This paper proposes a new paradigm for tactical demand chain planning (DCP), called robust planning, based on risk assessment of the supply and demand chain. The concepts of supply chain management (SCM), and its extension demand chain management (DCM), have been at the center of much recent research. One of the reasons for this is that, over the last years, a significant number of information systems have emerged, which claim to support the concept. The paper argues that these systems mostly adopt a myopic view of planning, based on pure deterministic planning methods. It demonstrates that such an approach fails to coop with the considerable uncertainty of the planning information. The proposed robust planning paradigm is then introduced and its impact explained, using the well-known example of the beer game. It holds the promise of reducing the number of re-planning cycles, through a better characterization of the expected service level performance over a medium planning horizon. Finally, a case study will show the value of robust planning in a European chemical enterprise.

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In this paper we present a methodology and simulation environment for solving multi-echelon supply chain planning and optimization problems for industries with batch and semi-batch processes. The introduced methodology is aimed to analyze efficiency of a specific planning policy over the product life cycle within the entire supply chain for automated switching from a non-cyclic to cyclic and to optimize the cyclic planning policy for products at the maturity phase. For optimization of a multi-echelon cyclic schedule, the simulation optimization algorithm developed is based on integration of the multi-objective genetic algorithm (GA) and response surface-based local search to improve GA solutions. The comparative analysis of planning policies is based on estimation of the difference between mean values of their total costs by using the Paired-t confidence interval method and evaluation of an additional cost of the cyclic schedule. The simulation environment allows one to describe input data to build the supply chain network and store it in an external file, computing effective planning policies, automatically generating and running a network simulation model, generating production rules for switching from one planning policy to another and optimizing parameters of a multi-echelon cyclic schedule. Finally, a business case is described that illustrates the practical application of the presented methodology.

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Abstract:In practice, inventory managers are often confronted with a need to consider one or more aggregate constraints. These aggregate constraints result from available workspace, workforce, maximum investment or target service level. We consider independent multi-item inventory problems with aggregate constraints and one of the following characteristics: deterministic leadtime demand, newsvendor, basestock policy, rQ policy and sS policy. We analyze some recent relevant references and investigate the considered versions of the problem, the proposed model formulations and the algorithmic approaches. Finally we highlight the limitations from a practical viewpoint for these models and point out some possible direction for future improvements.