AI has the potential to provide supply chain cost savings. Learn how to lower costs with demand forecasting, logistics, warehouse automation, and more.
In brief:
- AI-driven supply chain optimization can reduce transportation costs by up to 30 percent, decrease inventory by 25 percent, and improve forecast accuracy by 75 percent. Yet, despite recognizing its transformative potential, 77 percent of supply chain professionals still haven’t integrated AI into their operations.
- AI-powered forecasting analyzes historical data, weather patterns, supplier disruptions, and market shifts to create predictive models and digital twins that optimize workforce management, capital investment, and production scheduling based on real-time insights.
- Implementing AI in supply chains begins with organizing existing data, moving from intuition-based to data-driven decision-making, researching suitable tools, and testing AI in specific areas, such as demand forecasting or inventory management, before scaling.
Supply chains processes are often a company’s largest operating expense, but with AI innovation they now have the highest potential for cost savings.
In fact, according to WorldMetrics, AI-driven supply chain optimization can:
- Reduce transportation costs by up to 30 percent,
- Decrease inventory by 25 percent
- Improve forecast accuracy by 75 percent.
With all that in mind, adopting AI to optimize supply chain costs sounds like a no-brainer, right?
Depending on the size of your organization, it might be easier said than done. While 82 percent of supply chain professionals believe new technology, like AI and machine learning, will significantly impact the industry, 77 percent still have not integrated it into their supply chain.
If you’re one of the 77 percent, you need a plan to get started. Supply chain AI consulting services can help you build and implement a strategy that immediately helps lower your supply chain costs.
How Can AI Reduce Your Supply Chain Operation Costs?
AI can process vast amounts of data at incredible speeds, identify patterns that may not be immediately obvious, and generate insights that lead to smarter, more proactive decisions and faster, more efficient processes. Here are some industry-agnostic cost-reduction strategies focused on using AI to reduce supply chain operations.
AI-Powered Forecasting and Planning
AI improves forecasting by analyzing historical data and trends much more efficiently than traditional methods. Beyond simply recognizing past patterns, AI can proactively predict future trends and disruptions, making your supply chain operations more agile.
By interpreting larger datasets — such as weather patterns, holidays, supplier disruptions, and market shifts — AI can build predictive models that improve demand forecasting. It adjusts forecasts based on these insights, making your supply chain more agile. Walmart, for example, uses AI to anticipate macroweather and macroeconomic trends alongside local demographics to better predict demand.
AI can also create a “digital twin” of your operations, simulating various production options to test what-if scenarios. This allows businesses to optimize workforce management, capital investment, and production scheduling, all based on data-driven insights.
AI-Power Supplier Sourcing
AI can help you optimize sourcing by dynamically identifying the best suppliers based on real-time factors such as supply chain cost, material specifications, and geopolitical risks. It can also factor in the impact of supplier disruptions, like natural disasters or strikes, on costs and availability, helping you make more informed purchasing decisions. Finally, it can help you reduce risk by exploring alternative sourcing options before they become urgent.
Siemens, for example, uses AI to find alternative suppliers during shortages. It recently used a tool called Scoutbee to quickly sort through and find distributors of a patented product, Surlyn, which was hard to track down. Scoutbee found 150 distributors after searching import and shipping documents.
AI-Powered Manufacturing Operations
AI can automate manufacturer production schedules and bolster legacy enterprise resource planning (ERP) and material requirements processing (MRP) system capabilities for more agile, real-time decision-making. For example, when it couldn’t find an efficient one on the market, Lenovo created its own AI-run advanced production scheduling system that improved production line capacity by 19 percent.
AI-driven supply chain operations can also help with capacity planning by identifying bottlenecks and suggesting adjustments to improve production flow, which leads to more efficient resource use and reduced downtime.
Lastly, AI can also provide recommendations for managing production disruptions, such as downtime or maintenance issues. It helps predict where delays might occur and suggests how to allocate resources best to keep things running efficiently.
AI-Powered Distribution
AI-powered logistics reduces supply chain costs by optimizing transportation routes to reduce shipping costs and improve overall distribution efficiency.
AI can further optimize delivery routes by factoring in real-time changes such as driver preferences, delivery windows, and unexpected disruptions (such as sick calls). This reduces fuel consumption, improves customer satisfaction, and increases delivery efficiency.
Maersk Line uses AI to optimize container loading, route planning, and scheduling, helping reduce transportation costs. It can even calculate the most fuel-efficient route based on weather data received in real-time.
Now, let’s explore the first steps toward implementing some of these strategies into your existing operations.
How to Integrate AI Into Your Supply Chain
Implementing AI in your supply chain doesn’t have to be an all-at-once overhaul. Here’s how to integrate AI gradually with minimal disruption to current operations:
1. Collect Data
Before you can implement an AI tool, your data must be ready. AI will be able to process a ton of data, but chances are, some of your data is unstructured, repetitive or irrelevant. You need to look at what data you have and organize it.
Start by gathering all your reliable and comprehensive data. This includes everything from historical performance data, production schedules, supplier information, and customer demand patterns to real-time information such as inventory levels, transportation conditions, and market trends.
A robust dataset creates a solid foundation for AI to analyze and draw insights. The more high-quality data AI has access to, the better it can make predictions and recommendations. Additionally, collecting data helps you identify inefficiencies, bottlenecks, and areas for improvement in your operations, enabling more informed decision-making across all departments.
2. Shift Your Mindset
Adopting AI successfully requires a mindset shift across the organization. Employees must move from relying on intuition and traditional methods to embracing data-driven decision-making.
When staff members trust the insights provided by AI, they are more likely to see the value of the technology and use it to make smarter choices. To encourage this, leaders must foster a culture that supports experimentation and learning, where employees feel comfortable trusting AI suggestions and using them to optimize their workflows.
Over time, this shift will enhance operational efficiency, as decisions are made faster and more accurately based on data rather than gut feelings or outdated models.
3. Research Tools
Depending on the size of your organization, you might have several tools to choose from.
While larger organizations may have access to comprehensive AI tools built for enterprises, mid-market companies usually work with specialized, piece-meal solutions.
Oracle and SAP, for example, have started incorporating AI into their ERP systems, so organizations with these tools already in place can start testing as soon as they’re ready. Mid-market companies, on the other hand, use tailored tools that are optimized to specific areas of the supply chain, like production scheduling, route optimization, or forecasting. If this is you, you’ll likely need to do more research to find which tool you’d like to start with.
4. Start Small
Introducing AI into your supply chain doesn’t have to be a massive, organization-wide change from the outset. A more effective approach is to start small by testing AI in specific areas, such as demand forecasting, production scheduling, or inventory management. This targeted use case allows you to evaluate AI’s effectiveness with minimal risk and allows your team to learn how the technology can benefit their operations.
For instance, implementing AI for demand forecasting could help you reduce stockouts or excess inventory, directly showing how AI can enhance efficiency and save supply chain costs. Once the small-scale AI application proves successful, it becomes easier to expand its use to other areas of the supply chain, gradually scaling up its impact without overwhelming the organization.
5. Manage Your Risk
As you integrate AI into your supply chain, you’ll need to take calculated risks. These risks can include making significant changes, such as adjusting production schedules or changing suppliers based on AI recommendations.
Initially, these decisions may feel uncomfortable, especially if they involve altering well-established processes. However, these adjustments are necessary to unlock the full potential of AI.
For example, AI may suggest switching to a new supplier offering better pricing or faster delivery times, which could lead to long-term supply chain cost savings and efficiency gains. While it can be challenging to shift away from traditional business methods, embracing AI’s insights will allow your supply chain to become more resilient, adaptable and capable of responding to changing market conditions.
With these steps in place, you’re on your way to harnessing AI’s potential in your supply chain. AI’s future in supply chain management promises even greater innovations, offering new ways to optimize processes and anticipate disruptions before they happen.
Continued Supply Chain Cost Reductions Anticipated as AI Technology Becomes More Sophisticated
AI will continue to bolster supply chain resilience by identifying risks such as overreliance on single suppliers or regions. It will empower companies to better anticipate and respond to large-scale disruptions like pandemics or natural disasters. By offering real-time analysis and predictive capabilities, AI will allow businesses to adapt quickly, minimize risks, and run their supply chains efficiently.
Investing in AI supply chain consulting now is how forward-thinking companies are building a more agile, cost-effective, and resilient supply chain for the future.
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