It’s certainly no longer “business as usual” for supply chains. A convergence of factors has placed significant pressure on organizations’ supply chains to address a wide range of new challenges and priorities that, in many cases, existing supply chain capabilities aren’t capable of handling. In particular, companies’ are hampered by a lack of visibility.
It’s been well documented, for instance, that COVID-19 caused large-scale supply chain disruptions, risks, and uncertainty that nearly paralyzed companies for a time, largely due to a combination of unforeseen demand spikes and insufficient capabilities to respond to them—and these challenges are still wreaking havoc in companies today.
The compounding effect of these disruptions is an unprecedented rise in costs. For instance, the average price to ship a 40-foot sea container has quadrupled ; truck driver salaries are soaring due to labor shortages in major economies ; and commodity prices are the highest since 2014. And then there’s the ever-growing interest in and demand for responsible, sustainable business practices from consumers, governments, investors, and other critical stakeholders.
Scaling AI in the supply chain: The next step toward intelligent, self-driving supply chains
Investments in emerging technology are not immune to these challenges. Unsurprisingly, 45 percent of the largest barriers to implementation of generative AI center on its ability to deliver return on investment (ROI). From opportunity value and use case selection to investment cost and technical requirements, ROI for AI has never been more critical.
Fortunately, supply chain organizations have experience in attaining value from digital and physical assets. Much can be gleaned from nearly a decade of investments made in machine learning, a form of AI used to predict a range of outcomes from supplier lead times to customer out-of-stocks. Leveraging the lessons learned from past AI and attuning them to the context of generative AI can help navigate a better path to healthier returns.
Implications for the supply chain
The lessons learned from discriminative AI serve as guideposts for the supply chain. The principles of value, scalability, and adoption all continue as key success criteria for generative AI, where deep learning models are used to understand and create new content.
AI-generated content can prove to be quite valuable depending on the use case. Generative pre-trained transformer (GPT) models can increase logistics productivity by formulating responses to shipment inquiries, helping to mediate between customers, carriers, and third-party logistics providers (3PLs). Generative adversarial network (GAN) models can increase warehouse productivity by creating synthetic data to train robotics in how to handle packages more accurately.
While criteria for success remains the same, the tactical implications to generative AI are quite different. As organizations make progress on their generative AI journey, there are key considerations that help facilitate a better return.
Do the math to articulate productivity value
Discriminative AI often provides a direct line-of-sight to benefits such as how higher demand prediction accuracy leads to reduced days of inventory. Generative AI, on the other hand, tends to center on employee productivity, which alone may not be enough to justify a supply chain investment.
To justify a proposed investment, quantify how a proposed initiative increases employee productivity and articulate attributable impacts to key performance indicators (KPIs), be it service level, cost, or inventory. For example, a generative AI-powered chatbot that creates forecast reports might improve productivity for a demand planner. But would it improve order fill rate beyond the planner’s ability? Or would it merely provide the planner with more time to focus on other value-added activities? In this case, there is a clear difference in productivity value. Determining this upfront along with performing periodic validation helps cut investment losses sooner rather than later.
From Proof of Concept to Proof of Scale
At its core, generative AI is built on the premise of scalability. A large language model (LLM) for example, can be tuned to serve multiple cognitive use cases. The advantage for a supply chain is the ability to scale how its workforce interacts with multiple parties, products, and assets.
Set scalability as a key objective for every project by setting AI targets to achieve high volumes of interaction among supply chain functions, network partners, operating equipment, or physical inventory across locations. A generative AI project that drives productivity at considerable scale is proof of success.
Deploy with – not for- operations
Generative AI relies on deep learning models or “black box systems” with inputs and outputs that are known but with inner workings that are opaque. This makes it difficult for supply chain professionals to interpret AI reasoning and reliability. Compound this with a fear of job security stemming from tech-enabled automation, and AI adoption is at risk of becoming AI opposition.
Recruit subject matter experts from the supply chain organization to validate and endorse data inputs and outputs. Given the lack of model explainability and potential for “AI hallucinations,” the quality of input used to tune AI and the output generated by AI becomes paramount, more so than is the case with discriminative models. To build competence in validation, the supply chain organization must develop a deeper understanding to help demystify generative AI and its technical impact on the workforce. Broadening the organization’s role and responsibilities in this way helps improve reliability and foster acceptance.
Key use cases of generative AI in supply chain
Generative AI has diverse applications in supply chain management, revolutionizing how companies plan, execute, and optimize their supply chain operations. Here’s a deep dive into some of the key applications:
Demand forecasting
Generative AI synthesizes historical sales data, market trends, consumer behavior, and external factors like economic indicators to predict future demand accurately. This dynamic forecasting enables companies to adjust their production schedules, manage inventory more effectively, and respond proactively to market changes, ensuring supply aligns with demand while minimizing waste and stockouts.
Generative AI enables the analysis of real-time data streams, allowing supply chain managers to make immediate adjustments in response to changing conditions, thereby optimizing operations continuously.
For example, Walmart employs Generative AI to tailor inventory management, accurately forecasting customer demand for different products and optimizing stock levels. The company tested a generative AI-powered negotiation bot from Pactum AI, achieving cost savings of about 3% on contracts. Interestingly, most suppliers preferred negotiating with the AI bot over human negotiators.
Inventory management
Generative AI identifies the best strategies for distribution and storage, factoring in delivery times, transport costs, and demand variability. The outcome is heightened operational efficiency and significant cost savings. The technology recommends reordering points and safety stock levels. This is crucial for Generative AI in manufacturing, where it aids in superior warehouse management, leading to fewer product shortages and lower storage costs.
AI algorithms continuously assess sales data and demand patterns, recommending real-time adjustments to inventory levels for different products to align with market demand. Moreover, these models determine the optimal safety stock levels, considering demand variability, seasonal trends, and market dynamics to prevent stockouts of popular items. Generative AI simulates potential market scenarios, such as sudden demand spikes or supply chain disruptions, enabling companies to prepare and implement effective restocking strategies. By identifying slow-moving items that incur high holding costs, generative AI suggests actions like pricing strategies or targeted marketing to enhance product turnover. It devises efficient storage and distribution methods tailored to different product categories.
A real-life example is Amazon’s use of Generative AI in its fulfillment centers showcases how the technology optimizes product placement, streamlining order processing processes, and reducing storage costs.
Procurement and supplier management
Generative AI enhances supply chain resilience by analyzing vast datasets to pinpoint optimal suppliers. It evaluates performance metrics, quality assessments, and cost factors, enabling effective supplier relationship management. Through analysis of historical interactions, contracts, and performance evaluations, generative AI identifies risks and opportunities for improvement, supporting proactive supplier management and fostering strong partnerships.
ASOS uses Generative AI to handle returns, analyzing return data to identify common causes and implement strategies to decrease return rates, thereby enhancing customer satisfaction and operational efficiency.
Logistics and distribution
Generative AI for supply chains enables greater transportation efficiency by analyzing traffic flows, weather conditions, vehicle capacities, and customer demands to optimize delivery routes, ensuring fast and cost-effective paths. For example, logistics companies can use generative AI to manage delivery truck fleets, continuously gathering data from GPS traffic updates, weather forecasts, and current locations.
AI models provide real-time monitoring and re-routing capabilities during transit to circumvent delays caused by traffic congestion, accidents, or other disruptions, enhancing on-time delivery rates.
Generative AI streamlines reverse logistics by evaluating data related to product returns, repairs, and refurbishment, optimizing the pathways for returned items, and deciding on the most efficient and effective methods for repair, recycling, or disposal. It assists in managing the inventory of refurbished goods, ensuring efficient redistribution and reducing waste.
Warehouse layout optimization
Generative AI enhances warehouse layout optimization in supply chains by dynamically adjusting layouts based on real-time operational data and predictive analytics. An important advantage is its ability to adapt layouts on the fly to meet changing operational needs. Unlike traditional static layouts, which are based on historical data and assumptions, Generative AI continuously analyzes incoming data streams to identify opportunities for improvement. This dynamic approach ensures that warehouse layouts remain optimized in response to fluctuations in demand, inventory levels, and other factors.
Furthermore, Generative AI can simulate various layout configurations and scenarios to identify the most efficient arrangement. For example, it can analyze historical order data to identify frequently accessed items and strategically place them closer to packing stations or shipping docks. Businesses can improve order fulfillment speed and customer satisfaction by reducing the distance traveled by warehouse workers and minimizing picking times.
AI models can simulate various layout configurations to identify the most efficient arrangement. It considers product dimensions, order frequency, and picking paths to optimize the layout for maximum throughput and storage capacity.
Real-World Example: Building AI Readiness through Cloud Data Modernization
A great example of how data modernization enables AI-driven agility in the retail supply chain comes from one of Dedicatted’s clients: a premium regional grocery chain known for its curated selection of high-quality products and commitment to local sourcing. Facing siloed data systems, inconsistent reporting, and limited real-time visibility across store locations, the company partnered with us to migrate its operations to a cloud-native AWS data platform.
The new architecture, built on Amazon S3, Redshift, and Kinesis, unified all sales, inventory, and supplier data into a single source of truth, enabling real-time analytics, automated reporting, and predictive insights. As a result, the retailer achieved a 50% faster insight cycle, a 65% reduction in manual data handling, and gained a fully AI-ready infrastructure – laying the foundation for smarter demand forecasting, dynamic inventory optimization, and data-driven decision-making across its supply chain.
See how we helped them: Retail Data Modernization: Elevating In‑Store & Digital Operations with AWS
Why choose Dedicatted for generative AI implementation
When the time comes to implement Generative AI within your supply chain operations, selecting the right technology partner can spell the difference between success and failure. Dedicatted is a reliable partner with a proven track record in implementing generative AI in supply chain management. Here are a few reasons why you should entrust this task to our tech experts:
- With over 10 years of experience in the tech industry, Dedicatted has delivered 20+ successful data science and AI projects. We have a team of skilled Generative AI developers, who are well-versed in various verticals.
- We cover every process step, from the discovery phase, consulting, and end-to-end development to product release and post-production support.
- We adhere strictly to security protocols and regulatory frameworks, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, to guarantee the protection and integrity of data.
