How AI Improves Inventory Management in Manufacturing

Smart Demand Forecasting

Beyond Basic Averages

The traditional method of forecasting depends on averages drawn from historical data. This works under conditions of stable demand. There are a variety of factors that affect manufacturing demand including disruption of supply chains, changes in the market, weather events, and consumer behavior.

AI-based forecasting models take into consideration several hundred factors at once to identify patterns that would be hard for humans to see. These forecast models make use of both historical and real-time data and give accurate forecasts of future demand.

Real-Time Market Forecast

Traditionally, forecasting was performed through periodic planning and based on market history. However, this methodology could not account for all market changes that might happen. Artificial intelligence-based solutions work continuously by analyzing live sales data, market information, and customer behavior to adjust inventory suggestions in real time.

The company that manufactures auto parts managed to reduce the loss of materials due to overproduction by 20% with the help of an artificial intelligence-based forecasting system.

Predicting Seasonal Trends

Artificial intelligence can assist manufacturers in preparing for expected changes in demand by suggesting inventory replenishment depending on actual lead time and demand patterns. Thus, the materials will be ready to use without relying on estimations.

As a result, it becomes possible to eliminate last-minute purchases and associated costs related to emergency delivery, interruptions in the supply chain, and dependency on alternative, more expensive suppliers.

Stock Automation

Now, when we have seen what manufacturing workflow automation can offer to manufacturers in terms of forecasting, it is time to move to another level of automation – the process that takes place at the warehouse floor.

Artificial Intelligence-Powered Warehouse Vision System

Dynamic Reorder Points

One of the biggest wastes in traditional inventory management systems is static reorder points. If you set your reorder point six months ago, you are assuming that the lead time is the same, the demand pattern is the same, and the reliability of suppliers is the same, as it was six months ago. Dynamic AI-driven systems will recalculate the reorder points based on the current lead times and demand patterns.

This is also how dead stock is avoided by AI-powered systems. If a system sees that your reorder point is out of sync with the sell-through rate of the particular SKU, it will tell you about it ahead of time to prevent tying more cash into your inventory.

Tip: Set up dynamic low stock alerts, rather than static quantity alerts. Static "reorder when there are 50 items left" alerts don’t understand what the actual velocity of sales of parts is.

Human Input Errors

Manual input continues to be a significant cause of inventory errors. AI scanning devices take away the discrepancy created between warehouse operations and system updates. Rather than manual key-ins that come at the end of a day, the back office receives the information in real time, meaning all users have access to the same information.

Improvement in Supply Chain Flows

Predictive Lead Time Modeling

The use of AI improves inventory planning through the ability to model lead times based not only on quotations but also on the actual delivery times of the suppliers. This allows for a proper determination of safety stocks and, therefore, better inventory decisions.

Many times, the existence of too much buffer stock has been due to a lack of visibility into how the supply chain performs. Through better predictions in lead time models, the manufacturer can reduce inventory levels.

Vendor Selection Using AI

The use of AI systems in vendor selection is a continuous one based on three factors: cost, speed, and reliability of the service provider, as opposed to using the annual review. Advanced systems are taking the selection and bidding of raw materials one step further by automating parts of the bidding process to reduce the time spent getting quotes and increasing negotiations from an information point.

JIT Manufacturing and 2.0 Management

There are no margins for error when it comes to just-in-time manufacturing due to the minimal inventory level. By analyzing the conditions of the supply chain, artificial intelligence prevents any possible disruptions to production, like supplier delays and material shortages, before they happen.

Obsolete Inventory Identification

Artificial intelligence allows manufacturers to identify obsolete stock by scanning through thousands of stock-keeping units (SKUs). This way, they can take proactive action before losing money on their stock.

Excess Inventory and Associated Costs

As a manufacturer carries excess inventory at a cost, reducing obsolete inventory will be a profit maker since carrying costs associated with the extra inventory include storing, insuring, depreciating, and capital costs.

Optimizing Space Usage

Through the heat maps developed based on the analyzed movement pattern, it becomes possible to identify the most frequently used items and their current placement in relation to the shipping areas. Bringing such items closer to the shipping area decreases the walking distance covered by an employee when processing one order.

Integrating Quality Control Process

The AI vision technology enables detecting defective parts right at the moment of their manufacture, and thus prevents any defective products from being logged in the inventory. Such an approach helps avoid waste, returns, and issues reported only after contacting the clients.

Implementation of AI in Your Facility

A failure of implementing artificial intelligence in the manufacturing process does not happen due to technological problems; instead, it happens when the implementation process does not consider several crucial steps that come before the process itself.

Selecting the Best-Fit Tools

One of the initial tasks during implementation is to choose between plug-and-play AI solutions and customized tools. The former option allows deploying AI much faster and is applicable for most small and mid-sized manufacturers.

Questions for assessing an AI inventory management software vendor:

• Does it integrate with your current ERP or inventory system without requiring a full system upgrade?
• Is it capable of processing data across multiple locations/vendors?
• Does it support dynamic reorder points or only provide dashboards?
• How much historical data is required for generating reliable forecasts?
• What is the actual process of implementation and customer support?

Inventory Management in Manufacturing and Dhumi

And here is how Dhumi becomes useful for manufacturers and small businesses that do not have a large data science team in their organizations. The Dhumi platform is designed as a low-code solution that allows manufacturers to create demand forecasting, automated reorders, and inventory dashboards without developing a custom integration. In other words, when a company seeks to implement AI solutions for inventory without spending years on IT projects, this low-code approach often makes all the difference.

Data Cleaning

The accuracy of AI depends on the quality of data. Incorrect data, or data that is inaccurate or duplicated, may provide the manufacturer with poor results. Before introducing AI to the business, it is necessary for manufacturers to clean up their inventory data, including the SKUs, lead times, and suppliers' information. Good data cleaning is an essential factor in the successful implementation of AI.

Training the Human Workforce

Instead of replacing inventory workers, AI changes the role of people who previously counted inventories manually or entered data into spreadsheets. Now, they need to be able to observe analytics, evaluate predictions, and respond to notifications. In order to do that, manufacturers need to provide proper training to the human workforce.

Using AI for inventory management makes the process much more proactive than traditional inventory control. Improved forecasting, automated processes, and real-time insight into the supply chain allow manufacturers to optimize inventories, prevent shortages, and make quicker decisions.

Many manufacturers who are still using spreadsheets may be working with old data. Therefore, they should start with inventory audits and data cleansing. Good quality data is always the starting point of a successful implementation of AI technology.