scholarly journals Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

2021 ◽  
Vol 11 (22) ◽  
pp. 10897
Author(s):  
Henna Tiensuu ◽  
Satu Tamminen ◽  
Esa Puukko ◽  
Juha Röning
Keyword(s):  
Stainless Steel ◽  
Decision Support ◽  
Prediction Models ◽  
Steel Strip ◽  
Quality Measurement ◽  
Smart Manufacturing ◽  
Evidence Based ◽  
Planning And Control ◽  
Manufacturing Process Planning ◽  
Definition Of

This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

scholarly journals Exploring the impact of a decision support algorithm to improve clinicians’ chemotherapy-induced peripheral neuropathy assessment and management practices: a two-phase, longitudinal study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Robert Knoerl ◽  
Emanuele Mazzola ◽  
Fangxin Hong ◽  
Elahe Salehi ◽  
Nadine McCleary ◽  
...  
Keyword(s):  
Decision Support ◽  
Peripheral Neuropathy ◽  
Management Practices ◽  
Management Strategies ◽  
Clinic Visit ◽  
Evidence Based ◽  
Two Phase ◽  
Assessment Index ◽  
Patient Reported ◽  
The Impact

Abstract Background Chemotherapy-induced peripheral neuropathy (CIPN) negatively affects physical function and chemotherapy dosing, yet, clinicians infrequently document CIPN assessment and/or adhere to evidence-based CIPN management in practice. The primary aims of this two-phase, pre-posttest study were to explore the impact of a CIPN clinician decision support algorithm on clinicians’ frequency of CIPN assessment documentation and adherence to evidence-based management. Methods One hundred sixty-two patients receiving neurotoxic chemotherapy (e.g., taxanes, platinums, or bortezomib) answered patient-reported outcome measures on CIPN severity and interference prior to three clinic visits at breast, gastrointestinal, or multiple myeloma outpatient clinics (n = 81 usual care phase [UCP], n = 81 algorithm phase [AP]). During the AP, study staff delivered a copy of the CIPN assessment and management algorithm to clinicians (N = 53) prior to each clinic visit. Changes in clinicians’ CIPN assessment documentation (i.e., index of numbness, tingling, and/or CIPN pain documentation) and adherence to evidence-based management at the third clinic visit were compared between the AP and UCP using Pearson’s chi-squared test. Results Clinicians’ frequency of adherence to evidence-based CIPN management was higher in the AP (29/52 [56%]) than the UCP (20/46 [43%]), but the change was not statistically significant (p = 0.31). There were no improvements in clinicians’ CIPN assessment frequency during the AP (assessment index = 0.5440) in comparison to during the UCP (assessment index = 0.6468). Conclusions Implementation of a clinician-decision support algorithm did not significantly improve clinicians’ CIPN assessment documentation or adherence to evidence-based management. Further research is needed to develop theory-based implementation interventions to bolster the frequency of CIPN assessment and use of evidence-based management strategies in practice. Trial registration ClinicalTrials.Gov, NCT03514680. Registered 21 April 2018.

Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription

2021 ◽  
Vol 10 (1) ◽  
pp. 121-125
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Gait Analysis ◽  
Support System ◽  
Patient Specific ◽  
Evidence Based ◽  
Extensive Literature ◽  
Analysis Tool ◽  
Care Providers

With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.

Ontological Knowledge Representation and Inference Rules for MTM Decision Support System

2014 ◽  
Author(s):  
Rahul Renu ◽  
Gregory Mocko
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Time Estimation ◽  
Time Measurement ◽  
Inference Rules ◽  
Assembly Time ◽  
Time Estimates ◽  
Definition Of

The objective of the research presented is to develop and implement an ontological knowledge representation for Methods-Time Measurement assembly time estimation process. The knowledge representation is used to drive a decision support system that provides the user with intelligent MTM table suggestions based on assembly work instructions. Inference rules are used to map work instructions to MTM tables. An explicit definition of the assembly time estimation domain is required. The contribution of this research, in addition to the decision support system, is an extensible knowledge representation that models work instructions, MTM tables and mapping rules between the two which will enable the establishment of assembly time estimates. Further, the ontology provides an extensible knowledge representation framework for linking time studies and assembly processes.

A Decision Support System for Containership Stowage Planning

1989 ◽  
Vol 26 (01) ◽  
pp. 47-61
Author(s):  
D. J. Saginaw ◽  
A. N. Perakis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Interactive Graphics ◽  
Maritime Industry ◽  
Automate Data ◽  
Plan Generation ◽  
Stowage Planning ◽  
Definition Of ◽  
Stowage Plan

The results of a project intending to design and develop a microcomputer-based, interactive graphics decision support system for containership stowage planning are presented. The objective was to create a working prototype that would automate data management tasks and provide computational capabilities to allow the stowage planner to continuously assess vessel trim, stability, and strength characteristics. The paper provides a complete description of the decision support system developed to meet this objective, including a definition of the containership stowage problem, and details on the design and development of the Automated Stowage Plan Generation Routine (ASPGR). The paper concludes with a discussion of issues relevant to the implementation of the system in the maritime industry.

Die synchrone Produktion*/Synchronous manufacturing - An approach for semi-autonomous production planning and control and human-based decision support

2015 ◽  
Vol 105 (04) ◽  
pp. 204-208
Author(s):  
D. Kreimeier ◽  
E. Müller ◽  
F. Morlock ◽  
D. Jentsch ◽  
H. Unger ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Production Planning ◽  
Data Exchange ◽  
Production Control ◽  
Production Planning And Control ◽  
Planning And Control ◽  
Manufacturing Execution Systems ◽  
And Control ◽  
Manufacturing Execution

Kurzfristige sowie ungeplante Änderungen – wie Auftragsschwankungen, Maschinenausfälle oder Krankheitstage der Mitarbeiter – beeinflussen die Produktionsplanung und -steuerung (PPS) von Industriefirmen. Trends wie Globalisierung und erhöhter Marktdruck verstärken diese Probleme. Zur Komplexitätsbewältigung bei der Entscheidungsfindung zur Fertigungssteuerung kommen in der Produktion Werkzeuge der „Digitalen Fabrik“, beispielsweise Simulationsprogramme, oder IT (Informationstechnologie)-Lösungen, wie Manufacturing Execution Systems (MES), zum Einsatz. Eine Verknüpfung dieser Bereiche würde einen echtzeitfähigen Datenaustausch erlauben, der wiederum eine echtzeitfähige Entscheidungsunterstützung bietet. Der Fachbeitrag stellt hierfür einen Lösungsansatz vor.   Sudden and unsystematic changes, such as fluctuations in order flow, machine failures, or employee sick days affect the Production Planning and Control (PPC) activities of industrial companies. Trends like globalization and increased market pressure intensify these problems. To master the complexity of decision-making in production control, tools of the digital factory (e.g. simulation systems) or IT systems (e.g. Manufacturing Execution Systems (MES)) are applied in manufacturing. Combining these areas would enable real-time capable data exchange which, in turn, provides real-time capable decision support. This article presents an approach for solving this problem.

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