Inventory management forms a critical operational pillar for MSMEs, impacting working capital, production continuity, and customer satisfaction. Yet, most Indian MSMEs continue to rely on manual inventory practices due to limitations in capital, human resources, and technological adoption (21). Studies have indicated that conventional practices, such as handwritten ledgers or informal stock tallies, dominate in semi-urban industrial corridors, limiting the scope for demand forecasting and procurement optimization (22). Sharma et al. (23) highlight that in defense manufacturing clusters, particularly in supply chain-critical regions like Gwalior, operational delays are directly linked to outdated inventory practices. This points to a broader national issue wherein MSMEs across various sectors are unable to leverage modern inventory frameworks like VMI or Cloud Inventory Systems due to systemic and contextual barriers.

Foundations and Evolution: VMI is a collaborative inventory strategy that reallocates stock responsibility from the buyer to the supplier, aiming to streamline replenishment, reduce stockouts, and synchronize supply chains (24). In traditional procurement models, buyers forecast demand and place orders; under VMI, suppliers make replenishment decisions based on consumption data provided by buyers, thereby eliminating many inefficiencies associated with the bullwhip effect. Academic literature has rigorously explored the structural mechanics and outcomes of VMI. Aitken et al. (25) argue that VMI contributes significantly to demand chain responsiveness and is most effective when supported by integrated IT systems. Hendricks and Singhal (26) further reveal that supply chain disruptions, often stemming from inventory mismanagement, can result in shareholder value losses, particularly in small firms with tight cash flows. However, for MSMEs, successful VMI implementation depends on a delicate balance of trust, data sharing, and digital interfacing (27). While large firms can automate VMI through ERP systems, MSMEs often lack such capabilities, resulting in partial or hybrid deployments. As observed by Sahay and Mohan (28), most Indian MSMEs prefer to engage in “relationship-based” procurement where informal credit, personal rapport, and transaction flexibility take precedence over system-driven inventory policies.

Democratizing Digitization: Cloud-based inventory management systems—offered through SaaS platforms—have emerged as game-changers for SMEs by removing the need for on-premise infrastructure. These tools provide real-time inventory tracking, supplier integration, order automation, and analytics dashboards, all accessible via internet-enabled devices (29). Unlike traditional ERP suites that require high capital investment, cloud systems offer modular, pay-as-you-use models, making them ideal for MSMEs with limited budgets. Melnyk et al. (30) classify such tools as “outcome-driven” enablers that enhance forecasting accuracy and reduce inventory carrying costs. Flynn et al. (31) found that cloud-integrated supply chains show 23–35% higher responsiveness than manually managed systems. Nevertheless, challenges persist. Ganesan et al. (32) note that digital inventory systems often fail to gain traction in SMEs due to literacy barriers, resistance to change, and perceived complexity. Narasimhan and Kim (33) emphasize the need for configuration and training tailored to the organizational maturity of small enterprises. A poor implementation strategy may lead to underutilization of functionalities or operational confusion, defeating the purpose of automation. In Indian MSMEs, the adoption of cloud inventory systems is frequently hindered by poor infrastructure, lack of after-sales support, and concerns around data security (34). Additionally, MSME clusters often operate with minimal IT staff, requiring inventory tools that are not only cost-effective but also intuitive and low-maintenance.

Integrating VMI with cloud inventory systems offers a powerful model for seamless inventory control, especially in distributed supply networks. According to Seuring and Müller (35), sustainability in supply chains is increasingly driven by technological integration, and hybrid VMI-cloud frameworks allow real-time coordination between vendors and customers without costly intermediaries. Rosenzweig et al. (36) propose that organizations adopting integrated systems gain a competitive edge in cost-to-serve and customer responsiveness. In the MSME context, such integrations can ensure better vendor alignment, fewer stock discrepancies, and more transparent transaction histories. However, achieving this level of integration demands system interoperability and standardized data protocols—areas where Indian MSMEs continue to struggle (37). Studies from developing countries suggest that the hybridization of cloud tools and VMI practices leads to significant operational gains. For instance, in Indonesia and Vietnam, government-supported pilot programs have enabled textile SMEs to adopt low-code cloud interfaces linked to vendor portals, reducing average stock turnaround times by 25% (38).

Inventory systems do not function in isolation—they are embedded within broader supply chain networks. Supply chain integration, particularly internal (within enterprise functions) and external (with vendors and distributors), significantly determines the success of inventory models (31). In the MSME sector, where firm size often correlates with resource limitations, integration capabilities vary greatly. According to Chen and Paulraj (27), internal integration through systems like cloud ERP allows smoother demand planning, while external integration via VMI models ensures that inventory flows are demand-driven rather than supply-pushed. However, in MSMEs, both forms are frequently underdeveloped due to segmented operations, siloed information, and informal labor structures (34). Pagell and Wu (37) emphasize the need for strategic alignment between inventory policies and business models. For example, a make-to-stock MSME in automotive components may benefit more from VMI, while a make-to-order enterprise in custom textiles might find real-time cloud tracking more useful. The key lies in context-specific application rather than one-size-fits-all digital prescriptions.

Government and institutional support have a decisive role in inventory modernization. In India, several schemes—such as the Digital MSME initiative and MSME Champions platform—seek to encourage digital inventory adoption through subsidies, training, and vendor tie-ups. However, Mohite et al. (21) argue that such interventions often lack localization and fail to address cluster-specific needs. Sharma et al. (22) further note that awareness levels remain low, and uptake is fragmented, especially in second-tier clusters like Boisar MIDC. Global best practices suggest that targeted interventions—such as shared cloud platforms within industrial clusters, on-site VMI workshops, and cross-enterprise data sandboxes—can significantly improve MSME adoption rates (25). These measures, however, require coordination between industry associations, IT vendors, and government stakeholders.

While significant research exists on inventory models and supply chain technologies, a gap remains in understanding the real-world application of these systems in semi-urban and cluster-based MSME settings. Most existing literature focuses on large enterprises or developed economies, leaving MSME-specific adoption patterns underexplored. Studies such as that by Bourlakis and Bourlakis (38) suggest the importance of contextual variables—like vendor relationships, digital maturity, and workforce readiness—that are rarely modeled in empirical inventory research. The literature also lacks longitudinal studies that track the evolution of inventory practices over time within MSMEs. Melnyk et al. (30) recommend outcome-focused research that moves beyond technology adoption and measures operational impact through Key Performance Indicators (KPIs) such as stock turnover, order cycle time, and stockout frequency. In the Indian context, such research could offer valuable policy and managerial insights.

This study adopts a quantitative, survey-based cross-sectional research design to empirically assess the adoption and impact of VMI and cloud-based inventory management systems among MSMEs in the Boisar MIDC region of Maharashtra, India. This design was selected to provide statistically relevant generalizations and to capture measurable perceptions and operational indicators across multiple enterprise types. The unit of analysis is the enterprise (firm-level), and the respondents include operations managers, inventory supervisors, and enterprise owners. The study focuses exclusively on manufacturing MSMEs, as inventory control challenges and digitization demands are more critical in this sector compared to service-oriented MSMEs. In line with prior research by Mohite et al. (21), inventory performance among manufacturing MSMEs directly influences cost efficiency, customer satisfaction, and production planning. The conceptual framework builds on literature that links inventory model adoption (e.g., Economic Order Quantity (EOQ), Just-In-Time (JIT), VMI, cloud tools) to firm characteristics, such as size, digital maturity, and managerial skill level (4, 27). The dependent variables in this study include:

The study uses a stratified purposive sampling technique to ensure representation from all MSME categories—MMMEs—as per the Indian Ministry of MSME definitions (39). Stratification was applied to prevent over-representation of micro-enterprises, which form the majority of India’s MSME landscape. A total of 380 valid responses were obtained from MSMEs located in Boisar MIDC, a key industrial cluster in Maharashtra. The geographical focus was selected due to its diversity of manufacturing sub-sectors (engineering, chemicals, textiles, and food processing) and its blend of legacy and newly digitized firms (2).

Primary data were collected using a structured questionnaire comprising both closed-ended questions and five-point Likert scale statements. The instrument was developed and pre-tested with a pilot group of 20 MSME managers in Boisar to ensure clarity and validity.

The questionnaire consisted of five parts:

Structured interviews were also conducted with 25 respondents to validate questionnaire-based results, especially in the context of VMI collaborations.

The study categorizes inventory models into five types, in alignment with industry practice and prior studies (24, 40):

Firms could select multiple models, particularly those undergoing digital transition.

The following analytical tools and techniques were applied using IBM SPSS v26:

Despite robust planning, some methodological limitations must be acknowledged:

However, triangulation using both survey and interviews helps improve the internal validity of the study.

A total of 380 valid responses were collected from MMMEs within the Boisar MIDC region. The responses were stratified to ensure fair representation across enterprise types, capturing the diverse practices and readiness for inventory digitization. The data were cleaned using SPSS v26, and consistency checks were performed. Cronbach’s alpha for multi-item scales exceeded the 0.80 threshold, confirming internal reliability. The overall response rate was approximately 76%, with the highest participation from micro firms (n = 170), followed by small (n = 130) and medium enterprises (n = 80).

The inventory model adoption results demonstrate significant variations across enterprise categories. A detailed statistical analysis reveals the following distribution:

This stratified adoption trend echoes the technology maturity models proposed by Raut et al. (4), where operational digitization follows organizational complexity and resource availability.

The adoption of VMI and cloud-based inventory systems was explored through logistic regression, using the following independent variables: enterprise size, turnover, presence of dedicated IT staff, and previous training exposure.

Respondents were also asked to rank perceived benefits and challenges related to VMI and cloud-based inventory systems. Responses were recorded on a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree).

Among the 380 respondents, 92 firms had exposure to VMI in some capacity, mainly through raw material procurement from partner firms or distributors.

This supports the findings by Sharma et al. (22, 23), who noted similar hesitations in defense-sector MSMEs lacking digitized communication frameworks.

The empirical evidence underlines the need for tiered inventory modernization strategies, tailored to the unique constraints and enablers in each enterprise classification.

The findings echo the theoretical model by Chen and Paulraj (27), which emphasized resource capability as a key determinant in SCM model selection. The trend of greater VMI and cloud tool adoption in medium firms aligns with results from Aitken et al. (25), who argue that multi-tier visibility becomes more feasible with organizational scale. Further, the manual control dominance among micro firms confirms similar observations in Sharma et al. (22, 23) and Ghosh (2), who have repeatedly highlighted the friction in transitioning from paper-led to software-led inventory systems.