Statistical Sampling Methods in Large-Scale Inventory Management: Strengths, Weaknesses, and Cloud Implementation

Introduction

Inventory management is a critical function for large organizations, directly impacting operational efficiency and cost control. As organizations scale, the complexity and volume of inventory data necessitate robust statistical sampling methods to ensure accurate and cost-effective decision-making. With the advent of cloud computing platforms such as Amazon Web Services (AWS) and Microsoft Azure, organizations now have access to scalable analytics and machine learning tools that can further enhance the effectiveness of sampling strategies. This article examines five widely used statistical sampling methods—random, stratified, systematic, cluster, and judgmental sampling—evaluating their strengths and weaknesses in the context of large-scale inventory management. The analysis focuses on sample size, accuracy, efficiency, and the ability to model these methods on AWS and Azure.

Random Sampling

Random sampling is the most fundamental statistical sampling technique, where each item in the population has an equal probability of being selected. This method is straightforward to implement and does not require detailed prior knowledge of the population (Brown, 1963). Its simplicity makes it attractive for organizations with limited analytical resources or when the population is relatively homogeneous.

However, random sampling often necessitates larger sample sizes to achieve a desired level of precision, particularly in heterogeneous populations. This can increase both the cost and time required for inventory audits (Brown, 1963). While random sampling is supported by both AWS and Azure through basic analytics and machine learning tools, its efficiency and accuracy may be suboptimal compared to more advanced methods (Chebet & Mbandu, 2024).

Stratified Sampling

Stratified sampling involves dividing the population into distinct subgroups (strata) based on specific characteristics, such as product categories, locations, or value tiers. A random sample is then drawn from each stratum. This approach ensures that all key segments of the inventory are adequately represented, reducing sampling bias and increasing the precision of estimates (Chebet & Mbandu, 2024).

Research indicates that stratified sampling delivers significant efficiency gains and cost reductions in inventory management. Chebet and Mbandu (2024) report a 65% improvement in sampling efficiency and a correlation coefficient of 0.922 when using AWS-based stratified random sampling. Papa et al. (2020) observed a 41% reduction in sampling effort, while Yan et al. (2014) documented up to a 99% reduction in sample size compared to simple random sampling. These results highlight stratified sampling’s superiority in both accuracy and efficiency, especially for large, diverse organizations.

The main limitation of stratified sampling is the need for detailed population information to define appropriate strata. This can increase the initial planning and data collection burden. However, once implemented, stratified sampling is highly compatible with AWS, which offers scalable analytics and machine learning integration to automate the process (Chebet & Mbandu, 2024; Kotru & Batra, 2022). Evidence for Azure-based implementations is limited but suggests similar feasibility in principle (Himanshu & Chopra, 2025).

Systematic Sampling

Systematic sampling involves selecting items at regular intervals from an ordered list, such as every 10th or 100th item in an inventory database. This method is easier and faster to administer than random sampling and often results in smaller sample sizes and faster processing times (Pandey & Shukla, 2021).

The primary advantage of systematic sampling is its operational efficiency, making it suitable for large organizations with well-ordered inventory records. However, systematic sampling is vulnerable to ordering effects—if the inventory list contains hidden patterns or periodicity, the sample may become biased and unrepresentative (Pandey & Shukla, 2021). Despite this limitation, systematic sampling is well-supported on both AWS and Azure platforms, where automation can further enhance its efficiency.

Cluster Sampling

Cluster sampling divides the population into clusters, such as warehouses or geographic regions, and randomly selects entire clusters for sampling. This approach is particularly useful for organizations with geographically dispersed inventories, as it reduces logistical complexity and travel costs (Salido Magos et al., 2023).

While cluster sampling is logistically efficient, it carries a higher risk of sampling error and bias if the selected clusters are not representative of the entire population. The accuracy of results can be compromised, especially if clusters are internally homogeneous but differ significantly from one another (Salido Magos et al., 2023). Although AWS and Azure support cluster sampling techniques, there is less quantitative evidence of their effectiveness for cost-sensitive inventory management compared to stratified sampling (Chebet & Mbandu, 2024).

Judgmental (Expert) Sampling

Judgmental or expert sampling relies on the knowledge and experience of inventory managers to select items deemed most relevant or at risk. This method is valuable when statistical sampling is impractical or when dealing with rare or specialized inventory items (Kotru & Batra, 2022).

However, judgmental sampling is highly subjective and prone to bias, making results less reliable and harder to generalize. There is a lack of quantitative evidence supporting its cost or accuracy benefits in large-scale inventory management. Furthermore, judgmental sampling is not well-supported in AWS or Azure due to its lack of standardization and reproducibility (Himanshu & Chopra, 2025).

Cloud Implementation: AWS and Azure

Cloud platforms such as AWS and Azure provide scalable infrastructure for implementing statistical sampling methods. AWS, in particular, has demonstrated strong support for stratified sampling, offering machine learning and analytics tools that automate and optimize the sampling process (Chebet & Mbandu, 2024). Multi-cloud studies suggest that cost variance between providers can be significant, with Himanshu and Chopra (2025) reporting a 23% difference in implementation costs. While Azure supports standard sampling methods, there is limited published evidence of its effectiveness for advanced inventory management applications.

Conclusion

Among the five sampling methods evaluated, stratified sampling stands out as the most accurate and efficient for large-scale inventory management, particularly when implemented on AWS. Systematic sampling offers operational efficiency but is susceptible to ordering biases. Random sampling is simple but requires larger sample sizes, while cluster sampling is logistically efficient but less accurate. Judgmental sampling lacks statistical rigor and is not recommended for large organizations. Cloud-based implementations, especially on AWS, further enhance the scalability and effectiveness of these methods, enabling organizations to optimize inventory management processes and reduce costs.