Ways to Use AI for Supply Chain Optimization

TradeEdge Network, an end-to-end and multi-tenant architecture solution, brings execution agility into your supply chains. Its many-to-connectivity ability builds opportunities for swifter action by amalgamating businesses that react instantly to products, services, and information. Using iterative product design, artificial intelligence can identify the perfect combination of value, cost, and quality on the assembly line. Some of the areas that may be examined include downtime, lead time, cycle duration, costs, the margin of error, quantities, and supplier reliability.

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As it processes the data from these sources, this application can better gauge the market conditions and assess the growth drivers. Leveraging its sensory competencies, AI can reshape the capabilities of supply chain by improving capital expenditure and product portfolio. They may step back from purchasing even if the order is about to be delivered. This volatile order pattern can lead to miscommunication between your team and loss of unnecessary productivity loss. More often, an unstable customer behavior is hard to predict due to a surplus of orders from the online retailers.

Warehouse Automation and Management

These products are easily damaged and perishable and have a short shelf life. In general, ML & AI help organizations to automate & improve their inventory decision processes at scale, saving you time & money. Automated algorithms provide decision support, and optimization techniques recommend decisions for businesses to take based on the unique operating conditions of their supply chain. At the same time, supply chain managers have understood the importance of ESG, and global companies have started creating a sustainable supply chain.

For food supply chain owners, it means that they lost a vast amount of money. Creating and issuing BOLs can be automated, with the benefit being that the chance of a BOL being lost or misplaced is reduced. Crucially, video analytics is able to identify the movements of vehicles and people at a scene while ignoring any type of motion that’s irrelevant. Essentially, it gives you actionable data on the activity around your facility, thereby allowing you to quickly spot potential intruders. It’s really important that you think about security measures that prevent intrusion and theft, thus saving you money. Computer vision technology is used to monitor docks and parking lots and can help to guide trucks to vacant parking spaces.

AI for cost-saving and revenue boost in supply chain

The cloud also enables organizations to tap into new data sources to extend and enhance visibility and, thus, create greater opportunities for AI to deliver value. The above benefits prove nothing but the ever-expanding scope of AI and analytics in the supply chain and logistics. Your decision to invest in AI-based supply chain analytics solutions will be more simplified once you check out the use cases of these technologies in your business.

AI Use Cases for Supply Chain Optimization

Then, it relays all the necessary information to the warehouse worker , before guiding them along the right path so that they can either pick out a product or store one away. Pick-by-voice (also referred to as voice-directed warehouse procedures) combines mobile headsets with speech recognition software to determine the best pick paths on a warehouse floor. However, employing AGVs makes most sense if you have a large warehouse environment with lots of space. If your warehouse is more cramped and there’s a lot of human traffic, AGVs are much less useful.


The increased complexity might be daunting to some managers, but from a data scientists’ point of view, these flows produce a ton of information that is ripe for analysis and can enable discovery of new opportunities. Customers now expect a certain level of service, and companies need to manage a complex network of plants, providers, suppliers, and buyers that enable them to remain flexible, operate efficiently, and meet customer demand. Mosaic builds robust supply chain optimization solutions powered by artificial intelligence and machine learning. Supply chain optimization today uses technology to provide superior exception management.

  • You can use computer vision in logistics to prevent inventory shrinkage, stop theft before it happens, improve your quality control processes and monitor your shipment load better.
  • Generally, there are many inventory related variables like order processing, picking and packing, and this can become very time-consuming with a high tendency for error.
  • Fleet managers orchestrate the vital link between the supplier and the consumer and are responsible for the uninterrupted flow of commerce.
  • This way, you can avoid time waste and reduce manual error to invest more resources for business improvement.
  • Multiple cameras will need to be used for intrusion detection sensors to be successful, too.
  • In many supply chain industries, these products or parts can be defined using multiple characteristics that take a range of values.

Only the right combination of AI and supply chain can help companies tide over this crisis. To help you achieve this, you need to understand use cases and go through some well-established supply chain case studies. An in-depth reading of at least one modern supply chain case study may prepare you better to use AI and ML in the supply chain. Current methods often leverage human operators and relatively simple rule-based algorithms to dictate storage location upon loading and retrieval policies upon shipment. The immediate reaction to changes is the main difference between humans and AI. AI software recognizes patterns and considers potential areas for improvements.

Reduces Cost and Response Times

All in all, risk management is improved, and—thanks to AI-driven predictions—logistics companies are getting better demand prediction insights. Essentially, with AI in logistics and supply chain management, you’ll reduce error rates, bring down operational costs and ensure you experience minimal stockouts. Meanwhile, ML enables self-learning, predicting, prescribing, and optimizing supply chain performance automatically across functions. Machine learning can help businesses improve supply chain management by making it more resilient to disruptions. Supply chains across the world are adopting Machine Learning to improve their processes, reduce costs and risk, and increase revenue. Machine learning in the supply chain can help retailers and distributors deliver transformational changes in their businesses.

AI Use Cases for Supply Chain Optimization

At this stage, it can be useful to establish new KPIs to measure the impact of integrating AI in supply chain management. At a more granular level, professionals should understand what AI and automation would contribute to specific company operations. Digital transformation doesn’t occur in a vacuum —existing personnel and processes across the organization will be impacted, even if the implementation is on a relatively small scale. According to PwC, AI applications have the power to transform the way business is done and contribute up to $15.7 trillion to the global economy by 2030.

What Is Supply Chain Efficiency?

However, the interpretation of segments has to be done manually by business analysts/data scientists. Maybe in the future, an AI-based algorithm will be available which will provide a better and more interpretable solution to the clustering problem. Advanced modeling may include using advanced linear regression (derived variables, non-linear variables, ridge, lasso, etc.), decision trees, SVM, etc., or using the ensemble method. These models perform better than those embedded in the SCM solution due to the rigor involved in the process. Hence implementation of Supply Chain Management business processes is very crucial for the success (improving the bottom line!) of an organization. Organizations often procure an SCM solution from leading vendors and implement it after implementing an ERP solution.

How do I start using machine learning for supply chain management?

The machine learning model creation implies seven crucial steps that are:

Data collection Data preparationModel selectionModel trainingModel evaluationParameters tuningPrediction or answer

Today, AI can seed in the much needed exceptional agility and precision in supply chain optimization. It can also trigger a transformational increase in operational and supply chain efficiencies and a decrease in costs where repetitive manual tasks can be automated. The company’s staff gets a bird’s eye view of the number of packages in the delivery network, the expected peaks in the volume of goods en route, as well as potential disruptions. The AI-based supply chain solution relies on historical and real-time information, including weather and traffic data, to devise the fastest and safest ways to deliver packages.

AI Use Cases for Supply Chain Optimization

AI-enabled technologies such as cobots are helping drive efficiency, productivity, and safety in warehouse management. The recent global pandemic and other geopolitical disruptions demonstrated how weak supply chains could bring down whole organizations. Many companies are, therefore, investing in digital solutions to optimize their supply chain operations. However, AI Use Cases for Supply Chain Optimization merchants who outsource their supply chain can gain access to larger data sets across their industry and beyond. The longer a merchant works with a single supply chain partner, the smarter and more accurate machine learning algorithms become. Over time, the algorithms will learn that merchant’s particular business patterns, becoming even more efficient.

Manufacturers can improve both storage and retrieval operations by building an AI agent that can dynamically optimize and balance throughput and efficiency within the warehouse to maximize financial return. Warehouses store a wide range of products that require different storage and handling strategies. Inefficient storage and retrieval decisions can have severe negative financial impacts. 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.

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Leveraging AI in supply chain management can also help design better delivery routes and optimize fleet utilization. The last name in this list of companies using AI in the supply chain is DataArt. It has a range of AI and ML-enabled solutions designed to improve operational efficiencies of businesses. It helps them identify the shopping behavior of customers and better engage with them through customized solutions.

  • These methods are difficult to develop, but they’re also error-prone when there is a need to simultaneously manage the changing optimization goals and the current production goals.
  • SCM solutions offer configurable processes covering end-to-end supply chain operations right from the procurement of raw materials to the sale of the finished product.
  • This will allow you to take fleeting vehicles out of the chain before the performance issue causes any kind of delay in the deliveries.
  • AI in the supply chain helps reduce customer service-and-support time by predicting customer behavior with great precision.
  • For food supply chain owners, it means that they lost a vast amount of money.
  • This is an area many businesses struggle with and aim to tackle by designing supply chains to match demand with supply .