Next Article in Journal
3D Printing and Implementation of Digital Twins: Current Trends and Limitations
Previous Article in Journal
On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
 
 
Article
Peer-Review Record

New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

Appl. Syst. Innov. 2022, 5(1), 6; https://doi.org/10.3390/asi5010006
by Andrés Redchuk 1,* and Federico Walas Mateo 2
Reviewer 1:
Reviewer 2: Anonymous
Appl. Syst. Innov. 2022, 5(1), 6; https://doi.org/10.3390/asi5010006
Submission received: 22 November 2021 / Revised: 24 December 2021 / Accepted: 27 December 2021 / Published: 29 December 2021

Round 1

Reviewer 1 Report

Dear Authors, a nice and interesting work. Please take care of the English, I do not have amended all the errors, maybe ask a native speaker for grammar error correction. Maybe you could also point out more what is exactly the advantage of using this method versus not using this method. In the end, you say "several" vs. "a couple of months" which is not very convincing. Try to find an argument why AI/ML the proposed method, which I also think is very promising, can be judged with "decision criteria" so that people can be convinced to use this method. 

Comments for author File: Comments.pdf

Author Response

Dear reviewer, thanks for your kind feedback. 

We make several changes consiring your suggestions and the ones from the other reviewer. 

Taking into account your evaluation I am attaching the comments to what you kindly suggested that we change and take into consideration.

We hope that you find this versión of the manuscript much highly improved than the first one.

Best Regards

Federico Walas Mateo

Author Response File: Author Response.docx

Reviewer 2 Report

It is not clear which specific problem or knowledge gap is addressed with this paper. It is not presented how the particular features of the proposed solution (e.g. the no-code approach and direct use of the platform by business analysts) contributed to the success of the use case. No concrete, quantifiable results are presented. Overall it is unclear what the scientific contribution is.

The problem starts with the title. It actully consists of 3 different titles, separated by fullstops. There is no clear statement/hypothesis that could easily be understood by the readers.

The Abstract is not clearly written. Several sentences are repeated. What is presented as "results" and "conclusions" are not really results and conclusions. It is hard for readers to grasp what the paper is actually about.

The Introduction is very confusing. It contains a mix of related work in AI and data analytics, but doesn't introduce any particular problem or knowledge gap. It is not clear what problem the authors try to address by means of their case study.

Apart from the 7-step procedure in Figure 1, some information about the system architecture and what is being optimized, no information is provided about any concrete results and what are the distinguishing features of the approach with respect to other work.

Author Response

Dear reviewer, thanks for your kind feedback. 

We make several changes consiring your suggestions and the ones from the other reviewer. 

Taking into account your evaluation I am attaching the comments to what you kindly suggested that we change and take into consideration.

We hope that you find this versión of the manuscript much highly improved than the first one.

Best Regards

Federico Walas Mateo

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

There have been only minor revisions compared to the previous version. The contribution has been highlighted to be a no/low-code approach to developing an AI/ML solution for optimizing steel production. However, no details of this approach are shown, except for mentioning the existence of "templates". But how are they used, what do they look like, and who has used them? The description of the use case still does not elaborate on this. The role of the tool user seems to be unclear: In section 3 it is "process engineers and business analysts", and in a later section (and the abstract) it is "process operators".

  • Abstract: There is still confusion about the actual content and contributions of the paper. A "conceptual framework on key factors" is proposed but cannot be found in the paper. The claim of "democratizing AI" is also not used in the rest of the paper. And I'm not sure what is meant by the "new business models" and how the paper addresses this. To me it's about a solution for optimizing an existing process, so there is no new business model in my understanding.
  • Introduction: The proposed contribution or value proposal is announced without showing there is a need for it. It is not explained that the implementation process is difficult or slow etc., so why do you propose a new solution?
  • The Discussion section summarises the no/low-code approach, but no details are provided in the Case Study section. 

Author Response

Dear reviewer, thanks for your kind feedback.

We make several changes considering your suggestions. I´m sure this could help to improve the article.

Taking into account your evaluation I am attaching the comments to what you kindly suggested to change and take into consideration.

We hope that you find this versión of the manuscript much highly improved than the second one.

Best Regards

Federico Walas Mateo

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

all comments addressed; a few typos and grammar errors remaining

Back to TopTop