Improving supply chain visibility using machine learning

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Kazakh-British Technical University (Almaty, Kazakhstan)

Improving supply chain visibility using machine learning

Mazhitova Z.Z.

Аннотация

in today's world, effective supply chain management is a key success factor for many companies. Artificial intelligence is playing an increasingly important role in this area, providing new opportunities and benefits. This article discussed the definition of a supply chain, the role of artificial intelligence in its management, the benefits and challenges of applying artificial intelligence.

Ключевые слова: artificial intelligence, machine learning, supply chain.

supply chain artificial intelligence

Artificial intelligence and machine learning have the potential to significantly improve supply chain efficiencies for consumer packaged goods (CPG) companies. However, for now, most companies are limited in their approach: investing in a set of point solutions that work well for individual processes, but do not communicate with each other or integrate data. The problem with this approach is that it still requires direct input from COOs and operations teams into decision making and oversight to manage the intersections and interdependencies between individual applications.

To benefit from the true potential of analytics, CPG companies are better off integrating the entire end-to-end supply chain so they can manage most processes and make decisions with real-time, autonomous planning. Predicted changes in demand can be automatically factored into all processes and decisions throughout the chain, right down to inventory, production planning and scheduling, and raw material procurement.

Experience from several large CPG companies shows that autonomous supply chain planning can lead to revenue increases of up to 4 percent, inventory reductions of up to 20 percent, and supply chain cost reductions of up to 10 percent. But achieving these benefits is a journey, not a one-time transaction, and it requires thinking beyond technology to include process redesign, talent acquisition, performance management and other aspects of operations [1].

Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant benefits being achieved through advanced resource planning systems. Machine learning and artificial intelligence- based techniques underpin a wide range of next-generation logistics and supply chain technologies currently in development. The biggest advances are where machine learning can help solve the complex constraints, cost and delivery challenges that companies face today. McKinsey predicts that machine learning's most significant contribution will be in providing supply chain operators with more meaningful insights into how supply chain performance can be improved by anticipating anomalies in logistics costs and performance before they occur. Machine learning also provides insight into where automation can provide the most significant scaling benefits [2].

A Supply Chain is a network of organizations, companies and processes that are interconnected and work together to produce and deliver goods or services from suppliers to end consumers.

The supply chain includes all stages of the process, from the procurement of raw materials and components, production of goods, storage and packaging, logistics and delivery, as well as returns management and reverse logistics.

The main goal of a supply chain is to ensure the efficient and timely movement of goods or services from suppliers to consumers, minimizing costs and optimizing processes.

Artificial intelligence (AI) is playing an increasingly important role in supply chain management, enabling more efficient and accurate forecasting, process optimization and data-driven decision making.

Forecasting and planning. AI can analyze large amounts of data, including historical sales data, weather conditions, economic indicators and other factors to predict demand and optimize production and supply plans. This allows you to reduce the risk of shortages or excess inventory, as well as improve resource planning and logistics.

Optimization of inventory and logistics. AI can help optimize inventory levels and distribution of goods in the supply chain. Machine learning algorithms can analyze sales data, demand forecasts, seasonality and other parameters to determine optimal inventory levels and optimize delivery routes. This reduces storage and delivery costs and improves customer service.

Improved transparency and traceability. AI can help improve visibility and traceability of goods in the supply chain.

Automation and optimization of processes. AI can automate many routine tasks in supply chain management, such as order processing, delivery route planning, and warehouse management. This reduces the time and cost of these tasks and improves the accuracy and reliability of processes.

Overall, AI plays a key role in supply chain management, enabling more efficient and accurate forecasting, process optimization and data-driven decision making. This allows companies to reduce costs, improve customer service and improve competitiveness in the marketplace.

Artificial intelligence allows you to automate and optimize various processes in the supply chain. Machine learning and data analytics algorithms allow you to optimize production planning, inventory management, demand forecasting and optimization of delivery routes. This reduces costs, improves process efficiency and accuracy, and improves customer service.

More accurate demand forecasting. Artificial intelligence makes it possible to more accurately predict demand for goods and services. Machine learning algorithms analyze large amounts of data, take into account various factors such as seasonality, weather conditions, marketing activities and others, and predict future demand.

Improved inventory management. Artificial intelligence helps improve inventory management in the supply chain. Machine learning algorithms analyze data on sales, demand, supply and other factors, and suggest optimal inventory levels for each product or product category.

Challenges and limitations of artificial intelligence in supply chain management

Data quality.

One of the main challenges in applying artificial intelligence in supply chain management is data quality. For machine learning algorithms to work effectively, artificial intelligence requires a large amount of data that must be accurate, current and complete. However, in real life, data may be incomplete, contain errors, or be heterogeneous. This can lead to incorrect forecasts and decisions, which can negatively impact the effectiveness of supply chain management.

Integration of existing systems.

Another challenge is the integration of artificial intelligence with existing supply chain management systems. Many companies already use various software and hardware systems to manage their supply chains. Integrating artificial intelligence with these systems can be challenging, requiring additional development and configuration costs. In addition, it is necessary to ensure the compatibility of artificial intelligence with existing systems and ensure the security and confidentiality of data.

Lack of expertise.

Successful application of artificial intelligence in supply chain management requires expertise in artificial intelligence and supply chain management. However, specialists with such knowledge and skills can be limited in number and expensive for companies. This can create problems when implementing artificial intelligence and require additional costs for training and staff training [3].

The bottom line: Enterprises today use machine learning to achieve doubledigit improvements in forecast error, demand planning performance, cost reduction and on-time delivery, revolutionizing supply chain management. Machine learning algorithms and the models they are based on excel at finding anomalies, patterns, and predictive information in large data sets. Many supply chain problems involve time, cost and resource constraints, making machine learning the ideal technology to solve them. Artificial intelligence provides many benefits such as increased efficiency, streamlined processes and more accurate decisions. The future of artificial intelligence in supply chain management promises even more innovation and improvement. Overall, artificial intelligence plays an important role in modern business and can significantly improve supply chain management.

Список литературы

Autonomous supply chain planning for companies producing consumer goods [Electronic resource]. URL: www.mckinsey.com/capabilities/operations/our-

insights/autonomous-supply-chain-planning-for-consumer-goods-companies (date accessed: 03.01.2024);

Ashutosh Dekhne, Greg Hastings, John Murnane and Florian Neuhaus

McKinsey & Company, Automation in Logistics: Big Opportunities, Big Uncertainties, April 2019 [Online]. URL:

https://www.forbes.com/sites/louiscolumbus/2019/04/28/how-to-improve-supply- chains-with-machine-learning-10-proven-ways/?sh=50d0cedb3f3c (date accessed:;

Artificial intelligence in supply chain management: advantages, examples and future [Electronic resource]. URL: https://nauchniestati.ru/spravka/ii-v-upravlenii- czepochkami-postavok/ (date accessed: 03.01.2024)

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