# Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization
## 2 Bayesian Network Model Construction
### Model Prediction Process
The algorithm introduces the particle update strategy of the adaptive decision mechanism and adopts nonlinear inertia weight. The effectiveness of the model and algorithm is proved by comparative experiments. Accurate prediction of terminal customer demand is the basis for formulating production planning, inventory strategy, facility planning, and distribution strategy in the supply chain. The demand-forecasting model proposed in this paper provides a new idea for demand forecasting in the supply chain. In particular, with the in-depth development of the industrial internet, improving the accuracy of customer demand forecasting in the supply chain is of great practical significance for enterprises to improve the efficiency of production resource allocation. This study does not consider the specific factors affecting demand and the correlation between different products. The factors affecting product demand and the correlation between products need to be quantified according to the actual situation. The correction factors in the multilayer Bayesian network model can be replaced by quantitative influencing factors. In this case, the model is more complex. The model proposed in this paper has strong applicability when it is difficult to quantify the factors affecting demand or impossible to quantify the influencing factors. In addition, ways to further expand the proposed model and then study how to make the optimal production plan, inventory, and distribution plan will be the focus of future attention. Conceptualization, K.L. ; Methodology, S.H. ; Data curation, S.H. ; Writing--original draft, S.H. ; Project administration, K.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China grant number 71602143 and 61403277. Not applicable. The authors declare no conflict of interest. ## References

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