Supply Chain Management
Problem: Preventing Shortages in Supply Chain
If you’ve been in business for the last two years, you’ve most likely become an unwilling expert on supply chain shortage issues. COVID-19 has introduced unprecedented uncertainties into supply chains — making already difficult jobs even harder. As a result, many manufacturers are struggling to align production and stocking with shifting purchasing demands.
Your company needs to prevent parts shortages, especially when they occur at the last minute. Parts shortages lead to underutilized machines and transportation, causing a domino effect of late deliveries through the network. The difference between forecasted and actual number of parts that arrive on time prevent supply chain managers from optimizing their materials plans.
Parts shortages are often caused by delays in their shipment. In many cases, late shipments persist until supply chain managers can evaluate root causes and then implement short-term and long-term adjustments that prevent them from occurring in the future. Unfortunately, supply chain managers have been unable to efficiently analyze historical data available in MRP systems because of the time and resources required.
Solution: AI in Supply Chain Management
Injecting AI into supply chain management can greatly enhance the ability of manufacturers to predict future demand for goods, even in uncertain and dynamic times. AI in supply chain management can empower decision makers with unprecedented insights, enabling them to make more informed choices across all aspects of supply chain management.
Machine learning in supply chain management enables:
AI-Driven Demand Forecasting: Using a range of historic data sources to inform the level of future demand, manufacturers have increased availability in many cases by more than five percent, decreased waste by over eight percent, and reduced losses due to write-offs.
Forecasted Returns: By predicting how much stock will be returned, retailers need to procure less stock from suppliers, minimizing the risk of excessive inventory across the supply chain.
Reduced Out-of-Stock: With better forecasting, retailers can rely on better granularity to reduce out-of-stocks.
New Product Forecasting: Machine learning can predict likely sales in the first few weeks and months of selling a new product.
Price Optimization: Identify optimal price points influenced by multiple factors, such as competition, product, brand, category, and location, thus optimizing alignment of demand and supply constraints or imbalances.
Why DataRobot: The Leader in AI Solutions for Supply Chain Management
To meet these challenges, you need an optimized inventory management and pricing engine with accurate item-level demand forecasting. You also require a manufacturing-specific forecasting tool that works in an imperfect, unpredictable, and rapidly changing business landscape. And you need a forecasting solution that can accommodate even the most detailed, business-related nuances.
DataRobot AI offers a seamless way to significantly improve business results by automating, simplifying, and democratizing AI-driven supply chain management.