Several studies have highlighted the significance of effective inventory management in significantly increasing company profitability, especially for SMEs; effective inventory management significantly affects financial performance, operational profit margin, and net income in various production sectors; the key is to maintain an appropriate inventory level to prevent shortages and overstocking ( Ahmad et al., 2022; Anisere & Bodunde, 2021; Sultan, 2021; Gamariel & Annet, 2021; Golas, 2020).

Mathematically simple models such as the Economic Order Quantity (EOQ) are frequently employed to determine optimal stock levels and reduce operational costs associated with purchasing, warehousing, and stockouts ( Çalışkan, 2021; Condeixa et al., 2020; Nobil et al., 2023; Ayatollahi & Jafari, 2022; Liao et al., 2019); on the other hand, the Wilson model takes into account volume discounts and variable storage costs, adding a temporal dimension, which makes it more flexible in response to changing and unpredictable demand environments ( Samadi et al., 2020; Kazemi et al., 2018; Wray et al., 2023; Shepherd et al., 2023; Liu et al., 2020).

Despite queuing theory’s original intent, researchers have found ways to apply it to inventory management. This has led to improvements in replenishment times and shorter lead times ( Ahmed et al., 2019; Wu et al., 2023; Petrovic et al., 2023; Ala et al., 2023; Qandeel et al., 2023); integrated inventory management, which includes accurate valuation methods, comprehensive controls, and regular measurements using key performance indicators (KPIs), improves operational visibility, optimizes space utilization, and reduces costs ( Chunxia & Shunfu, 2018; Chen & Kong, 2019); finally, from a business lifecycle and Porter’s five competitive forces point of view, MSEs can clearly see their competitive landscape and change their inventory strategies to make the most money, which gives them advantages like being different and running their businesses more efficiently ( Pizzan et al., 2022; Jerónimo et al., 2022; Nirmala et al., 2022; Becerra et al., 2022); similarly, the sustainable growth model provides a framework for balancing growth and profitability, making decisions that ensure long-term financial stability ( Epizitone & Nxumalo, 2021; Drakaki & Tzionas, 2021; Nozari et al., 2022; Gammelli et al., 2022; Preil & Krapp, 2022; Walt & Bean, 2022; Moncayo & Tarrillo, 2020; Zhou, 2023; Jiang et al., 2022; Khuong & Anh, 2023; Dias et al., 2023; Baxter, 2019; Maartens & Hutmacher, 2022; Fernandez et al., 2020; Chien-Chiang et al., 2023; Henderson & Loreau, 2023; Hu & Liu, 2022; Harbiankova & Scherbina, 2021; Lan et al., 2019; Copeland & Weston, 1988; Pissan et al., 2022; Luna et al., 2021; Mundt et al., 2022; Merino et al., 2021; Setianto et al., 2022; Belloso et al., 2021; Caiza et al., 2020; Moncayo & Tarrillo, 2020; Ramón & Bañón, 2022; Sánchez et al., 2022; Kogan et al., 2022; Flores, 2019).

This research follows a quantitative approach, complemented by a descriptive-explanatory design, directly aligned with the established objectives; the use of this approach allowed for the systematization and statistical analysis of the data, facilitating the generation of generalizable and verifiable results through hypothesis testing; additionally, the study did not limit itself to merely describing phenomena but also measured and analysed the causal relationships between the different variables and dimensions involved; as an applied study, the results provided direct practical implications, offering valuable information for more informed decision-making regarding inventory management and its impact on profitability.

A total of 83 MSEs in the city of Bagua were contacted, resulting in the inclusion of 415 individuals, as five key members from each MSE were considered: the manager, warehouse staff, general accountant, and accounting assistants; consent from the managers was sought for participation in the questionnaires designed for the research; these companies represented various sectors, including hardware stores, multipurpose services, and construction companies; using the finite proportion formula, the final sample consisted of 200 individuals, representing 40 microenterprises, with participants holding roles such as managers, warehouse supervisors, accountants, and accounting assistants.

A non-probability convenience sampling method was employed, selecting the most accessible MSEs for the researchers; inclusion criteria for selecting MSEs included: voluntary participation, registration as active businesses, up-to-date payments in accordance with the Unique The study required MSEs to be registered with the Taxpayer Registry and to have a minimum of five active employees; the study excluded MSEs that failed to meet these criteria or couldn’t commit the required time.

Prior to the final application of the questionnaire, a pilot test was conducted with a reduced sample to evaluate the reliability of the items using Cronbach’s alpha coefficient; this test allowed for the adjustment and refinement of the questionnaire items to ensure they were clear and consistent, ensuring the quality and reliability of the data collected; the second section of the questionnaire explained the scales used for data collection, such as the Likert scale, which offered five response options: “Never” (scored as 1), “Rarely” (2), “Sometimes” (3), “Almost always” (4), and “Always” (5); the third section measured participants’ perceptions and attitudes regarding the central theme of the study through Likert scales and multiple-choice questions; finally, the fourth section included items aimed at gathering information about participants’ experiences and behaviour’s related to the investigated phenomenon.

The questionnaire, consisting of 21 items, was previously validated by three experts holding master’s and doctoral degrees; after validation, the collected data were organized in an Excel spreadsheet and transferred to SPSS 27 for statistical analysis; the first two specific objectives were addressed using descriptive statistical methods to examine the levels of the respective variables and dimensions; subsequently, inferential statistical analysis was applied to determine whether the data were parametric or non-parametric, which was crucial for hypothesis testing and analysing the relationships between variables.

Table 1 shows a summary of the different types of statistics that describe inventory management and how it directly affects key indicators of MSEs’ profitability in Bagua; a moderate performance is observed across all evaluated dimensions, with inventory measurement showing the highest mean value (2.62); this finding suggests that inventory measurement is prioritized by firms in their operational practices; the negative skewness across variables indicates a trend toward higher self-assessed performance ratings, especially in inventory measurement; in important areas related to profitability, the mean scores for all variables fall between 2.37 and 2.62, indicating generally moderate performance; additionally, the median value of 3 further supports the assertion that the majority of firms achieve acceptable performance levels; the negative skewness values, which are most noticeable in Return on Assets (ROA: -0.947) and Gross Margin (-1.007), suggest that while many firms report pretty good performance, a smaller group with worse performance may be changing the overall mean.

The variables in Table 2 related to inventory management explain about 39% of the differences in how profitable Bagua’s MSEs are; this is shown by an R-squared value of 0.402 and a significant correlation coefficient (R = 0.634, p < 0.001); inventory measurement emerges as the variable with the most substantial positive effect on profitability, displaying a standardized beta coefficient of 0.383 (p = 0.001); inventory control also significantly contributes to profitability, albeit to a lesser extent (Beta = 0.257, p = 0.013); conversely, inventory valuation methods and control records were statistically insignificant, suggesting limited direct influence on profitability within this context.

The study shows that residuals are close to a normal distribution, which proves a key regression assumption and shows that the regression model is robust and reliable; normality in residuals ensures the precision of estimates and strengthens the credibility of findings, particularly regarding inventory management practices and their significant relationship to profitability; the regression results underscore inventory measurement as the predominant factor influencing profitability, with a standardized coefficient Beta of 0.383; this aligns with extant literature highlighting effective inventory measurement as critical for profitability enhancement, notably within manufacturing and similar sectors Ahmad et al. (2022) and Gamariel and Annet (2021); additionally, inventory control also demonstrated significance (Beta = 0.257), corroborating findings from previous studies ( Sultan, 2021) the fact that inventory valuation methods and control records don’t have any statistical significance shows that these practices need to be carefully looked at again and maybe changed to fit Bagua’s business environment.

The empirical results underscore that inventory management variables, particularly inventory measurement, have a moderate yet meaningful impact on the profitability of Bagua’s MSEs; these findings confirm the critical role inventory measurement and control play in enhancing operational efficiency and financial performance. This research aligns with prior empirical investigations that report a positive association between efficient inventory management and enhanced profitability Nobil et al. (2023) and Wray et al. (2023); such alignment reinforces the notion that improved inventory practices consistently yield measurable financial benefits across diverse economic sectors and regions.

Implementing precise inventory measurement practices could significantly enhance resource optimization and profitability within Bagua’s MSEs; firms are encouraged to integrate advanced inventory tracking technologies to streamline processes, reduce operational expenses, and improve competitive positioning; additionally, revisiting current inventory valuation methods may further improve accuracy and profitability outcomes.

This research is constrained by its cross-sectional design and geographically focused sample, potentially limiting the generalizability of findings; furthermore, the static nature of cross-sectional analysis precludes exploration of longitudinal causal relationships and profitability trends over time.

Future research should adopt longitudinal designs to investigate the sustained effects of inventory management practices on MSE profitability over extended periods; comparative analyses across different regions or industrial sectors could further enrich understanding and yield more profound insights into best practices and their adaptability within varied business environments.