Better supply-chain planning with AI and machine learning

In the following paragraphs, we briefly describe the concept of each DL algorithm and its applications in the SCM in the reviewed papers. In this section, we intend to answer the main research questions using the defined structural dimensions and content analysis of collected material. To answer the fourth question of descriptive analysis, we explored the sources of selected papers. This analysis represents the key forums of DL applications in the SCM and can help the authors to find the most appropriate journals to submit their work. The number of papers and citations of journals with more than five citations has been displayed in Fig.

It then discusses the key challenges faced by supply chain professionals and how ML can address these challenges. The paper presents an overview of different ML techniques, including regression analysis, clustering, classification, time series analysis, neural networks, genetic algorithms, reinforcement learning, and ensemble methods, highlighting their specific applications in SCM. 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. The paper also explores the integration of ML with emergent technologies such as blockchain, IoT, and edge computing in the context of SCM.

Build supplier-retailer relationships

The findings of the review indicate that ML has demonstrated significant potential in improving decision-making, optimizing operations, enhancing supply chain resilience, and addressing sustainability challenges. Accordingly, through answering the RQ1, we found that forecasting, quality management, financial management, product classification, inventory management, traceability, and information security in SCM were discussed as the most addressed areas. Forecasting was the most popular area investigated in 17 papers specially demand forecasting (8 papers) followed by sales forecasting (3 papers). After that, quality management was the second widely studied domain with 7 publications.

Select the most suitable supply chain optimization tools and solutions for your business based on your specific needs and objectives. Research the available technologies and partner with reputable vendors to increase the likelihood of your chosen solutions delivering the desired results. The effectiveness of machine learning models depends on the accuracy and reliability of the data they use. Invest in robust data collection and management processes to provide your AI solutions with access to accurate, reliable, and up-to-date information. Successfully integrating AI solutions into your supply chain operations requires careful planning, collaboration, and ongoing commitment.

Optimize Your Supply Chain with AI and ML

These enable businesses to maintain optimal inventory levels, reducing storage costs and waste while making products readily available to meet customer demand. The success of supply chain optimization techniques relies on the creation of a detailed plan that includes several stages. The design stage determines where physical locations such as factories, warehouses, and distribution centers should be located. In the planning stage, managers develop production plans that consider product storage costs and fluctuations in transportation availability to ensure that the right products are produced at the right time.

Although those relationships are important, strong supply chain relationships rest on a foundation of quality, timely data. Manufacturers need to understand which suppliers are performing well and which are unable to meet demands and for what reasons. They need a real-time view into their own customer machine learning supply chain optimization demand so they can quickly communicate changes to suppliers. And at a time when manufacturers need a clear understanding of the environmental and social impact of their supplier and product choices, they need to prioritize companies that also have a clear view into their own suppliers’ actions.

The system analyses the current and voltage measurements to detect the measurements that are maliciously inserted by attackers. Price forecasting Price forecasting is an important aspect of economic decision-making. Individuals can use forecasts to earn profits from speculative activities, decide on the best government policies, or make financial decisions (Stockman 1987). In this category, Weng et al. (2019a, b) investigated three different methods to forecast the price of horticultural products in the short term (daily) and long term (weekly and monthly) based on prices available on websites. Forecasting the price of horticultural products can be also beneficial for designing a cropping plan. In the cross-border e-commerce field, (Guo 2020) proposed a hybrid model to encode image features, and capture the image features of commodities.

Piccialli et al. (2021) proposed a hybrid framework consisting of LSTM-Autoencoder, CNN, and several machine learning algorithms to forecast the respiratory diseases bookings. In this framework, the LSTM-Autoencoder extracts the features, and the CNN recognizes short-term behaviors and relations between the forecasts provided by machine learning algorithms of Ridge, Lasso, RFR, and XGB. Yasutomi and Enoki (2020) proposed a hybrid architecture based on the CNN and LSTM for inspection of belt conveyors (to find any anomalies in belt lines) in production or distribution processes. In this method, the convolutional layers of CNN, extract signal features from each window and the LSTM find the relation between the windows. The good news is that AI-based solutions are available and accessible to help companies achieve next-level performance in supply-chain management.

The amount of time it takes to develop a supply chain strategy depends on the complexity of the supply chain, the industry, the size of the business, and which kinds of technologies and processes the business already uses to manage its supply chain. In general, such planning can take as little as six months for small, simple supply chains to three years or longer for large, complex supply chains. Optimizing your supply chain—making it more efficient, cost-effective, and partner friendly—isn’t a goal that can simply be checked off as completed. Changes in the economy, market conditions, and customer expectations will require new technologies and processes as well as adjustments to current ones. Inventory optimization involves manufacturing the precise amount of product needed to meet customer demand.

In terms of evaluation metrics, RMSE, Accuracy, F1-Score, Precision, and MAE have been the most popular metrics. Regarding DL algorithms (RQ2), the RNN and its variants (especially the LSTM) for forecasting problem, and the CNN for quality management and forecasting problems have been the most frequently used techniques. Moreover, recently researchers tend to combine several methods together to make advantage of all them, eliminate the drawbacks of each method, and obtain higher performance. Meanwhile, DL methods can be integrated with new technologies such as IoT and blockchain to improve the performance and integrity of supply chains and bring about smart supply chains. Hu (2020) used particle swarm optimization (PSO) to train their BP feed-forward neural network to be used in the prediction of the financial credit level of a supply chain. Ahmadimanesh et al. (2020) used a feed-forward neural network to design an inventory management model.

In this figure, the left field displays the keywords, the middle field represents the authors and the right field shows the frequently used words in the titles. Forecasting is one of the important problems of supply chain management that have recently made advantage of DL methods. One of the areas of forecasting in SCM is demand forecasting and we can see that the word “demand” is the other frequently used word in the titles of the papers. Among deep learning methods, the LSTM method has mostly appeared in the keywords of papers. Besides the suppliers and products themselves, the building blocks of most supply chains are distributors, transport providers, warehousers, and end customers. Although every element of the supply chain can be optimized, some areas are more under the control of the manufacturer than others.

It’s a constant challenge for supply chains to manage multiple tasks at the same time — and with precision. The complexity of supply chains—from demand forecasting to planning optimization and digital-execution tracking—means that finding one provider that can meet all of these needs is increasingly unlikely. Executives should recognize that the right answer for their company won’t necessarily be the one recommended by the providers, whose goal is often to push for a single end-to-end solution. Such approaches have stretched supply-chain functions, which must now operate as a “central cross-functional brain” within large corporations. In many organizations, supply-chain management has shifted to concentrate on dynamically optimizing the company’s global value rather than simply improving the performance of local functions. In several process industries (such as chemicals, agriculture, and metals and mining), sales-and-operations planning has evolved into integrated business planning.

Stay informed about industry trends, continuously innovate, and foster a data-driven culture to maximize the benefits of AI-driven supply chain optimization. A manufacturer’s transportation network brings in supplies; transports goods between factories, warehouses, and other facilities; and ships finished products to distributors, retailers, and end customers. Minimizing transportation costs is crucial, especially with customer expectations for low- or no-cost shipping and speedy delivery. Cost optimization is all about honing forecasts to produce enough supply to meet demand—so-called just-in-time manufacturing—without missing any sales or promised orders. You can’t improve what you don’t measure, a time-worn adage that applies as much to improving supplier performance as just about anything else.

You will be able to use statistical models and machine learning to analyze retail stock and predict future sales as well as how many items to stock. LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it. End-to-end visibility into every stage of the supply chain is the top priority in supply chain optimization. There’s also blockchain technology — often linked to cryptocurrencies — which is finding its way into supply chain management, offering greater transparency, traceability, and security.

• Machine learning can support sustainable supply chain practices by optimizing resource utilization, reducing waste, and minimizing the environmental impact of transportation and production processes.
• Today’s customers have high expectations regarding product availability, delivery speed, and overall shopping experience.
• For example, during the pandemic one global restaurant chain used predictive analytics capabilities built into its planning software to accurately anticipate ingredient shortages.
• FCRBM (Taylor et al. 2011) is constructed by adding styles and the concept of factored, multiplicative, three-way interactions to the CRBM (Mocanu et al. 2016).