AI has the potential to provide supply chain cost savings. Learn how to lower costs with demand forecasting, logistics, warehouse automation, and more.
Supply chains are often a company’s largest operating expense, and they also hold the highest potential for cost savings — especially when using AI.
In fact, according to WorldMetrics, 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. With all that in mind, adopting AI 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 in 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. In this blog, we’ll help you do that, starting with how AI can help lower your supply chain costs.
How AI Improves Your Supply Chain Costs
Traditionally, many supply chain processes were manual, relying on human intuition and historical data to make critical decisions. While these methods worked to some degree, they were often slow and prone to errors. AI, however, 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.
From predictive analytics that help forecast demand more accurately to automated systems that optimize transportation routes, AI is playing a pivotal role in revolutionizing supply chains. Here’s how.
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.
By analyzing 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.
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 your supply chain, 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.
Manufacturing
In manufacturing, AI can automate production schedules, enabling faster decision-making. Lenovo created its own AI-run advanced production scheduling system when it couldn’t find an efficient one on the market, improving production line capacity by 19 percent.
AI also improves legacy ERP and MRP systems by adjusting real-time production plans, ensuring your operations run smoothly. It 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.
Distributing
AI enhances logistics by optimizing transportation routes, reducing shipping costs, and improving overall distribution efficiency. The tool can 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.
Let’s explore your first steps toward implementation and how these can help with adoption and streamlining your operations from forecasting to delivery.
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, ensuring 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 more than simply implementing the technology — it demands 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 key 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.
AI’s Future in Supply Chain Management
As AI evolves, it will transform manufacturing and improve supply chain costs by optimizing product design and production. By analyzing data, AI can help businesses design easier and more cost-effective products, ultimately reducing production costs and improving overall customer service.
AI will also bolster supply chain resilience by identifying risks such as overreliance on single suppliers or regions. It will even enable 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.
Embracing AI now is the key to building a more agile, cost-effective, and resilient supply chain for the future.
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