Artificial intelligence has seemed to grab everyone’s attention. This is reflected in the percentage of investors’ buying artificial intelligence stocks and the number is constantly increasing. The fields of logistics and inventory management see AI as a safe harbor after a long storm in the raging sea. Large enterprises face big challenges in managing their warehouses and establishing a smooth flow of incoming and outgoing items. If left to the mercy of conventional methods (human staff moving items and keeping track of all entries), inventory management can grow to an insanely big size, where operational budgets get consumed faster than light by the black hole. By 2025, the worldwide market for AI-based services, software, and hardware is predicted to rise at a staggering 15 to 25% annual rate, reaching USD 130 billion.
Logistics management software development with embedded AI can offer massive support to companies in several ways. The bigger the size of an inventory — the better use can be found for a trained AI.
Biggest Issues of Inventory Management and Supply Chain
To better understand the possible applications of an AI for logistical purposes, let's focus our sights on the most common problems that companies face while managing the inventory and establishing efficient supply chains.
- An overabundance of data. Data inventory management becomes a tedious process due to how much of it is usually accumulated by any company. While carefully built and supported inventory management software can track and store data, it would still require tremendous efforts from the human staff to process all of it.
Furthermore, data handling during this magnitude may necessitate collaboration amongst many departments to maintain performance and quality. Raw materials, work in progress, and completed items are the three primary forms of inventories. To track and report the data, each form of inventory necessitates database management software and specialized methods.
- Tracking issues. It is getting harder to track every item in the inventory and to get relevant analysis out of it. Failure to follow every incoming and outgoing item can have devastating effects on turnover, an adequate response to increasing or decreasing demand, timely delivery decisions, etc.
Moreover, when a company doesn't have any advanced inventory management systems in place, managers must physically measure supplies and analyze stock transactions for symptoms of low or excessive stock levels.
- Difficulties with business planning. Inventory management is a big part of successful planning for the companies in any industry, especially the ones that deal with CPGs. Successful growth methodologies require real-time collection and processing of all data, and according to decisions and responses to situations.
The appropriate business strategy requires proper planning. Applicable data analyses that take into account the company's strategic positioning and future drive the right level.
- High operational budgets. As the size of inventory grows, the company struggles more with maintaining cost-effectiveness across the branch. Whether it be the delivery service, analytical and data mining teams or staff that keeps track of stored items, the funding increases alongside the size of the company. And often these numbers are far from being acceptable.
IT inventory management. Keeping every IT asset within an organization requires thorough control. If done wisely, it will bring benefits to a company and reduce expenses on the assets. AI inventory management systems help companies to run this process properly.
Let’s delve into the actual discussion and see how an AI can improve inventory management at the moment.
5 ways to Improve Inventory Management Using AI
AI-based inventory management can prove to be invaluable in most of the situations described above. Careful implementation can result in a company-wide boost that brings cost-effectiveness, increased turnover, customer satisfaction, and retention. Here's what the companies can do today:
1. Dealing with Planning, Predictions, and Modeling in the Inventory Management Process
Inventory management is far beyond storing and delivering items from one place to another. It both generates and relies on substantial amounts of data to be effective in terms of time, money, and workforce.
Overstocking and understocking are usually caused by failure to see and respond to the change in demand for a specific product. The ability to predict that, as a company, requires extremely competent analysts and experts in business modeling. There are other complicating factors, such as more than one warehouse, location-specific demand, and more. Each product niche has its specifics that managers and analysts have to adapt to.
Businesses can reduce the risks of overstocking and understocking by deploying Innovative approaches since the technology can:
- Analyze and correlate demand data accurately;
- Detect and respond to a shift in demand for a particular product;
- Evaluate the demand in a certain place.
Artificial Intelligence is capable of providing insights that were previously unavailable. It can take care of inventory management models, created for inventory regulations, with the help of advanced AI inventory models, which provide a solid control of the operations by making and acting on the predictions. Moreover, AI can analyze more than 50 elements, which is vital for successful planning, stocking, and scheduling deliveries.
As per Mckinsey Digital, AI-powered planning decreases supply chain network mistakes by 30-50%. A certain result in enhanced accuracy and a corresponding decrease in missed sales, which is mostly due to goods being out-of-stock, as well as a 10-40% lower in storage expenditures.
2. Data Mining
An AI tech is showing serious competence in transforming the data into timely actions that can help the business to evolve or respond well to a particular situation. Say, a local hockey team progressed in the Stanley Cup, and there will be a much bigger demand for beverages and snacks in the next matchday in that specific region. Artificial Intelligence can analyze that and come up with a suggestion to overstock for that particular region.
Trained intelligence acts as a supervisor and a watchdog: weather conditions, events, economic situation, how trendy is this or that product, and much more. It can take the decisions made by the management from error-prone assumptions (even though backed up by data) to almost a guaranteed result. While, obviously, significantly reducing the workload of the said staff. AI allows businesses to forecast their consumers' upcoming wants and create product inventories appropriately. As a result, the worldwide data mining industry is predicted to rise at a 24,5% annual pace of $21.5 billion in 2025.
3. Stocking Management and Fulfillment
Inventory management has a significant impact on customer satisfaction and a sense of fulfillment. Planning errors or inadequate stock monitoring in any warehouse can lead to shortages and delays, negatively impacting the revenue.
As explained in previous sections, AI is already competent in analyzing the customer behavior patterns and a big number of other factors that help to plan the stocking right.
Customers' data may be collected and analyzed using technology to detect behavior patterns and other important characteristics that help:
- Arrange your stockings carefully;
- Inventory and fulfillment procedures can be automated.
- Utilize and respond to incoming client needs in a timely manner;
- Implement better transportation, among other things.
Furthermore, a well-trained intelligence can automate the process of stocking and increase the efficiency in delivery, suggesting the best routes.
AI inventory management minimizes the risks of mismanaging the stocking process and helps respond to the customer demand accordingly. With the insights provided by data mining, an AI can also help to establish efficient factory-to-warehouse transportation, which is extremely important for more volatile products that have shorter expiry times.
4. AI-based Robotics
Robots are not a new thing on the market. Companies like Amazon are already using them in their day-to-day logistical tasks, and there are a number of benefits that put robots over human staff, especially in routine operations.
Robots can tirelessly move items around the warehouses, locate wares and scan their conditions. The machines can work around the clock and with a more optimal time per action. This alone can save a big chunk of the operational budget and allows to allocate more employees to more urgent and vital tasks that require human cognition.
Artificial Intelligence can enhance the process further. United with an intelligence that is capable of analyzing the data, predicting the demand patterns, and suggesting optimal delivery routes, it becomes a potent tool that can completely automate internal processes in any warehouse.
By 2023, the robot automation industry is expected to be worth $10 billion or more. Here are several advantages of using AI-based robots over human employees:
- It may work nonstop for 24 hours a day, 7 days a week;
- Robots are more efficient in terms of time spent on each action.
- It may identify goods and inspect them, gathering information for subsequent study.
- It gives actual track and monitor;
- Orders may be selected and moved by robots, reducing human error.
- It does things like stock management and other things.
5. Optimization of Logistics Routes
Route optimization is among the most important aspects of logistics. Businesses can minimize hours spent stuck in a traffic jam, enable faster delivery times, and save money by integrating AI technologies. This is because AI may assist in the following areas:
- Lower shipping costs by understanding all of the options and determining the far more efficient and valuable methods of delivering products to clients.
- Creating as most efficient paths. AI may learn traffic patterns over time, assess received data, and route based on a variety of parameters. All of this makes it easier for vehicles to escape traffic congestion.
- Getting a rather more specific delivery estimated time. AI technology could more correctly determine transit times by using complicated algorithms that take into consideration historical and real-time data, best routes, and other factors that might impact system throughput.
What are the Possible Setbacks while Implementing AI into Inventory Management?
The managers must be aware of the complications that may follow while using the AI supply chain and inventory management. While the advantages shadow the potential risks and difficulties, it is still vital to learn them in advance.
A Huge Reliance on Data
One of the strongest advantages of an AI may be its biggest complication. To build and train a custom and efficient artificial intelligence, the software developers require tons of prior data. The development process will get easier the more data you present to the team. It varies depending on what your requirements are: lists of items, under/overstocking statistics, consumer demands, errors and victories in planning, etc.
Everything you can spare will make the AI work more efficiently and with lower risks. And if the data is lacking the required information, the process gets more tedious.
Integration into Your Company’s Software
An AI must help your business, not disrupt it. One must be aware that it is a hefty and time-consuming process to embed the AI into your systems without breaking them. Naturally, the bigger and heavier your systems and software are - the more effort it will require to install an AI.
Regardless of the software that you’re using, separate dashboards for the team will most likely be necessary to keep everything as smooth as possible.
Is AI a Must-have Asset for Inventory Management?
Artificial intelligence in inventory management helps to automate processes connected to warehouses, stocking and more. It can offer aid both in physical tasks, such as relocating and tracking items or more complex situations where an advanced insight is required to complete an error-free planning or consumer demand forecast.
If the enterprise is rather small in size, utilizing an AI may seem like an unnecessary move. But as the amount of operations and data to track grows, it becomes harder to handle inventory management processes manually and protect the company from errors.
An AI is also good and provides cost-effectiveness in the operational department, leaving the routine work for the machinery and enabling human workers to focus their attention elsewhere. Furthermore, an automated warehouse can be expanded without the urgent need to increase the staff accordingly.
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