Applications of Machine Learning in Supply Chain Management A Review SpringerLink

By analyzing various data sources, including weather conditions, and political instability, ANNs can identify and mitigate risks in terms of safety enhancement of supply chain processes. Artificial neural networks in supply chain management is studied in the research work to analyze and enhance performances of supply chain management in process of part manufacturing. New ideas and concepts of future research works are presented by reviewing and analyzing of recent achievements in applications of artificial neural networks in supply chain management.

• The first step is a thorough analysis of all elements of the current supply chain, followed by developing or adjusting production and inventory plans that align with demand forecasts.
• Furthermore, the review discusses the recent research trends and developments in the field, focusing on demand forecasting, inventory optimization, supplier selection and risk assessment, logistics optimization, supply chain risk management, and sustainability initiatives.
• Meanwhile, DL methods can be integrated with other technologies such as IoT and blockchain to improve the performance and integrity of supply chains.

Moreover, considering the target audience including both researchers and industry managers, providing the details of DL algorithms was impossible and just general explanations to familiarize both of them with the process of using DL algorithms in the SCM were brought forth. It goes without saying that the nature of the industry, as well as the type and volume of data, have a considerable impact on choosing an appropriate algorithm. So, it is recommended that DL algorithms should be selected carefully in order to the suitability of that with the nature of the data and its interpretability for the industry.

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Overall, ML is applied for supplier management, risk management, transport and distribution, and the circular economy. Some of the areas of study we review, based on a bibliometric analysis, include frameworks, performance management, and artificial intelligence (AI) challenges for supply chain management. Conversely, issues rarely discussed include the selection of ML techniques for supply chain management (SCM), sustainability issues, the future of ML in supply chain management, and system requirements for ML in supply chain management.

Industrial enterprises have recently become keenly interested in the highly value-adding potential of DL (Addo-Tenkorang 2016). Supply chain Key factors including inventory planning, supplier quality evaluation, demand forecasting, procure-to-pay, order-to-cash, production planning, logistics management, and more are becoming worthy paradigms for companies (Skjott-Larsen et al. 2007). DL approaches may be used to equip supply chains with a self-aware mechanism that connects many operational items at the same time, while constantly learning in the process (Tirkolaee et al. 2021). In the retail industry, daily operations are cost-oriented (Shavaki and Jolai 2021; Hossein Nia Shavaki and Jolai 2021) so, retailers need to manage their stock to have lower financial risks (Kilimci et al. 2019). They also require demand forecasts to make their stock-related decisions (Punia et al. 2020).

What Is Supply Chain Optimization?

The system uses the photos taken from products as they pass along the production line. Machine learning is a powerful tool with the potential to revolutionize supply chain management, delivering unprecedented levels of efficiency, agility, and customer-centricity. Integrating AI technologies into supply chain operations lets businesses optimize key components like demand forecasting, inventory control, and transportation management.

Machine learning can play a pivotal role in enhancing supply chain resilience by providing real-time insights and

predictive analytics that enable businesses to proactively address potential issues and minimize their impact. Today’s customers have high expectations regarding product availability, delivery speed, and overall shopping experience. To meet these expectations, companies must create responsive supply chains that quickly adapt to fluctuations in demand machine learning supply chain optimization and provide seamless service across various channels. Beyond the traditional focus on efficiency and cost reduction, machine learning offers a fresh perspective, enabling supply chains to become more agile, resilient, and customer-centric. Because it is so comprehensive, autonomous supply chain planning leads can improve performance in a range of processes across the supply chain (see sidebar, “A CPG company’s initial success with autonomous planning”).

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Invest in training and development programs to upskill your existing workforce, and consider hiring new team members with AI and machine learning backgrounds. Our ML model took into account a variety of data, including historical sales, current stock levels, warehousing capacity, logistics data from TMS, and predictive demand patterns. Based on these variables, we were able to implement an automated inventory replenishment system that could precisely adjust stock levels according to the anticipated demand. In addition, KPIs will likely need to be defined for the entire supply chain organization, with everyone incentivized to strive for the right target behaviors.

Two real-world sales datasets from a supermarket and a company selling pesticides have been used to verify the performance of the model. In the healthcare industry, Piccialli et al. (2021) proposed a predictive framework to forecast a 7-day sequence of respiratory disease bookings based on a hybrid neural network. Bookings time series data of the healthcare authorities of Campania Region in Italy as well as air quality and weather data have been used in the forecasting model. One of the best ways to improve supply chain efficiency is to automate routine tasks, which can free up employees to focus on higher-level tasks. For example, manufacturers can automate the replenishment of raw materials to automatically order more when supplies reach a certain threshold and to update customers on delivery status.

The success of this supply chain optimization solution illustrates the immense potential of machine learning and AI in streamlining the procurement and distribution processes in the retail sector. IBM, a multinational technology company, has leveraged machine learning to improve supplier management and mitigate supply chain risks. Through the use of AI-driven analytics, IBM has been able to identify potential supplier issues in order to take proactive measures that aim at minimizing possible disruptions. Adopting machine learning technologies in supply chain optimization offers a multitude of advantages.

For example, the average payment period for production materials KPI measures the time between receipt of materials and the time you pay for them, so you can favor suppliers that offer better billing terms. For example, quality managers can use specifications created at the beginning of product design to develop inspection plans for critical stages of the manufacturing process to identify raw material defects or damaged parts. The data gathered can be used to develop corrective action plans, which in the case of raw material defects might involve reviewing the initial specifications, determining whether the supplier can provide materials that meet those specifications, and testing a sample. At its most basic level, supply chain optimization has the goal of maximizing profits and minimizing costs.

Research gaps and future directions

Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors. Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent (software or hardware) learns from interacting with an environment to obtain rewards (Mousavi et al. 2016). Unlike the other machine learning algorithms, RL is defined by characterizing a learning problem instead of the learning method. Deep reinforcement learning (DRL) is the combination of DL and RL that has been able to solve various complex decision-making problems in many domains such as healthcare, robotics, smart grids, finance, and so on (Francois-Lavet et al. 2018).

Table 1 summarizes the related literature reviews’ directions and their differences compared to our review. As mentioned in almost all of the above literature reviews, in recent years, DL algorithms are becoming widespread in different areas of decision-making, especially in the future’s smart supply chains. However, there is a lack of a comprehensive systematic literature review investigating various angles of using DL in SCM, clarifying its advantages and disadvantages, and highlighting future research directions. To successfully implement AI-based supply chain optimization solutions, assess your supply chain’s readiness, set clear objectives, invest in high-quality data, and build a skilled and collaborative team.

In this section, we offer ten practical tips to guide you on your journey toward AI-driven supply chain optimization. The effective distribution and inventory management of drugs is a critical element in healthcare operations, more so in the intricate network of hospital pharmacies. One pharmaceutical company sought to optimize these crucial activities, recognizing the potential for considerable savings and efficiency. Machine learning (ML) plays a crucial role in enabling businesses to develop agile, customer-centric supply chains capable of thriving in a rapidly changing marketplace. Join us as we uncover the untapped potential of machine learning in supply chain management and learn how to navigate this uncharted territory for a future of innovation and sustainable growth. Data can be sourced from many areas like the marketplace environment, seasonal trends, promotions, sales and historic analysis.

Tosida et al. (2020) used a CNN model to classify telematics companies based on their need for assistance analyzing their general information, financial information, business obstacles, and prospects. Zhou et al. (2020) applied CNN on a supply chain financial dataset for fraud detection. Guo (2020) constructed a CNN to extract commodities’ features from their images to be used for price forecasting in e-commerce.

• For example, only 25 percent of respondents working with a system integrator reported that their objectives and the system integrator’s incentives are aligned.
• Manufacturers are constantly adjusting their supply chains to eke out cost savings and refine their processes.
• A comprehensive audit will measure the value of a specific vendor and make it clear how readily other vendors can be brought on board to fill any gaps.
• To answer the fourth question of descriptive analysis, we explored the sources of selected papers.