Innovations in Inventory Management to Improve the Profitability of Local SMEs

Víctor Hugo Puican Rodríguez; RITA DE JESUS TORO LÓPEZ; Waldemar Ramón García Vera

doi:10.12688/f1000research.168900.2

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Research Article

Revised

Innovations in Inventory Management to Improve the Profitability of Local SMEs

[version 2; peer review: 2 approved with reservations, 1 not approved]

Víctor Hugo Puican Rodríguez ¹, RITA DE JESUS TORO LÓPEZ², Waldemar Ramón García Vera³

PUBLISHED 07 Nov 2025

Author details Author details

¹ Universidad Cesar Vallejo, Trujillo, La Libertad, Peru
² Universidad Cesar Vallejo, Trujillo, La Libertad, Peru
³ Universidad Cesar Vallejo, Trujillo, La Libertad, Peru

Víctor Hugo Puican Rodríguez
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

RITA DE JESUS TORO LÓPEZ
Roles: Methodology, Software, Supervision, Validation

Waldemar Ramón García Vera
Roles: Data Curation, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Efficient inventory management is a critical internal capability for ensuring the financial sustainability of micro and small enterprises, especially in emerging economies affected by post-pandemic disruptions. In Bagua, Peru, MSMEs often lack digitised control systems, leading to frequent stock imbalances, rushed purchases at high prices, and lower profitability; a deeper understanding of inventory practices that drive financial performance can contribute to more informed and strategic decision-making.

Method

This quantitative, descriptive, and explanatory study examined the effects of four components of inventory management—control, valuation methods, control records, and measurement—on profitability; a convenience sample of 83 MSMEs yielded 200 valid responses from key personnel involved in inventory decisions; A 21-item Likert scale validated by experts was applied, and the data were analysed using descriptive statistics and multiple linear regression in SPSS v27. Profitability indicators included return on assets (ROA), gross margin (GM), and return on equity (ROE).

Results

All dimensions of the inventory showed moderate levels of implementation (means: 2.37–2.62 on a five-point scale). The regression model demonstrated satisfactory predictive power (R² = 0.402; F = 32.81; p < 0.001). Inventory measurement was the strongest and only highly significant predictor of profitability (β = 0.383; p < 0.001), followed by inventory control, with a smaller but significant contribution (β = 0.257; p = 0.013). Inventory valuation methods and control records did not show statistically significant direct effects (p > 0.40).

Conclusion

The findings highlight that decision-oriented measurement practices, supported by systematic control, are essential factors for the financial performance of micro and small enterprises; investment in digital monitoring tools, replenishment based on key performance indicators, and staff training could improve operational and financial efficiency; due to its cross-sectional design and localised context, future studies should incorporate longitudinal data and broader geographical comparisons to strengthen generalisation and explore possible mediation pathways between accounting-oriented practices and profitability.

Keywords

Inventory, profitability, control, measurement, financial efficiency.

Corresponding author: Víctor Hugo Puican Rodríguez

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Puican Rodríguez VH et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Puican Rodríguez VH, TORO LÓPEZ RDJ and García Vera WR. Innovations in Inventory Management to Improve the Profitability of Local SMEs [version 2; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2025, 14:853 (https://doi.org/10.12688/f1000research.168900.2) First published: 02 Sep 2025, 14:853 (https://doi.org/10.12688/f1000research.168900.1) Latest published: 07 Nov 2025, 14:853 (https://doi.org/10.12688/f1000research.168900.2)

Revised Amendments from Version 1

This new version of the article incorporates significant improvements over the previously published version. First, the Introduction and Literature Review were enriched with a more critical and analytical synthesis of previous studies, integrating the Resource and Capability and Dynamic Capability approaches to provide a more solid foundation for the study's theoretical contribution. In addition, the research gap and the practical value of analysing inventory management in MSMEs located in Bagua, a little-explored context, were defined more clearly.
Secondly, the Methodology section was expanded to describe more precisely the research design, sampling criteria, instrument construction, expert validation, and reliability indicators used. Likewise, the limitations of convenience sampling and the risks of bias associated with the use of surveys are acknowledged.
Thirdly, the Results and Discussion sections were improved by incorporating complete regression coefficients, model fit measures, and a conceptual figure with standardised values. The interpretation of the findings was strengthened with a more in-depth analysis of their relationship with recent literature and practical implications for business management.
Finally, the conclusions were adjusted to strictly reflect the evidence obtained, highlighting the contextual scope of the study and proposing new lines of research aimed at improving the future generalisation of the results.

Overall, this version presents a more methodologically rigorous and better-founded study with clearer and more relevant applied contributions.

See the authors' detailed response to the review by Juan Carlos Quiroz-Flores

Introduction

Effective inventory management is a fundamental lever for working capital discipline and the profitability of MSMEs; beyond preventing shrinkage or obsolescence, inventory policies determine cash conversion and margin realisation through days of inventory outstanding (DIO), cash conversion cycle (CCC) and inventory profitability ratios such as GMROI; Recent reviews show that inventory practices, when complemented by technology and management expertise, substantially improve the operational performance of SMEs under conditions of resource scarcity (Panigrahi RR, et al., 2024).

From a mechanistic point of view, reordering policies with continuous review (R, Q) and demand classification options are important for shortages, maintenance costs and service levels; in the case of intermittent or irregular demand, established evidence recommends Croston-type methods and their bias-corrected variants, which consistently outperform naive smoothing for low-turnover items common in local assortments (Zhou, 2023; Wilson et al., 2022; Wilson, 2023).

Research gap

Empirical associations between specific inventory practices and profitability have been documented mainly in large companies or urban settings; there is little evidence on micro and small enterprise (MSE) ecosystems in the Peruvian Amazon, where company size, informality, and logistical constraints may alter the magnitude of the effects (e.g., variable delivery times, higher risk of spoilage). In 2023, the Amazon region accounted for around 96% of micro-enterprises, with a predominance of commercial activities, conditions that amplify the operational impact of replenishment and record-keeping decisions (Ministry of Production, 2024).

Why Bagua, Peru? Bagua is a representative province of the Amazon where the heterogeneity of the road surface and connectivity deficiencies complicate supplier delivery times and replenishment frequency, plausibly altering optimal targets (R, Q) and safety stock policies relative to coastal or metropolitan references. This context offers external validity for similar secondary cities, while providing policy-relevant insights (Suárez et al., 2024).

Contribution

The association between inventory control and measurement practices was quantified, emphasising accurate record keeping, safety stock policies and valuation methods consistent with IFRS/IAS 2 (i.e. FIFO and weighted average; LIFO is not permitted under IFRS) and profitability indicators (ROA, gross margin, ROE) in micro and small enterprises located in Bagua. By focusing on an underserved geographical area and aligning accounting treatment with IFRS, evidence was provided on how specific and applicable inventory control practices are linked to financial performance in constrained environments (KPMG, 2021).

Literature review

Inventory management is a fundamental operational capability within the resource-based view (RBV) framework: organisations reap financial benefits when internal resources, such as inventory control processes, ICT systems and forecasting skills, create cost advantages or improve responsiveness (Barney, 1991; El Nemar et al., 2022). From a dynamic capabilities perspective, the ability to detect fluctuations in demand, take advantage of replenishment opportunities and reconfigure stock policies is particularly important for micro and small enterprises exposed to volatile environments, such as the Amazon region of Peru (Teece, 2007; Chen et al., 2024).

Definitions and mechanisms

Inventory control refers to coordinated decisions about the timing and quantity of replenishment, commonly represented by continuous review policies (R, Q), which determine stock-out costs, carrying costs, and service levels; basic profitability channels operate through days of inventory outstanding (DIO), cash conversion cycle (CCC), and inventory profitability ratios such as GMROI; safety stock policies mitigate delivery time uncertainty but must balance capital tie-up and service performance (Kotecha and del Rio, 2025).

Empirical evidence

Current studies have revealed a consistent positive relationship between CI practices and the profitability of micro and small enterprises, especially those where management skills and digital visibility remain scarce (Li and Mizuno, 2022; Panigrahi A, et al., 2024). In terms of replenishment decisions, EOQ-type models and their extensions remain viable, low-cost solutions for companies with limited analytical capabilities, as they reduce ordering and storage costs (Condeixa et al., 2020; Çalışkan, 2021). However, demand in small retailers is often intermittent or irregular, favouring Croston-type methods over naive smoothing to avoid systematic biases and service losses (Syntetos et al., 2005; Erjiang et al., 2022).

Accurate record keeping and IFRS-aligned valuation methods (IAS 2), i.e. FIFO or weighted average, improve cost allocation, margin visibility and the quality of financial reporting (Ala et al., 2023). Similarly, KPIs such as compliance rate, inventory turnover, and shrinkage rate have proven to be highly predictive of operating margins (Sánchez-Córdova et al., 2024). Therefore, integrated inventory control systems, whether manual or digital, impact profitability not only through cost containment but also through strategic differentiation, especially in short-life-cycle categories susceptible to deterioration.

Despite these data, geographical coverage remains skewed towards the urban and well-digitised ecosystems of Asia and Europe, with limited evidence in secondary cities or Amazonian economies (Granillo-Macías et al., 2024). The structural conditions of these environments—informality, long delivery times, concentration of suppliers—may moderate the link between inventory policies and profitability (Singh et al., 2022). This contingency suggests that practices that are effective in metropolitan contexts may not produce similar effects in regions such as Bagua, Peru.

Summary and gap

Previous studies corroborate that inventory capabilities create financial value, but few studies integrate RBV with dynamic capabilities to explain profitability outcomes in SMEs under the specific constraints of the Amazon; Consequently, this study examines how inventory control and measurement practices affect ROA, GM, and ROE in Bagua-based SMEs, addressing a geographical and theoretical gap with practical implications for similar contexts in developing economies.

Methods

Research design and rationale

A quantitative, descriptive and explanatory design was adopted, giving priority to inventory control ((R, Q), safety stock), valuation (FIFO or weighted average in line with IFRS/IAS 2), accuracy and timeliness in record keeping and inventory measurement (DIO, turnover, GMROI, compliance rate) as internal capabilities that link stock depletion, maintenance and replacement costs to profitability through the cash conversion cycle.

Population and sampling frame

The target population consisted of micro and small enterprises registered and active in Bagua with ≥5 employees; 83 micro and small enterprises in the trade/services and construction sectors were contacted, inviting five informants per company (manager, warehouse manager, general accountant and two accounting assistants) to mitigate single source bias; inclusion required active tax registration, operational status, and willingness/commitment of time; the valid sample included 200 respondents from 40 micro-enterprises.

Sampling strategy, limitations, and future research

Non-probability convenience sampling was used due to access restrictions and management authorisation; this implies coverage/selection bias and limits statistical generalisation; future studies should apply probability-based stratified sampling (by sector/size), multi-wave panels, and triangulate survey responses with accounting/ERP records.

Variables and operationalisation

Inventory control comprised three dimensions: inventory valuation methods (P1-P4), inventory control records (P5-P8) and inventory measurement (P9-P12). Profitability included ROA (P13-P16), gross margin (P17-P20), and ROE (P21-P24). All items used 5-point Likert scales (Never = 1 to Always = 5). The complete correspondence is shown in Table X (instrument source: Questionnaires).

Development and validation of the instrument

The items were adapted from operations/accounting sources and adjusted to the SMEs; content validity was based on expert judgement (CVI/CVR). A pilot test was conducted to assess clarity and timeliness. Reliability and dimensionality were assessed using Cronbach’s alpha and EFA (KMO, Bartlett); where possible, CFA was estimated and composite reliability (CR) and average variance extracted (AVE) were calculated (criteria CR ≥ 0.70; AVE ≥ 0.50).

Measurement properties

Internal consistency was acceptable to excellent: α (DIM1) = 0.749; α (DIM2) = 0.858; α (DIM3) = 0.842; α (DIM4) = 0.888; α (DIM5) = 0.798; α (DIM6) = 0.829; sampling adequacy supported factor analysis (IV: KMO = 0.866; DV: KMO = 0.913; all items: KMO = 0.910); Bartlett’s tests rejected sphericity (IV: χ² = 1111.59, df = 66; DV: χ² = 1432.63, df = 66; all items: χ² = 2818.27, df = 276).

Bias mitigation and diagnosis

Procedural corrective measures included anonymity, balanced wording, separate blocks, and a multiple informant approach; statistically, a Harman factor test was performed as exploratory screening, a marker variable adjustment, and, subject to the sample, MTMM/CFA.

Statistical analysis plan

Assumption checks (Shapiro-Wilk, Levene) preceded inference. Main models used OLS with industry/size/age controls; robustness checks included alternative specifications, exclusion of influential cases, and marker-adjusted correlations.

Results and discussion

Descriptive results

Descriptive statistics for the inventory management and profitability variables are presented in Table 1; overall performance was moderate across all dimensions, with inventory measurement receiving the highest mean score (M = 2.62), indicating an operational emphasis on tracking turnover and days of inventory. The average score of 3 across most variables reinforces a prevailing level of acceptable performance among the micro and small enterprises surveyed; a negative skew was observed across all dimensions, particularly in profitability metrics such as ROA (sk = -0.947) and GM (sk = -1.007), indicating a tendency for profitability to be lower than expected. A negative asymmetry was observed across all dimensions, particularly in profitability metrics such as ROA (sk = -0.947) and GM (sk = -1.007), suggesting that most firms are performing relatively well, while a smaller subset with poor performance is dragging down the mean. These results highlight that companies show greater maturity in metrics and monitoring than in planning and valuation, in line with studies in which MSMEs adopt visible control tools before strengthening their accounting bases (Ahmad et al., 2022; Sultan, 2021).

Table 1. Multidimensional statistical characterization of inventory management and profitability indicators.

Variables		IC	IVM	ICR	IM	P	ROA	GM	ROE
N	Valid	200	200	200	200	200	200	200
N	Lost	0	0	0	0	0	0	0
Mean		2,37	2,40	2,50	2,62	2,44	2,49	2,55	2,54
Median		2,00	2,50	3,00	3,00	2,50	3,00	3,00	3,00
Mode		2	3	3	3	3	3	3	3
Standard deviation		,619	,672	,665	,647	,599	,665	,608	,625
Variance		,384	,451	,442	,419	,359	,442	,369	,390
Skewness		-,434	-,665	-,965	-1,448	-,565	-,947	-1,007	-1,027
Standard		,172	,172	,172	,172	,172	,172	,172	,172
Kurtosis		-,650	-,633	-,228	,833	-,594	-,257	,003	,001
Standard error of Skewness		,342	,342	,342	,342	,342	,342	,342	,342
Rango		2	2	2	2	2	2	2	2
Minimum		1	1	1	1	1	1	1	1
Maximum		3	3	3	3	3	3	3	3

Regression analysis

The variables in Table 2 Inventory measurement was the strongest indicator and the only robust predictor of profitability (β = 0.383; p = 0.001), highlighting that companies capable of effectively tracking turnover, days of inventory, and obsolescence costs strengthen their margins and returns. This is consistent with Vahdani and Sazvar (2022), who argue that monitoring real-time inventory dynamics reduces capital immobilisation and improves ROA/ROE.

Table 2. Regression model summary, ANOVA, and standardized coefficients.

A. Regression Model and ANOVA
Estadístico	Valor
R	0.634
R²	0.402
R² Ajustado	0.390
Error estándar de la estimación	0.468
F	32.81
gl (Regresión/Residual)	4/195
Sig. F	0.001
Durbin-Watson	1.525

B. Model coefficients
Predictor	B	SE	β	t	Sig.	VIF
(Constant)	0.941	0.201	—	4.68	0.001	—
Inventory Control (IC)	0.198	0.079	0.257	2.50	0.013	1.74
Inventory Valuation Methods (IVM)	0.039	0.049	0.053	0.78	0.436	1.66
Inventory Control Records (ICR)	0.026	0.060	0.037	0.44	0.663	1.82
Inventory Measurement (IM)	0.321	0.064	0.383	5.04	0.001	1.55

Figure 1. Conceptual model of inventory-management effects on profitability (standardized coefficients).

Inventory control also showed a statistically significant effect (β = 0.257; p = 0.013), suggesting that replenishment policies, safety stock, and order tracking help companies avoid stockouts and reduce emergency purchases, in line with Wilson (2023) and Panigrahi A, et al. (2024).

In contrast, inventory valuation methods and control records showed no direct statistical effects (p > 0.40), supporting contingency-based views: valuation decisions produce indirect effects mediated by the costs of goods sold (IAS 2; Chen and Kong, 2019); records may help internally, but they require integration with planning systems to be reflected in profitability (Becerra et al., 2022). These findings reinforce the dynamic capabilities approach: capabilities must not only exist, but must be strategically deployed to convert operational data into economic benefits.

The model illustrates that inventory measurement (IM) has the strongest and only statistically significant relationship with profitability (β = 0.383, p < 0.001); MSEs that continuously evaluate turnover, ageing, stockouts, and replenishment rates demonstrate greater operational responsiveness, leading to better cost control and higher financial returns.

In contrast, inventory valuation methods (IVM) (β = 0.053, p > 0.05) and inventory control records (ICR) (β = 0.037, p > 0.05) show no significant direct effect on profitability; these areas, while relevant to accounting accuracy and compliance, appear insufficient on their own to drive financial results without being connected to tactical decision-making.

These data reinforce the idea that measurement capability is a dynamic capability that strengthens strategic decision-making in purchasing and replenishment; in line with the resource-based view (RBV), inventory management becomes a competitive resource when it generates financial benefits and cannot be easily imitated by competitors.

The results are also consistent with contingency theory, which indicates that operational practices must adapt to the environmental uncertainty typical of micro and small enterprises in Bagua, where high demand variability and limited administrative experience increase vulnerability to financial losses.

From a management perspective, the model suggests that micro-enterprises should prioritise inventory analysis dashboards over purely documentary control. In addition, they should strengthen forecasting, reorder points and safety stock policies related to measured KPIs. Similarly, they should integrate IVM and ICR into pricing, purchasing, and logistics workflows to unlock their indirect effect on returns.

Conclusions

This study provides empirical evidence that inventory measurement and control are the most relevant internal capabilities influencing the profitability of micro and small enterprises (MSEs) in Bagua; organisations that actively monitor stock turnover, days of inventory on hand, and obsolescence risks, and that implement periodic replenishment policies, tend to achieve better financial results, including improved ROA, gross margin, and ROE.

In contrast, inventory valuation methods and record-keeping practices did not show significant direct effects on profitability, suggesting that such practices may contribute indirectly and that their positive financial impact depends on complementary administrative capabilities, such as integration with cost control, demand forecasting, and pricing decisions.

The findings should be interpreted taking into account the contextual limitations of the study: non-probabilistic sampling, self-reported measures, and a restricted geographical scope focused on micro-enterprises in Bagua, Peru; these factors limit the external validity of the results, which should not be generalised to other sectors or regions without caution.

Despite these limitations, the results offer relevant implications for management, such as strengthening digital inventory monitoring, improving staff training in performance metrics (e.g., turnover, GMROI), and incorporating affordable technological solutions (basic ERP/WMS tools) can help MSEs improve operational visibility and reduce pressures on working capital. Policy makers and business support programmes can also leverage these insights to promote capacity-building strategies focused on financial efficiency.

Future research should explore probability-based sampling, triangulation with accounting and enterprise resource planning (ERP) records to reduce common method bias, and SEM-PLS modelling to examine indirect paths and confirm proposed causal mechanisms. such studies would help validate whether the relationships observed in Bagua hold in broader contexts, contributing to the practical and theoretical development of inventory management in emerging economies.

Ethics and consent

This research was approved by the Ethics Committee of César Vallejo University (Resolution No. 447-2023-VI-UCV of the Vice-Rectorate for Research), in accordance with the principles of the Declaration of Helsinki. All procedures complied with national and international ethical standards applicable to research involving human participants.

Written informed consent was obtained from all participants before administering the questionnaire. All respondents were over 18 years of age and were fully informed about the objectives of the study, the voluntary nature of their participation, the confidentiality of their data, and the exclusive use of the information for academic and scientific purposes.

Data availability

All data supporting the results of this study including the values underlying the reported means, standard deviations, and other measures; the values used to generate the figures; and the data points extracted from images for analysis are available in the Data availability the Victor Puican repository.

Excel data base

Process identifier

https://doi.org/10.23728/B2SHARE.C7BA1BDAF7B8423A8D0E467A446EB0F9 (Rodriguez et al., 2025).

Additional materials and data

The additional data used in this study, including the complete questionnaire, instrument application guide, and supplementary tables of statistical results, are publicly available in the same data repository.

Process identifier

https://doi.org/10.23728/B2SHARE.C7BA1BDAF7B8423A8D0E467A446EB0F9 (Rodriguez et al., 2025).

These materials allow for open replication and review of the study.

The full dataset is accessible without restrictions or embargoes under a CC-BY 4.0 license.

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Version 2

VERSION 2 PUBLISHED 02 Sep 2025

Author details Author details

¹ Universidad Cesar Vallejo, Trujillo, La Libertad, Peru
² Universidad Cesar Vallejo, Trujillo, La Libertad, Peru
³ Universidad Cesar Vallejo, Trujillo, La Libertad, Peru

Víctor Hugo Puican Rodríguez
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Writing – Original Draft Preparation

RITA DE JESUS TORO LÓPEZ
Roles: Methodology, Software, Supervision, Validation

Waldemar Ramón García Vera
Roles: Data Curation, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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https://doi.org/10.12688/f1000research.168900.2

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© 2025 Puican Rodríguez VH et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Puican Rodríguez VH, TORO LÓPEZ RDJ and García Vera WR. Innovations in Inventory Management to Improve the Profitability of Local SMEs [version 2; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2025, 14:853 (https://doi.org/10.12688/f1000research.168900.2)

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Approved with Reservations

https://doi.org/10.5256/f1000research.190753.r431317

I have analyzed the paper and I believe that the topic has good potential. However, in my opinion, the entire work is shallow. The introduction is too brief, the literature has ignored almost majority of the relevant recent studies. The methodology is precise and the analysis is also very precise. the topic is good, but every step needs explanation and support of the most relevant and current literature. I am recommending a few studies, but you must enhance your writing to more literature and add at least the review of 30 40 more articles and strengthen introduction with theoretical gaps that your study is trying to fill. Likewise, for the literature, expand methodology, follow the tests needed to support the kind of analysis you are making.
I am just recommending a few papers related to innovation and profitability, make sure you need to add more studies into it.
Asad, M., Sulaiman, M. A., Awain, A. M., Alsoud, M., Allam, Z., & Asif, M. U. (2024). Green entrepreneurial leadership, and performance of entrepreneurial firms: Does green product innovation mediates? Cogent Business & Management, 11(1), 2355685. doi:https://doi.org/10.1080/23311975.2024.2355685
Awain, A. M., Asad, M., Sulaiman, M. A., Asif, M. U., & Shanfari, K. S. (2025). Impact of supply chain risk management on product innovation performance of Omani SMEs: Synergetic moderation of technological turbulence and entrepreneurial networking. Sustainability, 17(7), 2903. doi:https://doi.org/10.3390/su17072903
Sulaiman, M. A., Asad, M., Awain, A. M., Asif, M. U., & Shanfari, K. S. (2024). Entrepreneurial marketing and performance: Contingent role of market turbulence. Discover Sustainability, 5, 1-20. doi:https://doi.org/10.1007/s43621-024-00710-8

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Entrepreneurship and SMEs performance

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Version 1

VERSION 1

PUBLISHED 02 Sep 2025

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Reviewer Report 16 Sep 2025

Juan Carlos Quiroz-Flores, Carrera de Ingeniería Industrial, Universidad de Lima, Santiago de Surco, Lima, Peru

Approved with Reservations

https://doi.org/10.5256/f1000research.186120.r411543

The manuscript addresses a relevant and timely problem in the field of industrial engineering and presents findings that could be of interest to both academics and practitioners. Nevertheless, there are aspects that limit its current scientific soundness. First, the presentation of the work is only partly clear. Some sections lack precise definitions and the review of current literature is limited; expanding and updating the state of the art with more recent high-impact references would help to strengthen the context and highlight the research gap.
Regarding the study design, it is partly appropriate but not fully explained. The methodological section does not provide sufficient detail on sampling criteria, operationalization of variables, or the validation of instruments, which makes replication by others difficult. Although the statistical analysis is included, its justification and interpretation are only partly appropriate; the authors should reinforce the robustness of the chosen techniques and explain why they are suitable for the research objectives.
An important strength of the article is the open availability of all underlying data and supplementary materials in a public repository under a CC-BY 4.0 license. This commitment to transparency significantly increases the reproducibility and value of the work, enabling replication and secondary analysis by other researchers. However, even with data availability, the methods and analytical steps should be described more systematically in the manuscript itself to ensure clarity and replicability without external consultation.
Finally, the conclusions are only partly supported by the results. While they are aligned with the stated objectives, in some cases they are presented with greater breadth than the evidence can fully sustain. A more cautious and data-driven interpretation is recommended.
In summary, the article makes a potentially valuable contribution but requires improvements to achieve the necessary level of scientific rigor. Specifically, the authors should: (1) strengthen and update the literature review; (2) provide a more detailed methodological description; (3) improve the justification and clarity of the statistical analysis; and (4) refine the conclusions so that they are fully supported by the presented evidence. Addressing these points would considerably enhance the impact and credibility of the study.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Industrial Engineering, Process Improvement, Lean Manufacturing, Supply Chain Management, Six Sigma, Digital Transformation, Industry 4.0, Machine Learning.

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Author Response 07 Nov 2025

VICTOR HUGO PUICAN RODRIGUEZ, Universidad Cesar Vallejo, Trujillo, Peru

07 Nov 2025

Author Response

Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,
... Continue reading Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,

Dr Victor Puican
Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,

Dr Victor Puican
Competing Interests: No hay conflicto de intereses. Close
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COMMENTS ON THIS REPORT

Author Response 07 Nov 2025

VICTOR HUGO PUICAN RODRIGUEZ, Universidad Cesar Vallejo, Trujillo, Peru

07 Nov 2025

Author Response

Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,
... Continue reading Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,

Dr Victor Puican
Greetings, esteemed reviewer.

Thank you very much for your comments on our work, which have enabled us to improve our research.
All comments have been addressed.

With gratitude,

Dr Victor Puican
Competing Interests: No hay conflicto de intereses. Close
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Reviewer Report 16 Sep 2025

Vina Sari Yosephine, Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Special Capital Region of Jakarta, Indonesia

Not Approved

https://doi.org/10.5256/f1000research.186120.r411548

General Summary

The article, “Innovations in Inventory Management to Improve the Profitability of Local SMEs,” investigates the impact of inventory practices (control, valuation, record-keeping, and measurement) on the profitability of micro and small enterprises (MSEs) in Bagua, Peru. Using a quantitative, descriptive-explanatory design, the authors collected 200 valid responses from 83 MSEs via a 21-item Likert-scale questionnaire. Data were analyzed using descriptive statistics and multiple regression in SPSS v27, focusing on profitability indicators such as ROA, gross margin, and ROE.
The results suggest that inventory measurement and inventory control significantly influence profitability, whereas valuation methods and record-keeping do not show significant effects. The authors conclude that improving measurement and control practices, along with adoption of digital systems, could enhance the financial performance of local SMEs.

1. Clarity
The article is generally well written and follows a standard IMRAD structure. However, the writing style is sometimes descriptive and informal, especially in the introduction and discussion (“the most important thing this paper does…”). The literature cited is up to date (2020–2023) and relevant, but the review is presented more as a list of studies rather than a critical synthesis. There is no clear theoretical framework or conceptual model guiding the study.

Suggestion:
Revise the Introduction and Literature Review to critically synthesize prior work rather than listing references.
Clearly identify the research gap and justify why Bagua, Peru, provides a meaningful contribution beyond confirming prior findings.
Introduce or align with an established theoretical framework (e.g., Resource-Based View, Dynamic Capabilities, or Contingency Theory) to strengthen novelty.

2. Design
The quantitative survey approach is acceptable, but the use of convenience sampling weakens generalizability. The paper labels the design as “descriptive-explanatory” but does not justify why this is the most suitable design. Only four inventory practice dimensions are considered without clear rationale or theoretical justification.

Suggestion: Provide a stronger justification for the study design and selection of variables. Why were control, valuation, record-keeping, and measurement prioritized? Consider addressing common method bias and response bias, which are common risks in survey-based studies. Discuss limitations of convenience sampling more explicitly and propose how future research can address this.

3. Methodology
Some methodological details are included (e.g., pilot test, Cronbach’s alpha, validation by experts), but more transparency is needed.
Tables are incomplete (Table 1 shows only mean values; regression results are summarized but lack full coefficients, p-values, and confidence intervals).

Suggestion

Expand the Methods section with more detail on: Questionnaire design and item sources, reliability and validity results (report Cronbach’s alpha values, factor analysis results if available)., sampling logic (why 83 MSEs, how sectors were chosen).

4. Interpretation of Statistical Analysis
The regression model is appropriate at a basic level and explains 40% of variance in profitability. However, the analysis is limited to simple regression. No robustness checks, mediation/moderation analysis, or multivariate modeling were attempted. Interpretation of results is shallow—conclusions mainly restate statistical findings without linking back to theory.

Suggestion: Report full regression output, including coefficients, SE, t-values, and significance. Consider using more advanced methods (e.g., SEM-PLS or CFA) to validate the measurement model. Deepen the discussion by linking findings to prior studies and theoretical implications, not only descriptive interpretations.

5. ConclusionThe main conclusions (measurement and control matter more than valuation and record-keeping) are supported by the data. However, broader claims about competitiveness and profitability improvement are overstated given the narrow sample and cross-sectional design.

Suggestion: Revise the Conclusion to better align with study limitations. Emphasize that findings are context-specific (Bagua SMEs) and cannot be generalized without caution.
Add practical managerial implications for SME managers (e.g., training, adoption of digital systems).

Minor Improvements: Revise language for conciseness and academic tone. Improve tables and figures (add clarity, labels, descriptive statistics).Remove informal phrasing in abstract and discussion.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Industrial Engineering, Supply Chain Management, Production Scheduling, Inventory Control, Digital Transformation in SMEs, and IoT-based Logistics Systems

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Version 2

VERSION 2 PUBLISHED 02 Sep 2025

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Version 2 (revision) 07 Nov 25			read
Version 1 02 Sep 25	read	read

Vina Sari Yosephine, Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia
Juan Carlos Quiroz-Flores, Universidad de Lima, Santiago de Surco, Peru
Muzaffar Asad, Tecnológico de Monterrey,, Monterrey, Mexico

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6 Views

20 Nov 2025 | for Version 2

Muzaffar Asad, Entrepreneurship, Tecnológico de Monterrey,, Monterrey, Nuevo Leon, Mexico

6 Views Cite this report Responses(0)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Entrepreneurship and SMEs performance

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Responses (0)

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Reviewer Report

18 Views

16 Sep 2025 | for Version 1

Juan Carlos Quiroz-Flores, Carrera de Ingeniería Industrial, Universidad de Lima, Santiago de Surco, Lima, Peru

18 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Industrial Engineering, Process Improvement, Lean Manufacturing, Supply Chain Management, Six Sigma, Digital Transformation, Industry 4.0, Machine Learning.

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Responses (1)

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Reviewer Report

15 Views

16 Sep 2025 | for Version 1

Vina Sari Yosephine, Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Special Capital Region of Jakarta, Indonesia

15 Views Cite this report Responses(0)

Not Approved

Expand the Methods section with more detail on: Questionnaire design and item sources, reliability and validity results (report Cronbach’s alpha values, factor analysis results if available)., sampling logic (why 83 MSEs, how sectors were chosen).

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Industrial Engineering, Supply Chain Management, Production Scheduling, Inventory Control, Digital Transformation in SMEs, and IoT-based Logistics Systems

Respond to this report

Responses (0)

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Innovations in Inventory Management to Improve the Profitability of Local SMEs

Abstract

Background

Method

Results

Conclusion

Keywords

Revised Amendments from Version 1

Introduction

Research gap

Contribution

Literature review

Definitions and mechanisms

Empirical evidence

Summary and gap

Methods

Research design and rationale

Population and sampling frame

Sampling strategy, limitations, and future research

Variables and operationalisation

Development and validation of the instrument

Measurement properties

Bias mitigation and diagnosis

Statistical analysis plan

Results and discussion

Descriptive results

Table 1. Multidimensional statistical characterization of inventory management and profitability indicators.

Regression analysis

Table 2. Regression model summary, ANOVA, and standardized coefficients.

Figure 1. Conceptual model of inventory-management effects on profitability (standardized coefficients).

Conclusions

Ethics and consent

Data availability

Excel data base

References

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