The need for predictive analytics in food access is evident in that 43% of food assistance needs remain unmet among households earning less than $50,000 annually (UCSC, 2019). The number of individuals experiencing food insecurity is expected to rise by a predicted 17.1 million people by the end of this year (Feeding America, 2020). Individuals who experience food insecurity are more likely to have poor health in general and diet-related conditions like diabetes (Seligman, 2010).
Not enough information is known about what inventory exists at different food access facilities. Predictive Analytics empowers these organizations with the most detailed overview of their community clientele and ensure nobody is left unheard or unfed. Predictive Analytics is an advanced branch of data science and engineering. It is defined primarily by its focus on calculating likely outcomes through running tests on massive databases. These statistical and analytics techniques can be leveraged to optimize supply chains and empower Food-Access resources.