Artificial intelligence has spelled the beginning of a new revolution in technology. It has been utilizing the massive volumes of operational data emerging from different devices and cloud applications to generate viable insights. Interestingly, the applications of AI in supply chain management have been gaining the attention of experts.
What is the primary reason for using AI in the domain of supply chain management or SCM? How will SCM benefit from AI? What are the setbacks of using AI in SCM applications? What is the ideal roadmap for implementing artificial intelligence in supply chain management? You might have many questions like these on your mind when you think of supply chain management applications of AI.
Artificial intelligence uses advanced mathematics and data analytics to create products, systems, and processes that can adapt and learn. The interplay between AI and supply chain has been one of the most discussed topics in discussions about technology. The development of smarter supply chains depends on how they embrace artificial intelligence technology. Supply chains can become smarter by developing important traits such as instrumented, intelligent and interconnected. Let us learn more about the value of AI in SCM applications.
Why Should You Use AI in Supply Chain Management?
The global pandemic in 2019 provided the ideal foundation for increasing the pace of technological innovation. As companies all over the world experienced bottlenecks in their supply chain management operations, digitalization came to the rescue. Do you know what played a major role in digitalization with no people around? Artificial Intelligence. Yes, AI turned the tables in a good way for different industries.
The review of AI in supply chain examples shows that AI has a massive transformative impact on supply chain management. Artificial intelligence has the capability to manage massive volumes of data, draw predictions, and establish connections between multiple data sources. Supply chain managers have to rely on all these tasks and deal with the complexities of each task.
According to research findings by McKinsey, early adopters of AI have successfully reduced their logistics costs by 15%. In addition, supply chain artificial intelligence has also improved service excellence by almost 65%, alongside elevating inventory levels by 35%. While industries had to deal with the aftermath of the pandemic, supply chain management discovered the opportunity for adopting artificial intelligence on a large scale. Supply chain management must embrace AI as a viable tool for revolutionizing overall efficiency, decision-making, and processes.
The interplay between supply chain and AI could help in ensuring enhanced inventory management, real-time delivery controls, smart manufacturing and dynamic logistics systems. One of the primary objectives of utilizing AI in SCM and logistics revolves around ensuring better productivity and efficiency. On top of it, introduction of artificial intelligence in supply chain has increased prospects for sustainability.
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Ways for Using Artificial Intelligence in Supply Chain
You can discover answers to “How AI is revolutionizing supply chain management?” from different perspectives. Why? The existing supply chain landscape is extremely complicated, with multiple partners, escalating costs, market volatility, and large product portfolios. At the same time, the growing demand for environmental sustainability in supply chain management has also emerged as a formidable challenge.
Artificial intelligence could support businesses in connecting all the data points in one ecosystem alongside developing an end-to-end approach for supply chain management. AI can provide prediction, analysis and advantages of automation to facilitate comprehensive visibility. Here are some of the most noticeable ways in which you can put AI into action in supply chain management.
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Demand Forecasting
Artificial intelligence models have the power to incorporate multiple data points for drawing precise predictions that could help in guiding business decisions. The future of AI in supply chain would revolve primarily around capability of AI for analysis of different market segments and relationships. AI could also help in analyzing other elements, such as trends in consumer buying behavior and upcoming holidays, for making informed supply chain decisions.
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Real-time Monitoring
Another noticeable use case of AI in the domain of supply chain management focuses on real-time monitoring. Artificial intelligence can help in achieving end-to-end transparency in supply chains with the help of responsive monitoring. The common AI in supply chain examples showcases how artificial intelligence can help keep tabs on perishable items. Such use cases of AI in SCM also enable faster implementation of corrective measures when perishable products have crossed their expiry dates.
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Real-time Inventory Management
Artificial intelligence also helps supply chain managers monitor their inventory closely. The benefits of real-time inventory management could help in preventing overstocking and lack of stock. Real-time inventory management with AI in supply chain management could help in ensuring automatic adjustments to the inventory according to existing conditions. For example, artificial intelligence could help in determining the ideal time for re-ordering certain items according to the demand and supply trends for similar products.
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Robotics and Automation
Artificial intelligence offers the best potential for achieving automation of supply chain management operations. The relationship between AI and supply chain ensures that the supply chain would become dynamically responsive to problems and emerging demands, such as requirements for problem solving. On top of it, AI could also support effective working of automated systems.
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Importance of Data Analytics for Decision-Making in Supply Chain
If you want to find out more about the future of AI in supply chain, then you must know that AI boosts data analytics. Subsequently, data analytics for supply chains utilize advanced algorithms and machine learning techniques for extracting valuable insights to make informed decisions. AI-powered data analytics solutions for supply chain help in optimization of the workflow to ensure that large volumes of data can offer accurate forecasts, fuel innovation, and recognize inefficiencies. Here is an outline of the different types of data analytics in supply chain management that you can improve with AI.
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Predictive Analytics
Most of the answers to ‘How AI is revolutionizing supply chain management?’ would point to the use of predictive analytics. It is a technique that utilizes regression analysis and statistical modeling techniques for identifying and understanding trends from historical data.
You can use the insights obtained from historical data to make predictions about expected trends. The use of AI in predictive analytics helps supply chain managers in predicting the likely outcome of certain conditions. For example, predictive analytics can help in ensuring effective mitigation of risks and possibilities of downtime.
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Prescriptive Analytics
Prescriptive analytics is another prominent tool involved in the domain of supply chain management. It helps in exploring the impact of specific modifications on the outcomes of the supply chain processes. As a result, managers could identify potential improvements and offer a valuable resource for optimization of supply chain operations.
The supply chain and AI equation in prescriptive analytics would focus on collaboration across multiple endpoints. Collaboration with logistics partners can reduce time and effort alongside providing the opportunity for achieving maximum business value. One of the popular examples of prescriptive analytics points to Supplier Relationship Management systems.
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Cognitive Analytics
If you want to dive deeper into the use of advanced data analytics and AI in the domain of supply chain management, then you should rely on cognitive analytics. It is an ideal tool for improving customer experiences and relationships in supply chain management. An overview of AI in supply chain examples would show that AI-powered systems utilize feedback data for analysis and execution in the form of reports and dashboards for answering complex questions. The applications of artificial intelligence in supply chain could help in pursuing breakthrough ideas based on customer feedback.
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Descriptive Analytics
Another popular technique in the domain of AI that fits the bill for supply chain management is descriptive analytics. It is a type of data mining exercise that focuses on analyzing large datasets for identifying patterns and generating summaries that allow users to extract insights on a specific situation.
Such variants of analytics utilize historical data for uncovering trends and drawing conclusions to make better decisions. Descriptive analytics establishes a strong link between AI and supply chain as it showcases the importance of data analytics for the supply chain. It can help in offering visibility alongside certainty for all types of internal and external data throughout the supply chain.
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What are the Advantages of Artificial Intelligence for Supply Chains?
Effective supply chain management could help in optimization of the flow of products and services alongside ensuring effective streamlining of business operations. The supply chain works effectively only when all the points in the supply chain can work efficiently. Many companies have already identified the future of AI in supply chain and used artificial intelligence for automation of supply chain tasks.
AI can support automation of back-office processes and warehouse logistics, supplier relationship management, inventory management, and quality checks. Companies could leverage AI to ensure quality control in supplier management and automate manual tasks with better accuracy. Here are some of the notable benefits of using artificial intelligence in supply chain management.
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Reduced Operating Costs
Companies could reduce the operational costs of supply chain management by lowering the buying and production expenses. For example, a grocery store purchases fresh vegetables from the farmer directly. If the store eliminates the third party for purchasing products, it can save money and could have vegetables faster in their home. On top of it, AI also offers data transparency to ensure improved supply chain visibility alongside saving costs.
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Improved Relationships with Supply Chain Partners
The responses to “How AI is revolutionizing supply chain management?” also draw attention to the different stakeholders in the supply chain. Suppliers, distributors, and manufacturers are important supply chain partners. Companies could avoid unprecedented situations by managing relationships with everyone in the supply chain.
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Shorter Delivery Times
AI could help in tracking shipments alongside ensuring timely delivery of goods through analysis of data and identification of important patterns. The use of supply chain artificial intelligence could help in ensuring that managers can make better-informed decisions. For example, Walmart utilizes AI for analysis of sales patterns alongside optimization of inventory levels and reducing the chances of products running out of stock.
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Reduced Risk
Artificial intelligence could help in improving supply chain management by analyzing massive volumes of data in minutes. As a result, it can help in identifying different risks, weaknesses, and best prospects for critical decision making. Businesses could use AI to create safer work environments alongside identifying potential risks in the supply chain. However, it does not replace humans and only offers a tool for supporting decisions about supply chain management.
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What are the Challenges of Using AI in Supply Chain?
The applications of AI in supply chain management could also lead to certain challenges. You can face problems with the use of artificial intelligence in supply chain management, such as bias algorithms. The design flaws in AI algorithms or the use of faulty data in the algorithms could lead to limitations for supply chain management. On top of it, the lack of transparency in certain AI systems could lead to selection of biased data sets to create scenarios that have poor visibility.
On top of it, disinformation could also create setbacks for use of artificial intelligence in supply chain management. Furthermore, the use of AI in supply chains could also lead to replacement of skilled workers. In addition, AI could also create the possibility of cybersecurity risks, such as phishing schemes and malware.
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Conclusion
The outline of the different ways in which AI can help in improving supply chain management shows that AI could revolutionize supply chain management. Some of the notable benefits of using AI and supply chain together include cost reduction, better decision-making capabilities, and better productivity. On the other hand, AI can also create formidable concerns for supply chain management.
For example, biased AI algorithms, cybersecurity risks, and lack of transparency can create setbacks for adoption of artificial intelligence in supply chain management. You must learn more about the value benefits of AI for supply chain management and the best practices for deploying artificial intelligence for SCM systems. Find more about AI fundamentals and its benefits for different industries right now.
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