Top 12 AI Use Cases for Supply Chain Optimization in 2023

They may cancel their placed order even if the order is about to deliver and this just leads to a waste of time and effort on your logistics. Companies will want to consolidate their business and operations data — regardless of the amount — to assess overall data readiness. When stakeholders claim there isn’t enough data, that it isn’t clean, or that they’re unsure which data is relevant, they are succumbing to a common fallacy. Data is the fuel that feeds AI, and you’ll need a lot of it to maximize your returns. Most business leaders know this, and they assume that they don’t have enough data to make an AI investment worthwhile.

This AI software will help you find the most feasible path for delivering products by processing driver, consumer, and vehicle information. Furthermore, expanding interest in precision and well-being in distribution centers would drive the development of AI in the supply chain industry soon. Within most organizations, there is usually an abundance of data being generated, stored and forgotten. For these companies, the challenge isn’t collecting new data — it’s locating, consolidating and analyzing existing data.

Salesforce and AI – Generative CRMs Use Cases for Service Cloud to Increase Productivity

For small to mid-sized businesses and global corporations alike, artificial intelligence (AI) has far-ranging supply chain applications. AI-enabled solutions can ingest vast reserves of supply chain data, spot patterns, and generate human-sounding advice. And they can learn on the job—integrating with other technologies to sharpen their capabilities over time.

The information on KPIs can be made available to management in real-time using a suitable dashboard. Further, in addition to the above, one can implement a weighted average or ranking approach to consolidate demand numbers captured or derived from different sources viz. AI/ML applications that can be explored and implemented in the SCM space have been discussed in this section.

AI in Production

Incorporating generative AI promises to be a game-changer for supply chain management, propelling it into an era of unprecedented innovation. By harnessing the power of generative models, businesses can evaluate various scenarios, model diverse strategies, and fine-tune their decision-making mechanisms. For instance, generative AI could be the key to architecting highly streamlined warehouse layouts, fine-tuning production lines, or formulating creative packaging approaches. Through constant trial and innovation, generative AI equips businesses with the tools to discover novel efficiencies, pinpoint opportunities, and promote ongoing refinement within their supply chains.

Through sentiment analysis, companies can separate good and bad products based on the review and the ratings the customer provides. With AI and deep learning systems we can find patterns for human behaviour from data such as weather, employment, seasons, and help companies make fine investments in storing products in warehouses and optimising the delivery system. Ware2Go’s free network planning tool, NetworkVu, uses machine learning and AI to show merchants where they should storing inventory ship faster. Merchants are given a two-warehouse scenario and a three warehouse scenario with cost comparisons and the percentage of customers that fall within a 1- or 2-day ground delivery footprint. Generative AI models can analyze factors such as customer demand, competitor pricing, and market conditions to generate optimal pricing strategies.

Many companies are, therefore, investing in digital solutions to optimize their supply chain operations to get ahead of the curve (see figure 1). The more you automate, the more you save on time and the unnecessary deployment of staff for routine tasks. For example, automated Pick-to-Light systems improve pick rate productivity in fulfillment warehouses by 30-50%. Supply chain departments are evolving into the central nervous systems of the operation.

Machine learning in supply chain with its models, techniques and forecasting features can also solve the problem of both under or overstocking and completely transform your warehouse management for the better. An increasing number of B2C companies are leveraging machine learning techniques to trigger automated responses and handle demand-to-supply imbalances, thus minimising the costs and improving customer experience. Logistics hubs usually conduct manual quality inspections to inspect containers or packages for any kind of damage during transit. The growth of artificial intelligence and machine learning have increased the scope of automating quality inspections in the supply chain lifecycle. Keeping supply lead times up to date in a planning system is harder than ever, and the more complicated the bill of materials, the less likely a planner is able to do more than spread outdated assumptions across parts. The software will underline the areas and processes it can improve, and your management should identify the organizational challenges that have to be made to improve performance in the future.

In this post, we examine four use cases that are key to the future of intelligent supply chain management. Learn about the trends reshaping the logistics industry and the solutions machine learning offers to problems that have dollar figures in the trillions. The use of AI in supply chain management is rapidly becoming more prevalent as technology advances, and this will only continue. As IoT and blockchain technologies become more widely adopted, AI-enabled predictive analytics will become even more important for managing supply chains.

AI in Logistics relies on a range of technologies, including process mining, customer service, synthetic data, autonomous vehicles, and autonomous things. Process mining helps provide valuable insights into data by monitoring and analyzing logistic activity. Synthetic data helps build models based on realistic data points to provide more accurate predictions.

Operations teams can reduce the amount of time it takes to analyze data by leveraging AI tools. Analysts can use those insights to identify potential areas of improvement, forecast demand and inventory levels, schedule maintenance and downtime activities, and predict potential equipment failures. Solutions include supply chain planning, procure-to-pay automation, supply chain finance, supply management, supply chain visibility, transportation management and warehouse management. Equipment breakdowns and unplanned downtime can disrupt supply chain operations and lead to substantial financial losses. Generative AI can be vital in implementing predictive maintenance strategies by analyzing sensor data, historical maintenance records, and equipment performance metrics. By identifying anomalies and patterns in the data, Generative AI models can predict when maintenance is required, enabling organizations to schedule repairs or replacements proactively.

Use cases of generative AI in supply chain

They found that the fault does not lie in the technology but in where and how it is applied. AI-based solutions can also enable more energy-efficient sea voyages, a recent trial found. AI-enabled short-range transportation planning helps optimize loads and increase efficiencies. “Using reinforcement learning, we can construct realistic candidate loads very quickly that know that bricks can’t be stacked on eggs and have excellent weight/cube mixes of products to ‘max out’ the loads,” says Dr. Schutt.

Prescriptive analytics is a powerful tool for supply chain operations, allowing for the exploration of how specific changes will affect outcomes. Through this, potential improvements can be identified and recommended, providing a valuable resource for optimizing supply chain operations. Descriptive analytics is a form of data mining that involves the analysis of large datasets to identify patterns and generate summaries that allow users to gain insight into a given situation. This type of analytics utilizes historical data to uncover trends and draw conclusions that can be used to inform decision-making. Predictive analytics is a technique that leverages the power of statistical modeling and regression analysis to identify and understand trends from historical data in order to make predictions about future trends.

Despite these potential returns, 96% of retailers find it difficult to build effective AI models, and 90% report trouble moving models into production4. Collaboration across data science, business, and IT teams throughout the AI lifecycle also greatly impacts AI success. Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. Our feeling remains that tech marketing teams need to tone down the hype and instead focus on what specific business and societal needs that generative AI can and will address. Further, prospects need to educate as to what business areas can best benefit from this technology vs. other available technologies. Similarly, in a Supply Chain environment, the RL algorithm can observe planned & actual production movements, and production declarations, and award them appropriately.

This agility helps mitigate risks and minimize the impact of disruptions on the overall supply chain. Predictive analytics also enables insight into future trends and demand patterns, allowing SCM professionals to better anticipate customer needs and optimize supply chains accordingly. Now, the experts would integrate AI capabilities into the infrastructure and technologies that now run your supply chain. In order to do this, enterprise resource planning (ERP), warehouse management (WMS), transportation management (TMS), or other pertinent software may need to be linked with AI models. The experts would ensure that the systems’ integration is seamless and permits data transfer. This helps supply chain companies predict the most likely future outcome and its business implications.

However, AI and ML in the supply chain can track metrics, develop benchmarks, and recommend vendor selection. Generative design algorithms can propose countless design iterations by just setting specific criteria, driving innovation, and reducing time-to-market for new products. This involves developing synthetic datasets that can be used to train and improve AI systems.

• Generative AI can contribute to sustainable supply chain management by optimizing transportation routes to minimize fuel consumption and emissions.
• They bring their expertise, contextual knowledge, and judgment to make informed decisions based on the AI-generated insights and recommendations.
• Explainability and democratization build trustworthiness that fosters adoption, when delivered on a foundation of responsible AI.
• Supply chain AI can also provide detailed region-specific demand to help business leaders make better decisions.

Managing the end-to-end process of a delivery system from acquiring data, managing data, understanding it and making decisions, can be difficult and tiring. Demand planning and scenario mapping are more important than ever for companies looking to build a more resilient supply chain. According to McKinsey, we can expect a disruption to the supply chain lasting more than one month every 3.7 years. For more information on such technologies, you can check our article on the AI uses cases for supply chain optimization. Supply chains bear a significant environmental footprint, which includes carbon emissions from transporting goods, deforestation due to producing raw materials, overuse of water, and habitat destruction.

The lack of essential data affects efficient predictions, but this issue can be solved with cloud-computing logistics systems. Advances in machine learning and uses cases will continue to accelerate as companies synchronize their entire supply chain ecosystem to remove silos, maximize resources and gain end-to-end visibility. Another powerful use case is the use Multi-Echelon Inventory Optimization (MEIO) to automatically adjust inventory positions.

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