
Recently, a complaint was filed against Volkswagen, BMW, and Mercedes Benz with the German Federal Office for Economic Affairs and Export Control because these companies failed to detect unethical practices in their supply chains. Apparently, these automotive giants relied on raw materials extracted through forced labor of the oppressed Uyghur minorities.
Nowadays, it's challenging to control your own inventory, let alone monitor the entire supply chain. Fortunately, generative AI seems to have the tools necessary to address this problem. You can hire a generative AI consulting firm to help you predict customer demand, identify any dubious practices in your supply chain, and find new suppliers that align with your environmental and ethical goals.
Interested? Then let's explore what else generative AI can do for supply chains and what challenges to expect during implementation.
What is Generative AI in Supply Chains?
Generative AI is a technology capable of creating new content such as text, images, and even documents that resemble examples it has seen during training. It's like a smart assistant that can produce new content on demand without being specifically programmed for each content type.
In the context of supply chains, generative AI is trained on vast amounts of supply chain-related data, including logistics information, sales history, inventory records, and more. It then provides various types of analytical outputs, including optimized route maps, demand forecasts, supplier evaluation reports, restocking strategies, and much more.
How Does Generative AI Differ from Traditional AI Technology?
Traditional artificial intelligence excels at analyzing historical data and identifying patterns. It encompasses a wide range of capabilities, including natural language processing, computer vision, machine learning, and more. In contrast, generative AI focuses exclusively on creating content that appears to be human-generated.
Examples of traditional AI in our daily lives include self-driving cars, recommendation systems on your favorite shopping websites, and voice assistants like Siri or Alexa. Generative AI examples revolve around content creation, such as ChatGPT, which produces human-like text, and DeepDream, which generates images.
For more information on the role of traditional AI in ensuring supply chain sustainability, you can refer to our blog.
Benefits of Generative AI for Your Business's Supply Chain
After implementing generative AI, even if applied to just one or two use cases, your company can reap some or all of the following benefits:
Increased Efficiency: Generative AI can optimize processes like inventory replenishment and procurement, as well as identify faster and cheaper delivery alternatives.
Reduced Labor Costs: Automating tedious tasks such as forecasting and report generation lowers the need for manual labor.
Improved Scalability: AI can handle additional workloads without the necessity of hiring more staff.
Enhanced Customer Satisfaction: Algorithms can predict demand and ensure that your customers' favorite products are always in stock.
Optimized Operations: AI's ability to forecast and resolve supply chain issues leads to smoother operations.
More Productive Employees: Staff can focus on tasks better suited to their skills while AI models handle extensive report creation and other monotonous duties.
Is It Worth Creating Custom or Refining Existing Generative AI Models?
There are off-the-shelf generative AI solutions available, such as C3 Generative AI, which can be used to improve supply chain visibility. While these solutions can be quite powerful, companies using them for supply chain optimization may encounter the following issues:
Lack of Domain Expertise: These solutions are often developed as general-purpose models and may not cater specifically to your industry's needs.
Dependence on Training Data Quality: If the training data is of low quality, biased, or simply doesn't match your company's data, the generated content will reflect these issues.
Irrelevant Content: Algorithms may produce outputs unrelated to your business because they don't understand the specifics of your data.
For optimal performance, organizations can hire an IT consultant specializing in supply chains to develop new or customize existing AI models, adding domain-specific knowledge. This approach offers the following advantages:
Enhanced Accuracy
Alignment with Your Organization's Needs
Seamless Integration into Your Processes
Full Ownership of the Technology in the Case of Custom Development
Compliance with Industry Standards and Regulations
However, keep in mind that custom-built algorithms are more expensive and require more time to deploy since they are created from scratch and need thorough training and validation. Therefore, the final choice is a trade-off between your business needs and available budget.
5 Key Use Cases of Generative AI in Supply Chains
Use Case 1: Efficient Inventory Management
Generative AI can analyze vast amounts of data and develop policies and suggestions on how to better manage inventory based on current trends. Here's how this technology can aid inventory management:
Dynamic Inventory Policy Recommendations: Algorithms continuously analyze sales information and demand trends to suggest real-time adjustments to inventory levels of various products to meet market needs.
Calculating Safety Stock Levels: Ensures popular products don't run out of stock by computing optimal safety levels based on demand fluctuations, seasons, and other factors.
Scenario Modeling: Simulates various situations that could impact inventory, such as sudden demand surges and supply disruptions, allowing companies to develop contingency plans for restocking as needed.
Reducing Inventory Waste: Identifies slow-moving items in the warehouse that incur high storage costs and recommends strategies to improve product flow, such as discounts and marketing campaigns.
Developing Efficient Storage and Distribution Tactics: Determines the most effective methods for storing and distributing different products.
Real-Life Example:
Stitch Fix, a fashion company headquartered in California, trained generative AI algorithms on its extensive data about customer preferences and other information. The model predicted which clothing items would be in high demand and provided restocking recommendations. As a result, the company reported a 25% reduction in costs associated with storing and handling goods.
Use Case 2: Faster and Cheaper Freight Delivery
Companies can utilize generative AI for supply chain management by analyzing large volumes of data related to weather conditions, traffic patterns, shipments, and more to create optimized route maps, enabling suppliers to deliver products/materials more quickly and cost-effectively.
These models can also monitor real-time data to reroute shipments already in transit if there are traffic jams, accidents, or other issues on the planned path. Such dynamic routing plans help drivers adapt on the go and avoid wasting hours stuck in traffic.
Businesses recognize this advantage, and the generative AI market in logistics is rapidly growing. In 2022, it was valued at $412 million, and it's expected to soar to $13.948 billion by the end of 2024, marking a staggering 43.5% compound annual growth rate.
Real-Life Example:
A manufacturer integrated generative AI into its operations for inventory management and supply chain process optimization. The system analyzed real-time data and recommended rerouting options. The company reported a 12% reduction in logistics costs within the first six months of AI implementation.
Use Case 3: Ensuring a Sustainable and Ethical Supply Chain
Research shows that business leaders are moving towards ethical supply chains that incorporate sustainability efforts, and generative AI can assist in this initiative. Algorithms can analyze publicly available supplier data, such as energy efficiency, waste production, sustainable production methods, and sources of raw materials, to determine which supplier best aligns with your environmental impact goals.
Additionally, AI models can identify areas where your existing contractor can reduce waste. For example, it might suggest changes in packaging design or the logistics process. You can share these ideas with your supplier if they are open to adopting environmentally friendly practices. This way, you can still achieve your sustainability goals without terminating the partnership with the supplier.
Real-Life Example:
Companies rely on generative AI algorithms to identify unsustainable and unethical practices within their supply chains. For instance, Siemens and Unilever use this approach to identify suppliers associated with the repression of Uyghur Muslims, as mentioned in the introduction.
Another example is the California-based company Frenzy AI, which developed a generative AI model that analyzes data such as customs declarations and shipping documents to trace products back to various suppliers and verify whether their products are ethically sourced.
Use Case 4: Anticipating Customer Needs
Generative AI models can process various types of data, such as historical sales, seasonal trends, economic data, competitor actions, customer sentiment, and more, to predict demand.
Algorithms can monitor all of this in real-time, informing you of upcoming trends as soon as they appear. Generative AI can perform the following tasks:
Predicting demand for various products and services, enabling companies to notify suppliers, replenish stock, and better serve customers.
Modeling different scenarios of how demand might change, allowing companies to prepare. For example, it can show how changes in pricing and marketing strategies affect demand.
Real-Life Example:
Walmart relies on a generative AI-based demand forecasting system to anticipate what customers will need in each store. Additionally, the retail giant uses the technology to analyze customer behavior during Black Friday events and foresee any potential bottlenecks.
Use Case 5: Finding the Right Supplier and Negotiating with Them
Since it can analyze large volumes of supply chain data, generative AI can provide valuable recommendations and assist in vetting suppliers. Here's what this technology can do:
Ranking suppliers: Algorithms can rank suppliers based on predefined criteria such as prices and raw material quality.
Evaluating sustainability practices: This includes assessing a potential supplier's environmental impact, social responsibility, waste production, and more.
Assessing risks associated with each supplier, such as geopolitical risks, economic factors, and other vulnerabilities.
Developing negotiation strategies tailored to each supplier.
Additionally, AI algorithms can continue to monitor supplier partners to ensure they meet their contractual obligations and maintain the expected quality levels.
Real-Life Example:
Walmart experimented with a generative AI bot from Pactum AI that can negotiate with suppliers. This approach helped the retailer save about 3% on contract expenses. Surprisingly (or not), three out of four suppliers actually preferred negotiating with the bot.
You'll find more examples of generative AI use cases for businesses in our blog.
Challenges You May Encounter When Using Generative AI in Supply Chain Management
If you're interested in implementing generative AI, be prepared to face the following set of challenges:
Data-related Challenges: Generative AI models need large volumes of high-quality data to perform their tasks. If the data is fragmented, incomplete, or outdated, the results will be inaccurate. And you can't control what supplier data is publicly available, so try to set reasonable expectations when relying on data offered by others.
Integration with Existing Systems: Your new AI solution should seamlessly fit into the existing system and connect to other applications to access their data. This may require adapting legacy systems, which is a huge challenge. You may also need to redesign some of your processes. Additionally, generative AI is very powerful and requires significant computing resources and data storage capacity. Consider adapting your infrastructure or arranging for cloud hosting.
AI Usage Challenges: Sometimes, the lack of AI explainability can be a problem. It's not always possible to explain why generative AI produced a particular answer/recommendation/strategy. Take compliance reports as an example. If an organization wants to obtain ISO certification, it needs to document its processes to demonstrate compliance. But if it relies too heavily on generative AI for reporting, it may not be able to do so.
You need to adhere to industry standards for AI usage and general privacy regulations in your field.
Post-Deployment Challenges:
Security: Any AI system must be designed with security in mind, as it handles large volumes of sensitive information. There is a set of practices that companies should follow to ensure data security. This includes encrypting data in transit and at rest, implementing authentication mechanisms, and monitoring for unauthorized access, to name a few. Moreover, you will need to share data with your supplier network. Ensure that this is secure as well.
Maintenance: AI models require regular audits, performance assessments, and updates to remain effective and relevant.
Human Factor: After implementing generative AI for supply chain management, you want employees to adopt it, use it, and contribute to its improvement. It’s best to formalize rules that will govern human-AI collaboration and specify who is responsible for the final outcomes. And this is a challenge. Who is to blame if the inventory was overstocked with products that no one wanted to buy? And who is at fault if the supplier chosen by AI fails to deliver on time twice in a row?
The company is also responsible for training its employees on how to work with AI and adhere to safe data handling practices.
Convinced You Need Generative AI? Here's What to Do Next
Here are nine tips to help you start implementing AI:
Define your business goals and what you want to achieve with generative AI for your supply chain. This will determine what data your models need access to.
Consider automated data collection so your algorithms have access to up-to-date information.
Prepare your data for use by artificial intelligence and machine learning algorithms.
Ensure you obtain consent for using training data when necessary and comply with data privacy regulations.
Implement effective data management practices or use third-party data management services.
Foster data collaboration between your organization and suppliers.
Hire a trusted AI provider to create or customize generative AI algorithms that can meet your unique needs.
Start with a small pilot project and learn from failures.
Monitor models post-deployment. Implement a feedback loop that allows users to report their issues and recommendations.
If you're interested in the costs associated with implementing AI, check out our detailed article on how much artificial intelligence costs.
Contact us if you have questions about using generative AI for supply chain management or if you want an accurate estimate for your Gen AI project. We will help you build/customize AI models, support you in data collection and cleaning, and audit your models on request.