Inventory management is the science of managing, storing, and distributing goods to ensure that the correct product is always available in an optimal condition at the right time and place. Unfortunately, many businesses are struggling with inventory management, primarily in manufacturing. With companies producing more and more products worldwide, the issue of keeping track of enormous amounts of parts and materials used in the manufacturing process has grown increasingly complex. In addition, these parts frequently use several departments or plants within a company. As a result, when ordering a component, it may have to be shipped from one end to the other.
We’ll look at how Artificial Intelligence will change inventory management and what needs to be done to prepare for that.
Facts and Factors say Industry 4.0 Market Size Growth Will Reach USD 210 Billion by 2026.
AI in Inventory Management
- Inventory management and supply chain challenges
1.1. An excess size of data
1.2. Inventory Tracking Issues
1.3. Barriers in Business planning
1.4. Operations Budgets are very high - Four ways to use AI to enhance inventory management
2.1. How is AI used in inventory management?
2.1.1 Reinforcement learning Inventory Management
2.2. IBM Inventory management
2.3. How is machine learning used in inventory management?
2.3.1. Amazon Inventory management with Machine learning
2.4. How is AI used in logistics?
2.4.1. AI in order management - International Inventory Management
- Future of Artificial Intelligence Inventory Management
1. Inventory management and supply chain challenges
A computer-based inventory system calculates the optimum levels for each product to help with stock management and achieve the best inventory balance for a business. Artificial Intelligence (AI) has arrived and transformed how we do inventory management. AI is already showing an impact on the way companies manage their inventory.
Source: Forbes
1.1. An excess size of data
Without computerized data management, any small company would have employees simply collecting and grooming data from its systems. Even if this seems like a trivial problem, it is not the case for giant corporations. Moreover, data management at this scale can require multiple departments working in close coordination to ensure efficiency and accuracy.
There are three main types of inventories: raw materials, work in progress (WIP), and finished goods. Each type of inventory requires database management software and specific processes to track and report the data.
1.2. Inventory tracking issues
The biggest threat to a big business is losing track of inventory, whether it’s a single essential item or hundreds of individual items. When inventory tracking becomes difficult, management decisions become slow, and customers lose trust in the company. In addition, if a business does not have sophisticated inventory systems in place, management has to look for signs of stock levels getting low or high by manually counting supplies and checking stock receipts.
1.3. Barriers in business planning
Proper planning is the fuel of all business plans. The right is level driven by adequate data analyses that take into account the strategic positioning and future of the business. Different companies in different spheres deal with planning methods; some are suitable for short-term goals, while others define long-term objectives.
1.4. Operations budgets are very high.
Effective management is a key to growth for any business. One of the most critical aspects that need attention is a company’s operational budget. Since the operating budget is tieing to inventory growth, it can be a complex task to operate the business. An excess supply leads to an excessive number of stock positions and, thus, a higher operating budget. In modern times of technological development, inventory as a factor in business – from small trading companies to large retail chains – has changed significantly. To keep a steady profit, As a result, operational costs have become an important issue for businesses.
2. Three ways to use AI to enhance inventory management
2.1. How is AI used in inventory management?
2.1.1. Reinforcement learning Inventory Management
RL can be used to solve the inventory ordering management problem. The steps in this approach are as follows: formulation of a multi-agent, serial supply chain as an RL model, implementation of a 1-step Q-learning algorithm to tackle the inventory ordering problem, optimization, and analysis of the results from the 1-step Q-learning algorithm to produce an optimal order size.
The goal is to develop a single-agent model for inventory management. The model should find the optimal solution and estimate how quickly the following action will perform.
2.2. IBM Inventory management
IBM Maximo MRO Inventory Optimization will scan an organization’s parts and supply chain, examine how parts using in equipment throughout their lifecycle, and determine which components should stock where and in what quantities. Implementation involves adding data feeds from the equipment and materials, configuring the solution to calculate optimal parts placements, and scheduling updates to reflect changes in the supply.
Source: IBM
2.3. How is machine learning used in inventory management?
2.3.1. Amazon Inventory management with Machine learning
To keep the goods moving through their network of warehouses with machine learning, Amazon has built an army of automated robots. It became crucial to Amazon’s operations, powering its fulfillment centers, moving items from place to place, and tending its vast network of distribution centers. The company now has tens of thousands of robots across the globe.
Source: addepto
2.4. How is AI used in logistics?
2.4.1. AI in order management
At Present, we have large retailers using artificial intelligence in order management. The system will give much of the data needed to choose the best suppliers. These suppliers are selecting mathematical models developed using Bayesian methods. AI is rapidly evolving as it is used more and more in order management software. It offers a significant opportunity for the order management industry to improve through more intelligent software and better existing channels.
Source: reliable plant
Grand view research says The global artificial intelligence market size was valued at USD 62.35 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028
3. International Inventory Management
International inventory management is the evaluation of inventories from a global perspective. The analysis of stocks must consider all possible factors, such as marketing, location, financial aspects, and the target market it intends to sell the product. Artificial intelligence has changed the rules of the game. Now you can operate at maximum capacity with minimal cost and resources required by using intelligent inventory management software anywhere.
4. Future of Artificial Intelligence Inventory Management
We stand to witness the future of Artificial Intelligence inventory management that will be smarter, faster, and more efficient than anything in existence today. With the help of AI, inventory management will track thousands of orders simultaneously and identify problems or delays in production or shipping times. In addition, a forecasting system fueled by AI will know before that happens, based on data pulled from every interaction the inventory department has had with past and present clients, whether it be an increase in customer demand or a shortage of materials. We are entering an era where artificial intelligence will make our lives easier and improve the experience for everyone involved.
Conclusion
Artificial intelligence in inventory management can help companies eliminate time-consuming and tedious inventory tasks. It can also provide companies with better data to make decisions about inventory, which will result in higher profits and lower losses. Artificial intelligence has a great deal of potential to revolutionize how inventory management is working.
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