Top Use Cases of AI in Supply Chain Optimization

If Amcor can’t accurately predict shortages, it can’t stock up ahead of time on raw materials. More importantly, the company needs to predict price changes, so it can buy more at lower prices before a hike, or less if it looks like a drop is on the horizon. It also notifies company agents about events such as a new order placed or a schedule change.

This data can predict whether a product should be repaired, refurbished, recycled, or disposed of. Generative AI can analyze product dimensions, fragility, and other factors for packaging optimization to suggest the most efficient and environmentally friendly packaging solutions. In transportation, for instance, generative AI in supply chain can analyze traffic data, vehicle capacities, and delivery routes to optimize logistics and minimize environmental impact. Choosing the most fuel-efficient routes and schedules can reduce carbon emissions and contribute to sustainability goals. Generative AI models can predict when maintenance is needed by identifying data anomalies and patterns.

Harnessing autonomous, self-regulated supply chains

This enables businesses to examine different demand scenarios, test the influence of various factors, and make more informed decisions. Rather than solely depending on historical data, it creates new data that mirror the training dataset. Generative AI algorithms, such as GANs or Variational Autoencoders (VAEs), learn the underlying patterns and characteristics of the data, utilizing this understanding to generate new data points. In this article, we will delve deeper into the applications and impact of generative AI within the supply chain sector.

• Transportation management company Echo uses AI to provide supply chain solutions that optimize transportation and logistics needs so customers can ship their goods quickly, securely and cost-effectively.
• Bad customer experiences arise due to ignoring customers’ needs, failing to give quality customer service, lengthy delays, and company representatives who lack knowledge and etiquette.
• The supply chain system of the technology giant Microsoft heavily relies on predictive insights driven by machine learning and business intelligence.
• 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.

For instance, DHL, a global leader in logistics and supply chain management, harnesses the power of machine learning to enhance the upkeep of its fleet and equipment. Predictive maintenance alerts are generated, allowing DHL to schedule maintenance proactively and minimize vehicle downtime. 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.

Back-office automation

The demand numbers thus finalized are released to the next module (Supply Planning) in the desired time buckets (day, week, etc.). SCM definition, purpose, and key processes have been summarized in the following paragraphs. The finance industry has embraced generative AI and is extensively harnessing its power as an invaluable tool for its operations.

The AI tool can convert text into the harmonized tariff schedule (HTS) code and provide tiered classification data based on suggestion algorithms. Users can enter specific product descriptions to improve accuracy; meanwhile, machine learning models continuously improve the algorithms. It’s easy to forget the importance of customer service to a company’s logistics and supply chain processes. However, without good customer service, you won’t have many customers left whose demand maintains these processes. AI’s prowess extends beyond integrating business plans across companies; it extends to generating cognitive predictions and recommendations for supply chain planning.

Deeply understanding the source of demand—the individual customers—so it can be met most precisely has never been more difficult, with customer expectations changing rapidly and becoming more diverse. And as we saw in the early days of COVID-19, getting a good handle on demand during times of disruption is virtually impossible without the right information. The good news is that the data and AI-powered tools a company needs to generate insights into demand are now available.

AI integration in the production process can prove to be a significant cost-saver for any organization. By automating processes using AI and robotic automation, businesses can increase production speed and accuracy, resulting in cost savings through reducing human labor costs as well as improving product quality. The costs of automating this process, although high, do outweigh the labor costs in the long term. And because robotic automation does not require downtime, it can work significantly faster than human labor. We can empower your business with the best possible services, from supply chain analytics and data visualization to data warehousing and business intelligence solutions.

Getting Started with AI/ML to Build Intelligent Supply Chains

A recent study by Dimensional Research found that 96% of enterprises embarking on ML projects encountered difficulties with data quality. For example, UPS has begun to operate with an AI-powered navigation system that automatically updates drivers’ routes, constantly creating the most efficient route. An example is in the healthcare industry, where AI is being utilized for medical diagnosis through image recognition technology. Therefore, companies must plan these investments or address their needs to a verified IT outsourcing vendor for cost-effective implementation. Forecasted to reach $771.38 billion by 2032, the projected CAGR will grow by 35.09% from 2023 to 2032. One use case that’s becoming increasingly important in the wake of COVID-19 is scenario modeling, often done with the help of a digital twin.

Accurate, fast, and cheap delivery where goods are tracked to their final destinations is now the new normal. Real-time access to supplier data can enable companies to hold suppliers accountable for where and how they’re sourcing materials—allowing brands to cut off a supplier that’s not meeting ethical or sustainable standards. Gaining similar visibility into the full supplier base is also critical so a company can understand how its suppliers are performing and see potential risks across the supplier base. The keys to simultaneously addressing relevance, resilience and responsibility are advanced analytics and AI. Our study shows that Leaders are adopting these powerful tools at scale and, in the process, getting a head start in capitalizing on the significant opportunities created by human and machine collaboration.

As an autonomous, full-service development firm, The App Solutions specializes in crafting distinctive products that align with the specific

objectives and principles of startup and tech companies. They also need to decide the data types to ensure the supply chain has enough information. In this way, your development team will concentrate on the most critical aspects of your supply chain. As a result, you will receive the right type of AI that drives meaningful outcomes and uncover a clear path for further improvements.

This can help improve the overall equipment effectiveness (OEE) — one of the most important manufacturing metrics. Contact us for custom built low code data and AI solutions for your business challenges and check out supply chain AI solutions built for our clients, including Fortune 500 companies. Generative AI can analyze vast amounts of supplier data to rank and suggest the best possible options based on multiple factors like cost, reliability, and lead time, streamlining and enhancing the supplier selection process.

Generative AI for time-series

Automated systems accelerate traditional warehouse procedures, removing operational bottlenecks along the value chain with minimal effort to achieve delivery targets. Organizations seeking to optimize their sales and purchasing build rules-based analyses. Existing processes combine various natural language processing (NLP) techniques to index contracts and learn about their business. However, variability of contract language and format leads to complex, custom rules that are fragile and require ongoing maintenance by specialists. Let’s walk through one example of a Generative AI use case in supply chain management that would have immediate value in augmenting your workforce. That is Generative AI-enhanced — or more precisely, large language model (LLM)-enhanced — demand forecasting.

For instance, AI programs excel in anticipating product life cycles, flagging potential declines, and strategizing for end-of-life cycles on sales channels. By seamlessly transitioning to models for emerging products, companies can extend the lifecycle of their offerings, demonstrating the transformative impact of AI on demand forecasting. According to several studies, artificial intelligence can provide supply chain and logistics operations unparalleled value, as has been mentioned above. Companies across the world are beginning to favor AI for supply chain improvement and management.

Modern warehouses aren’t just storage centers; they are lively hubs where every square foot counts. Artificial intelligence technology speeds up the digitization of warehouses, automating picking and packing of goods, inventory, order fulfillment, and product transportation. It also equips business deep insights into their warehouses, which leads to smart and informed decisions such as where to place goods, how to route orders, and which staff to hire. According to a McKinsey report AI-driven systems aid in cutting warehousing expenditures by up to 15%. Since AI-powered forecasts can help maintain optimal inventory levels, carbon emissions attached to storage and movement of excess inventory can be reduced. Smart energy usage solutions can also reduce carbon emissions related to warehouse energy consumption.

Microsoft Supply Chain Copilot, with its AI capabilities, greatly enhances the precision of demand forecasting, an area already employing machine learning. However, due to recent supply chain disruptions, there remains a need for careful manual review, leading to substantial time investments from demand planners in manual analysis and demand plan adjustments. Copilot leverages generative AI to proactively identify external issues, such as severe weather, financial fluctuations, or geopolitical events, that could impact critical supply chain operations.

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. DP also includes many other functionalities such as splitting demand entered at a higher level of hierarchy (e.g., product group) to a lower level of granularity (e.g., product grade) based on the proportions derived earlier, etc. The article explores AI/ML use cases that will further improve SCM processes thus making them far more effective.

• The more advanced planning the automation of this process affords also facilitates a better division and specialization between and within different departments.
• Implementing machine learning in logistics can be expensive, including data collection, infrastructure settings, and IT staff-related costs.
• Obtaining sufficient and reliable data can be challenging in the supply chain, mainly when dealing with complex and dynamic data sources such as customer demand, production parameters, and logistics information.
• IT systems can independently find solutions to problems that arise using artificial intelligence.

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