A decision support framework is proposed for assisting managers and executives to possibly utilise lean production control strategies to coordinate work authorisations and inventory management in supply chains. The framework allows decision makers to evaluate and compare the suitability of various strategies to their system especially when considering conflicting objectives, such as maximising customer service levels while minimising Work in Process (WIP) in a business environment distressed by variabilities and uncertainties in demand stemmed from customer power. Also, the framework provides decision guidance in selecting and testing optimal solutions of selected policies control parameters. The framework is demonstrated by application to a four-node serial supply-chain operating under three different pull-based supply chain strategies; namely CONWIP, Kanban, and Hybrid Kanban-CONWIP and exhibiting low, medium, and high variability in customer demand (i.e., coefficient of variation of 25%, 112.5%, and 200%). The framework consists of three phases; namely Modelling, Optimisation and Decision Support; and is applicable to both Simulation-Based and Metamodel-Based Optimisation. The Modelling phase includes conceptual modelling, discrete event simulation modelling and metamodels development. The Optimisation phase requires the application of multi-criteria optimisation methods to generate WIP-Service Level trade-off curves. The Curvature and Risk Analysis of the trade-off curves are utilised in the Decision Support phase to provide guidance to the decision maker in selecting and testing the best settings for the control parameters of the system. The inflection point of the curvature function indicates the point at which further increases in Service Level are only achievable by incurring an unacceptably higher cost in terms of average WIP. Risk analysis quantifies the risk associated with designing a supply chain system under specific environmental parameters. This research contributes an efficient framework that is applicable to solve real supply chain problems and better understanding of the potential impacts and expected effectiveness of different pull control mechanisms, and offers valuable insights on future research opportunities in this field to production and supply chain managers.