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Peer-Review Record

Adoption Case of IIoT and Machine Learning to Improve Energy Consumption at a Process Manufacturing Firm, under Industry 5.0 Model

Big Data Cogn. Comput. 2023, 7(1), 42; https://doi.org/10.3390/bdcc7010042

by Andrés Redchuk¹

, Federico Walas Mateo^2,3,*

, Guadalupe Pascal² and Julian Eloy Tornillo²

Reviewer 1:

Archana Prabahar

Reviewer 2:

Riccardo Zambon

Reviewer 3: Anonymous

Big Data Cogn. Comput. 2023, 7(1), 42; https://doi.org/10.3390/bdcc7010042

Submission received: 15 November 2022 / Revised: 17 February 2023 / Accepted: 20 February 2023 / Published: 24 February 2023

(This article belongs to the Special Issue Energy-Efficient IoT (Internet of Things) and Big Data Challenges for Connected Intelligence)

Round 1

Reviewer 1 Report

This is an interesting work that describes a case study implemented in a food industry where authors applied a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution 17 integrated with an IIoT architecture that generates data to achieve process optimization. The case 18 shows how a low-code AI platform can ease energy efficiency, considering people in the process, 19 empowering them and giving a central role in the improvement opportunity.

Although this paper uses IIoT architecture and performs optimization and implements low-code AI techniques, I suggest few improvements for the manuscript. Though methods are clear, results generated for optimizer model may be described in more detail. Different parameters that affect the fuel consumption may be described in the results section and can be discussed. A chart of these parameters would be more informative and a comparison with previous techniques used for this kind of problem would be added value to the manuscript. In addition, more information about LCP platform protocols and implementation process is required in the manuscript.

Author Response

I have revised the results and they are described in more detail. Besides the parameters that affect the fuel consumption are described in the results section and can be discussed.

Finally more information about LCP platform protocols and implementation process are presented

Reviewer 2 Report

The work describes a low-code AI implementation for food industry. It lacks on theoretical background, and does not show quantitative results obtained with such implementation.

I suggest to add more literature analysis, theoretical study and experiments showing the good performances of the proposed work in order to be better evaluated.

Author Response

More literature analysis was added regarding IIoT and AI/ML, and low code platforms cases in traditional industries

Reviewer 3 Report

Energy consumption and lower environmental footprint impact of industries operations is challenging task that occupies many researchers. Your paper faces a significant problem in industry.

In order to improve this work:

· In the first section, you should present the task more detailed. As example in line is referred some other studies that are not analyzed.

· In section 2, you can expand your literature review with other similar studies

· In section 3, you could describe with more details your approach:

· Low-Code software (details, some exports for its operation, presentation of its tools)

· Lean Startup methodology (a short description)

· In section 4, there is a description of procedure however a presentation of historical data (18 months) is important if possible

· In section 5, the results are not described detailed some diagrams may stress the results

· In section 6, you may stress the novelty of this work and underline its weaknesses

Some figures of data and results are going to improve this work.

Author Response

Dear reviewer, all sugestion were addressed.

In the first section, you should present the task more detailed. As example in line is referred some other studies that are not analyzed.

In section 2, you can expand your literature review with other similar studies
In section 3, you could describe with more details your approach:
Low-Code software (details, some exports for its operation, presentation of its tools), more detail was added
Lean Startup methodology is better described
In section 4, there is a description of procedure however a presentation of historical data (18 months) is important if possible. major parameters were explained
In section 5, the results are describnbed with more detail

Round 2

Reviewer 1 Report

Authors have explained the top features viz, Value Positions, water supply temperature and ambient temperature. While they described that 42 features were used. A table describing the same would be of interest to the reader.

Results and discussion is not descriptive and needs to be more elaborative for better understanding of the manuscript.

Author Response

Dear reviewer,

We´ll be working to answer to your suggetions. First of all we include a figure to make more clear the way the LCP works and ease the adoption of the solution to industrial people.

We add more information about features that were considered in the model, but we didn´t use a table because we couldn´t found how to make it clear to the reader.

Finally we improve the results discussion, and conclusions items.

Hope you find the article suitable to be published.

Best Regards

Federico Walas Mateo