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How can you optimize inventory carrying costs with predictive analytics?

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Inventory carrying costs are the expenses associated with holding and storing inventory over a certain period of time. They include costs such as warehousing, insurance, taxes, depreciation, obsolescence, and opportunity costs. Inventory carrying costs can account for up to 25% of the total inventory value, so reducing them can have a significant impact on profitability and cash flow. One way to optimize inventory carrying costs is to use predictive analytics, which is the process of using data, statistical models, and machine learning to forecast future demand and supply scenarios. Predictive analytics can help you make smarter decisions about how much inventory to order, when to order it, where to store it, and how to distribute it. In this article, we will explore how predictive analytics can help you optimize inventory carrying costs in four key areas: demand forecasting, inventory optimization, warehouse management, and transportation management.

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Rajesh Hoskote

1 Demand forecasting

Demand forecasting is the process of estimating the future demand for your products or services based on historical data, market trends, customer behavior, and external factors. Demand forecasting is crucial for inventory management, as it helps you avoid overstocking or understocking, which can lead to high inventory carrying costs, lost sales, or dissatisfied customers. Predictive analytics can help you improve your demand forecasting accuracy and reliability by using advanced algorithms and machine learning to analyze large and complex data sets, identify patterns and trends, and account for uncertainty and variability. Predictive analytics can also help you segment your demand by different factors, such as geography, seasonality, product category, or customer type, and adjust your inventory levels accordingly.

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Rajesh Hoskote
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As a strong promoter of JIT - Just In Time Inventory management concepts, I propose the use of productive analytics to list out the items causing major impact on Inventory cost, after a critical evaluation. The entire supply chain should be subjected to predictive analytics so that, any laxity gets arrested. The predictive analytics should also consider past few year's historical trend of items impacting Inventory cost and specific reasons for the same. Based on factors such as procurement lead time, perishability, criticality to main product, discount consideration on bulk buying etc. an algorithm can be built to predict the demand.

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2 Inventory optimization

Inventory optimization is the process of determining the optimal inventory levels and reorder points for each product or location in your supply chain, based on your demand forecast, inventory costs, service levels, and lead times. Inventory optimization aims to balance the trade-off between having enough inventory to meet customer demand and minimizing inventory carrying costs. Predictive analytics can help you achieve inventory optimization by using mathematical models and optimization techniques to calculate the optimal inventory levels and reorder points for each product or location, considering multiple factors, such as demand variability, inventory holding costs, ordering costs, stockout costs, and service level requirements. Predictive analytics can also help you monitor your inventory performance and adjust your inventory policies in real time based on changing demand and supply conditions.

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3 Warehouse management

Warehouse management is the process of managing the physical storage and movement of inventory within a warehouse or distribution center. Warehouse management involves activities such as receiving, storing, picking, packing, and shipping inventory. Warehouse management affects inventory carrying costs by influencing the space utilization, labor efficiency, inventory accuracy, and customer satisfaction. Predictive analytics can help you improve your warehouse management by using data and machine learning to optimize your warehouse layout, design, and operations. Predictive analytics can help you allocate the optimal space and location for each product based on its demand, size, weight, and compatibility. Predictive analytics can also help you optimize your labor allocation, scheduling, and routing based on the workload, productivity, and skill level of your workers. Predictive analytics can also help you enhance your inventory accuracy and visibility by using sensors, RFID tags, and barcode scanners to track and verify your inventory movements and status.

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4 Transportation management

Transportation management is the process of planning, executing, and controlling the transportation of inventory from the point of origin to the point of consumption. Transportation management involves activities such as selecting the best mode, carrier, and route for each shipment, consolidating shipments, tracking and tracing shipments, and managing freight costs and delivery performance. Transportation management affects inventory carrying costs by influencing the transportation costs, lead times, and service quality. Predictive analytics can help you improve your transportation management by using data and machine learning to optimize your transportation planning, execution, and control. Predictive analytics can help you select the best mode, carrier, and route for each shipment based on the cost, speed, reliability, and environmental impact. Predictive analytics can also help you consolidate shipments and reduce empty miles by using algorithms and simulation to find the optimal combination of orders, vehicles, and routes. Predictive analytics can also help you track and trace your shipments and manage exceptions by using GPS, IoT, and blockchain to monitor and communicate the location, status, and condition of your shipments and resolve any issues or delays.

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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?

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Thomas Dent, CPIM, CSCP, CPSM, CPSD

Director of Program Management
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Python can significantly assist with optimizing inventory carrying costs through predictive analytics. By using libraries like pandas for data manipulation, numpy for numerical calculations, and scikit-learn for machine learning, Python enables you to analyze historical sales data, identify trends, and forecast future demand. For example, you can train a predictive model using historical sales data to forecast future demand, adjusting inventory levels accordingly to minimize carrying costs.

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How can you optimize inventory carrying costs with predictive analytics?

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1 Demand forecasting

2 Inventory optimization

3 Warehouse management

4 Transportation management

5 Here’s what else to consider

Inventory Management

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How can you optimize inventory carrying costs with predictive analytics?

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1 Demand forecasting

2 Inventory optimization

3 Warehouse management

4 Transportation management

5 Here’s what else to consider

Inventory Management

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