Computational offloading happens to be a prominent solution for leveraging the performance of handheld devices. It raises the feasibility of executing computation-intensive and latency-conscious tasks with the help of task migration to proximate cloud servers. However, longer Wide Area Network latencies of cloud and greater mobile data consumption paved the way to adopting opportunistic offloading instead of remote offloading. This proposed work uses an edge-based solution of Fog computing to handle such tasks and toprovide the users with a high Quality of Experience. This paper presents a Capability-Aware Supply Chain Management Model (CASCM²) as an extension of traditional Supply Chain Management (SCM) model. CASCM² dynamically selects a crowd of competent mobile devices within a Foglet that is in proximity to theusers and offloads the complex computational tasks to them. The proposed model aims at optimizing two parameters, such as communication overhead and conductance cost, as they possess a remarkable impact on offloading delay-sensitive tasks. Hence an overall objective optimization is achieved using a dual Lagrangian decomposition method, which subdivides and solves the optimization of parameters in parallel. Experimental analysis of the participator selection is performed for a single period as well as multiple periods. The performanceresults yield a considerable contribution that alleviates the issues in delay-sensitive applications deployed in the Fog framework.