How Machine Learning Optimizes the Supply Chain

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research. A wheat trader has harnessed AI to optimize its harvesting and collection plan spanning 22,000 fields and more than 400 storage silos, in the process capturing value from wheat-quality segregation and logistic costs. A global basic chemicals company transformed its sales-and-operations planning process in a monthly exercise of end-to-end integrated business planning. It generated more value by optimizing product and customer mix, volume allocation across plants, and raw material and supplier mix, among other factors.

In this model, the CNN processes review information of users and tourism service items, the DNN processes the necessary information of users and tourism service items, and the factorization machine technology learns the interaction between the extracted features. Chien et al. (2020) proposed a demand forecasting framework using the DRL model of DQN to select the optimal forecasting model among Naïve, Simple moving average, Single exponential smooth, Syntetos–Boylan approximation (SBA), ANN, RNN, and SVR models. RBM is a type of neural network that consists of a visible layer and a hidden layer with no visible-visible or hidden-hidden connections (LeCun and Bengio, Convolutional networks for images, speech, and time series 1995).

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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.

• The system uses the photos taken from products as they pass along the production line.
• 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.
• 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 general, supply chain financial management refers to the control of capital inflow and outflow with the ultimate purpose of increasing the financial efficacy of the system as a whole (Wang et al. 2008).

“Technological awe aside, autonomous delivery has proven incredibly useful during the pandemic,” she notes. The organizational design of the supply chain function can have a critical impact on overall performance; even with the right solution in place, execution can be nearly impossible if individual components of the system are not aligned. As companies better understand and capture variability of future demand through forecasting, they can predict customer behaviors more accurately and meet their demand with a higher level of confidence—and with significantly reduced lead times from order to delivery. Demand is more granular and segmented, to satisfy differing fulfillment requirements in various categories and regional markets, while tolerating promotions and other variables that enhance volatility.

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Beyond automation, manufacturers are beginning to benefit from advanced tools such as scenario modeling, logistics network modeling, and robotics. Ernst & Young research says that by 2035, 45% of supply chains are expected to be largely autonomous, using technologies such as robotics, autonomous vehicles for manufacturing and delivery, and automated planning. AI will also play a greater role in every phase of the supply chain, supporting predictive decision-making. A 2022 KPMG survey noted that 6 of 10 respondents plan to invest in digital technologies to improve their supply chain processes, synthesize data, and boost their analytics capabilities. Some of the authors combined two or more DL networks to solve their research problem. Tang and Ge (2021) combined CNN and LSTM to design a material forecast model analyzing three independent variables including sales demand forecast, transit warehouse inventory, and material features.

In the last decades of the twentieth century, the supply chain area has grown considerably into international locations which motivated both practitioner and academic interests. Shukla et al. (2011) highlighted that the supply chain in its classical form is a network of facilities that produce raw materials, transform them into intermediate goods or final products, and deliver them to customers by the distribution system. Nowadays, different industries especially the automobile, computer, and high-tech companies witnessed that physical logistics are becoming more reliant on information technology, which may also be used to enable new cooperative arrangements (Meixell and Gargeya 2005). Having an advanced supply chain network for participating companies becomes a source of competitive advantage in the technology era (Louw and Pienaar 2011). Machine learning can facilitate this by integrating data from multiple sources to provide real-time insights into the status of inventory, shipments, and manufacturing operations.

Over-ordering ties up capital, complicates warehouse management and could result in a loss due to an outdated or expired product the company can no longer use or sell. Analytics Insight® is an influential platform dedicated machine learning supply chain optimization to insights, trends, and opinion from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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.

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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.

Mao et al. (2018) presented a credit evaluation system based on blockchain technology and an LSTM network. The system analyzes the traders’ transactions and credit evaluation text and categorizes them into two classes “positive” and “negative”. Wichmann et al. (2020) proposed a bidirectional LSTM (BiLSTM) model to extract buyer–supplier relationship maps in multi-tier supply chains by analyzing natural language text such as news reports or blog posts. In bidirectional RNNs, the model can train the data in both normal and reverse sequences of data which may be insightful in some contexts.

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.

Dynamic allocation of inventory based on customer demand patterns means that machine learning algorithms can minimize stockouts and overstocks, leading to a more efficient and responsive supply chain. In a complex and volatile environment, CPG manufacturers can no longer rely on the supply chain planning processes of the past. Instead, they have a clear opportunity to improve financial and operational performance by implementing autonomous planning across the entire end-to-end supply chain. Capturing this potential will not be easy, particularly given that many companies have long legacies and deeply entrenched ways of working.

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.