Supply Chain Optimization
During Covid-19 lockdowns, supply chain risk and optimization were brought to the forefront. Companies that had ignored their supply chain now recognized the risk associated with a black swan event like Covid-19 and began to invest and prioritize resources for their supply chain practitioners.
Many companies soon realized they did not have the personnel or the processes to deal with the disruptions associated with a broken supply chain. Here are the three most common uses in supply chain optimization today.
- Demand Forecasting: AI can help to forecast demand more accurately, considering historical sales data, market trends, and weather patterns. With this data, AI can make more accurate predictions about future demand, allowing supply chains to optimize inventory levels and reduce waste.
- Inventory Management: AI can help to optimize inventory levels, reducing the risk of stockouts and overstocking. AI can learn from past inventory patterns and identify the optimal inventory levels for each SKU, considering factors such as lead times and demand variability.
- Logistics Optimization: AI can help to optimize logistics operations, improving efficiency and reducing costs. AI can identify the most efficient shipping routes, carriers, and modes of transportation for each shipment.
We are in the birth of the AI era. In just a few short months since being released to a wider user base, we are already seeing quick adoption and increasingly powerful uses of AI. AI is already a tool that has surpassed the simple expectation of automating repetitive tasks and is now an essential tool for any practitioner managing supply chains in the 21st century.