Parameter Setting for Strategic Buffers in Demand-Driven Material Resource Planning through Statistical Analysis and Optimisation of Buffer Levels

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Authors focused on The Demand Driven Material Resource Planning (DDMRP) method as a method of inventory management in an enterprise. There are some comparable studies. Authors mentioned that their approach is connected with MRP, MRPII. I would ask them to emphasize the differences. I would agree that DDMRP is an extension of MRP, but if authors have a separate opinion, please, insert into the article.

Authors present a literature survey, and in my opinion, the references are appropriate.

In Table 1, authors provided exemplar values of parameters, authors should justify/explain why just these values are accepted. They should explain the analysis way, it is important because authors further use those values in simulations.

Authors emphasize simulations and buffers. My question is what it means for producers. How they should prepare the production process to cope with the challenges i.e., simulations and buffers, and what consequences are if they apply this method DDMRP.

There are my figures in the text, however, for readers they are not useful, as they do not have data, I would propose to reduce the figures, but add more case study data for exemplification.

Author Response

Dear reviewer,

thank you for offering your time to review our article. We are grateful for your comments which will help to improve our research and our manuscript.

Allow us to respond to individual comments.

In this paper, the authors describe a new approach to calculating strategic buffers parameters in the DDMRP framework as developed and described by Ptak and Smith in [1]. Original methodology of DDMRP represents an extension of the MRP system. In the review of the state of the art, publications that transform the original DDMRP into a resource constrained planning and control setting (MRP II system extension) were also mentioned. These are e.g. publications [34], [35] and [36]. The authors did not address that issue in this paper, but it is one of the directions for future research in the authors' department.

The parameters in Table 1 represent the parameters of the example item. These parameters are based on the delivery terms of the item (MOQ, DOC), the analysis of historical consumption data (ADU), the inventory cost structure (ordering cost, carrying cost, stockout cost) and the basic DDMRP system settings (DLT, LTF, VF). An extended explanation has been added to the introduction of Chapter 2.3 (rows 432 – 436).

The authors' own methodology for parameters setting of the strategic buffers does not require the methodology user (the manufacturer) to use simulations. Simulations were used in the research as a tool for verification of the proposed methodology and its comparison with standard approaches for basic parameters calculation of strategic buffers. The simulation results in Chapter 3 confirmed the validity of the proposed methodology. In applying the methodology, the manufacturer will only work with the calculation formulas described in Chapter 2.2.

Based on reviewers' comments, graphs of the main effects of all three input parameters (LTF, VF, DOC) were added as part of the results evaluation of the multifactor analysis (Chapter 3.3, new Figure 14).

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, motivated by the complex business environment, authors investigated the problem of parameter setting for strategic buffers in DDMRP system so as to realize a more satisfying inventory management. Statistical analytical approaches on daily data and optimization techniques were taken as the solution technology, which results in a new proposed methodology. Feasibility and effectiveness of the methodology were verified through several comparisons.

In my opinion, the research gap subject to the existing studies is well-examined. The technical route is reasonable, the simulation experiments are appropriate and the results are fruitful. I recommend that this paper can be accepted after the following comments are properly addressed:

(1) To formulate the green zone and red zone in a buffer, Eq. (2) and Eq. (6) are used. However, they share a same expression without any modifications.

(2) In Section 2, many equations are used to formulate the problem. However, some key embodied parameters are not explained. For example, RB in Section 2.1.3.

(3) Three types historical data are selected. Among them, the histogram of daily usage distribution in Figure 3 is ambiguous.

(4) For multi-factor analysis results of buffer input parameters, authors stated that a parameter combination of 0.5, 0.5 and 6 made a minimum inventory cost. I think it is not a convincing conclusion subject to the presented tables and figures. In fact, authors need take inventory cost as the response value and draw the main effects plot of the three input parameters.

(5) To better suit the scope of this journal, some highly-related studies published in Applied Sciences are suggested to be reviewed and referenced.

Author Response

Dear reviewer,

thank you for offering your time to review our article. We are grateful for your comments which will help to improve our research and our manuscript.

Allow us to respond to individual comments.

1. Formulas 2 and 6 were taken from the original methodology for strategic buffers dimensiioning published in [1]. In the case of formula 2, it is one of three alternatives for calculating the Green Zone presented by Ptak and Smith [1]. The other two include calculation based on Desired Order Cycle (DOC) - formula 1 and calculation based on Minimum Order Quantity (MOQ) - formula 3. The resulting green zone value is the maximum value calculated using formulas 1, 2, 3 (formula 4).
2. RB (Red Base) and RS (Red Safety) create the resulting Red Zone. A short description of both components (RB and RS) has been added in the article in chapter 2.1.3 (rows 246 – 247, 250 - 251)
3. To ensure clarity of histogram interpretation, a description of the axes has been added in Figure 3.
4. This result is confirmed by the data in Table 10 (total inventory cost - average). In order to confirm the result, graphs of the main effects of all three input parameters (LTF, VF, DOC) have been added to the paper (new Figure 14).
5. As a part of the analysis of the current state in the subject area, the authors focused on a comprehensive assessment of the sources within the scientific databases Web of Science and Scopus, which included publications published by MDPI. Two related studies published in Applied Sciences have been added to the article (reference [44] at row 571, reference [46] at row 699)