Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Abstract

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

Application of Industry 4.0 in the Procurement Processes of Supply Chains: A Systematic Literature Review

“…The outcomes of this paper are original and novel in comparison with the relevant papers that studied P4.0 in the literature [28,95,121,144,145]. Whereas, previous papers had a focus on comprehensive models [95], reviewing the impact of P4.0 on business functions of a supply chain [28,[146][147][148][149][150], manufacturing modeling to optimize organizational procurement processes [121,151], developing resiliency in supply chains against disruptions [144], and resource planning to develop productivity in remanufacturing operations [145], this paper is the first contribution to the subject that employs a systematic literature review to emphasize the values obtained from the adoption of P4.0 into supply chains. Despite the sprouting literature in this field, it is obvious that there is a lack of comprehensive and systematic frameworks, strategies, and approaches for the implementation of I4.0 in the procurement processes of SCs.…”

The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda

“…In the management of health supply chains and pharmaceutical commodities, timeseries models are used the most (52 percent) and causal models are used the least (24 percent), according to a 2002 study by Jain, while judgmental models account for 19 percent and mixed or combination models account for 5% [30]. Furthermore, leveraging AI technology such as ML applications to improve the accuracy in forecasting and demand predictions for essential medicines might potentially improve their availability and reshape their well-ordered distribution [31,32].…”

Determining the Level of Use of the Industry 4.0 Solutions in the COVID-19 Pandemic Era: Results of Empirical Research

“…The literature shows the diversity of Industry 4.0 technologies. This collection includes, among others, (1) big data and analytics [20][21][22][23], (2) autonomous robots [24], (3) simulations [25][26][27][28][29][30], (4) horizontal and vertical system integration [31,32], (5) Internet of Things-IoT [31], (6) cyber-security [33,34], (7) the cloud, (8) additive manufacturing, (9) augmented reality, (10) artificial intelligence [35], (11) mobile technologies, and (12) RFID and RTLS technologies [12,30,[36][37][38][39][40][41][42]. Each solution could be independently implemented as a separate project for the organisation.…”