Workload Prediction of Business Processes -- An Approach Based on Process Mining and Recurrent Neural Networks

• URL: http://arxiv.org/abs/2002.11675v1
• Date: Fri, 14 Feb 2020 08:19:23 GMT
• Title: Workload Prediction of Business Processes -- An Approach Based on Process Mining and Recurrent Neural Networks
• Authors: Fabrizio Albertetti, Hatem Ghorbel
• Abstract summary: We propose a process mining approach that reconstructs the historical workload of a company and predicts the workload using neural networks. Our method relies on logs, representing the history of business processes related to manufacturing. An evaluation and illustration of the method is performed on the administrative processes of Heraeus Materials SA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in the interconnectedness and digitization of industrial machines, known as Industry 4.0, pave the way for new analytical techniques. Indeed, the availability and the richness of production-related data enables new data-driven methods. In this paper, we propose a process mining approach augmented with artificial intelligence that (1) reconstructs the historical workload of a company and (2) predicts the workload using neural networks. Our method relies on logs, representing the history of business processes related to manufacturing. These logs are used to quantify the supply and demand and are fed into a recurrent neural network model to predict customer orders. The corresponding activities to fulfill these orders are then sampled from history with a replay mechanism, based on criteria such as trace frequency and activities similarity. An evaluation and illustration of the method is performed on the administrative processes of Heraeus Materials SA. The workload prediction on a one-year test set achieves an MAPE score of 19% for a one-week forecast. The case study suggests a reasonable accuracy and confirms that a good understanding of the historical workload combined to articulated predictions are of great help for supporting management decisions and can decrease costs with better resources planning on a medium-term level.

Related papers

• A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management [0.0]
  Business Process Management and Operations Research aim to enhance value creation in organizations. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. Results indicate a strong focus on resource allocation and scheduling problems.
  arXiv  Detail & Related papers  (2024-09-02T14:13:14Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE [68.6018458996143]
  We propose a more general dynamic network that can combine both quantization and early exit dynamic network: QuEE. Our algorithm can be seen as a form of soft early exiting or input-dependent compression. The crucial factor of our approach is accurate prediction of the potential accuracy improvement achievable through further computation.
  arXiv  Detail & Related papers  (2024-06-20T15:25:13Z)
• Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins [53.70191138561039]
  We propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. We adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines.
  arXiv  Detail & Related papers  (2024-05-20T09:28:23Z)
• Continual Learning in Predictive Autoscaling [17.438074717702726]
  Predictive Autoscaling is used to forecast the workloads of servers and prepare resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. We propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM) Our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy.
  arXiv  Detail & Related papers  (2023-07-29T09:29:09Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories [0.0]
  The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field.
  arXiv  Detail & Related papers  (2022-12-12T23:45:59Z)
• Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization [0.0]
  We introduce a novel mathematically sound method that integrates theoretical process models with interrelated minimal Hidden Markov Models. Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection.
  arXiv  Detail & Related papers  (2022-10-03T16:19:27Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• ProcK: Machine Learning for Knowledge-Intensive Processes [30.371382331613532]
  ProcK (Process & Knowledge) is a novel pipeline to build business process prediction models. Components to extract inter-linked event logs and knowledge bases from relational databases are part of the pipeline. We demonstrate the power of ProcK by training it for prediction tasks on the OULAD e-learning dataset.
  arXiv  Detail & Related papers  (2021-09-10T13:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.