In the technology enabled globalizing world with shrinking margins, IT offshore outsourcing is a well established practice in global software development. This is in line with the strategy of focusing only on core businesses of the enterprises. Despite this increasingly popular trend, the initial expectations of cost reduction of offshore outsourcing are not realized. Typically many hidden costs, risks of transition, learning needs, communications overheads, setup-times, ramping up durations, scope creeps, government regulations, etc, are not taken into account in the initial estimation of the relationships. This leads to ineffective value realization of offshore outsourcing which can be avoided by precision in cost estimates that are sustainable in execution cycles. It is thus imperative to develop a structured, objective and consistent approach to determine cost and productivity of application offshore outsourcing engagements. Wipro Offshore Outsourcing Methodology (WQOM), described in this paper, specifies such an approach, taking a holistic view of the end to end process. The driver for the methodology has been the focus to build quality in the complete process, starting from, pre-sales till the life cycle of the relationship. This methodology has been developed by incorporating the experience, judgment, intuition and expertise of multiple experts who managed and were part of many successful long-term offshore-outsourcing engagements. It provides guidelines to practitioners and decision makers to estimate the cost of IT application offshore outsourcing which include application assessment and to plan transition and steady state productivity achievement in a predictable and systematic manner. The core of the methodology is Wipro Application Assessment Model (WAAM), which leads to a consistent, robust and experience based estimate of time required to transition the application to offshore and the offshore onsite resource mix to maintain the application during its life. It facilitates the practitioners to recognize risks and optimal bidding