Artificial intelligence (AI) has the potential to revolutionize supply chain management by enhancing efficiency, optimizing logistics, and reducing costs. Through machine learning, AI-powered supply chain management systems can analyze vast amounts of data to predict demand, optimize routes, and improve inventory management. In this context, we will explore the transformative impact of AI in supply chain management and its applications in various industries.
What is Supply Chain Management (SCM)?
Supply chain management includes all procedures that convert raw materials into finished goods. It is the management of the flow of goods and services. It entails the deliberate simplification of a company’s supply-side operations to optimize customer value and achieve a competitive edge in the market.
Businesses must plan the movement of goods and services from suppliers to customers. These plans should also be planned, coordinated, and under strict control. This is a characteristic of supply chain management, and it is an essential component of business operations.
It is a complicated process that calls for the fusion of several operations, including production, logistics, and distribution. The timely, accurate, and cheap delivery of goods and services to customers depends on the efficient operation of the logistics network.
How AI Transforms Supply Chain Management
AI in supply chains can provide the potent optimization capabilities necessary for better demand forecasting. It also allows for more precise capacity planning, efficiency, cheaper supply chain costs, and higher output. It does this while promoting safer working conditions.
Common Supply Chain Tasks That Can Be Automated
AI can automate tasks in the supply chain to save time and money compared to performing them manually. Businesses can automate supply chain tasks that can by using a variety of automation techniques:
- Warehouse robotics: They move materials and carry out other duties. A business can use automated equipment and specialized software to perform these tasks without human labor.
- IoT: Automation can also provide IoT. These are physical tools equipped with sensors, processing power, and software that connect to other devices. It also includes other communication networks and send or receive data from them.
- AI/ML: Automated supply chains can learn to anticipate user behavior with the use of artificial intelligence (AI) and machine learning (ML).
- Predictive analytics: Predictive analytics analyzes past and present data to create predictions about what might happen in the future. It uses data mining, predictive modeling, and machine learning to automate supply chains.
- Digital process automation (DPA) automates many supply chain operations across various applications. Supply chains enjoy optical character recognition (OCR), a type of text recognition.
Automation of data entry allows supply chain companies to obtain the information they need. This is without having to perform any manual operations, despite the fact that data entry can be time-consuming. In order for any supply chain to stay up with the rapidly changing sectors, AI automation is a game-changer and a must.
Benefits of AI in supply chain and logistics management
Supply chain and logistics management can gain a lot from AI. Among these advantages are:
- Operational efficiency: Automating jobs like demand forecasting, route optimization, and inventory management enables businesses to work more effectively.
- Decreased costs: AI can assist businesses in lowering labor costs by automating tasks and detecting inefficiencies.
- Increased income: Artificial intelligence can assist businesses in increasing revenue and strengthening their bottom line by enhancing efficiency and lowering costs.
- Improved judgment: AI systems can examine data to find trends and patterns, giving businesses insightful data that may guide strategic judgment.
- Better customer service: By giving customers access to real-time tracking information, artificial intelligence can help businesses become more responsive to their customers’ requirements.
AI in Supply Chain Management: Use Cases
Supply chain automation
Modern supply chain automation is not viable without AI. AI gives supply chain automation technologies such as digital workers, warehouse robots, autonomous vehicles, and RPA the ability to perform repetitive, error-prone tasks automatically.
AI can automate the following supply chain tasks:
Automation in the back office
Intelligent automation, or digital workers that combine conversational AI with RPA, can automate tasks like document processing.
Logistics automation
Businesses can achieve efficient logistics in a supply chain through AI & automation. Businesses like Amazon, Tusimple, and Nuro are substantially investing in transport automation technology such as driverless trucks.
Warehouse automation
AI-enabled technologies are helping drive efficiency, productivity, and safety through automated warehouse management. Ocado is one of the leading warehouse automation market players. Businesses like Ocado often employ ‘cobots’ to increase efficiency.
Automated quality checks
AI-enabled computer vision (CV) systems can help automate quality checks for products. Since these systems do not tire, they can help improve productivity and accuracy in production lines. For instance, AI-powered computer vision systems can automate and improve the quality assurance of finished products.
Automated inventory management
Individuals can use bots enabled with computer vision and AI/ML to automate repetitive tasks in inventory management. It can perform tasks such as scanning inventory in real-time. Retail stores can also use inventory scanning bots to streamline their processes.
While implementing such solutions, you need to assess their viability and calculate their long-term benefits; otherwise, such endeavors can lead to failure.
Predictive analytics/forecasting
Knowing what demand or market trends would look like in the future would be a supply chain manager’s holy grail. Managers can create more accurate predictions by utilizing machine learning.
Applications for demand forecasting with AI capabilities can greatly improve forecast accuracy. High-level accuracy has numerous advantages, including but not restricted to the following:
Inventory management
AI-powered systems that examine historical demand and supply data and patterns can assist in determining the ideal inventory levels. This can reduce storage expenses and excess production.
Forecasts for regions
To aid corporate leaders in making wiser decisions, supply chain AI may also provide comprehensive regional demand information. Each region, for instance, has its own festivals, customs, fashions, etc.
AI-powered forecasting tools can assist in customizing fulfillment procedures. These procedures should be by region-specific requirements by employing region-specific parameters.
Preventing the bullwhip effect
The bullwhip effect is a significant source of frustration for supply chain managers. The supply chain is able to amplify tiny changes as they move upstream or downstream. When this happens, the phenomenon takes place.
AI-powered forecasting solutions can assist in reducing demand and supply swings to regulate bullwhip. This is possible by utilizing information gathered from customers, suppliers, manufacturers, and distributors. Businesses can lessen stock shortages and backlogs as a result.
Enhanced supplier relationship management
Weak supplier relationship management is a major contributor to many of the current problems in global supply chains. During the 2020 COVID-19 pandemic, several supply chains experienced significant disruptions. These industries included in the food and automobile industries. The disruptions were also a result of a lack of collaboration and integration with suppliers.
AI can enable businesses to make supplier relationship management (SRM) more efficient and consistent.
Increased supplier choice
SRM software with AI capabilities can help with supplier selection based on criteria like cost, previous purchasing history and sustainability. AI-powered systems can also assist in tracking, analyzing, and ranking supplier performance data.
More effective supplier communications
Automating normal supplier communications like invoice sharing and payment reminders is another benefit of AI-powered solutions like RPA. Automating these processes can help avoid stupid snags that, for instance, result from late payment to a vendor and negatively impact shipment and production.
Improved sustainability
Sustainability is a significant concern for supply chain management. This is because a company’s supply chain generates the majority of its indirect emissions. AI can help supply chain operations be more sustainable and environmentally friendly.
Greener logistics for transportation
By taking into account variables like traffic, road closures, and weather, AI-powered technologies can assist in transportation route optimization and help reduce the number of miles traveled.
For instance, DHL employs AI to streamline transportation routes and utilize less fuel, which lowers emissions and enhances sustainability.
Sustainable warehousing
Businesses can decrease the carbon emissions they produce. The carbon emissions associated with the storage and transportation of surplus inventory. AI-powered forecasts’ ability assist in maintaining optimal inventory levels.
Solutions for smart energy use can help cut carbon emissions from energy use in warehouses. Big data and AI-powered supply chains can help them become robust and sustainable at the same time.
Disadvantages of AI in supply chain and logistics management
While using AI in logistics management has many potential advantages, there are also difficulties to take into account.
- Complexity: Integrating artificial intelligence into logistics management and networks can be challenging and expensive in terms of both technology and resources.
- Data quality: To operate efficiently, AI systems need high-quality data. It can be difficult to guarantee that data is correct and full.
- Human resources: Employers may need to undergo retraining, and HR may need to find newer, more qualified candidates.
How to Implement AI in Supply Chains
Try an AI simulation
The capacity of AI to forecast the results of actions is one of its advantages. Supply chains can experiment with this capability to improve their operations through AI simulations. Supply chain companies can more easily optimize operations through the use of simulations that mimic real-world situations.
The supply chain can benefit greatly from the use of AI simulation tools. Supply chain managers can create an exact digital replica of the entire warehouse they work in using AI simulation. The AI logistics can then test various optimization techniques using a simulation on the digital copy.
Decide what should be automated
An inefficient supply network could result in major issues for the entire supply chain. Inventory management may employ AI to automate various sections of their warehouses, which, when done, can save time and money.
IoT tags are another device that can watch the status of various goods. An AI hub that oversees all these inventory data updates on data changes receives communication from the IoT tags. The supply chain company can then be informed of any issues by the AI.
See the benefits of AI in cybersecurity
Cybersecurity is now essential for every supply chain firm since it is a critical component of handling data. Cyberattacks are frequent, and cyber criminals frequently use a variety of methods to steal sensitive data.
AI on supply chains can identify the most frequent patterns. When they might change, it is a highly effective tool for helping organizations keep ahead of changes or threats.
AI analysis for supply and demand
Supply chains can employ AI data analysis to predict how demand and supply may change over the next quarters. AI algorithms that use data analysis can forecast the amount and kind of demand for certain goods.
Demand forecasting can help various supply chain connections ease supply pressure.
The supply chain business can make better decisions about the quantities needed if they know how much of a given product they will require.
Less risk of company error
Because of the capabilities of ML, systems can learn to allow various processes, such as infrastructure vision, to automate in accordance with the requirements of the supply chain organization.
IoT devices can gather information on how many materials are being utilized in addition to ML and AI. Artificial intelligence (AI) data analysis algorithms can pinpoint where and which materials are being wasted.
Understanding the future of AI in supply chain: Best supply chain predictions for 2023 and beyond
By 2026, experts predict that more than 75% of large businesses would handle their warehouse operations with intralogistics smart robots. These robots will help to supplement the requirement for human labor while ensuring that operations are more quick and more inexpensive.
FAQs
How can AI be a game changer for supply chain management?
Supply chains are using AI-enhanced solutions to become more efficient, lessen the effects of a global workforce shortage, and find better, safer ways to move commodities from one location to another.
What are the important factors of AI in supply chain management?
Modern AI software features educate computers on how to give real-time information from raw data. It is then used to influence important decisions. Testing of cutting-edge applications, such as autonomous vehicles, has yielded conflicting findings.
According to DHL, its usage of autonomous forklifts in warehouse operations has “reached a degree of maturity.”
How can you use artificial intelligence to solve problems in supply chain design and management?
Overall, AI may help businesses make data-driven decisions and optimize their operations, increasing the efficiency and effectiveness of supply chain design and management.
Is AI the future of supply chain?
The use of AI in supply chain management appears to have a bright future. AI will probably play a bigger part in logistics network operations as technology develops and data becomes more accessible.
