Manual inventory management and human errors negatively impact your business. Try AI-based inventory management and let machines and algorithms do the work!

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What AI Stands for Today?

Artificial Intelligence (AI) is a top-notch technology today used to help machines process large amounts of data, discover data patterns, and carry out tasks based on the human experience. Even 30 years ago it was considered impossible for a computer to perform human-like tasks, have cognition and reasoning of its own, solve problems, self-learn, and advance in data analysis that much, as it was simply beyond belief. However, nowadays, AI is used to:

  • make repetitive learning and discovery automated through data
  • increase product intelligence
  • adapt algorithm skill acquisition through constant learning
  • analyze wide capacities of information on the deeper level
  • be a step more accurate the more you use it
  • assist in getting the most value out of any data

AI-based technologies include self-driving cars, facial recognition, search suggestions, gaming platforms, algorithms of behavior, mapping applications, Alexa, SIRI, and chatbots. The top ten inventory management and artificial intelligence stocks today according to the stock market news are Nvidia (NVDA), Alphabet (GOOG, GOOGL), Salesforce (CRM), Alteryx (AYX), (AMZN), Microsoft (MSFT), Twilio (TWLO), IBM (IBM), Facebook (FB), and Tencent (TCEHY). More about AI stock management can be found at Investor’s Business Daily. There are no boundaries to AI’s achievements with the current technological progress, humans’ curiosity to dig deeper, and the world’s pace.

What AI stands for today?

With its vast capabilities, AI is highly demanded practically in every existing present-day industry. For example, in banking, healthcare, retail, and manufacturing, where it can ensure legal solutions, medical research, patent searches, and risk management, respectively. As a result, AI enhances the analytical skills of domains and industries, increases technology’s analytical performance, and eliminates language and economic barriers. Also, AI reinforces our abilities to become greater at what we can do, allows us to obtain profound understanding, broaden our vision, excel at memory support, and much more.

One of the greatest usages of AI falls onto the manufacturing and retail businesses, specifically, their inventory management. It is crucial for businesses that sell products to manage and control inventory starting from the manufacturing stage up to product distribution. Essentially, data inventory management is a postulate of running business coherence as it helps you gain a positive customer attitude by providing fast and quality services and growing the percentage of sales. Thus, smart cybernation has all the potential to become a crucial part of inventory management systems within the next 10 years or so.

Also, learn more about AI in web development!

Read more: benefits of warehouse management system

Inventory Management: What? Where? And How?

Inventory management is the process of controlling inventory within every business. It includes monitoring of buying, manufacturing, storing, and using goods from the purchasing phase up to the direct store sales. By managing this process, you ensure that goods provided for customers are here and now, and of good quality. Correct management can significantly impact your ROI by spending fewer costs on malfunctions and creating an amiable atmosphere for client services based on the demand and expectations of the latter. There are five types of inventory a business can own. These are:

  • finished goods
  • raw materials
  • work-in-progress
  • maintenance, repair, operation goods and
  • safety stock

Based on the types of inventory, there are also five inventory management steps. These are:

  • purchasing of raw materials or ready-made products for further realization
  • producing goods or semi-manufacturing products
  • holding stock or storing manufactured goods or raw materials before the realization
  • selling goods to customers
  • reporting statistical data and tracking profit based on sales

IT inventory management is one of the examples of how inventory management works. It enlists all the technical property of the company such as desktop computers, keyboards, servers, load balancers, routers, firewalls, switches, headphones, and other items that participate in internet connectivity or in carrying out developers’ mundane duties. Together with hardware, loads of references about this property require appropriate storage.

Inventory Management Explained

For instance, the brand of any device, model, or serial number, the configuration of the server, the location of the equipment, data about assigned computers, and much more information, which the inventory management process monitors. One more inventory management asset in IT companies is the installed software on each personal computer, which also requires control.

Accounting for an abundance of information, companies need specific tools to automatically obtain, optimize, and store the existing data in one place. That is when automated inventory management tools become handy. Inventory management is important for your company’s growth and profit accumulation, so it is vital to use the best artificial intelligence models to keep it cutting-edge.

Inventory Management Models

Generally, there are two models of inventory management. These are created to find out the perfect level of inventory that requires maintenance within businesses. These models are:

  • independent inventory demand (perpetual inventory and periodic review)
  • dependent inventory demand (e.g. EOQ – Economic Ordering Quantity, ABC – Activity-Based Costing, and JIT – Just-in-Time).

If independent models depend on a certain non-decisiveness based on the demand levels and time of restocking, dependent models are based on the assumption that everything is decided according to demand and restocking. Here, perpetual inventory is a process of reordering when the item stock decreases to its minimum level and periodic review is a regular process of stock management and reordering control.

In comparison, the EOQ will give answers to questions of How often should you buy? When to buy? What kind of reverse stock should you have? ABC model will help you measure and position inventory items, where A will be the most valuable ones and C – the least. The JIT model helps in waste cost reduction as it prefers only produced and received goods of a certain quantity. Each model is designed specifically to meet the organization’s needs and control both the demand and supply to ensure a positive experience in inventory management.

How AI Enables Inventory Optimization Today

Currently, the role of inventory management AI is not well-defined. So, let’s overview the current situation:

Business AI applications

AI brings advantages to business-facing applications that traditional methods often can’t achieve. Basic inventory software tracks stock levels and generates reports but may not offer advanced features like demand forecasting, dynamic pricing, or supply chain optimization.

AI can quickly process and analyze vast amounts of data, identifying patterns, trends, and anomalies that may be missed by humans or traditional software. With such models, predicting future trends, demand, and consumer behavior is also possible. That means you get answers about what consumers want, what suppliers deliver, when to reorder, and how much to order.

Compliance and inventory optimization

Every business has unique requirements and constraints. Customized compliance standards take these specifics into account. They help ensure inventory management practices align with your goals, regulatory requirements, and industry standards.

When items are out of stock, you get lost sales, dissatisfied customers, and missed opportunities. Conversely, excessive safety stock can tie up capital and warehouse space. AI inventory management software analyzes vast datasets and provides insights into how much inventory to order and when to order it, considering demand patterns, seasonality, etc.

Better simulation

While employees may analyze a limited set of scenarios, AI can explore a wide range of combinations based on historical data. This capability enables businesses to consider a broader spectrum of variables and potential outcomes, enhancing decision-making.

For example, through historical data simulation, you estimate the probability of various scenarios, including stockouts or excess inventory. With this information, you can make more informed decisions regarding safety stock levels. This might involve adjusting order quantities, reorder points, or supply chain strategies.

Supply chain improvements

The pandemic exposed vulnerabilities in supply chains, disrupting traditional models. In response, companies are increasingly prioritizing resilience in their strategies. AI, machine learning (ML), and data science can manage uncertainty and mitigate risks in supply chains. These technologies analyze vast amounts of real-time data, providing a more accurate understanding of market dynamics, demand fluctuations, and potential disruptions.

Also, AI-driven systems enhance anomaly detection by continuously monitoring various data sources and identifying deviations. As a result, you respond swiftly to unexpected events, whether they are supply chain disruptions or shifts in consumer behavior.

The Most Valuable Areas for AI Inventory Optimization

According to the IBM Global AI Adoption Index 2022, 1 in 4 companies (26%) use AI for real-time inventory management. With the automation of AI-based inventory management, manual tracking of inventory becomes impractical. Thus, it is an extra advantage for employees, who can concentrate on carrying out other tasks and be more productive while machines do all the work.

Inventory monitoring automation

According to the IBM Global AI Adoption Index 2022, 1 in 4 companies (26%) use AI for real-time inventory management. With the automation of AI-based inventory management, manual tracking of inventory becomes impractical. Thus, it is an extra advantage for employees, who can concentrate on carrying out other tasks and be more productive while machines do all the work.

Warehouse optimization

The global warehouse automation market is expected to grow 15% annually between 2022 and 2027. With AI, you will streamline warehouse operations by optimizing storage, picking, and packing processes. Such models can determine the most efficient layout, minimize travel time for workers, and suggest optimal storage locations for products. As a result, you get improved warehouse efficiency and cost savings.

Supply chain visibility

Employ AI to get valuable insights into the entire supply chain, from suppliers to end customers. For example, advanced systems can track the movement of goods, identify potential delays, and offer recommendations for route optimization.

Risk management

AI assists in predicting and alerting businesses to potential issues, such as supplier disruptions, shifts in customer demand, or economic fluctuations. By providing early warning signals, you are able to adapt and make informed decisions to minimize risk.

Risk management

AI assists in predicting and alerting businesses to potential issues, such as supplier disruptions, shifts in customer demand, or economic fluctuations. By providing early warning signals, you are able to adapt and make informed decisions to minimize risk.

Data mining

Data-driven insights hold the key to business success. To unlock this potential, you should extract value from the vast amount of unstructured data, which grows at a staggering rate. According to Gartner’s survey, four out of eight Data and Analytics Research Board members reported a 25% increase in unstructured data from January 2022 to January 2023.

Data mining with the help of AI becomes an easier process of gathering information. Hence, by tracking and recording the interests of every consumer through algorithms, companies form a better picture of consumer demands and plan their business development. Thus, businesses get a pre-plan of future customer needs and stock products accordingly.

Robot automation

Inventory checking, fulfillment, and restocking can also be done with the help of AI inventory management software. Algorithms guide robots to select and move orders with the help of their sensors and system requirements. Robot automation saves the day by retaining large amounts of time on task completion, which is impossible to do manually. After a decline in revenues in 2023, there is potential for the global warehouse automation market to see growth in 2024.

Error reduction in forecasting

Error reduction in forecasting is a priority to businesses as it impacts supply chain management. Of course, companies make attempts to understand what amounts of products to stock optimally to gain consumer satisfaction and profit. It is where AI makes valuable predictions and updates data non-stop to ensure demands are being calculated and there are going to be enough products stocked in the future.

According to McKinsey, 4 out of 5 supply chain executives expect to or already use AI and machine learning in planning.

Dictating the demand, preferred product quality, and quantity, as well as deadlines, is inherent to customers, who contribute to business growth financially. Overstocking and understocking suggest that the company does not meet customer demand and, this way makes consumers’ experience less decent.

AI and machine learning in stocking

Approximately 70% of consumers (or even more) will buy the product somewhere else (e.g. the Amazon Effect) and, the revenue of $1.75 trillion will be lost all around the globe annually. This number is not final and tends to grow without appropriate interventions. The downturn occurring leaves little space for cost accumulation and savings. Thus, conscious inventory management is a priority. Besides, the biggest focus in sales should be dedicated to input quality, timely delivery, and customer satisfaction with the output.

Moreover, AI has the potential to search, scan, and identify the needed products or offers, which saves customers’ time and effort. These two factors directly influence customer satisfaction and leave big chances customers will come back to you again.

How Are Famous Companies Using AI to Enhance Their Sales and User Experience?

A positive customer experience promotes the feeling of satisfaction while making a purchase and makes these customers potential brand buyers. For this reason, companies turn to use newer technologies, one of which is artificial intelligence. Let’s review some of the use cases and technologies used by Google, Amazon, and IBM.

Google (Pluto7)

Pluto 7 is a technology that uses machine learning to forecast customer demand accurately. To improve demand, Pluto 7 leverages Google Machine Learning Engine, enhancing the system performance and reliability with Google Cloud Platform at its core.

The drive towards Google Cloud Platform was to get beyond performance bottlenecks and leverage Google machine learning on a cloud platform that scales and is cost-effective. – Salil Amonkar, COO and AI/ML Professional Services leader, Pluto 7

The Google Cloud Platform is remarkable because it:

  • Helps retailers deliver the most accurate demand forecasts
  • Shifts the company’s focus on product development and new features rather than problems occurring
  • Creates chatbots within one day, which saves time significantly
  • Is reliable and scalable, and delivers great value
  • Enhances and leverages businesses, which have the potential to grow

If there is an inventory dilemma, Pluto7 Planning In A Box will surely solve it for its retail customers.

Google Cloud Platform lets us free up engineering resources so we can deliver greater value to customers. – John Nikhil, Head of Growth and Sales, Planning in a Box, Pluto 7

Amazon (Intellify)

Intellify is an AWS Advanced Consulting Partner and an AWS Machine Learning Competency Partner that delivers one of the best machine learning solutions in the supply chain industry.

Intellify, an AI-powered inventory Management AWS Solutions Consulting Offer is designed to improve inventory health in warehouses. It works by automating inventory forecasting to reduce both time and unneeded guessing from inventory management. To create trustworthy demand forecasts, consultants at Intellify use machine learning possibilities. When forecasts are done, they recommend specific inventory purchases.

Among the benefits of AI-Powered Inventory Management are:

  • Enhanced forecast accuracy
  • Increased inventory turns
  • Managed stock in demand in the right place
  • Simple system integration

The system proposed, i.e. AI-Powered Inventory Management can integrate with the already-existing enterprise resource management (ERP) system and business intelligence (BI) system to warn about the problem stock beforehand. Being able to address problems at their early stage is a good perspective that reduces time and costs.


With the help of IBM, AI is reshaping the good old supply chain industry. Not that long ago, IBM introduced the AI-enhanced inventory control system, which has the potential to assist companies in optimizing their decision-making processes and building effective and resilient supply chains. This system is called the IBM Sterling Inventory Control Tower. It provides insights to see the inventory’s location at the moment, identifies external event impact, and predicts disruptions on the spot, taking actions to mitigate the effects.

The IBM Sterling Inventory Control Tower provides real-time insights by

  • expanding inventory visibility beyond warehouses, in-store locations, and supply in transit in grocery stores
  • providing visibility into supply and demand gaps for critical items, e.g. lifesaving equipment and supplies, making them available 24/7 in hospitals
  • getting visibility into aftermarket service parts, ensuring critical parts are in stock with regard to customer expectations in the automotive market

More than 20 years ago, experts predicted that every company would become an internet company… I’m predicting today that every company will become an AI company — not because they can, but because they must. – Arvind Krishna, IBM’s CEO

AI-Based Inventory Management Improvement

Though AI is an ideal deposit into inventory management, there is still space for improvement. For example, gradually improving inventory management and AI will help your business thrive and accumulate profits. Therefore, the ways of improvement should consist of:

AI-Based Inventory Management Improvement

  • focusing on your needs
  • engaging with suppliers
  • planning the use of AI in inventory management system
  • using only present-day data
  • going mobile

With the right attitudes and actions, major improvements in inventory management can be achieved using AI, its scenario-predicting capabilities, recommendations, and solutions to the problems occurring in the future. Analytics presented by AI and a bit of independent decision-making allow data enrichment, standardization, consolidation, and, basically, everything that employees are incapable of doing within short periods, without human errors and, most importantly, manually.

Challenges of Implementing Artificial Intelligence in Inventory Management

Using AI in inventory management offers significant benefits, such as reducing costs, improving customer satisfaction, and streamlining operations. However, it also presents various challenges you need to address:

Data Quality and Availability

AI algorithms require accurate, complete, consistent, and relevant data to make the right predictions. Inaccurate data can mislead AI algorithms, leading to poor inventory decisions. Also, it’s equally important to use historical and real-time data, integrating it from internal (sales and supply chain data, customer feedback) and external (weather forecasts and economic indicators) sources.


AI models, like deep neural networks, can be highly complex and have numerous parameters. As a result, they provide outcomes without revealing their decision-making process. Inventory planners and managers may hesitate to rely on AI recommendations if they cannot discern how and why they are made. Providing training and education to the stakeholders, as well as improving the interpretability of AI algorithms, boosts trust in AI insights.

Demand Forecasting

Predicting future demand is crucial for inventory optimization. However, developing algorithms that can adapt to different seasons and holidays is a complex challenge. Forecasting demand for new products is also complicated because there is limited or no historical data to draw upon. For this, AI models must use market research, product attributes, and similar product performance.

Model Selection

Different models may be more appropriate for various scenarios. For example, time series forecasting models like ARIMA and exponential smoothing are useful for predicting future demand based on historical data. Machine learning algorithms like decision trees, random forests, and neural networks can capture complex patterns and dependencies. Selecting the most suitable demand forecasting model depends on the data nature, the available historical information, and the complexity of demand patterns.

Cost of Implementation

Implementing AI systems can be expensive, requiring investments in technology, infrastructure, talent, maintenance, and support. You should weigh the expected benefits, such as improved inventory accuracy, reduced costs, and increased efficiency, against the upfront and ongoing expenses associated with AI.

Change Management

Change is often met with resistance. Employees may be hesitant to embrace AI for inventory management due to a fear of job displacement, a lack of understanding, or concerns about changing roles. Provide clear and consistent information about why it is being adopted, how it will benefit the organization, and what it means for their roles. Also, ensure your staff has the necessary skills to operate and collaborate with AI-driven tools.

Integration with Legacy Systems

Legacy systems often use outdated technologies, programming languages, and databases. They may not be designed to interact with modern AI tools and platforms. Compatibility issues, data format mismatches, and technical hurdles can pose significant challenges. So, you may need a complex system audit and modernization.

Human-AI Collaboration

Overreliance on AI recommendations can be risky. It can lead to decisions that lack critical human insights, especially in situations where historical data may not fully capture unique circumstances. It’s crucial to combine AI’s data-driven insights with human judgment to make informed choices. The challenge lies in defining clear roles and responsibilities for both parties.

6 Tips How to use AI for inventory management

Based on the ways of improvement discussed above, there are 6 steps to optimize your inventory management using Artificial Intelligence.

Use inventory monitoring and robotic automation to reduce time and costs spent on manual work

The retail industry (and other industries) revolves around selling numerous types of goods/services to customers using different types of channels of distribution. Here, the major pain point is to sort these goods/services correctly. The better is the logic of storing and searching for goods the faster they will be found and shipped to the customers. But, when there are big amounts of products in the warehouse, the solution should be based on technologies such as AI. Thus, Intelligent Robotic Sorting and Visual Inspection is something all the warehouses need.

Use a warehouse management system to enhance warehouse functionality, and optimize it

In healthcare, where there are warehouses with medicine/drugs and medical devices or spare parts to those devices it is necessary to have a warehouse management system to support and optimize warehouse functionality. The system operates and excels in daily planning, organization, staffing, directing, and controlling the usage of available resources, movement, and storing of materials into, within, and out of a warehouse, providing support for the staff in the performance of material arrangement and storage in and around a warehouse.

Most of the workers cannot perform these tasks manually as there’s too much to remember and a lot to do throughout the day. What’s more, the items to be moved are heavy. Thus, even the most cautious employee might be subject to errors and errors are the biggest pain points in business. AI helps in this case as well. It allows workers to have a system with all the basic information and use it whenever needed.

Use supply chain planning techniques to be one step ahead of your competitors

Supply chain planning belongs to the process of accurate planning of the journey a material or a product goes through from the raw material stage to the end-user. This may include supply planning, demand planning, production planning, distribution planning, operations, and sales planning. For example, in the automotive industry, this technique is very useful as it allows safer AI management of automobile parts to be distributed and delivered per request.

For instance, in factories, where cars are being assembled, a constant flow of car parts is essential. With the right attitude and planned shippings, these parts can achieve their final destination as scheduled. If the planning stage is neglected, this becomes a huge pain point for both the car parts vendor and the customer.

Use Risk Management/Network Management to achieve the company’s goals and leverage profits.

The correct development of a stock policy will ensure you will know when, how much, and what to order, or what to keep in stock.

For example, the AI-based risk management scheme will make sure the delivery of the needed products to your stock will be done according to the schedule set beforehand. This system of networking is applicable to all the businesses having warehouses, stocks, and delivering products/services to customers, who demand them.

Use Predictive Demand/Capacity Planning to fit into the budget and demand

With the help of AI/ML, it is quite easy to understand and predict the consumer demand for a certain product. Moreover, AI has all means to forecast the potential capacity needed to satisfy customers. Be it hardware or software, medicine or food, etc., with prediction and planning, your business will be able to meet all the demand and grow.

Use Intelligent Route Optimization to succeed in the dynamic environment

Most of the route planning problems happening today are subject to various time-varying factors, e.g. equipment failures, traffic accidents, traffic congestion, and uncertainties in road networks. To ensure, the delivery of your goods to the warehouse, or the stores, or even to the customers’ doorstep will not be a failure, it is essential to use AI in composing an intelligent route with backup plans in case of any accidents. Intelligent road network planning is a good thing to try if you had no chance to do so yet. Excellence in delivery requires being prepared.

Are There AI Usage Risks?

Unfortunately, AI technology advances and develops through analyzing consumer demand patterns and adjusts AI-based inventory management to well-known or forecasted data. However, sometimes customers change their preferences quite suddenly and AI cannot adapt to the new patterns and improve customer demand immediately as if it was a human. AI in inventory management acts on common knowledge and it becomes hard for it to adjust to the new algorithms of the consumption environment within short periods. With no references applicable to new knowledge AI is deprived of performing quick predictions.

Moreover, on implementing new AI-based inventory management software there is a chance it will be conflictual with the previous older software versions while the integration stage and result in unpredictable damage. Thus, human occasional presence in inventory operations control and constant monitoring of AI is rather a must to ensure a smooth course of events. AI rarely reports errors if the technology is given the right commands and information, but that does not mean errors never occur. Having the ability to produce cognitive tasks and reasoning, AI still needs human help to function better and improve inventory management.

Despite the AI usage risks, which are nominal, the advantages of this technological breakthrough are immense. Perhaps, nowadays artificial intelligence for inventory management has limits to its functions in inventory management and more, but in the nearest future, it might supersede all the bold expectations. It is only a matter of time.

Our Company’s Experience

As AI development company Inoxoft, has met all the challenges and benefits of boosting enterprise logistics and organizational monitoring for an Israel-based company B.O.S. Better Online Solutions and produced a case study. The client’s request was to make inventory management more functional, reliable, performative, and supportive, and required waste expenditure reduction.

Inoxoft’s experience in inventory AI management development

Inoxoft’s team developed a web platform and a mobile app to enhance inventory and customize multiple types of businesses. For example, database and RFID readers’ communication improved reaching the highest possible optimization of 0.2 milliseconds. Inoxoft achieved the following:

  • cutting-edge Android app
  • inventory accounting configuration
  • internal ERP systems integration through the web platform
  • speedy database processing optimization
  • Bluetooth, RFID, FTP data transfer support
  • 500k row database management
  • system setup boost
  • online/offline barcode scanning
  • 130% process performance improvement

More information on the case study can be found here.

To Sum Up

Artificial intelligence breaks into the future with unbelievable speed and power. Almost every business has shifted towards using AI daily. Especially, to inventory AI management, as it changed the whole picture of stocking and storing for industries. With the help of AI, inventory management became automated, pre-planned based on customer demands, carried out by robots and machines, allowed employees’ productivity to increase in other fields, reduced errors to a minimum, and further eliminated malfunctions by a set of appropriate algorithms.

These interventions brought consumer satisfaction, high sales, and companies’ growth. As AI develops and advances its possibilities with every minute, there is still space for constant and gradual improvement in producing better and faster results. If you still wonder whether to use AI in your business, you can always try out the Inoxoft discovery phase. Your choice depends on the needs of your inventory management procedures as well as the sums you can invest to achieve bigger profits and save costs from unwanted waste. But results can be outstanding. AI is the future you should embrace to thrive.