With the pandemic and wars disrupting the global supply chain and causing significant losses to businesses worldwide, a new solution is emerging to improve resilience and minimize risks. Dive deep into the potential of Supply Chain AI and learn what it can do for your company.
The global supply chain is more at risk than ever.
Since COVID-19, the global supply chain has seen major disruptions. First, the pandemic and then the Russia-Ukraine conflict triggered fundamental shifts in demand and supply, extreme price volatility, and raw materials and labor shortages.
In 2022, Forbes described the war in Ukraine as the most significant challenge in supply chain management. Two years later, the global supply chain situation is still extremely delicate and uncertain, posing several essential issues to organizations worldwide. Uncertainty and disruptions create barriers in the market, blocking the movement of commodities and affecting prices, therefore fueling inflations. As a result, both businesses and consumers are struggling.
At the beginning of 2024, Accenture conducted a study on the economic cost of supply chain disruptions. According to the results, the potential revenue growth opportunity missed each year is a staggering $1.6 trillion.
This money doesn’t have to go down the drain. To survive and thrive today, strengthening your supply chain is an absolute imperative; leveraging the power of technology like Supply Chain AI is the best way to do it.
Why Supply Chain AI Might be the Right Solution to Global Disruptions
Big players across many industries are starting to experiment with Supply Chain AI to improve visibility, anticipate problems, and respond faster to disruptions. While most companies are still trying to figure out a Supply Chain AI strategy, it is clear that those that can move quickly stand to gain a significant advantage.
Early adopters are already reaping the benefits. Successfully implemented Supply Chain analytics has allowed companies to improve:
- Service levels by 65%
- Inventory levels by 35%
- Logistics costs by 15%
Source: McKinsey
Given the significant value at stake, companies need to hurry up and start thinking about their Supply Chain AI strategies now. Designing and implementing one isn’t easy, but missing this opportunity is likely to result in being overtaken by competitors.
But what can Supply Chain AI do? Here are the primary use cases.
How Supply Chain AI Helps You Find the Best Suppliers
Supply Chain AI tools are wonderful at finding new suppliers. By scraping the Internet, the software can generate a list of potential new suppliers, highlighting their strengths and giving valuable insights to make informed decisions. It chooses and ranks suppliers based on public, but often difficult or time-consuming to find, information:
- Financial situation;
- Customer ratings;
- Sustainability scorecards;
- Patents and awards;
- Customs documents;
- Real-time alerts and sentiment.
AI-powered supply chain analytics allow executives to find new partners and alternative suppliers when existing ones face shortages or disruptions.
Improving Relationships with Stakeholders And Negotiating Power
Supply chain leaders can use AI to automate numerous essential processes:
- Identifying suppliers, retailers, and logistics partners;
- Cooperating and constantly communicating with all partners involved in the supply chain;
- Working with regulatory bodies for reporting and compliance.
The advent of Generative AI opened the doors for even more advancements in supply chain management. In a famous case study, a US retailer built a Generative AI-powered chatbot to conduct negotiations with vendors. Ultimately, over 65% of vendors preferred interacting with the bot over humans, thanks to the bot’s quickness, efficiency, and problem-solving.
Scenario planning with Supply Chain AI
Not accurately predicting demand causes two severe issues in supply chain management: overstocking and understocking. Getting your inventory wrong can cost you much more than you think:
Overstocks and out-of-stocks cost retailers $1.1 trillion globally in lost revenue. (Source: IHL Group)
How is it possible to minimize such a loss? The solution is performing data analytics for demand forecasting to improve inventory management and avoid understocking and overstocking. Demand forecasting is fundamental for supply chain risk management, as it allows leaders to prepare for shifts in the market and surges or declines in demand and optimize resources.
AI can perform advanced analytics and provide accurate forecast models to make informed decisions. Combined with an automated, AI-powered dynamic system, it is possible to adjust inventory levels based on real-time data, such as current stock, lead times, and demand fluctuations.
Increasing Supply Chain Visibility with AI-Powered Control Towers
A control tower is a supply chain analytics platform fully customizable and designed to provide all stakeholders with end-to-end visibility and insights across the whole supply chain.
Such a central approach minimizes risks, gives access to relevant data at all times to all stakeholders involved, and allows real-time monitoring to respond to every issue promptly.
Five Steps to Start Your Supply Chain AI Strategy
While supply chain AI has proved its value when it comes to improving efficiency and strength, it is not easy to implement it in business processes. It requires specialized knowledge, access to advanced technology, consistent economic investments, and a commitment to learn from everyone involved in supply chain management.
The good news is that there are some steps that can facilitate the adoption of a comprehensive AI approach.
- Step 1: Have a clear strategy. A detailed road map is mandatory to funnel money, time, and effort toward the right goal.
- Step 2: Encourage adoption. People at all levels of the organizations should be on board with adopting AI solutions. Encourage learning, identify value metrics and KPIs, and promote a tracking and reporting system.
- Step 3: Invest in the exemplary architecture. Choose a flexible, scalable, resilient architecture that supports data collection, modeling, and workflow integration. Making the wrong architecture decision will have long-term negative effects and hinder the ultimate success of your Supply Chain AI strategy.
- Step 4: Ensure data quality. Data is the most critical aspect of every AI application. Ensure the data you collect is clean, relevant, and prepared correctly before feeding it to the AI.
- Step 5: Identify the best use cases for your company. You don’t have to use Supply Chain AI for everything. Knowing where not to use AI is as important as knowing which applications could provide the most value in your situation.
Start Now Your Journey To A Stronger Supply Chain
Supply Chain AI has been defined as “a critical tool to combat supply chain entropy”.
In a historical time when supply chains worldwide suffer constant disruptions and significant political, economic and natural events bring uncertainty everywhere, the advent of Supply Chain AI comes providential.
Companies now have the chance to leverage AI-powered technology to counteract disruptions, boost efficiency, and drive seamless operations. Access to AI-powered insights will improve your business’s ability to respond to dynamic shifts, increase visibility across the whole supply chain, and increase your forecasting abilities.
In our current scenario, the alternative continues to be exposure to constant disruptions, shortages, inefficiencies, and wrong inventory levels, which is a death sentence for businesses wanting to thrive.
