How can data analytics improve inventory forecasting?

1 Demand analysis

Data analytics can help plant engineers to understand and predict customer demand better, by using data from multiple channels, such as sales, marketing, customer feedback, social media, or external factors. Data analytics can also apply machine learning, artificial intelligence, or statistical models to identify patterns, trends, anomalies, or correlations in demand data, and to forecast future demand based on various scenarios and assumptions. By doing so, data analytics can help plant engineers to anticipate changes in demand, adjust production plans accordingly, and avoid overstocking or understocking.

2 Supply analysis

Data analytics can also help plant engineers to monitor and optimize their supply chain, by using data from suppliers, logistics, quality, or inventory. Data analytics can help to track and evaluate supplier performance, delivery times, costs, or risks, and to identify potential issues or opportunities for improvement. Data analytics can also help to optimize the sourcing, ordering, and replenishment of materials and components, by using data-driven algorithms or optimization models to determine the optimal quantity, timing, and location of orders. By doing so, data analytics can help plant engineers to reduce supply chain costs, improve quality and reliability, and ensure availability of materials and components.

3 Inventory analysis

Data analytics can also help plant engineers to manage and control their inventory levels, by using data from production, inventory, sales, or demand. Data analytics can help to measure and optimize inventory turnover, carrying costs, service levels, or safety stocks, and to balance inventory across multiple locations or products. Data analytics can also help to implement inventory policies, such as just-in-time, lean, or agile, by using data-driven rules or triggers to initiate or adjust inventory actions. By doing so, data analytics can help plant engineers to minimize inventory waste, maximize inventory efficiency, and enhance customer satisfaction.

4 Performance analysis

Data analytics can also help plant engineers to evaluate and improve their inventory forecasting performance, by using data from actual and forecasted inventory levels, demand, supply, or costs. Data analytics can help to measure and compare the accuracy, bias, or error of different forecasting methods, models, or parameters, and to identify the sources of deviation or uncertainty. Data analytics can also help to test and refine the forecasting models, methods, or parameters, by using data-driven feedback or learning mechanisms to incorporate new data or information. By doing so, data analytics can help plant engineers to enhance their forecasting confidence, quality, and agility.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?