Abstract: Because the procurement management of steel enterprises lacks reasonable planning in cyclical procurement amounts， the production demand forecasts for supply enterprises are inaccurate. To address these issues， a model based on improved cuckoo search algorithm to optimize Elman neural network （BASCS-Elman） is proposed. Taking material iron ore of Desheng Company of Baosteel as the research object， this model is used to predict demand to achieve accurate prediction， reduce resource waste， and improve enterprise profits. In this paper， the initial CS population is optimized by Logistic chaotic mapping to maintain the diversity of the population and improve the uniformity of the algorithm’s search. The traversal global search capability is increased by updating cuckoo locations through adaptive Levy flight. The multi-stage dynamic disturbance strategy helps global optimization. The local optimization speed is accelerated by the cow whiskers beetle antennae search algorithm. Finally the simulation experiment results show that， the average absolute error of the proposed model is 1.5042， the average absolute percentage error is 0.33423%， and the fastest stable time is 1.18 s， which is better than other prediction models.

Key words:  , demand forecast, improved cuckoo search algorithm, cow whiskers algorithm, Logistic chaos mapping