This paper is aimed at employing analytical target cascading (ATC) to solve the distributed production planning problem. In the ATC hierarchy, by setting a parent element to the core enterprise and a child element to each manufacturer, the core enterprise can negotiate the plans with the manufactures. Individual manufactures are able to make production plans autonomously, and finally achieve the global objective under the coordination of the core enterprise. ATC offers an independent decision making ability to each member, a decentralize coordination strategy to the core enterprise and a parallel computing method to the planning model. In order to obtain the concrete production quantities and balance the costs and the service levels, a multi-objective integer linear programming has also been introduced in the model, where different costs have different weights. By setting different weights to different optimization objectives, the core enterprise can obtain the lowest overall cost or the best service quantity. Meanwhile, from a strategic point of view, two coordination patterns, competitive and cooperative, through modifying the iterative mode of ATC, are addressed here. The competitive pattern make the core enterprise achieve a lower cost, while the cooperative pattern make the core enterprise maintain a better relationship with the manufactures. A series of comparative analyses are conducted to identify the strengths of the ATC method and demonstrate the effectiveness of the proposed coordination patterns. In particular, one of these experiments, which considers the quantity discount, has been done to further verify the effectiveness of the ATC based coordination mechanism while encountering the newly added decision-making factors.