Round 1

Reviewer 1 Report

The article presents interesting research results dealing with the order-picking processes in the warehouse. The topic is up-to-date considering the dynamic development of logistics.

However, the following improvements should be introduced in text:

1. The abstract should be rewritten. The research gap and motivation are not clearly defined. The article does not present the “model solution”.
2. The Introduction section should better highlight the research gap, justifying why there is a need to develop new model compared to existing solutions.
3. Table 1 and Table 2 refer to proposed model, however, the model wasn’t described in detail yet.
4. Equation (18) is not clear. One “form” is shown (line 572). There is “if”, but there is a lack of “then” in this equation.
5. It is not clear, whether the Pickup Simulo model was developed by Authors or it was used as a basis for modification. There is a contradiction in its description in lines 635, 800 and 716. Is it a developed model or software used to implement the developed solution?
6. Table 7 – there is a lack of Variant 1.
7. The Conclusions should be rewritten. The article analyses mainly the picking time, however, the Conclusions section does not contain the information about the time and its efficiency, paying attention to the costs (that were not the topic of the analysis).
8. The discussion should be added, referencing the achieved results to similar research available in literature.
9. The quality English should be improved, as well as punctuation is missed (e.g. line 183, 813).

Some editorial improvements needed:

1. Capital letter should be put after the dot (lines 138, 210).
2. Line 144, is it “method” or “model”?
3. Please rewrite the sentence “The developed model starts with the sales data…” (line 209).
4. Line 363, please correct “examinedand”.
5. Line 381, please correct “If the results after change is better…”.
6. Line 402, please correct “The selected ANN structure give high quality results”.
7. Page 16, the numbers of equations in text do not match the real numbers (lines 509, 511, 537).
8. Line 556, there is a lack of this position in References section.
9. Line 731, please correct “the size of 1000 picking list was established…”.
10. Line 894, the lack of initials near the authors’ surnames.

Author Response

Reviewer 1

Thank you for your review and comments. We improved the article according to the suggestions sent. Please find below the list of changes.

The article presents interesting research results dealing with the order-picking processes in the warehouse. The topic is up-to-date considering the dynamic development of logistics.

However, the following improvements should be introduced in text:

1. The abstract should be rewritten. The research gap and motivation are not clearly defined. The article does not present the “model solution”.

We rewritten the abstract to show the motivation and gap. New abstract:

“In this paper, a new model supporting decisions about product allocation in an order-picking shelf warehouse is presented. Industry 4.0 pay attention to insight the processes, self-analysis and self-optimization, short response time to market changes and maximum use of related data. Methods for solving Product Allocation Problem (PAP) are not enough to meet the requirements of Industry 4.0. Authors present a new approach for solving PAP.

The novelty introduced in the model is based on correlated data – products parameters, clients’ orders and warehouse layout. The proposed model contains elements of intelligence. The model, after product classification and allocation, analyzes its effectiveness by a simulation of the order-picking process. The application of Artificial Neural Networks (ANN) as a part of the computing model enables the analysis of large data sets in a short time.

The presented study has proved the proposed model, both for practical and scientific purposes. Relying on the research results, the total warehouse cost could be reduced from 10 to 16 per cent of the total warehouse costs. With the use of the proposed model, it is possible to predict the effect of future actions before their execution. The model can be implemented in most conventional warehouses to raise the throughput performance of the order-picking process.”.

1. The Introduction section should better highlight the research gap, justifying why there is a need to develop new model compared to existing solutions.

We extend the “Introduction” section. We add among others:

“In XXI century new ideology – Industry 4.0. was formed. The growth of technology, a large base of the data and customer expectations forced the companies to be more effective. Modern companies must pay attention to insight the processes, self-analysis and self-optimization, short response time to market changes and maximum use of related data. It is a way for a company to be competitive on the market.

Industry 4.0 is also important for intralogistics in a warehouse. The main process in the warehouse which takes the most time is the products’ picking. The effectiveness of this process can be optimized by using new technology like robots, Voice Picking, Augmented Reality support o by the better insight the data from WMS to reduce the distance of picking route. This distance can be reduced by solving Product Allocation Problem (PAP).”

We also made small changes in this section.

1. Table 1 and Table 2 refer to proposed model, however, the model wasn’t described in detail yet.

We move the Table 1 and Table 2 to the section “3.2 The model description”.

1. Equation (18) is not clear. One “form” is shown (line 572). There is “if”, but there is a lack of “then” in this equation.

We add the”then” to the equation.

1. It is not clear, whether the Pickup Simulo model was developed by Authors or it was used as a basis for modification. There is a contradiction in its description in lines 635, 800 and 716. Is it a developed model or software used to implement the developed solution?

We changed the sentence to explain the difference between model and its implementation as a software.

In line 635: “The proposed model was used as base algorithm for implemented in authors own software called Pickup Simulo. The system was coded in PHP language and MySQL relational databases and has been tested for code integrity and standards compliance.”.

In line 717: “The proposed model implemented in software Pickup Simulo was used to perform simulations for five warehouses of different size (2188, 7149, 12.491, 17.225 and 22.021 m²).”.

In line 800: “In the proposed research, a new model was developed and implemented as a software called Pickup Simulo has been presented along with a discrete event simulation analysis.”.

1. Table 7 – there is a lack of Variant 1.

The Variant 1 is Reference variant, that was the reason why it was omitted. We add Variant 1 to the table with annotation to improve the readability of the table.

1. The Conclusions should be rewritten. The article analyses mainly the picking time, however, the Conclusions section does not contain the information about the time and its efficiency, paying attention to the costs (that were not the topic of the analysis).

The abstract was corrected and rewritten:

“In the proposed research, a new model was developed and implemented as a software called Pickup Simulo has been presented along with a discrete event simulation analysis. The model is quite complex and not only does it contain methods of ANN but also enables the classification of products by ABC analysis according to the selected criteria, the definition of COI and, finally, product allocation planning. Based on the results of our case study, a decrease from 10 to 16 % of the total orders’ picking time can be expected by using our model. Implementation of the presented model enables us to find the optimal location for products, so the distance of products picking is reduced. This results in increased efficiency of the orders’ picking.

The research results are, therefore, of significant importance for warehouse managers. Thus it can be used in companies which use standard warehouse system in storage racks. Moreover, the discussed model could be used for the evaluation of potential solutions before their practical implementation in a warehouse, which will, consequently, result in reducing the risk of implementing an ineffective solution. The cost of the order-picking process is approximated from 30 to 50% of all warehouse costs. Because of this, product layout planning plays an important role in operational decisions. The model can support that decision, and decrease the risk of the bad decision of product location planning. At present, the implementation of the proposed model Pickup Simulo is being used in selected companies. Based on the results of the proposed research the authors model and its implementation as software called Pickup Simulo could be helpful in the selection of products classification model when planning products allocation in a new warehouse.

In future works, the authors want to upgrade the proposed model Pickup Simulo with the possibility of simulating different warehouse systems.”.

1. The discussion should be added, referencing the achieved results to similar research available in literature.

We add in the end of section 3 discussion about the results:

“Based on the analyzes performed, it was found that there is a statistical dependence of the picking time on the warehouse area. The results of the research presented can provide a solid basis to assist in choosing a product classification method for planning the distribution of products in a warehouse.

Based on the simulations for a small warehouse, i.e. allowing the storage of a maximum of 2.700 pallet units, it was found that the largest difference between the median values occurs for the COI Index compared to the results obtained from the ABC analysis (2.56%) and classical analysis (3.05). Therefore, it was found that for warehouses of the considered size, the selection of the product classification criterion is irrelevant – just a few per cent. The organization of the product storage system is also irrelevant - the allocation of storage areas or the free storage space method improves efficiency by only 2.5%. Therefore, for warehouses of a similar size, it is not possible to significantly improve the efficiency of its operation as a result of the application of product classification. In this case, it is more rational to reduce the costs of warehouse operation by reducing the storage area.

On the other hand, when conducting simulations for medium and large warehouses (Variant IV and V) it was found that the best results were obtained when the products were distributed in the warehouse based on ABC analysis (weight) and Analysis of ABC and COI (weight).

The worst results were obtained using the ABC analysis combined with the COI Index according to the popularity of products - the median value of the picking time was 1797 sec., and the range of results 8610 sec. It can therefore be concluded that for large warehouses and products with low susceptibility to damming loads are best suited to methods where the decisive criteria are the volume and weight of products, such as the COI Index.

On the other hand, considering the use of the presented method, allows the warehouse to operate more efficiently by approximately 16.8%. This is a result more than twice as good as that which can be achieved with the use of classical classification methods, regardless of the adopted criterion. Due to the lower labour-consumption and relatively high speed of computing, this method can be successfully used in warehouses, where the quantitative share of individual products and the assortment often change. Both the method of searching for links and the use of artificial neural networks can be successfully implemented for practical applications.

The developed method has a comprehensive approach, i.e. it allows taking into account the relationships between products - based on the orders. The analyses have proved that there is a statistical dependence of picking time on warehouse area size. The simulations indicate that the picking time decreases for warehouse-size larger than 10.000 m2. For small warehouses product choosing method for allocation plays the second role.”.

1. The quality English should be improved, as well as punctuation is missed (e.g. line 183, 813).

We corrected the English and the punctuation in the paper.

 

Some editorial improvements needed:

1. Capital letter should be put after the dot (lines 138, 210).

We change the low letter to the capital.

1. Line 144, is it “method” or “model”?

You have got right it should be model. We change the “method” to the “model”.

1. Please rewrite the sentence “The developed model starts with the sales data…” (line 209).

We rewrited the sentence to: „ The developed model use as the input data: sales data (clients order-picking lists), stored products properties and the warehouse layout. The input data are analysed to find the correlation between products.”.

1. Line 363, please correct “examinedand”.

We add the space to separate it: “ examined and”.

1. Line 381, please correct “If the results after change is better…”.

We change this sentence and added an additional explanation: “If the quality of model after change the structure is better, this structure is chosen. After this the change of the weight of ANN and performance analysis is done again. This loop is done until the stop condition is achieved (number of loops). The final structure of the ANN was chosen for the network of high quality – the results from the simulation are similar to the real data.”.

1. Line 402, please correct “The selected ANN structure give high quality results”.

We have change this sentence to the: “Developed ANN structure give high quality results – similar to the target values (Figure 6, on left).”.

1. Page 16, the numbers of equations in text do not match the real numbers (lines 509, 511, 537).

We change the numbers in the text to the proper ones (12, 13, 14, 15).

1. Line 556, there is a lack of this position in References section.

We correct the reference to the proper one: “Lorenc and Lerher, 2019”.

1. Line 731, please correct “the size of 1000 picking list was established…”.

We changed this sentence to the: “Consequently, the size of the research sample for the model was set at 1000 cases of picking list for every simulation process.”.

1. Line 894, the lack of initials near the authors’ surnames.

We add the initials to the authors’ surnames: “Straka, M.; Lenort, R.; Khouri, S.; Feliks, J.”.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper explores the development of a model to improve product allocation in warehouses. This seems to be an active and very dynamic topic due to very recent publications that highlight from a quick review on the topic.

The paper is well structured and written. It is found to have minor corrections and one section adjustment.

Despite the number of references the literature review can be improved. Some more recent or older papers were identifies as having close relation with this paper, but none seems to have been considered.

The non-exhaustive list of identified related papers is the following:

https://doi.org/10.1016/S0925-5273(03)00007-0

https://doi.org/10.1016/j.compind.2020.103343

https://doi.org/10.1016/j.apm.2012.09.015

https://doi.org/10.1016/j.apm.2015.02.047

https://doi.org/10.1016/j.ejor.2020.08.024

The literature review and the paper should be revised and updated to consider some of these developments, namely the most recent ones.

Going into detail on the revision it worth’s to highlight the following:

• The keywords should be revised to eliminate duplications with the title, ex: order-picking;
• Line 101 “are of major challenges”, consider to eliminate the “of” or consider to rephrase the sentence;
• Line 120 “branch ,”, eliminate the space between the word and the comma;
• Lines 134 to 150 could be included on a new section “Contribution to the body of knowledge” and more contextualized;
• Lines 139 and 140 “the” comes two times, erase one;
• Table 2, third column is confusing on the last part, consider more space to improve reading;
• Line 182 – “begins with in Section”, consider replacing by “begins in Section”;
• Lines 319 to 324 – adjust the spacing that is different from the rest of the paper;
• Line 325 – table 4 – the “s” from “statistics” is in bold;
• Line 377 – figure 5, change to Figure 5;
• Line 729 – Statistical analysis showed, replace by “Statistical analysis evidenced”;
• Line 814 – replace “our” by “the authors”;
• Line 816 – replace “we” by “the authors”;

Author Response

Reviewer 2

Thank you very much for your review. Thanks to the comments contained in it, the article has gained quality. Above we present the changes which we made.

 

The paper explores the development of a model to improve product allocation in warehouses. This seems to be an active and very dynamic topic due to very recent publications that highlight from a quick review on the topic.

 

The paper is well structured and written. It is found to have minor corrections and one section adjustment.

Despite the number of references the literature review can be improved. Some more recent or older papers were identifies as having close relation with this paper, but none seems to have been considered.

The non-exhaustive list of identified related papers is the following:

https://doi.org/10.1016/S0925-5273(03)00007-0

https://doi.org/10.1016/j.compind.2020.103343

https://doi.org/10.1016/j.apm.2012.09.015

https://doi.org/10.1016/j.apm.2015.02.047

https://doi.org/10.1016/j.ejor.2020.08.024

The literature review and the paper should be revised and updated to consider some of these developments, namely the most recent ones.

We got acquainted with these publications and add it to the references. We extend the section “Introduction” and “Literature review”. In the “Introduction” section we add: “The newest research papers pay attention to the reorganization of the warehouse regards to the Internet of Things (IoT) ideology. The researchers [16] analyses the processes in traditional warehouses and the modern one. They propose a Business Process Model of a Smart Restocking Process. Authors pay attention to the optimization of the intralogistics.

Proposed in this paper model uses the clients’ orders, products’ demand and products’ properties to optimize the allocation of the product. This model can be implemented as part of the Warehouse Management System (WMS) or can be used as separate software. This research is consistent with the ideology of Industry 4.0.”.

In the “Literature review” we add: “In 2013 the research team from Italy [22] proposed a mathematical model for the Multi-Levels Product Allocation Problem with compatibility constraints. The goal of this research was to reduce, as much as possible, the delivery times, the inventories, the total logistic costs and to guarantee, at the same time, higher service levels. Researchers used the mathematical modelling sensitivity analysis, then Iterated Local Search based heuristic was used to reduce the computing time. Two years later similar team compared the heuristics’ for solving the product allocation problem in the multi-level warehouse. This authors present a Rollout-based heuristic whose performances are evaluated on the basis of a detailed computational phase, including also a real case study and compared with Iterated Local Search-based Heuristic and Cluster-based Heuristic [23].

[…]. In 2003 Also Gengui et al. [25] propose using the genetic algorithm to allocate a set of customers to multiple warehouses. The proposed solution procedure utilizes a genetic algorithm that is designed to find Pareto optimal solutions for this problem in a short period. Shan Zhu et al. [26] proposed the method for optimization of product category allocation in multiple warehouses to minimize splitting of online supermarket customer orders. Multi-item customer orders often need to be split into multiple shipments because the ordered items may be stored in different warehouses. Developed by the authors' method used a heuristic clustering algorithm to optimize the product category allocation among multiple warehouses based on the distribution of multi-item orders to minimize the total number of order splits.”.

Going into detail on the revision it worth’s to highlight the following:

• The keywords should be revised to eliminate duplications with the title, ex: order-picking;

There is one “order-picking” position in the keywords.

• Line 101 “are of major challenges”, consider to eliminate the “of” or consider to rephrase the sentence;

We eliminate the “of” from this sentence.

• Line 120 “branch ,”, eliminate the space between the word and the comma;

We delete the space between this word and comma.

• Lines 134 to 150 could be included on a new section “Contribution to the body of knowledge” and more contextualized;

We add the section “Contribution to the body of knowledge”. We also extend this section to be more contextualized.

• Lines 139 and 140 “the” comes two times, erase one;

We delete one word “the”.

• Table 2, third column is confusing on the last part, consider more space to improve reading;

We change the heading row height, now the last word is readable.

• Line 182 – “begins with in Section”, consider replacing by “begins in Section”;

We deleted the word “with” from the sentence.

• Lines 319 to 324 – adjust the spacing that is different from the rest of the paper;

We change the line spacing to the 13 px. Now it is the same like in the rest of the paper.

• Line 325 – table 4 – the “s” from “statistics” is in bold;

We change the “s” letter to the normal style.

• Line 377 – figure 5, change to Figure 5;

We change the first letter of “figure 5” to the upper letter.

• Line 729 – Statistical analysis showed, replace by “Statistical analysis evidenced”;

We change word “showed” to the “evidenced”.

• Line 814 – replace “our” by “the authors”;

We change “our” to “the authors”.

• Line 816 – replace “we” by “the authors”;

We change “we” to “the authors”.

Author Response File: Author Response.docx

Reviewer 3 Report

I find the paper significant in contributing towards new knowledge 

Author Response

Reviewer 3

 

Thank you for the kind of made review of our paper.

 

I find the paper significant in contributing towards new knowledge

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Thank you for explanations. The quality of papier is much better in the presented form.