Top 5 AI Use Cases for Supply Chain Optimization

In that way, AI promotes transparency and visibility among suppliers, manufacturers, distributors, and customers. Ikea has developed a dedicated system that allows it to forecast demand in a smarter way. A good example in this category is ChainLink, a platform that enables supply chain process automation by connecting external data sources and APIs to enable smart contracts. AI-powered, autonomous vehicles can be used to transport goods within warehouses, between different locations, or even shipping directly to the customer. These robots can autonomously navigate through the warehouse or other environments, optimizing the transportation process and reducing the need for human labor. It’s time for modern supply chain enterprises to empower their business with reliable and automated data visual analytics platforms.

Essentially, Generative AI involves generative models, the two most popular ones being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs use a generator to produce samples from random input and a discriminator to differentiate real from generated samples. Through competitive learning, GANs refine the generator to produce more convincing content while enhancing the discriminator’s ability to distinguish between the two, creating realistic content.

Why is Machine Learning Important to Supply Chain Management?

As a supply chain owner or C-level executive, you struggle to reduce inventory imbalances. At the same time, you want to achieve ultimate visibility and transparency across all departments. Unfortunately, the supply chain generates too much data, complicated to store and analyze. The project showed how AI and machine learning can enable more energy-efficient voyage planning for ship operators. The results demonstrated successful energy efficiency optimization based on estimated time of arrival. AI-based solutions can also enable more energy-efficient sea voyages, a recent trial found.

• While the technology isn’t new, recent advances make it simpler to use and realize value from.
• No supply chain firm would want to halt their company’s production while they launch a hunt to find another supplier.
• COVID-19 has only accelerated this process making companies revisit their global supply chain strategies amid the new reality.
• Furthermore, by adequately distributing products among the hundreds of vessels that pass through a port, terminal traffic may be decreased, optimizing schedules and reducing shipping costs.
• To improve production planning and solve these limitations, one can build an AI agent using DRL to optimize production by determining amounts of which product SKUs to manufacture and how to best schedule their production.

Due to the delicate balance of stock levels, inventory management plays a vital role in this regard. Overstocking escalates holding expenses, whereas understocking leads to stockouts and customer dissatisfaction. Achieving optimal inventory levels necessitates reliable demand projection, adept demand-supply alignment, and streamlined inventory replenishment tactics. Generative AI systems can analyze vast amounts of data, uncover patterns and generate actionable insights to enhance decision-making and operational efficiency. In addition, generative AI enables the creation of synthetic data and models, facilitating simulations, scenario planning, and risk analysis and sensing, thereby improving supply chain resilience and adaptability.

Use cases of AI in supply chain management that increase resilience

As our reliance on international freight transportation continues to grow, effective and technology-driven supply chain management becomes the backbone of staying on top of the competition. There is a plethora of use cases within supply chains that would benefit from the application of AI/ML technology. Supply chain executives are typically looking for areas where to invest the time and effort of their teams (which are already stretched) to derive the most value from these approaches. In this article, we explore a small but diverse set of use cases that can serve as a starting point for a supply chain organization’s foray into AI/ML.

This can impact business efficiency as supply chain partners will need to work closely with the AI providers to create a training solution that is impactful and at the same time, affordable during the integration phase. Accurate inventory management can ensure the right flow of items in and out of a warehouse. Simply put, it can help prevent overstocking, inadequate stocking and unexpected stock-outs. But the inventory management process involves multiple inventory related variables (order processing, picking and packing) that can make the process both, time consuming and highly prone to errors.

It tracks weather and road conditions and gives recommendations on how to optimize the route and reduce driving time. This way, trucks can be diverted any time on their way when a more cost-effective route is possible. It allows supply chain professionals to track the location of goods during transportation.

Another benefit of using AI with a dimensioning system in invoice control is that it can help to improve the accuracy of master data. By analyzing SKU or pallet dimensions, AI-powered software can identify the size and weight of packages and notify on missing information, incorrect dimensional data, or incorrect shipping costs ensuring that invoices are accurate. For enterprises, taking advantage of these tools is essential to future-proofing and maximizing their organization’s capabilities. As companies focus on optimizing their content supply chain, new roles that leverage tools like AI and machine learning (ML) are emerging to streamline workflows. University of the Cumberlands Helps Drive the AI RevolutionThose looking to impact the AI supply chain ecosystem must possess the skills to create real change.

Use Cases of AI in Supply Chain

AI technology also allows for predictive analysis of customer data to better anticipate customer needs and automate the fulfillment process. At Acropolium, we tailor IoT, AI, and ML-based bespoke software solutions to help businesses modernize their logistics. Whether you need a transportation system upgrade or complete product development, we offer a subscription-based pricing model for any goals and budget. Machine learning in warehouse management is applied to automate manual tasks, proactively spot potential issues, and minimize paperwork for warehouse staff. The technology also plays a significant role in programming robots within these warehouses. Furthermore, progressive warehouse management systems involve computer vision that aids in identifying incoming packages and scanning barcodes.

We saw the importance of having greater visibility into the supplier base in the early days of the pandemic, which caused massive disruptions in supply in virtually every industry around the world. Artificial intelligence algorithms have been used in a wide range of applications within accounting and finance. Rohlik, the e-commerce unicorn, helps connect food producers to retail consumers via its food The recent advances in deep learning neural networks are pushing beyond what we thought AI technology could do. Warehouse slotting lot of the time can be seen as a never-ending puzzle game, but the solution can be less overwhelming and easier than you think. Artificial intelligence (AI) has demonstrated its limitless possibilities, use cases, and potential to help maximize efficiency in many industries and areas.

Demand planning

It can also develop order profiles, recognizing customer demands, preferences, and usage history to improve customer satisfaction. The more data your logistics software processes automatically, the more adaptive your business gets to the ever-changing market. Machine learning use cases in supply chain management can be as diverse as your company’s scope of tasks. The popularity of machine learning in the logistics industry is caused by the technology’s ability to foresee potential disruptions. It helps logistics businesses minimize human-factor risks by getting the most out of automatically collected and smartly processed data.

Predictive analytics helps determine optimal inventory levels, production schedules, reorder points and optimal stock levels. Evaluate how artificial intelligence in supply chain is transforming your business processes over time. Make necessary changes in your AI-based supply chain management to increase productivity, accuracy, and decision-making. Keep abreast of current AI breakthroughs and look at supply chain innovation and optimization prospects.

What is artificial intelligence (AI)?

Read more about Top 3 AI Use Cases for Supply Chain Optimization here.

• Depending on a business’ needs, NLP can solve a specific problem or provide a complete technology solution for automating an entire back-office function using natural language processing and custom software development.
• Ware2Go’s free network planning tool, NetworkVu, uses machine learning and AI to show merchants where they should storing inventory ship faster.
• Without an agile and resilient supply chain, organizations struggle to respond quickly and effectively to these disruptions.
• Knowing this information, companies can save money and avoid potential charges or penalties.