A new approach for supply chain material requirement planning

At present, more and more researches are focused on material requirement planning (MRP), which is an important role in supply chain management. In this paper, a new approach for material requirement planning is proposed based on data field and cloud model, which can find out the important materials automatically in supply chain material requirement planning. The clustering method by data field is based on the distribution of equipotential line (surface) of the natural nested data structures in the field and the aggregation properties of self-organization among data objects to achieve the level division. The use of cloud model to describe the characteristics of the concept for clustering results on material requirement planning is not limited by the amount of data and dimension, and it can effectively reflect the clustering the uncertainty of knowledge, especially the randomness and fuzziness. An actual case study and its results analysis are given based on the proposed model. The results show that this approach is effective and feasible in the area of supply chain material requirement planning.