Integration of Structural Equation Modeling and Machine Learning in Supply Chain Management

Introduction

Supply chain management (SCM) means the goods and services are transferred from providers to end users. The evolution of data analytics and machine learning (ML) have transformed the supply chain systems drastically. The use of Data analytics and ML gives more accurate demand forecasts, better inventory management, and better production scheduling. Structural Equation Modelling is becoming increasingly famous in the supply chain to model hidden variables and interactions that are not obvious. Both SEM and ML have resulted positively when used separately, but when used together, they give positive results and solve many of the problems in supply chain management.

Structured equation modeling (SEM) and machine learning (ML) are two effective statistical tools that can be used to look at data in supply chain management (SCM). Machine learning can be used to predict the future, like demand or inventory levels, and SEM can be used to look at the links between things like economic success and customer happiness. Adding SEM and machine learning to SCM can help in solving several problems, such as difficulty which can solve complex relationships these things and find growth possibilities. It helps in projection by which future can be predicted which will help make better decisions about handling inventory, moving goods, and making things. By using SEM and AI processes can be optimized and makes processes better. These methods will improve, making them even more useful tools for improving the efficiency of the supply chain.

Here are some examples of how SEM and machine learning are used in SCM:

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  A study from the University of Tennessee used SEM to determine that price, product quality and reliability affect the factors that affect the supply chain process

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  Using machine learning, IBM did a study on how to predict what things people will want to buy in a supply chain. It revealed that machine learning has a higher ability to make more accurate predictions about demand than other methods.

The research in supply chain management has evolved over a while. Researchers have used both machine learning and structure equation modeling in this stream to understand fully how the determinants influence the efficiency of the supply chain in the organization. SEM helps in determining the underlying factors responsible for change and examining the relationship. Whereas machine learning algorithms are great at the management of high-volume data. This allows us to make real-time estimates and helps us in improving the efficiency of supply chain networks.

Literature Review

According to Cho and Kim (2020), one of the core benefits of using SEM in supply chain modeling is that it helps in determining the vital factors that affect the success of the supply chain. Similarly, Li et al. (2019) found that ML algorithms help in accurately predicting demand patterns and exploring inventory quantity. Chen et al., (2021), discussed the likelihood of utilizing both SEM and ML to assess the effectiveness of supply chain operations. The supply chain is vital in today’s business. Firms need to improve SCM to stay ahead of the competition.