Fundamentals of Machine Learning for Supply Chain

Artificial intelligence in supply chain management: A systematic literature review

machine learning supply chain optimization

Less than one-third of companies perform an independent diagnostic at the outset, but this exercise can ensure companies have an accurate list of all the value-creation opportunities. Following the foundation of this study, where the emphasis is on how the different authors have applied different DL algorithms on the SCM and lack of consensus on SCM dimensions, different machine learning supply chain optimization aspects were recognized far from being utterly considered. Consequently, there is a marvelous opportunity to encourage researchers to advance the available knowledge in this area. To draw a future research agenda, we propose the research framework presented in Fig. This framework categorizes different areas that should be considered when using DL algorithms.

machine learning supply chain optimization

It’s also important to have multiple suppliers in different geographic locations so manufacturers can quickly switch from one supplier to another. Reliance on a single supplier for a critical component or raw material can cause production to grind to a halt. But even with these advances, global supply chains add several layers of complexity to inventory control. In light of the logistical and cost challenges faced in the past year, companies must do a better job stressing the importance of discipline.

Build a multichannel, responsive supply chain

The authors of Meisheri et al. (2021) addressed these challenges in a multi-period and multi-product system using DRL. Embracing change and fostering a culture of innovation will enable your organization to harness the full potential of AI-driven supply chain optimization and maintain a competitive edge in the future. Regularly assess the impact of AI solutions on supply chain performance and make adjustments as needed to maximize their effectiveness. Monitor key performance indicators and gather feedback from your team to identify areas for improvement and fine-tune your AI strategies.

Encourage a mindset of continuous improvement and responsiveness among your employees by promoting a data-driven culture. Empower your team to leverage insights from AI solutions and make data-driven decisions to improve performance across the supply chain. To successfully implement AI solutions, you need a team that combines supply chain management expertise with AI talent.

The Benefits of Supply Chain Network Optimization With AI

Journals of “SUSTAINABLE ENERGY GRIDS AND NETWORKS”, “REMOTE SENSING OF ENVIRONMENT”, and “INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH” have the highest citations that are 270, 138, and 64. “INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH” published three papers which is the highest number of papers with 38 total citations. “COMPUTERS IN INDUSTRY”, “INDUSTRIAL MANAGEMENT AND DATA SYSTEMS”, and “SENSORS”, each published two papers with 6, 14, and 23 total citations, respectively. Although we did not set any time limitation in our research query, as can be seen in Fig.

machine learning supply chain optimization

Finally, we’ll put our new skills to the test by optimizing a supply constraint problem using linear programming techniques. By checking the box below, you consent to GEP using your personal information to send you thought leadership content – such as white papers, research reports, case studies – and other communications. GEP representatives may contact you to provide additional information or answer questions. To ensure adoption of new solutions, companies must invest in change management and capability building. Employees will need to embrace new ways of working, and a coordinated effort is required to educate the workforce on why changes are necessary, as are incentives to reinforce the desired behaviors. This paper also proposes a conceptual framework that enables us to succinctly understand DL applications in SCM from a philosophy of knowledge perspective, and chart an agenda as a guideline for both practitioners and academic enthusiasts.

Machine learning and deep learning

During the training process, internal parameters of the unit including weight and biases are learned to produce outputs (Schmidhuber 2015). ANNs (also known as feed-forward NNs) are Multilayer Perceptrons (MLP) containing one or more hidden layers so that each layer has multiple hidden units (Alom et al. 2019). DNNs, by employing deep architectures in ANNs, are capable of representing learning functions with more complexity when the number of layers as well as units in a layer increases (Liu et al. 2017). In the retail industry, having multiple products with uncertain demands and different lead times makes determining the optimal inventory replenishment policy highly challenging (Meisheri et al. 2021).

AI in Supply Chain Market to be Worth $41.23 Billion by 2030 – Exclusive Report by Meticulous Research – GlobeNewswire

AI in Supply Chain Market to be Worth $41.23 Billion by 2030 – Exclusive Report by Meticulous Research.

Posted: Tue, 22 Aug 2023 07:00:00 GMT [source]

Then, we’ll dive deeper into some of the specific techniques and use cases such as using neural networks to predict product demand and random forests to classify products. An important part to using these models is understanding their assumptions and required preprocessing steps. We’ll end with a project incorporating advanced techniques with an image classification problem to find faulty products coming out of a machine.

Systematic review results

The search was conducted on the most used academic search engine Scopus (Portugal et al. 2018) on August 14, 2021. Then we limited the selected material to articles published in peer-reviewed journals that have been 59 papers and excluded the other publications (8 reviews, 70 conference papers, 46 conference reviews, 3 books, 2 book chapters, and 1 Erratum). Finally, we studied the abstracts of 59 remained papers to make sure whether they are related to the scope of our review or not. Through this investigation, 16 publications that were not directly related to the supply chain or did not use DL algorithms have been excluded, and a final sample of 43 publications was selected for further review. The resulting benefits include enhanced forecasting accuracy, reduced lead times, improved customer satisfaction, cost reduction, and a more resilient and sustainable supply chain.

machine learning supply chain optimization