In a linear programming model, infeasibility means that there isn't a solution that concurrently meets all of the specified requirements. Unrealistic parameter values, contradictory requirements, or unduly restrictive limits can all lead to this predicament. The ramifications are profound: decision-makers need to reconsider the model, possibly loosening restrictions, changing goals, or reevaluating data sources. Because it may reveal inconsistencies between operational objectives and resource constraints, infeasibility signals the need for a more thorough examination of the problem context, which will ultimately direct strategy changes to produce better workable solutions.