Artificial neural networks in supply chain management, a review

machine learning supply chain optimization

This AI-driven solution resulted in a significantly optimized inventory, minimizing the occurrences of overstock and stock-outs, reducing waste, and improving cost-efficiency across the hospital pharmacies network. Challenged with the task of improving large-scale procurement processes, the company reached out to us, turning to applied analytics to better manage drug stocking and distribution across an extensive network of US hospitals. According to

IBM’s own case study, the company has reduced supply chain costs by $160 million thanks to its deployment of a cognitive supply chain. In addition, many organizations underestimate the time and effort that will be involved in ensuring data quality and availability when transitioning to an AI-based solution. “If the data is imprecise or incomplete, the tool will not be able to produce useful results, following the well-known garbage-in garbage-out principle,” Rigonat warns.

machine learning supply chain optimization

Autonomous planning is a continuous, closed-loop planning approach built on a fully automated technology platform, designed to optimize S&OP processes in real time. For large, complex CPG companies, autonomous planning can help supply chains function more effectively in volatile environments, and with less direct human oversight and decision making required. It combines big data (internal, external, and customer information) and advanced analytics at every step of the supply chain planning process. For that, supply chain managers rely on KPIs that measure how efficiently they can acquire raw materials, turn them into finished products, and deliver those products to customers. A financial KPI is cash-to-cash cycle time, which measures the time that elapses between paying a supplier for materials and receiving payment from a customer.

Invest in High-Quality Data Collection and Management

Chuaysi and Kiattisin (2020) combined KNN classifier with MLP on statistical and trajectory features of fishing vessels to enable their traceability at sea. Yalan and Wei (2021) used DNN as part of a deep logistic learning framework (DLLF) to improve data security and purchasing waiting time problems in e-commerce transactions. The process of coordination, integration, and optimization of quality operations across supply chain members is referred to as supply chain quality management. It effectively controls product quality and procedures to obtain a competitive edge, customer satisfaction, and market share (Robinson and Malhotra 2005).

machine learning supply chain optimization

Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows. As a result, companies and stakeholders have become more focused on supply-chain resilience. At the end of this section, it should be noted that these results were found using the material collection process described in Sect.

Why Supply Chain Inventory Management Skills Are Critical

For this reason, they may have the struggle to do proper action that is most suitable for their supply chain performance. But with the help of DL, they can understand the underlying trends that shape supply chains and develop the next generation supply chain. However, the performance of the LSTM has been better than many of the employed methods.

  • On the other hand, most of the papers developed a theoretical model and investigated the results through simulation and experimental analysis.
  • Accordingly, companies may need to redesign their performance-management systems to be more integrated and cohesive.
  • This chapter uses some literature and a bibliometric analysis to provide an overview of the field.
  • Better collaboration among suppliers and retailers can have a tangible impact on customer satisfaction, for example, by ensuring that retailers either have the products they need at a given time or can inform customers about potential delays.

In terms of supply chain functions, the production and manufacturing, as well as sales and retailing had the maximum share each with 11 papers. We also found that among all industries, consumer goods supply chains, food supply chains, and agricultural supply chains were studied more than the other domains. It shows the extensive potential of deep learning techniques to solve the problems of different members of supply chains in various sectors.

What Is Supply Chain Optimization?

This will represent a major change at many companies, a large number of which still set performance targets within individual functions or business units. Accordingly, companies may need to redesign their performance-management systems to be more integrated and cohesive. Machine learning can analyze timings and handovers as products move through the supply chain. It can compare this data to benchmarks and historic performance to identify potential holdups and bottlenecks and make suggestions to speed up the supply chain. Manufacturers are constantly adjusting their supply chains to eke out cost savings and refine their processes. When it comes to fulfillment, manufacturers must take care to balance customer expectations with profitability by optimizing order management.

Integrated planning enables companies to balance trade-offs across functions and optimize earnings before interest, taxes, depreciation, and amortization (EBITDA) for the organization as a whole. According to the outcomes of this study, the DL has come to the forefront in driving the rapid increase of data, helping SCM to be optimized. This review accelerates the research advances and business growth for both academic researchers and practitioners. Besides, it can be a reference for scholars to have an overview of different applications of DL algorithms and issues to work on in future studies. The study contributes to the literature on SCM by being the first study focusing on applications of DL in SCM. A conceptual framework is developed, which may be followed up by researchers as a roadmap in further directions.

2 Previous literature reviews

From this table, it can be seen that the majority of the reviewed papers have a theoretical approach. Moreover, the papers with a practical approach used CNN and RNN algorithms that show the potential of these methods to have different applications. In practical applications, Kong et al. (2021) and Vo et al. (2020) used CNN for product classification in the agriculture and food supply chains respectively. Garillos-Manliguez and Chiang (2021), Chakraborty et al. (2021), Jagtap et al. (2019), Yasutomi and Enoki (2020), Thota et al. (2020), Cavallo et al. (2018), and Guo et al. (2020) used DL for quality management of agricultural, food, textile, and consumer products. Liu et al. (2020) proposed a decision-making platform for sales forecasting using RNN.

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Quality is vital to good SCM as waste and faulty products create unnecessary rework and increase costs. There’s no beginning or end to supply chain management—it’s an ongoing process that requires constant oversight and fine-tuning and a strong focus on the capabilities of the manufacturer’s suppliers and the needs of its customers. A manufacturer’s resources include the people and physical assets it needs to produce, store, sell, repair, maintain, and deliver its goods. Physical assets include factories, warehouses, machines, and vehicles (such as trucks or tractors). Before supply chain optimization can even begin, companies need to audit all these resources to ensure they have the right skills, technologies, equipment, and processes in place. But the cost of production is often the biggest factor, which is why negotiating and renegotiating with direct suppliers is the first step to finding that balance between high quality and affordability.

Learn how Oracle can help you build a resilient supply chain and deliver exceptional customer service.

As the same DL approach can be used with different data types (Alom et al. 2019), Table 5 also demonstrates the type of data used in the papers to explore what algorithms with what data types are most frequently used in the SCM. From this table, it can be seen that the structured data types (25 papers) have been used more than unstructured data types (17 papers) in the SCM field. To answer the first question, we explored the selected publications based on the first dimension which is the supply chain problem. To be more specific, the categories of each reviewed paper have been specified in Table 4, and the corresponding shares of categories in the sample publications have been displayed in Fig.

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As the forecasting problem consists of several sub-problems, the sub-categories and the papers that belonged to each are also shown in this illustration to give a complete picture to readers. It is evident that using RNN in the forecasting problem has the maximum number of papers (13 papers), followed by using CNN for the quality management (7 papers), and CNN for the forecasting (5 papers), respectively. The quality management in the food supply chain using CNN is the second most frequent combination, followed by forecasting in the consumer products supply chain applying CNN. The literature review papers here analyzed are the output of a search performed on the Scopus database under the terms “deep learning”, “deep machine learning” or “deep neural network” in titles, abstracts, or keywords, and the term “supply chain” anywhere in the paper. In the current technological era with complex industrial developments, the agility and effectiveness of supply chains play a vital role in the improvement of their profits. The supply chain concept has a long history since Oliver and Weber (1982) proposed primary definition in their academic work.

COMPANY

For many companies, supply chain optimization is a constant low rumble in the background as different people managing different parts of the supply chain work to make their areas more efficient and cost-effective. It sometimes takes a big event—say, an acquisition, a financial downturn, a labor strike, or a pandemic—to move supply chain optimization to the forefront. Inventory management experience is one of the core competencies of a prospective supply chain manager. Poor inventory management machine learning supply chain optimization has repercussions throughout the organization, detrimentally affecting the bottom line. If a supply chain manager for a hospital chain fails to secure an adequate inventory of a crucial medical supply, such as syringes or personal protective equipment (PPE), the institution’s capacity to provide medical services would be impacted negatively. In this third module, we’ll take our Pandas and Numpy skills to the next level, learning how to effectively combine and reshape data.

machine learning supply chain optimization

The goal here is to avoid overproducing products that languish in warehouses, need to be discounted, or, worse, go to waste as well as to avoid underproducing so that customers can’t get the products they want when they want them. Supply-chain management solutions based on artificial intelligence (AI) are expected to be potent instruments to help organizations tackle these challenges. An integrated end-to-end approach can address the opportunities and constraints of all business functions, from procurement to sales. AI’s ability to analyze huge volumes of data, understand relationships, provide visibility into operations, and support better decision making makes AI a potential game changer. Getting the most out of these solutions is not simply a matter of technology, however; companies must take organizational steps to capture the full value from AI.

It used enterprise performance management software to identify vendor billing discrepancies and recoup the overspend, saving millions of dollars a year. Another company, a coffee wholesaler based in Europe with a history of growth through acquisitions, faced huge challenges around financial and supply chain consolidation. Its solution was a cloud-based application suite that integrated finance, supply chain, procurement, manufacturing, and performance management and allowed for acquired companies to be added to the suite relatively easily.

  • Suppose a supply chain manager for a clothing manufacturer does not closely monitor customer demand.
  • A supply chain optimization plan outlines the strategies involved in improving the efficiency and cost-effectiveness of a company’s supply chain.
  • Although transportation managers have been using this software for many years, advances in machine learning, IoT tracking, cloud computing, and other technologies are making real-time fleet monitoring a reality.
  • The best way to begin the optimization process is to determine why certain levels of inventory are held and rationalize that inventory to meet demand while keeping logistics and storage costs to a minimum.
  • Frost & Sullivan says manufacturers overproduce by an estimated 20% to account for market volatility and demand fluctuations.
  • In recent years, supply chain disruptions have become increasingly common due to factors such as geopolitical tensions, climate change, and global health crises.

Previously introduced DL methods namely DNN and CNN have some limitations that RNN can eliminate. Firstly, DNN and CNN accept a fixed-size input vector and produce a fixed-size output vector. An RNN can be considered as several short-term memory units consisting of input layer X, hidden layer S, and output layer Y (Pouyanfar et al. 2018).

machine learning supply chain optimization