Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Round 1

Reviewer 1 Report (Previous Reviewer 2)

In my opinion, the novel ideas are not well described. Especially, in the first section, I would expect many references to existing works and comparison with these works. Instead of the way, we can read (page 2): ... that the current approaches to optimizing production systems usually come down to building a .. Which approaches? Add citations. Similarly, in the new table, Table 7, it is not clear what is the method described in this article. I would expect a comparison of the new method with other existing methods. Moreover, this article includes a lot of many various directions, but the improvement is not clear. The quality of a paper does not depend on the number of various directions.

Other notices:

abstract:

- Therefore, it is necessary in this process to consider the enterprise as a whole ... : Therefore, it is necessary to consider the enterprise as a whole in this process ...

- missing article: enterprise architecture 

-  ANN (artificial neural network) - add a reference

p10: artificial neural network (ANN): artificial neural network is used again instead of only ANN.

p11: was formulated in the form (11) - What does it mean: form (11)?

Author Response

Answers in the attachment file.

Author Response File: Author Response.pdf

Reviewer 2 Report (New Reviewer)

The typographical work on the text has not been completed. The resulting copy shows parts of the text with a yellow background on the font.

................................................................

The article is written according to all requirements for the presentation of scientific research. The scientific task is presented clearly. The authors propose a neural network approach to a problem previously solved by conventional mathematical methods. The basis of the modelling is the meta-model of production architecture developed by them. The optimization model enables the utilization of production resources and risk reduction.

Author Response

Answers in the attachment file

Author Response File: Author Response.pdf

Reviewer 3 Report (New Reviewer)

Dear Authors

This is an interesting manuscript that determines the “Application of enterprise architecture and artificial neural net-works to optimize the production process”. The methods are not novel but are acceptable. It should be noted the correction of several items is necessary in this manuscript. Specially, English language of writing should be modified in the whole of manuscript. Please study the “Guide for Authors” of journal, carefully and correct the manuscript based on the guideline. Moreover literature review is not up to date. You should use appropriate papers for this section such as Principle of life cycle assessment and cumulative exergy demand for biodiesel production: Artificial neural networks and adaptive neuro-fuzzy inference system in energy modeling of agricultural products.

Accordingly, I recommend accepting it, with major revision.

Best Regards

Author Response

Answers in the attachment file

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Major remarks:
- Lack of discussion of the obtained results.
- How did you select the presented network architecture (2 hidden layers and the number of units in hidden layers)?

Minor remarks:
- Diagrams seem too small.
- Page 15: 12 and 10 don't mean "number of hidden layers" but "number of hidden units".
- Page 15: Text below and above Fig. 10 is duplicated.

Reviewer 2 Report

It seems that this article does not include any new ideas. If there are any new ideas, the manuscript should be rewriten, especially the novel approach has to be compared with other existing approaches. Moreover, the structure of the article is not good: a lot of short sections, relationships among sections are not clear and so on. References are inadequate, for example, ANN are often cited, however there are no references related to ANN. I detect some typos (p1: decision making, It is necessary ;  and IT techiques such, ...), but it is not the main issue of this article.

Reviewer 3 Report

Concern 1#

 

The literature survey section must be included and expanded and the author is requested to cite the following latest paper related to this field 

 

Md AQ, Jha K, Haneef S, Sivaraman AK, Tee KF. A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0. Processes. 2022; 10(10):1966. https://doi.org/10.3390/pr10101966

 

Md AQ, Jaiswal D, Daftari J, Haneef S, Iwendi C, Jain SK. Efficient Dynamic Phishing Safeguard System Using Neural Boost Phishing Protection. Electronics. 2022; 11(19):3133. https://doi.org/10.3390/electronics11193133

 

Concern 2#

The author must include the problem statement and must clearly say what problem the paper is trying to solve

 

Concern 3#

 

The author must clearly explain the flow of work through the proposed architectural diagram. The diagram must have contents related to the detailed process

 

Concern4#

 

The study's fundamental structure has to be explained with the help of an algorithm

 

Concern 5#

 

 How have the outcomes been ensured in light of the major uncertainties?

 

Concern6#

The conclusion has to be revised to incorporate the following advice: - Highlight your analysis and just present the most important takeaways from the full paper.

 

- Mention the advantages.

- In the final sentence of this section, mention the inference.

 

- Make sure the topic of the Conclusion differs from what is discussed in the abstract.

- include future work with multi-direction