The Emerging Role of AI in Inventory Management (with Examples)

From demand forecasting and warehouse optimization to image recognition and predictive maintenance, various AI applications are set to significantly impact inventory management practices in the near future.

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What exactly is AI?

Artificial Intelligence (AI) is a field of computer science that aims to create machines and software systems capable of performing tasks that typically require human intelligence. It encompasses a wide range of techniques and approaches, including machine learning, natural language processing, computer vision, and expert systems.

At its core, AI seeks to replicate and simulate human cognitive functions such as problem-solving, reasoning, learning, perception, and language comprehension. Machine learning, a subset of AI, enables systems to learn from data and improve their performance over time without explicit programming.

AI applications are diverse and include virtual personal assistants (like Siri or Alexa), image and speech recognition, autonomous vehicles, large language models (like ChatGPT), recommendation systems (such as those used by Netflix and Amazon), medical diagnosis, and even AI-driven manufacturing processes and quality control.

AI’s potential impact on a growing number of industries is profound, ranging from automation and efficiency improvements to the development of new products and services. It is a transformative technology that continues to evolve, offering exciting possibilities and challenges in many fields, including manufacturing and inventory management.

Why is inventory management important?

Inventory management deals with making goods available at the right time, place, and price. Determining and maintaining optimal inventory levels, however, is a persistent challenge for any company dealing with physical inventory. Balancing the benefits of bulk material purchases with financial constraints is crucial and while purchasing just enough materials for production is cost-effective, it poses a risk of production stoppages whenever supply is disrupted. Maintaining this delicate balance requires continuous and careful inventory management.

In essence, inventory management is about finding a balance between having enough stock to meet customer demand while minimizing inventory costs and risks associated with excess inventory. It’s a strategic function that impacts various aspects of a business’s operations, financial health, and customer relationships.

How AI can transform inventory management

AI technologies are poised to revolutionize how businesses handle inventory, promising to streamline processes, cut costs, and enhance overall efficiency. This transformation is expected to influence not only large enterprises but also extend to small manufacturers and distributors. Although humans are capable of performing the tasks artificial intelligence is used for, AI is rapidly outperforming us in terms of speed and efficiency.

Below are some ways AI can be used in inventory management. Some of these have already been implemented by manufacturers while others await enthusiasts and innovators.

Demand forecasting

AI algorithms can analyze historical sales data to predict future demand, aiding businesses in maintaining optimal inventory levels and preventing overstocking or stockouts. Additionally, AI can consider market trends, seasonal patterns, and other factors for heightened forecasting accuracy.

For instance, AI can identify a pattern where certain products see increased demand during major sporting events. It can then predict these demand spikes by monitoring event calendars and past sales patterns. The company can adjust inventory levels and marketing strategies accordingly to capture these opportunities.

Inventory classification

AI can find new correlations between phenomena. For example, inventory items can be classified into categories based on their demand patterns, value, and other characteristics. This will help businesses prioritize the management of certain SKUs and physically organize the warehouse for better efficiency.

Consider a company producing consumer electronics where AI is used to analyze the demand volatility of various electronic components. From the datasets fed to it, the AI identifies that smartphone screens exhibit higher demand fluctuations compared to charging cables. By recognizing this, the company can adjust its stocking strategy, keeping a larger buffer of screens to handle demand spikes, while maintaining a leaner stock of charging cables.

Optimizing warehouse operations

Utilizing AI enables improving warehouse layout, streamlining pick-and-pack processes, and elevating the overall efficiency of logistics.

For example, AI can analyze order history and real-time traffic within the warehouse to discover whether certain products are frequently ordered together. AI could then suggest storing these products closer to each other. As a result, pickers can efficiently gather required items in a single trip, minimizing travel time and optimizing the packing process.

Image recognition

Besides common QR codes, barcodes, and RFID systems, AI-driven image recognition can visually inspect and identify inventory items, reducing errors and improving accuracy. Its application is already evident in several quality assurance and warehouse management systems.

For example, AI image recognition technology can be deployed to inspect manufactured parts for defects. Cameras capture images of each part, and AI algorithms meticulously analyze them for anomalies like scratches or misalignments. When a flaw is detected, the system immediately alerts operators, preventing faulty parts from going further in the supply chain.

Replenishment planning and optimal stock levels

AI can calculate reorder timing and quantities using live data, supplier and production lead times, and demand shifts. 

For example, it can identify that a particular material experiences a surge in demand during a certain season. Leveraging this insight, it can then calculate the ideal reorder timing, ensuring that the material arrives just in time to meet the increased demand. Additionally, AI can determine the appropriate reorder quantity, considering lead times and demand variability.

Supplier management 

AI can analyze supplier performance based on historical data. While most buyers are familiar with their suppliers’ strengths and weaknesses, AI aids in the unbiased selection of the optimal choice. It analyzes additional variables to recommend top suppliers, negotiate favorable terms, and enhance relationship management.

For example, we had an AI system analyze historical data from transactions with two vendors. Below are the results of the analysis:

“Vendor A demonstrates a mix of on-time deliveries and occasional delays. The quality control shows a range of passed items with some rejected items as well. Overall, Vendor A exhibits both reliable and inconsistent behavior in terms of delivery and quality control.

Vendor B, on the other hand, consistently experiences delays in delivering the items. The delays range from 2 to 4 days. In terms of quality control, Vendor B has a mix of passed and rejected items.

Based on this analysis, it appears that Vendor A generally performs better in terms of on-time deliveries and quality control, although occasional quality issues are observed. Vendor B, on the other hand, consistently faces delays and may need to improve its delivery performance. Both vendors could benefit from closer attention to quality control to minimize rejected items.”

Supply chain optimization

AI-driven analysis optimizes supply chains by fine-tuning shipping routes, warehouse placements, stock movements, and other variables, leading to cost and lead time reductions. AI can decipher complex supply dynamics, identifying prime routes, ideal warehouse spots, and efficiency influencers. It enables flexible order strategies based on real-time data, ensuring a smooth flow from makers to customers.

As an example, consider a company with multiple production facilities. AI is leveraged to optimize the transportation of raw materials and finished goods between these locations. By analyzing production schedules, inventory levels, and transportation costs, AI identifies the most efficient routes and schedules for transporting goods. For instance, it might determine that putting shipments from different factories onto a single truck for a specific destination would minimize transportation costs.

Predictive maintenance

While MRP systems enable scheduled equipment maintenance, AI can be used to forecast upkeep requirements using machinery uptime data. If sensor data on variables like temperature and vibration is also added, the system can further predict when the machine is likely to fail.

Consider sensors installed on a CNC milling machine that can continuously monitor spindle temperature, tool wear, and vibration levels. AI algorithms can analyze this data and start recognizing patterns. For example, the spindle temperature tends to rise significantly after a certain number of machining cycles, indicating that the spindle bearings are close to failing. Using this insight, maintenance personnel can proactively replace the bearings before failure, avoiding unplanned downtime and minimizing repair costs. 

Perishable goods inventory management

For industries dealing with perishable goods, AI can predict the shelf life of products based on various factors like temperature and humidity, helping businesses minimize waste due to expired products.

For example, AI can use sensors to continuously monitor the chemical composition of products in storage. It analyzes factors like temperature, humidity, and chemical reactions, triggering alerts when storage conditions approach a set threshold.

Enhancing internal processes with NLP

AI-driven Natural Language Processing (NLP) promises to become a transformative force in warehousing and manufacturing operations. It enables digital systems to understand and act upon both verbal and textual instructions, facilitating communication between human operators and automated machinery.

For example, chatbots can be trained to answer employees’ or customers’ questions regarding product specifications, lead times, company policies, IT troubleshooting, etc. A customer may ask a chatbot on the company website, “Can you help me track my recent order?” The NLP chatbot would not only understand the query but also retrieve the order information from the company’s database and provide an immediate response with tracking details. MRPeasy has a chatbot that helps customers with questions related to the software’s functionality and implementation.

In summary

AI can significantly raise inventory management efficiency, improve decision-making, reduce human error, and ultimately lead to improved customer satisfaction and increased profitability for businesses. However, it’s important to note that successful implementation requires quality data, integration with existing systems, and ongoing monitoring and adaptation to ensure optimal results. The topics covered above represent only a fraction of AI’s potential applications in inventory management. Beyond these, numerous less apparent possibilities are sure to emerge in the near future.

Key takeaways

• Artificial Intelligence (AI) is a field of computer science that aims to create machines and software systems capable of performing tasks that typically require human intelligence.
• AI’s potential impact on a growing number of industries is profound, ranging from automation and efficiency improvements to the development of new products and services.
• AI technologies are poised to make inventory management much more efficient, promising to streamline processes and cut costs.

Frequently asked questions

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