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Inventory Stock Prediction

What Is Inventory Stock Prediction?

Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.

This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.

Why Is It Important to Have a Good Inventory Stock Prediction Model?

Inventory stock prediction models can drastically influence business profitability by accurately forecasting customer needs and desires. Instead of business owners making potentially inaccurate predictions themselves, AI models provide more accurate, data-driven predictions. These models give business owners a competitive advantage—and free them to focus on other business goals.

What Internal Data Should I Have for a Good Inventory Stock Prediction Model?

The first step in any artificial intelligence project is to gather, interpret, and analyze data. To implement a good forecasting model, you should have historic inventory data for each day of the week. Many models even use data on how many items of single products were sold per day.

Models should also collect data on when each product was promoted, on which days. Also helpful is information on how many hours the business was open per season. All these statistics will critically impact the algorithm’s resulting predictions.

What External Data is Essential for a Good Inventory Stock Prediction Model?

Inventory managers need to collect both long-term and short-term factors that influence demand prediction. Short-term prediction may include seasonal demand figures. Long-term factors, on the other hand, may include population and demographic data.

What External Data May Prove Useful for a Prediction Model?

Inventory management deserves more attention especially under a long spending cycle and complicated supply chain. A business owner should take care to use effective inventory forecasting methods so that there is no unforeseen shortage or expiry of products. A successful and professional inventory stock prediction model coordinates many variable factors that align with your business goals and with all the other processes in your company.

There are many factors to consider when choosing inventory planning solutions. These might include the total cost, reliability, and quality of support of the inventory solution chosen.

What Are the Main Challenges of This Use Case?

Traditional methods of inventory planning can be imprecise and the data is often outdated. Inventory forecasting and demand planning becomes much more difficult when stock levels are hard to keep track of or lack visibility. Without effortless access to stock level data, a business can quickly build up unessential excess stock levels or have stock shortage when demand spikes for specific products. And when operation managers request new inventory, Just In Time (JIT) perspectives for stock management have manifested as less than practical in reality, delaying order fulfillment which can result in lost sales and order annulment. Planners should have a procedure in place that follows each supplier and vendor lead times for greater awareness into reorder point planning and rush-order specifications.

Interesting Case Studies and Blogs to Look Into

Researchgate: Inventory Management Maximization based on sales forecast: Case study
Production Planning & Control: Inventory management maximization based on sales forecast: case study

Tangible Examples of Impact

McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, overall inventory reductions of 20% to 50% are possible.

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