# MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
## 1 Introduction  
As digital transformation accelerates across the manufacturing industry, there is growing interest in predicting and mitigating process bottlenecks to maintain high throughput and yield. Because production time and yield directly impact profitability, any single task that becomes a bottleneck can severely degrade overall factory performance. Manufacturing processes form complex networks of tasks that exchange intermediate products. Each task receives inputs from one or more preceding tasks, performs its operation, and forwards its outputs to its follower. Although many processes are automated, cycle times can still vary for various reasons, particularly in operations like garment assembly, which continue to rely on manual labor. Accurately forecasting which task will become a bottleneck ahead is vital for maintaining throughput of manufacturing processes. For example, managers can take proactive measures, such as allocating additional workers to the precarious tasks, to avoid potential bottleneck by anticipating them. In practice, most production facilities lack high-performance servers and instead deploy low-cost, resource-constrained devices: for instance, embedded devices. Complexalgorithms that require substantial computing power are therefore impractical, while prediction latency is crucial to prevent production delays from bottlenecks. Thus, bottleneck prediction should run efficiently on embedded devices. To address these challenges, we propose MicroForest, a method to compress multiple random forests into a compact forest-based model. Researches on compressing random forests has been carried out with various approaches, including boosting-based methods [1, 2] and knowledge distillation [3]. However, we observed that such methods do not outperform others in all aspects for the given manufacturing process scenarios, particularly when simultaneous multi-task prediction is required. In this paper, we focus on bottleneck prediction performance, model size, and inference latency so as to exploit the bottleneck prediction in manufacturing processes even with resource-constrained edge devices. We first model the manufacturing process as a directed graph with nodes that represent tasks and edges that represent flows of products. Random forests are trained to predict which task will bottleneck after a certain number of cycles. Then, high-information-gain nodes from those random forests are selected in order to build a compact and ready-to-deploy decision tree. This paper proposes MicroForest for collecting those decision trees. To demonstrate the efficacy of the proposed method, we compared MicroForest with recent works and achieved up to 7.2\(\times\) faster inference latency compared to the original random forest even though it has the highest prediction performance against them. This paper makes the following key contributions:  
* We propose MicroTree, an effective random forest compression technique for bottleneck prediction in manufacturing process. MicroTree is built upon a set of high-information-gain nodes in the random forest, by which we generate a ready-to-deploy compact model without fine-tuning. * MicroForest architecture is proposed, which employs multiple MicroTrees to predict multiple bottlenecks across many manufacturing tasks simultaneously. * An extensive evaluation against four different models was conducted to test efficacy of the proposed method, and MicroForest achieves by up to 7.2\(\times\) latency reduction as well as the highest recall performance. * We evaluated the generality of the proposed method by applying it to several other classification tasks and experimentally confirmed its exceptional effectiveness for the specific problem of bottleneck prediction in manufacturing processes. The remainder of this paper is organized as follows. Section 2 reviews related studies. Then, we present our motivation and necessary background in Section 3. Section 4 details the proposed method called MicroForest. We evaluate the performance of MicroForest in Section 5.