How Inventory Control Management Evolves With Manufacturing Automation?
Inventory control is at the heart of successful manufacturing. AI-powered inventory management is more than just automation. It is the perfect marriage between data-driven decisions and human judgment. AI systems become increasingly advanced, operating based on large volumes of information that then serve as predictions or suggestions. This leads to the next level in proactive inventory control, wherein potential shortages or excesses are recognized and addressed so far in advance that only marginal variation can be seen. As a result, manufacturers maintain a smoother production line, minimal waste, and are better positioned to meet the demands of an ever-changing market for sustainable success in a keener competition environment.
Different Types of Inventory Control Methods
The current trend in inventory management involves many changes and improvements over the years to increase the resilience and adaptability of the system. The recent outbreak of the COVID-19 pandemic also highlighted the many weaknesses and shortcomings of the traditional inventory management system. These include the reliance on static data and the inability to provide real-time information and visibility. Other fixed and rigid processes and methodologies. Modern and more efficient solutions and strategies have been adopted by many organizations and businesses to improve inventory management and increase resilience to changes and uncertainties in the market and industry. Despite the challenges and limitations facing the industry and market today, there are many types and features of inventory control management. Here are the types of inventory control that are listed below-
1. ABC Analysis- ABC Analysis is the categorization of inventory into three categories based on value and importance. The “A” items are those of prime value or importance that need close surveillance. The “B” items consist of items of moderate value. The “C” items pertain to non-important items that do not warrant great attention.
2. Just-In-Time (JIT) Inventory- JIT inventory management involves receiving or producing merchandise exactly when it is required. JIT helps optimise inventory, minimise waste, reduce storage costs, and maximise storage capacity. Therefore, the relevant Wal-Mart strategy here is ‘Just-In-Time’ inventory.
3. Economic Order Quantity (EOQ)- EOQ is also an optimization approach with calculations related to the optimal order amount to optimize inventory costs. It usually takes into consideration the costs of ordering, carrying, and variation in demand.
4. First-In, First-Out (FIFO)- FIFO is an inventory costing method where it is assumed that the oldest products in the inventory will be sold or used prior to the newer ones. This will prevent the products or the inventory from becoming obsolete or spoiling. Such products would be consumed before others.
5. Vendor Managed Inventory- VMI is a joint inventory management process whereby the supplier monitors and maintains the level of inventory at the customer’s site. VMI enables this responsibility to shift from the customer to the supplier and thereby enables him to concentrate on his core business.
5 Key Elements of Automation in Inventory Management
1. Automated data collection and tracking– Automation helps in the automatic collection of data related to inventory, such as product information, quantities, and locations. This can be done by using technology such as barcode scanners and IoT sensors. Automation in data collection helps manufacturers avoid errors in data entry and also provides for automatic updates of data related to inventory in real-time. This provides manufacturers with up-to-date, accurate information for decision-making.
2. Real-time inventory management- Automation enables the real-time management of the current levels of the inventory. As sensors and IoT solutions are incorporated into the system, the manufacturer achieves real-time visibility into the levels of the inventory that are housed in different locations and warehouses. Real-time management of the levels of the inventory enables the manufacturer to take informed decisions on the reorders that need to be placed or the production schedule that needs to be modified due to the availability of the inventory.
3. Demand Forecasting & Predictive Analytics- Automation is complemented with analytics & Artificial Intelligence/Machine Learning capabilities, allowing for effective demand forecasting & predictive analytics in inventory management. The analysis of past sales patterns & other relevant variables helps in making accurate forecasts. It further assists in optimized inventory management & capacity alignment. The predictive analytics functionality of automation also helps in trend & anomaly identification.
4. Automated Order Processing and Replenishment- Automation makes the entire order processing and replenishment process quite simple. When connected to sales and ERP, an automatic system for controlling inventory can automatically process all orders, check their availability, and trigger replenishment orders based on certain criteria and levels. This also cuts down manual work, reduces the delay in fulfilling orders, and ensures that orders are replenished on time. There are no chances of errors, and automatic order processing ensures seamless coordination between sales and inventory functions.
5. Supply chain and logistics optimization- Automation is highly significant in streamlining and optimizing the supply chain and logistics in inventory management. The automation system is able to process and optimize all the required elements of inventory, supplier, and logistics to optimize sourcing, suppliers, and routes. Through the optimization of inventory replenishment, production distribution, and the implementation of efficient and optimized manufacturing and logistics. It is possible to obtain cost-effective management of the supply chain. Automation allows improved communication and synchronization of all parties involved in the manufacturing and logistics of products, such as suppliers, manufacturers, and distributors, to obtain efficient management of the supply chain. By recognizing the importance of automation in such critical applications, it is possible to ensure improved accuracy, efficiency, and productivity in inventory management.
How to Implement Sustainable Inventory Management With AI?
The ever-growing need for environmental sustainability has become a prominent theme in this modern industrial era. When the entire world is debating and trying to understand the effects of climate change, companies, particularly those in manufacturing, are under growing pressure and scrutiny regarding their concern for environmental sustainability. Being a crucial part of manufacturing automation, inventory management is at a crossroads regarding this environmentally responsible wave. AI and AI-powered solutions can thus provide a roadmap to mix and match inventory management along with this concern.
One of the biggest challenges that businesses must overcome is maintaining inventory while ensuring that waste is kept to a minimum. Too much waste can lead to inventory not being sold, which ultimately ends up in landfills, or it can lead to lost opportunities because products must be expedited. These can all be mitigated through predictive analysis powered by artificial intelligence. The ability to forecast with greater accuracy can better match production with demand.
However, the applications of AI do not end here. The matter of packaging and logistics is also benefited by the presence of AI, as the algorithms are capable of optimizing the packaging so that the material consumed is minimum and the safety of the product is maintained. Coming to logistics, AI is capable of optimizing the routes that will reduce fuel and emissions significantly.
Conclusion
With the increased interaction of manufacturing processes, the management of the inventory system will also keep pace with the latest technological developments. AI/ML technologies will soon become the need of the day for managing complex supply chains, inventory optimization, and market adaptability. In a savings-conscious market, the adoption of these technologies will become more necessary for enhancing savings and achieving excellence. With the changes occurring at a very rapid pace in the global supply chain, the future of manufacturing will require more agility and adaptability concerning inventory management, based on real-time information and AI-based automation.
