This paper is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a timecontinuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision-makers.

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Purpose -The purpose of this paper is to focus on the medium-term planning problem in supply chain networks. Based on a literature review, a comprehensive analytical planning model for the three echelon tactical planning problem in supply chain networks is developed which is applicable in a hierarchical planning frame. Design/methodology/approach -The approach used was mathematical mixed integer programming to model three echelon production-distribution networks embedded in the supply chain planning matrix frame. Application of the model in a multi-site planning process based on a case study from the industrial transformer supply chain was also undertaken. Findings -Integrated multi-period medium-term planning with customer oriented single sourcing is an efficient method to implement mathematical optimal solutions in practice as it provides comprehensive tactical plans and network designs. These can be used for scenario analysis in a coordination process with independent supply chain partners.Research limitations/implications -The implementation of a mathematical optimal plan in a complex business network structure requires a big-bucket model solution to grant the plan's stability via sufficient time buffers. Originality/value -The paper displays development of a multi-period three echelon tactical production-distribution-transportation model with different capacities, transportation modes, product types and single sourcing decisions.

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