# A Modified Bayesian Network Model to Predict Reorder Level of Printed Circuit Board
## 1 Introduction
Previous studies reveal that recency, frequency, and monetary (RFM) analysis and frequent pattern mining can be successfully used or integrated to discover valuable patterns of customer purchase behavior [3; 4; 5; 6; 7; 8]. [PERSON] and [PERSON] [3] took the RFM analysis for profiling profitable hotel customers and related customers were divided into eight groups. [PERSON] et al. [4] incorporated the RFM concept to define the RFM sequential pattern and developed a modified Apriori for generating all RFM sequential patterns from customers' purchasing data. [PERSON] and [PERSON] [5] proposed RFM-pattern-tree to compress and store entire transactional database and developed a patterned growth-based algorithm to discover all the RFM-patterns in the RFM-pattern-tree. [PERSON] et al. [6] employed RFM analysis, logistic regression, and decision trees for the customers' segmentation and identification. [PERSON] et al. [7] employed k-means clustering for identifying target patient customers and then conducted the prediction of customers churn behavior via the RFM model based on the decision tree classifier. [PERSON] et al. [8] employed RFM considering parameters with time series to cluster customers and identify target customers. Other data mining approaches have also been developed and many special factors were considered to excavate the customer pattern purchase behavior. [PERSON] [9] developed a fuzzy text mining approach to categorize textual data to analyze consumer behaviors for the accurate classification of customers. [PERSON] et al. [10] presented a consumer segmentation method using clustering based on consumers' purchase of sustainability and health-related products. [PERSON] et al. [11] combined clustering with time series analysis to create customer segments and segment-level forecasts and then applied the forecasts to individual customers. [PERSON] et al. (2018) proposed a logit leaf model for customer churn prediction in which customer segments are identified using decision rules and then a model is created for every segment using logistic regression. [PERSON] et al. (2018) provided a comprehensive review of CRM from four dimensions such as customer identification, customer attraction, customer retention, and customer development. [PERSON] et al. (2018) presented a review of Big Data-enabled CRM including research on customer evaluation and acquisition. However, the topic discussed in this paper has seldom been studied to the best of our knowledge and it was not involved in the two previously mentioned reviews either. Customer identification and a reorder level prediction are similar in that both of them aim to develop classified treatment strategy according to historical transactions. Nevertheless, there are differences from the following three aspects. First, the customer identification problem is to develop more accurate sales and advertising strategies based on customer transaction history and, therefore, better for retaining target customers ([PERSON] et al., 2018) while the final purpose of the problem discussed in this paper is to select orders for batch production with different misclassification risks based on accumulated manufacturing orders. Second, RFM of different purchase products (orders) should be considered for the customer pattern mining while, in this research study, we only consider the parameters of the same product ordered at different times. Third, RFM are the main parameters considered in the customer identification problem while the production scale including quantity, area, and lifecycle of orders should also be considered in this paper. However, the previously mentioned approaches cannot be employed directly for reorder level prediction. Therefore, more influential variables and misclassification loss should be considered and related approaches should be developed. In this paper, a prediction model based on modified Bayesian network (BN) with Monte Carlo simulations is presented to predict a reorder level of PCB orders.