A study on task management system

S, Jyothi N; Parkavi, A.

doi:10.1109/rains.2016.7764421

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…Once both the dominant loss class and the measure index are selected, the second step of the framework aims to discover the pairs of major anomalies that frequently occur concurrently. Subsequently, an Eisenhower matrix is filled to classify the major anomalies and, most importantly, to prioritize them based on their urgency and importance (Jyothi and Parkavi, 2016). The urgent priority anomalies could affect even more than one OEE factor; hence, the relationships between them and the OEE loss factors should be investigated to identify the best proactive action.…”

Section: Data Analyticsmentioning

confidence: 99%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

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PurposeThe Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.Design/methodology/approachAssociation Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.FindingsThe method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.Originality/valueThe originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

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Section: Data Analyticsmentioning

confidence: 99%

A rule-based machine learning methodology for the proactive improvement of OEE: a real case study

Lucantoni,

Antomarioni,

Ciarapica

et al. 2023

IJQRM

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show abstract

“…[11,18,23] In order to include further information to an element, additional components such as attachments, tags and comments can be added ( Table 2). [25,27,43] Both core elements and components have attributes that further specify the respective object (Table 3). While authors suggest a variety of potential attributes, the most common ones include a title, a description, and a deadline.…”

Section: Task Management Catalogmentioning

confidence: 99%

“…In this research, we investigate the aspect of coordination on the academic collaboration platform Uni-Connect by focusing on the use of Task Management (TM) features. As every project involves tasks, people, and deadlines, the success of a project heavily depends on TM [25]. Examples for this include appropriately prioritizing tasks, as well as time management [6,25,26].…”

Section: Introductionmentioning

confidence: 99%

“…As every project involves tasks, people, and deadlines, the success of a project heavily depends on TM [25]. Examples for this include appropriately prioritizing tasks, as well as time management [6,25,26]. Improper TM can also lead to an uncontrollable amount of unfinished tasks [7] and eventually, to a bad project outcome [25].…”

Section: Introductionmentioning

confidence: 99%

“…Examples for this include appropriately prioritizing tasks, as well as time management [6,25,26]. Improper TM can also lead to an uncontrollable amount of unfinished tasks [7] and eventually, to a bad project outcome [25]. Bellotti et al [1] argue that effective TM requires resources and knowledge on the use of such features.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Task Management in Virtual Academic Teams

Nagel

Schwade

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Social Collaboration Analytics (SCA) aims at measuring collaboration in Enterprise Collaboration Systems (ECS). In this paper, we apply SCA to investigate the use of Task Management (TM) features in virtual academic teams on a collaboration platform. This paper contributes to theory by developing the TM Catalog describing the elements and characteristics of TM. Our literature review identified only three studies analyzing the use of TM features in ECS. These studies base their analyses on transactional data (event logs). We propose to analyze both the structure and characteristics of tasks, as well as how tasks are used. In our paper, we show how SCA can be applied to gain insights on the use of TM features. Based on data from an academic collaboration platform, we demonstrate the characteristics of tasks and how different types of virtual academic teams make use of TM features.1. What are the characteristics of TM, and how do modern ECS implement TM functionalities?

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