Artificial intelligence (AI) is increasingly becoming a crucial part of inventory management processes due to its ability to minimize error, improve efficiency, and increase profitability. The article discusses the growing importance and applications of AI in inventory management.
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Importance of AI in Inventory Management
Efficient inventory management has become the key to success for companies in the ever-evolving business landscape as a shortage of inventory can lead to dissatisfied customers and missed sales opportunities, while an excess inventory can result in potential wastage and higher storage costs.
The use of AI in inventory management can assist businesses in navigating inventory management complexities easily and efficiently, transforming potential challenges into growth opportunities. AI can analyze substantial volumes of data to identify relevant patterns and trends and make accurate predictions.
In inventory management, AI can promote growth and innovation by enabling real-time decision-making, providing insightful analytics, and automating complex processes. AI techniques, including machine learning (ML), generative adversarial networks (GANs), natural language processing (NLP), computer vision, predictive stock replenishment, reinforcement learning (RL), sentiment analysis, and multi-echelon inventory optimization (MEIO), are used in inventory management.
ML algorithms, RL, NLP, and computer vision are used for demand forecasting, inventory optimization, data analysis, and automated inventory checks, respectively. Predictive analytics and GANs are employed for maintenance optimization and inventory simulation, respectively. Sentiment analysis is used for demand sensing, while MEIO is utilized for inventory optimization.
Benefits of Using AI
Data Analysis and Structuring: Dealing with messy and unstructured data is one of the major challenges faced by businesses in inventory management. AI can be used to structure and clean up the data to make it suitable for executive debriefs, visualizations, and analysis, which improves the quality of the data-derived insights and allows businesses to make more informed decisions. Thus, the application of AI can make data a crucial tool that can drive business growth and strategic decision-making.
Improved Accuracy: AI techniques can analyze data with higher accuracy compared to humans, which leads to more accurate inventory forecasts that significantly reduce the risk of understocking or overstocking and enable businesses to maintain optimal inventory levels.
Increased Efficiency: AI can automate several inventory management aspects, such as data analysis and collection and reordering processes, allowing staff to focus on other critical tasks, which improves overall productivity. The elimination of manual tasks also decreases the risk of manual errors, which further improves the inventory management process efficiency.
Cost-effectiveness: The application of AI can lead to significant cost savings for businesses as the technology reduces storage costs, decreases wastage due to overstocking, and improves sales through accurate forecasting through improved efficiency and accuracy. Cost-effectiveness can provide a critical edge to businesses in a competitive business landscape by enabling them to invest more in innovation.
Higher Customer Satisfaction: AI can ensure the availability of the right products in stock, which allows businesses to offer a superior shopping experience to their customers, leading to greater customer satisfaction and loyalty. Customer loyalty and satisfaction are crucial for business growth in an era of ever-rising customer expectations from businesses.
Employee Satisfaction: AI can also ensure employee satisfaction by automating repetitive tasks and allowing them to be involved in more impactful and strategic tasks. Higher job satisfaction motivates employees to contribute significantly to the success of their organization.
AI Applications in Inventory Management
Demand Forecasting: Conventional demand forecasting methods, such as exponential smoothing and autoregressive integrated moving averages, are becoming ineffective due to the ever-increasing data generation by businesses.
AI-powered inventory management systems can identify demand patterns and utilize this data for warehouse replenishment plan optimization and accurate forecasts. AI-driven inventory management provides instant and improved forecasts based on real-time data collected from external and internal sources, such as social media, online reviews and feedback, weather, and demographics.
Additionally, AI-powered demand forecasting can decrease supply chain errors, which results in a reduction in lost sales owing to incorrect consumer demand and stock numbers. Businesses using ML algorithms and external data in inventory operations can gain a competitive edge over their competitors who rely on manual methods and human data analysts.
Customer Support: AI chatbots can assist businesses to remain updated about their enterprise resource planning (ERP) inventory system and keep a track of all orders. Additionally, chatbots can provide top-level customer service with additional features apart from instant messaging.
Moreover, AI chatbots can quickly assist with issuing receipts and billing, processing orders, and delivery requests; improve customer service by allowing customers to easily make queries, solving customer queries, and track items; and enable businesses to collect feedback from suppliers and customers.
For instance, DHL currently offers a service that allows users to ask a smart device, such as Alexa, for instant updates on their parcel’s estimated delivery time and location. Thus, AI-powered customer support can improve customer experience, improving customer satisfaction and retention rates.
Warehouse Management: Using AI in inventory management can make warehouse management more focused, efficient, and easier by optimizing and streamlining warehouse management processes, reducing the possibility of human errors. Additionally, automated AI systems can quickly communicate accurate information compared to human operatives.
AI can also optimize logistics tasks such as counting pallets or designate the required equipment for staff to reduce human errors and processing time. Businesses can save significant budgets and resources spent on inventory control using AI.
Material Procurement: Procurement is a crucial part of inventory management that involves managing many suppliers and documents. Errors and inefficiencies in the material procurement process can reduce the overall inventory management efficiency. AI analytics can automate such processes from the first stage of quoting to managing materials throughout the supply chain.
Businesses can significantly improve their inventory levels and reduce logistics costs by introducing AI in their material procurement process. Automated material procurement examples include anomaly detection, supplier and market data collection, vendor matching, and procurement spend classification.
Marketing: AI-powered inventory management offers better insights into product demand, which can facilitate the development of lucrative marketing strategies. Specifically, AI and ML can identify the short-lived demand for products and their market and sporadic changes in product interest to enable businesses to tailor their marketing strategies and personalize them to target customers.
Thus, AI allows businesses to stay updated with the current trends in the market and about the services and products that are losing customer interest, which allows them to increase their revenue.
AI-based Robotics: Several companies, such as Amazon, already use robots for their routine logistical activities. Robots can become an effective tool that can entirely automate internal warehouse activities when AI techniques capable of recommending the best delivery routes, forecasting demand trends, and evaluating data are integrated with them.
AI-powered robots can analyze data and accurately predict demand patterns to significantly improve operational efficiency. Additionally, these robots reduce operational costs as they need only one-time acquisition and routine maintenance payments.
Dynamic Safety Stock Management: AI-driven safety stock management can ensure profitability in every fulfillment channel by balancing the fulfillment costs against the service demands, which encourages repeat purchase behavior, increases return on investment, and improves customer experience.
Recent Studies
In a study recently published in the journal Computers and Industrial Engineering, researchers proposed a decision support framework for inventory management combining ML and multicriteria decision-making (MCDM) approaches and applied the framework to a railway logistics operator to assist its maintenance, repair, and operation (MRO) inventory management decision-making process.
The first stage of the proposed framework involved applying a hybrid MCDM method that combined fuzzy logic with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and analytic hierarchy process (AHP) methods to rank and select stock-keeping units (SKUs) based on criticality and importance.
A second stage of the framework was introduced after the most critical SKUs were identified to predict the demand for these SKUs through an ML model, which combined a genetic algorithm and an artificial neural network (GA-ANN). Research findings demonstrated that applying the proposed decision support framework led to significant improvement in the accuracy of the SKU demand forecasts compared to the previous forecast by the operator.
References and Further Reading
De Paula Vidal, G. H., Caiado, R. G. G., Scavarda, L. F., Ivson, P., Garza-Reyes, J. A. (2022). Decision support framework for inventory management combining fuzzy multicriteria methods, genetic algorithm, and artificial neural network. Computers & Industrial Engineering, 174, 108777. https://doi.org/10.1016/j.cie.2022.108777
Dunlea, J. (2023) Revolutionizing Inventory Management: The Power of AI [Online] Available at https://www.akkio.com/post/ai-for-inventory-management (Accessed on 23 October 2023)
Takyar, A. AI IN INVENTORY MANAGEMENT: REDEFINING INVENTORY CONTROL FOR THE DIGITAL AGE [Online] Available at https://www.leewayhertz.com/ai-in-inventory-management/ (Accessed on 23 October 2023)
AI Inventory Management: 7 Ways Artificial Intelligence Can Enhance Inventory Management [Online] Available at https://www.liquidweb.com/blog/ai-inventory-management/ (Accessed on 23 October 2023)