regulations, but more important to remember that you are what you eat!