Clustering customer orders in a smart factory using sequential pattern mining

Abstract

In a smart factory, setting a production plan, relocating production equipment, and producing small batches of various products in real-time at a low cost is essential. This study discusses clustering customer orders with the same or similar process routes using a sequential pattern-mining technique, simplifying the overall production process and reducing the relocation and restructuring of equipment and machines in smart factories. We present a similarity measure to evaluate the similarity between two process routes and mathematically formulate integer programming to solve the problem of clustering similar routes. Considering process routes with alternatives, we use sequential pattern-mining techniques to cluster customer orders and determine alternative routes related to customer orders.We propose two sequential pattern-mining algorithms to expedite customer orders in smart factories. Algorithm 1 cluster customer orders and finds frequent process route patterns based on the similarity of the process routes. Algorithm 2 determines frequent sequential patterns based on the frequency of customer orders. We compared the results of 0-1 integer programming and Algorithm 1 and evaluated the algorithms' running time and memory space. This study demonstrates how data-mining techniques can be integrated into manufacturing systems to simplify process routes and reduce the complexity of the manufacturing process in the customer order phase.