## 4 Experiments and Results  
The data in this paper were from an enterprise that produces optoelectronic connectors and whose products are used in equipment in petroleum, electric power, aerospace, and other fields. This enterprise is a typical multi-variety, low-volume enterprise, and it has a high degree of information. We obtained order data covering half a year (about 15,000 customers) from this enterprise. After a downstream customer's order is received by the enterprise, the required products are decomposed into parts by the system. Standard parts should be purchased, and core parts should be developed. After these parts are fabricated in the production workshop, assembled by the assembly workshop, and tested by the quality inspection department, the orders are delivered to the downstream customer. The business process of the enterprise is shown in Figure 4, including marketing, purchase, research, production, assembly, and testing. The deferred delivery risk factors of each business process are listed in Table 2. A portion of the identification results of the association rules between the factors are listed in Table 3.  
\({}^{\dagger}\) The label of this item is indicated in parentheses, and this is the same below. \begin{table}
\begin{tabular}{c c} \hline \hline
**Business Process** & **Risk Factor** \\ \hline Market (\(A_{1}\))1  & Customer demand time in advance (\(B_{11}\)) \\  & Order quantity increasement (\(B_{12}\)) \\  & Market demand forecast inaccurate (\(B_{12}\)) \\ Purchase (\(A_{2}\)) & Supplier’s product unqualified (\(B_{21}\)) \\  & Delay of supplier delivery time (\(B_{22}\)) \\  & Material price change (\(B_{23}\)) \\ Research (\(A_{3}\)) & Changes in structure and process design (\(B_{31}\)) \\  & Increase of the difficulty of research task (\(B_{32}\)) \\  & Mobility of research personnel (\(B_{33}\)) \\ Production (\(A_{4}\)) & Order adjustment of production tasks (\(B_{41}\)) \\  & Increase in production task (\(B_{42}\)) \\  & Production equipment failure (\(B_{43}\)) \\ Assembly (\(A_{5}\)) & Delayed delivery of parts (\(B_{51}\)) \\  & Increasing assembly task (\(B_{52}\)) \\ Testing (\(A_{6}\)) & Lack of equipment precision (\(B_{61}\)) \\  & Fault detection equipment (\(B_{62}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Business processes and corresponding risk factors. Figure 4: Business process of the case enterprise. The delayed delivery risk Bayesian network was constructed, and then reasoning was carried out. The occurrences of \(B_{11}\), \(B_{13}\), \(B_{21}\), and \(B_{31}\) were established as evidence, and then forward reasoning was carried out. The results show that the probabilities of the occurrence of the deferred delivery risk, \(D\), were 85%, 50%, 87%, and 80%, respectively, which are shown in Figure 6a-d. The probability of delayed delivery was about 50%, which is due to the inaccurate forecast of the internal market demand in the enterprise and the relative increase in customer orders. The delayed delivery of parts due to various reasons of the suppliers can result in a high probability of delayed delivery. A sudden change in customer demand time can result in a high probability of delayed delivery. A high probability of delayed delivery can also happen when the order of production is affected due to the changes in the structure and process made by the research department. The occurrence of \(D\) was established as evidence, and then backward reasoning was carried out, which is shown in Figure 6e. The results show that the degree of an increasing probability of particular nodes, such as \(B_{11}\) and \(B_{41}\), was the highest.