Top 3 AI Use Cases for Supply Chain Optimization

Top 3 AI Use Cases for Supply Chain Optimization

The modern supply chain covers many business processes, from forming a raw material base to delivering finished goods to the buyer. It is a network of suppliers, warehouses, factories, logistics companies, distributors, and retailers. Therefore, organizations constantly seek to optimize their supply chains to reduce costs and increase work speed.

The global pandemic has shown conventional linear, or rule-based business process optimization algorithms do not have the flexibility to adapt to changing conditions quickly. AI-enhanced solutions are increasingly being used to improve the reliability of supply chains, as well as to reduce risks and costs. Let’s discuss the benefits of using intelligent technologies when working with different counterparties.

Use cases of AI in the supply chain

Artificial intelligence and machine learning have set new performance standards for efficient logistics. They also help companies perform automated procedures, analyze data, and serve customers. At times like these, choosing the right system product is critical. Businesses must use thoughtful solutions that meet their daily needs to manage a complex supply chain system. Most often, AI is implemented to solve the following warehouse and logistics tasks.

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Supply chain automation

Supply chain automation is a broad term that means reducing or eliminating the human factor in dealing with suppliers. Automation includes many new technologies, including machine learning, robotics, artificial intelligence, etc. Theoretically, almost all processes may be automated, but most often, companies choose the following supply chain tasks:

  • Back office tasks, e.g., document processing, can be easily solved with intelligent automation or digital workers who combine conversational AI with robotic process automation (RPA).
  • Transport automation holds great promise for the future of supply chains. Such improvement includes vehicles, alternative delivery devices, and route optimization.
  • Warehouse automation is revolutionizing warehouse management. Software may receive and confirm orders, and box algorithms select the optimal packaging to consolidate shipments and reduce waste.

Supply chain automation solves a wide range of problems faced by manufacturing, including shortage of workers, lack of materials, incorrect demand forecasting, transportation challenges, etc. As technology advances and costs decrease, automation becomes an integral part of the industry.

Top 3 AI Use Cases for Supply Chain Optimization

Building trust relationships with suppliers

AI helps to improve supplier relationship management (SRM), making it more consistent and efficient. Advanced AI-enabled SRM platforms allow choosing the right supplier based on cost, interaction history, sustainability, and more. Intelligence technologies let you evaluate suppliers and rank them correctly.

AI automates routine supplier communications, e.g., invoice sharing and payment reminders. Automating such procedures helps avoid failures associated with late payment for supplier services and negatively affects shipment and production.

Accurate predictive analytics and forecasting

Supply chain professionals want to know the future regarding demand, market trends, and more. Although correct prediction is rare, AI can more or less accurately predict future events.

Such forecasts are especially relevant if we speak about fresh produce suppliers. These goods are easily damaged and deteriorate quickly. According to UN reports, about 30% of the world’s food is wasted annually. For owners of goods, it may result in significant financial losses.

To solve the problem, you may utilize the ready-made platform to predict product demand in supply chains using machine learning algorithms and forming a demand model. AI-ruled software can make daily demand forecasts. The program determines the reasons for the increase in demand and even creates simulations of such situations.

Main benefits and challenges of adding AI in supply chain optimization

Intelligent systems improve supply chain management. AI-ruled programs analyze large amounts of information, identify key trends, and respond quickly. From quality assurance to warehouse management, AI can be used at all stages of the movement of goods. The benefits of artificial intelligence listed below confirm the importance of implementing AI in your business:

  • Productivity growth: more than 60% of CEOs say AI can automate decision-making and make supply chains more efficient.
  • A quick response to changes is the main difference between artificial intelligence techniques and the human brain. AI software identifies patterns and tries to find potential areas for improvement.
  • Optimizing warehouse operations and inventory are only possible with information analysis and machine learning. Intelligent algorithms plan and predict product popularity. Thus, the logistics department uses such information to choose the transport and send it to the places with the highest demand.
  • Improved pricing: if a company’s pricing considers market requirements and competitors’ plans, it allows it to remain competitive. Thus, AI insights in supply chain case studies analyze the impact and offer dynamic pricing based on customer psychology, value perceptions, and other vital factors.
  • Reduced operational costs: AI technologies optimize every minute and every mile in the delivery chain. So, United Parcel Service, an American logistics company, reports that the organization was able to save $50 million with AI and rapid route optimization.

Recently, supply chain management has become increasingly complex. The length of physical flows increases, and the connection between individual elements increases as the complexity of product portfolios increases. Market volatility increases the demand for delivery flexibility and adaptability. Let’s also discuss the main challenges of the AI ​​supply chain:

  • System issues: advanced AI systems are cloud-based and require significant bandwidth to power the system. In some cases, operators need to install special, expensive equipment to access the full functionality of AI.
  • Scalability: many AI and cloud systems scale well. Users face the problem that the initial run level must be optimal. Since every AI system is unique, supply chain participants should discuss this with all service providers.
  • Training costs: like any new technology, training is another aspect that requires a lot of time and money. It may impact business profitability as supply chain actors must partner with AI vendors to create affordable and effective learning solutions.
  • Significant operating costs: AI-based machines contain a network of processors, each of which needs maintenance and replacement from time to time. It may lead to a sharp increase in operating costs.

Any procedure and change have its price. Sometimes, users do not consider the long-term perspective of dealing with supply chains that consist of several partners and different systems. As a result, the whole system has to be rescheduled, which causes discomfort and high financial costs.

Top 3 AI Use Cases for Supply Chain Optimization

How to implement AI in your supply chain

According to PwC, AI applications could revolutionize the way we do business and contribute up to $15.7 trillion to the global economy by 2030. Advanced AI guarantees significant flexibility and precision in supply chain optimization. If you also plan to implement AI into your activities when working with counterparties, you need to perform several actions:

  • Identify business cases and strategic points to add AI: initially, you can digitize the one link in the supply chain that has the most potential to create value. After adopting the basic solution, it is possible to continue the development of AI vertically and horizontally.
  • Find the right supplier to bring your project to life: the supply chain is complex and multifaceted, so one supplier cannot meet all expectations. Explore different offers on the market and integrate all the necessary solutions into a system that suits your needs.
  • Monitor the development and implementation of solutions: Regularly align your development process with your initial supply chain digitization strategy, and keep the core values ​​of project delivery in mind.
  • Ensure smooth software adoption and scale solutions: Digitizing your supplier experience includes end-to-end change management and retraining. Explain to employees the value of AI and tell them how to use new methods in their work.

Modern supply chain management is one of the most complex business procedures, but it can be made more accessible. AI provides employees with deep data at greater frequency and granularity than ever before. But more than visibility is needed to get the most out of AI-powered supply chain solutions. In addition to the development of technology, it is necessary to think about organizational changes, update business processes, and improve employees’ skills. Only then can you expect a good return on investment.