How Is AI Revolutionizing Supply Chain Management?

The ever-evolving business landscape tends to change, and inaccuracies in supply chain management are more likely to bring counterproductive outcomes. But, there’s nothing to worry about, as AI has got your back. This blog is going to navigate you through comprehensive information on the role of AI in supply chain and how AI is revolutionizing supply chain management. Let’s dig deeper!

Artificial intelligence (AI) in supply chain revolutionizes almost all activities including demand forecasting, procurement, inventory management, and logistic operations, keeping every aspect of the supply chain balanced. AI processes a large amount of data, predicts customer behavior and trends; and conducts proper demand forecasting for smart inventory management. 

Accumulating data like customers’ purchasing history and searching behavior, AI-powered systems tend to predict future demand for specific products, thereby ensuring appropriate availability of the product while circumventing overstocks and stockouts. It promotes the next level of business efficiency and profitability. 

However, these are just a few aspects of the role of AI in supply chain, this blog is going to navigate you through a comprehensive analysis of how AI is revolutionizing the supply chain industry. So, read through this guide and explore all the aspects of the same before you get connected to an AI Development Company to leverage these benefits.  

What Is Supply Chain?

The supply chain is a complete process that includes the comprehensive conversion of raw materials into finished products along with delivery. It involves raw material procurement, product manufacturing, sales & marketing, and the final delivery of the products to their users. Ensuring an efficient supply chain management in place is more likely to lead to skyrocketed production at mitigated efforts and expenses which in turn, improves the overall business profitability. Businesses make use of AI to manage and optimize supply chain operations. 

Key Objectives of Artificial Intelligence In Supply Chain Management

The preliminary objective of AI in supply chain management is to bring efficiency in a variety of proceedings which includes: planning, procurement, manufacturing, management, and the delivery of the products to their final destination. AI processes a wide range of data to predict current and upcoming trends, based on this data ensures real-time decision-making for procurement; and helps businesses bring agility in inventory management and product delivery. 

ML is the subset of AI that owns the competencies to expand far and beyond the performance of traditional software solutions. ML gauges customers’ preferences and behavior, discovers patterns, conducts market prediction, interprets voice & written texts, and assesses a wide range of areas responsible for optimizing supply chain management. 

AI Adoption Rate In Supply Chain Worldwide 

Following are some of the key statistics, indicating the uses and adoption of Artificial intelligence in supply chain management:

• The supply chain marketplace is anticipated to exhibit a CAGR of 8.60% between the years 2022 to 2029.
• Around 70% of supply chain professionals use AI, seeking opportunities to cut down on operational costs.
• AI-powered predictive maintenance optimizes equipment performance, lessening the maintenance cost by up to 15% to 20%.

According to stats from Gartner, businesses using AI supply chain management solutions are more likely to be high performers compared to those missing out on using the same. Have a glance at the image below!

The image above shows how those who implemented AI-powered solutions became the high performers compared to those who didn’t do that. Whether it’s demand forecasting, order management, and fulfillment, supply chain planning, logistics & distribution, or else, using AI-enabled systems helped businesses get ahead of the curve. For more, you can refer to our recently published guide on how AI in manufacturing is redefining the industry.

Key Benefits of AI In Supply Chain Management

Now that we’ve been through the basics, it’s time to come to the key discussion point and dig deeper into the role and use of AI in supply chain management. However, the general benefits include demand forecasting, efficient procurement, inventory management, and process optimization, the benefits of AI don’t end here. Following are the top aspects through which AI brings complete transformation to the supply chain:

Demand Forecasting

AI algorithms provide a real-time tracking mechanism and perform intensive analysis of a large amount of internal data like previous sales, market trends, and external factors such as economic outlooks, broader market trends, and seasonal market trends to anticipate future demand. Machine learning, a subset of AI, learns from historical as well as new data and streamlines the forecasting system, ensuring maximizing accuracy in demand forecasting and minimizing overstocks and stockouts.

Smart Procurement

AI-powered systems own the competencies to perform smart and efficient procurement. AI-driven systems excel at real-time data analysis and analyze suppliers’ data aiming to make the right selection of suppliers according to various factors like reliability, services, cost, and many additional factors. It enables procurement professionals to optimize inventory levels and circumvent overstocks and stockouts while accelerating supply chain operations.

Inventory Management

Efficient inventory management is one of the key reasons for employing AI in supply chain and logistics. AI-based supply chain systems automate inventory tracking and management, thereby, optimizing stocks by intensive prediction of when and where the product will be required. These systems analyze demand patterns, assisting you in ascertaining appropriate inventory levels for certain locations. Through this, the entire supply chain system is able to alleviate the carrying cost and expand the service levels.

Mitigated Errors & Waste

AI can easily spot anomalous behaviors for both machines and humans way sooner than humans can. This is the reason why manufacturers, warehouse operators, and shipping companies tend to train AI algorithms to get rid of flaws in employee errors, workflows, and product defects. The cameras installed in assembly lines, logistics hubs, and delivery vehicles feed into computer vision systems that make use of AI for proper inspection of works intending to mitigate recalls, reworks, and returns.

AI-powered systems can easily identify mistakes made by machines as well as the workforce, circumventing any chances of wrongly manufactured products or delivery to a wrong destination. This, in turn, saves you time and material waste. It can also conduct root cause analysis, assessing a large amount of data aiming to find correlations, reveal failures, and equip teams to fix them timelessly.

Supply Chain Optimization

Making use of algorithms, AI-enabled systems optimize delivery routes, thereby minimizing transportation and the delivery costs of the product. It analyzes data obtained from machines, predicts potential causes for failures; and reduces any kind of feasibility of downtime while improving efficiency in the process. What’s more, AI is competent enough when it comes to assessing supplier performance, predicting potential risks, and suggesting alternative suppliers, resulting in an agile supply chain management.

Warehouse Automation

AI-enabled robots are employed for picking, packing, and sorting products aiming to lessen the labor cost while enhancing the speed and accuracy of the process. Moreover, they optimize the store locations within the warehouses as per the demand patterns which helps improve retrieval times.

Efficient Customer Service

AI-powered chatbots tend to efficiently handle customer inquiries, and provide them with real-time information about the order status, delivery time, and many more. The system customizes the customer experience by offering personalized recommendations for specific products based on their previous behaviors and preferences.

Risk Management

AI-powered systems provide robust risk management competencies to the supply chain industry. AI performs predictive analysis and anticipates potential disruptions in the supply chain, like natural disasters, allowing companies to implement predictive measures. Furthermore, it assists you in fraud detention by identifying unusual behaviors and patterns and resisting any feasibility of potential risks.

Seamless Collaboration

By providing real-time data insights to the stakeholders such as the warehouse manager, and logistic manager, the AI ensures the next level of collaboration among various departments of the supply chain. The system works with blockchain, intending to ensure extensive traceability and transparency across the supply chain ecosystem. It strengthens trust among partners that’s a key ingredient for business growth.

On-Time Delivery

Manufacturers assembling products through complex supply chains are more likely to be reliant on timely delivery. Even a small delay in the arrival of a single component can cause a setback in the entire production schedule. Thankfully, AI is taking on the tasks to mitigate these delivery holdups. Logistics businesses make use of machine learning (ML) to train models and make them competent enough to optimize and manage delivery routes through which components move throughout the supply chain.

Apart from this, they prioritize shipments based on order volumes, delivery promises, contractual deadlines, customer importance, or product availability. They also provide all nodes throughout the distribution network with accurate time of arrival by shipment identification.

Reduced Operational Costs

AI is embellished with the potential to learn complex behaviors and work under predictive circumstances. Repetitive tasks like tracking, counting, and documenting inventory can easily be accomplished with the next level of efficiency and accuracy. It reduces the performance bottleneck by identifying and lessening them, thereby reducing the overall operational costs.

Real-Life Implementations of AI In Supply Chain Management

AI implementation in the supply chain is rapidly growing in a variety of industries. Following are some of the real-life implementations of AI:

• Chatbots: AI-powered chatbots automate sales, marketing, and customer service, offering around-the-clock service availability for customers, and putting an end to any limitation.   
• Image Recognition: AI can recognize objects, faces, and additional visual elements in videos and images. The technology is employed in self-driving cars, security systems, social media platforms, etc.  
• Speech Recognition: AI-enabled speech recognition system is used in virtual assistants, dictation software solutions, and customer service apps. 
• Predictive Analytics: AI-based predictive analytics assist businesses in anticipating future trends, identifying patterns, and making decisions driven by historical data. 
• Fraud Detection: AI is utilized to detect fraudulent activities and behaviors in different cases like financial transactions, and insurance claims. 
• Recommendation Systems: AI-powered recommendation systems are utilized to suggest movies, content, or other products. eCommerce platforms, social media platforms, and streaming services use this technology for specific recommendations. 

AI In Supply Chain Management: Use Cases

AI for supply chain management is used in a variety of industry verticals such as manufacturing, retail, healthcare, and the like. Here are some of the real-life examples of the use of AI in supply chain management:

• Amazon: Amazon experienced a 75% increase in speed when implementing AI. The retail giant has invested in a new robotic inventory management system called “Sequoia”, that helps pinpoint and store inventory 75% faster. Using this tailored inventory management system Amazon can lessen human efforts and employee injury by 15%, and cut down the processing time by by 25%. 
• Walmart: Walmart uses many AI techniques like machine learning, computer vision, and natural language processing, for demand forecasting. Through ML, Walmart assesses the historical sales data. This process ensures accurate anticipation of the product demand, allowing it to have an updated inventory level. 

IBM and Maersk: IBM’s Artificial intelligence and blockchain technology which is combined with Maersk’s global shipping network, is an excellent example of the use of AI for supply chain and logistics. The system maximizes efficiency, minimizes paperwork, and accelerates the overall efficiency of shopping and tracking processes.

Challenges & Solutions In AI Implementation In Supply Chain

When integrating AI into the supply chain, there are challenges you may face. Following are a few of them you should be aware of to tackle them with ease:  

Infrastructure & Technology 

Challenge: When integrating AI into the supply chain, there may be issues related to technology and infrastructure. The outdated existing systems, inadequate infrastructure, and incompatible platforms may cause obstacles in AI integration in the supply chain environment.

Solution: Consider a detailed technology audit to find out gaps that may affect the AI integration. Upgrade the existing infrastructure and prefer implementing cloud-enabled solutions for scalability. You can contact an IT service provider for smooth integration. 

Data Quality & Availability

Challenge: The AI algorithms need high-quality data to work properly and produce findings. At times, it’s likely to be difficult to efficiently implement AI mode if the supply chain data is inconsistent, incomplete, or of low quality. 

Solution: In order to enrich the data quality, consider data cleaning and validation procedures. Employ data governance procedures to ensure data accuracy and consistency.

How to Get Your Supply Chain Ready for AI Implementation?

Many businesses may find it challenging and expensive enough to get AI run completely in their production environments. Following are the key steps using which you can get your supply chain ready for AI implementation with ease and convenience:

Develop a Roadmap

 A supply chain modernization involves many problems to resolve and benefits to leverage. Develop a roadmap and make sure each stage enables the next to have adequate funding. Decide on the priorities before you outline the specific project and then come up with a strategy that’s capable enough to address the most pressing concerns in the initial phases.

Design A Solution

Identify the specific areas of supply chain operation with the potential to bring optimal benefits by AI infusion and get the solutions designed appropriately. Determine the type of solution needed like cloud-enabled apps, data science platforms, edge servers, and internet-connected devices and sensors. Also, ascertain how they are required to integrate with each other and your existing resources.

Choose The Right AI Development Company

The selection of a service provider is something essential when it comes to nurturing a long-term relationship. Consider performing a careful assessment of the competencies of each bidder. It may involve assessing their technical capabilities, support models, price, and the like.

Implementation & Integration

Once done with the vendor selection, begin with the implementation and integration process. The implementation phase needs some downtime and employ training once it gets done. If you perform effective execution, the switch from staging to production is more likely to be completed with minimal effort.

Consider Change Management

Before you implement a new AI-powered solution, develop a strategy to prepare the entire workforce to adopt it. Create a change management plan which includes communicating with coworkers regarding the issues and aims for which the AI integration has taken place. Besides, let them know the productivity the organization intends to obtain and the benchmarks leaders are expected to use to evaluate project success.

Monitor and Adjust

AI tends to be a dynamic technology that keeps on evolving through a feedback loop of monitoring and adjustment. Even when AI-powered systems seem to be working properly, you should experiment with modification and accumulate data capable of tracking results intending to indicate further performance refinements.

Why Mindinventory for AI Development?

Whether it’s demand forecasting, smart procurement, or efficient inventory management, AI brings complete transformation to supply chain management. It provides a complete awareness of the marketplace and customers’ requirements, enabling you to manage inventory accordingly. The AI-based procurement, production, and distribution system streamlines the business productivity, maximizing the business’s ROI. 

The abundance of benefits of AI nudges businesses to implement AI for supply chain management. But, for this they need an experienced team of AI developers to bring ideas to reality. 

At Mindinventory, we realize the need of the hour and provide comprehensive AI development services to businesses from various domains. Be it AI development consulting, design, or development, our accomplished AI developers will be available right there to help you with complete AI development and integration with your supply chain management system. Contact us now, and share your project requirements!

FAQs On AI in Supply Chain