scholarly journals Understanding Process Mining for Data-Driven Optimization of Order Processing

2020 ◽  
Vol 45 ◽  
pp. 417-422
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Sven Cremer ◽  
Marco Schopen
Keyword(s):  
Process Mining ◽  
Data Driven ◽  
Order Processing

Process Mining in der additiven Auftragsabwicklung/Process Mining for additive manufacturing

2020 ◽  
Vol 110 (06) ◽  
pp. 429-434
Author(s):  
Philipp Scherwitz ◽  
Steffen Ziegler ◽  
Johannes Schilp
Keyword(s):  
Additive Manufacturing ◽  
Process Mining ◽  
Batch Size ◽  
Order Processing ◽  
Eine Hohe ◽  
Automated Process ◽  
High Degree

Die Fähigkeit der additiven Fertigung in Losgröße 1 zu fertigen, erzeugt eine hohe Komplexität in der Auftragsabwicklung. Dies stellt die datenbasierte Optimierung der Prozessabläufe vor große Herausforderungen. Durch die geringen Stückzahlen, bei einer hohen Variantenanzahl, ist die Prozessaufnahme in der additiven Fertigung mit signifikanten Aufwänden verbunden. Abhilfe kann hier eine automatisierte Prozessaufnahme schaffen. Deshalb soll in diesem Beitrag die Technologie des Process Mining untersucht und darauf aufbauend eine Vorgehensweise für die datenbasierte Optimierung in der additiven Fertigung vorgestellt werden.   The capability of additive manufacturing to produce in batch size 1 creates a high degree of complexity in order processing. This creates great challenges for the data-based optimization of process flows. Due to the low number of pieces, with a high number of variants, the process recording in additive manufacturing is connected with significant expenditures. This can be overcome by automated process recording. Therefore, this article will examine the technology of process mining and, based on this, present a procedure for data-based optimization in additive manufacturing.

A Methodology to Apply Process Mining in End-To-End Order Processing of Manufacturing Companies

2021 ◽  
pp. 127-137
Author(s):  
G. Schuh ◽  
A. Gützlaff ◽  
S. Schmitz ◽  
C. Kuhn ◽  
N. Klapper
Keyword(s):  
Process Mining ◽  
Manufacturing Companies ◽  
Order Processing ◽  
End To End

Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 1307-1323
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 30-46 ◽  
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

scholarly journals Data-Driven Approaches to Improve the Quality of Clinical Processes: A Systematic Review

2020 ◽  
Author(s):  
Parivash Khalili ◽  
Mohammad Reza Rasouli ◽  
Mohammad Fathian
Keyword(s):  
Process Mining ◽  
Research Question ◽  
Data Driven ◽  
Exclusion Criteria ◽  
Scientific Publications ◽  
Novel Approach ◽  
Event Based ◽  
Learning Data ◽  
Final Synthesis

Background: Considering the emergence of electronic health records and their related technologies, an increasing attention is paid to data driven approaches like machine learning, data mining, and process mining. The aim of this paper was to identify and classify these approaches to enhance the quality of clinical processes. Methods: In order to determine the knowledge related to the research question, a systematic literature review was conducted. To this end, the related studies were searched in the web of science documentation database, as a comprehensive and authoritative database covering 1536 scientific publications from 2000 to 2019. The studies found from the initial search were investigated and the relevance of their title with the inclusion and exclusion criteria was determined. As a result, 184 articles were selected. Further investigations resulted in 84 studies that remained after reviewing the abstracts and full texts of these articles. These studies were also evaluated with regard to their field of study and the quality of presented evidence. Consequently, the final synthesis was performed on the evidence extracted from these articles. Results: Examination of the identified evidences resulted in 4 general categories of "event-based approaches", "process intelligence", "clinical knowledge systems", and "data-driven control and monitoring" as data-driven approaches that can be used to manage the quality of clinical processes. Conclusion: The findings demonstrated that event-bases approaches had more applications as data driven approaches in the context of health care. Furthermore, process mining is a novel approach that can be used by future studies. The results of this study can be used to complement clinical governance procedures regarding emerging data driven opportunities.

scholarly journals Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

2016 ◽  
pp. 44-66
Author(s):  
Suriadi Suriadi ◽  
Teo Susnjak ◽  
Agate M. Ponder-Sutton ◽  
Paul A. Watters ◽  
Christoph Schumacher
Keyword(s):  
New Zealand ◽  
Process Mining ◽  
Data Driven ◽  
Problem Gamblers ◽  
Using Data

scholarly journals Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

2022 ◽  
Author(s):  
Vedat Bayram ◽  
Gohram Baloch ◽  
Fatma Gzara ◽  
Samir Elhedhli
Keyword(s):  
Processing Time ◽  
Prediction Models ◽  
Historical Data ◽  
Real Data ◽  
Data Driven ◽  
Optimal Order ◽  
Warehouse Management ◽  
Order Batching ◽  
Order Processing ◽  
Data Driven Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

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