The Impact of Agile Methodologies and Cost Management Success Factors: An Empirical Study

Iqbal, Mohammad S.

doi:10.21123/bsj.2019.16.2(si).0496

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The major difficulty in the field includes the foreseeing ability during the maintenance of assets is the most significant thing. Option for predictive maintenance improves the quality of production, control, cost, and machine downtime 7 . The courses and survey on industry 4.0 include machine learning techniques and data analytics for the improvement in practicing production and following up on predictive maintenance structure and techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry

Rao,

Jayasree

2024

Baghdad Sci.J

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Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should be followed. The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy. The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. By evaluating the performance results and confusion matrix through the training and testing output all the metrics for training and testing are classified in the confusion matrix. Our proposed predictive maintenance model with high accuracy for the detection of failures in earlier stages and maintenance of Indian ships can help in the avoidance of accidents in voyages and the loss of goods and money during transportation. The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models. The proposed Pd-MaaS helps in early detection of failures in the ships which is the greatest advantage in the Indian shipbuilding industry.

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Section: Literature Reviewmentioning

confidence: 99%

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry

Rao,

Jayasree

2024

Baghdad Sci.J

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“…Some of the elements that distinguish IT projects from others include: Dynamic development of technology, uniqueness of solutions, often resulting from dynamic changes in technology and an environment not encountered in other areas where changes are implemented, creativity, prototypicality or multidimensionality of implementation and complexity of IT systems requirements (Trzeciak 2020). Furthermore, the technical and technological aspect is also raised as a component that builds the success of an IT project (Iqbal et al 2019).…”

Section: Technical and Technological Environmentmentioning

confidence: 99%

Sustainable Risk Management in IT Enterprises

Trzeciak

2021

Risks

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A synthesis of literature studies covering the determinants of agile project management methods, risk management processes as well as factors influencing the shaping of project success and failure clearly indicates that in most publications on risk in agile managed projects, the human factor is heavily underestimated at the expense of often excessive favoring of procedures. Meanwhile, after analyzing the risk factors that arise in agile-managed IT projects, it became apparent that in addition to aspects such as technology, hardware, system, or even project schedule and cost, the project team is highlighted, which is also the second concept with the GPM P5 Standard for Sustainability in Project Management. Thus, the purpose of this article is to develop a model for risk management in IT projects. As a result of the empirical research carried out by means of an expert interview (108 experts) and a questionnaire survey (123 respondents), a risk management model was developed and six original risk management areas were identified, describing 73.92% of all risk factors that may occur during the implementation of an IT project. Furthermore, empirical studies confirm that basic processes such as risk factor identification, impact assessment, and key risk factor management are used by managers and/or team leaders during the implementation of IT projects.

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A Systematic Mapping Study on Issues and Importance of Documentation in Agile

Habib

Romli

2021

2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS)

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The Impact of Agile Methodologies and Cost Management Success Factors: An Empirical Study

Cited by 6 publications

References 3 publications

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry

Sustainable Risk Management in IT Enterprises

A Systematic Mapping Study on Issues and Importance of Documentation in Agile

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