Food Protection Trends - September 2009 - 574

FIGURE 2. Quality assurance model, adapted from Business Dynamics: Systems Thinking and Modeling for a Complex World The interactions among the contextual factors and pressures in this very large and complex food safety control structure can themselves be complex, and changes meant to fix one problem may be less effective than intended or may create unintended consequences. Computational and simulation models can be constructed to assist in understanding these interactions and to redesign the system to mitigate some of these contextual pressures. To accomplish this goal we use system dynamics (17). The field of system dynamics, created at MIT in the 1950s by computer pioneer Jay Forrester, is designed to help decision-makers learn about the structure and dynamics of complex systems, to design high leverage policies for sustained improvement, and to catalyze successful implementation and change. System dynamics provides a framework for dealing with dynamic complexity, where cause and effect are not obviously related. It is grounded in the theory of non-linear dynamics and feedback control, but also draws on cognitive and social psychology, organization theory, economics, and other social sciences. “All too often, well-intentioned efforts to solve pressing problems create unanticipated ‘side effects.’ Our decisions provoke reactions we did not foresee. Today’s solutions become tomorrow’s problems. The result is policy resistance, the tendency for interventions to be defeated by the response of the system to the intervention itself. From California’s failed electricity reforms, to road building programs that create suburban sprawl and actually increase traffic congestion, to pathogens that evolve resistance to antibiotics, our best efforts to solve problems often make them worse. At the root of this phenomenon lies the narrow, event-oriented, reductionist worldview most people live by. We have been trained to see the world as a series of events, to view our situation as the result of forces outside ourselves, forces largely unpredictable and uncontrollable… System dynamics helps us expand the boundaries of our mental models so that we become aware of and take responsibility for the feedbacks created by our decisions.” – John Sterman (17). In system dynamics models, behavior over time (the dynamics of the system) can be explained by the interaction of positive and negative feedback loops. Figure 2 shows a simple example of a causal loop diagram modeling the quality assurance process within a manufacturing firm. In Fig. 2 there are two main control loops, both of them balancing loops: Quality Control and Goal Erosion. An arrow denotes a variable that influences another variable. The “+” means the two variables connected by the arrow move in the same direction, while “–” denotes the values of the variables move in opposite directions. For example, as financial pressures increase, efforts devoted to quality assurance can degrade. Losses often occur when the controller’s process model becomes inconsistent with the true state of the process and inadequate control is therefore applied. For example, the FDA thinks that the food manufacturers themselves or state and local authorities are adequately monitoring operations and does not impose additional monitoring or inspection activities. Process models are kept updated and kept accurate through information provided by feedback or other communication channels. A potential cause of inadequate control (and system hazards) is missing or defective feedback channels. For example, the government usually does not have access to test results provided by plant managers, by private inspectors, or sometimes even by state inspectors. The FDA has a hard time keeping track of all the manufacturers and what they produce. Process models (and thus control actions based on these models) may be deficient simply because of inadequate scientific knowledge; for example, Salmonella has not commonly been considered a risk associated with peanut butter, and therefore inspectors may not test for it. Time lags can be an issue in process model accuracy. Test results, for example, may come back after products have already been distributed. Coordination among controllers Another common causal factor in accidents is inadequately coordinated controls exercised by multiple controllers. When a system or system component is controlled in multiple ways, it is easy to assume that the other controller is operating effectively (and thus not to feel it necessary to exercise one’s own controls) or for two controllers to conflict in the control actions they take, thus inadvertently leading to inadequate overall control and a loss event. As an example, in the recent peanut events, both the federal government regulatory agency (the FDA) and the Georgia food safety regulatory agency had responsibility for inspecting PCA. The FDA relied on the Georgia Department of Agriculture and therefore had not inspected the plant in over 8 years (7). However, the state did not have the budget to properly conduct those inspections because of rising needs and falling budgets: Georgia has only 60 agents to monitor over 16,000 plants (12), which means that each inspector has to take care of more than 260 plants. In general, no federal or state agency is mandated to take care of food safety exclusively. Different agencies have food safety responsibilities, usually on top of other competing responsibilities. Process models The process model is an important component of STAMP-based modeling. A basic theorem in control theory is that in order to provide effective control, a controller must have an accurate and complete model of the system it is controlling. The model is used to determine what control actions are necessary to provide to keep the system operating effectively (see Fig. 3). This process model includes assumptions about how the controlled process operates and the current state of the controlled process. 574 FOOD PROTECTION TRENDS | SEPTEMBER 2009

Food Protection Trends - September 2009

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