Automated planning is a rich technical filed in Artificial Intelligence (AI) and most of the existing research focused on path finding methods in a compact state-transition system where planning is decoupled from execution. The introduction of the Web has led to increasing emphasis in AI on the development of planning algorithms for real-world applications where planning is distributed and plan generation can happen concurrently with plan execution. An example of one such real-world application is logistics and supply chain. In this paper, we envisage a collaborative planning and execution framework for logistics and supply chain operations. The framework supports human planners for a collaborative plan construction. The planning is interleaved with execution where new information collected during execution is used to refine the plan if required. Additionally, planning is defeasible in nature. During planning either conflicting viewpoints may arise among planners and/or the new information collected during execution may result in conflicts among planned tasks (situations). Deliberation module in the proposed framework provides a platform to human planners where they can start an argumentative dialogue to resolve the conflicts by establishing preferences between conflicting tasks. We use situation calculus to model the framework and propose an algorithm to interleave collaborative planning with execution along with deliberation support.