7 ways AI can reinvent inventory management


By Dusty Alexander, President & CEO, Global Shop Solutions.

ERP for manufacturing was first introduced back in the 1960’s at a time when basic software systems were being developed to help manufacturers improve their ability to manage, track, and control inventory.

Since that time, inventory management has remained a reactive process based largely on historical data. The data tracked and stored in ERP systems has enabled manufacturers to forecast customer demands and material needs to some degree. However, their accuracy has been limited without the ability to peer into the future – until now.

It is my belief that ERP systems will likely experience more change and innovation over the next 10 years than all of the last 50 put together. Part of this can be attributed to the software reaching a new level of maturity, but the revolutionary changes, which have already begun, will result from the integration of ERP with artificial intelligence (AI) technology.

AI-integrated ERP is already having a significant impact on how manufacturers manage one of the most important aspects of production – inventory. Considering that AI technology is still in its formative stages, the promise of an incredible future lies just beyond the horizon.

A New Era of Efficiency and Precision

AI technology is designed to simulate human intelligence in computers and machines. When integrated with ERP software and applied to inventory management, it can analyze huge amounts of data, identify trends and patterns, and make accurate predictions to improve decision-making and raise the quality of inventory management. 

The ability to analyze and learn from internal and external data sources sets AI apart from well-established inventory management methods, many of which rely on manual input and analysis. AI can decipher complex patterns, predict future trends, and make informed decisions in real time, enabling manufacturing enterprises to anticipate changes, adapt quickly, and stay one step ahead of the competition.

Just as ERP reinvented inventory processes, integrating it with AI is redefining the way manufacturers manage their inventory. It is also turning the challenges of inventory management into opportunities for growth and innovation in many different ways.

AI can:

  • Analyze data with a level of accuracy far beyond human abilities. 
  • Organize and structure complex data, making it easier to analyze, visualize, and parse the data for decision-making.
  • Automate numerous inventory management processes, such as data collection, analysis, and reordering. 
  • Reduce the inventory costs of storage, waste and overstocking.
  • Ensure the right products are always in stock to make and deliver products on time.
  • Increase employee satisfaction by automating tedious, repetitive tasks, enabling them to engage in more impactful work. 

Power Inventory Management with AI-Driven ERP

The integration of AI with ERP will change the way inventory functions for every manufacturer. Here are seven ways AI is already transforming inventory management:

1. Optimized Stock Levels

AI-integrated ERP helps manufacturers optimize inventory management by predicting demand, identifying slow-moving products, and automating order fulfillment. According to a McKinsey study, companies that utilize AI to optimize inventory can reduce inventory levels by up to 50%.

In addition to examining historical data and forecasting upcoming needs, inventory software that incorporates AI can boost real-time tracking, automate the procurement of materials, and better manage costs. AI can also improve the precision of inventory data, streamline the ordering process by automating purchases based on predictive analytics, and fine-tune stock levels through the analysis of radio frequency identification (RFID) tag data.

2. Enhanced Quality Control

AI will notably augment the quality control of manufacturing inventory, guaranteeing precision in inventory management by scrutinizing real-time tracking data and identifying inconsistencies. It can dramatically reduce mistakes in the purchasing process by automating orders based on predictive analytics. It can also provide insights into product circulation and longevity for superior inventory management by using data from RFID tags.

By incorporating AI into operations, manufacturers can process large datasets to reduce forecasting errors and ensure the right products are in stock. Furthermore, AI can identify product quality issues before they are moved to inventory by analyzing visual data from the manufacturing process, discerning patterns, and facilitating real-time decisions related to product quality.

3. More Efficient Purchasing

Manufacturers with quality ERP systems have long been able to automate their purchasing processes. AI can make automated purchasing even more efficient and reduce costs by meticulously analyzing inventory information, incorporating RFID technology for product tracking, and refining shipping procedures. AI empowers manufacturers to handle inventory across multiple locations, scrutinize usage reports, and manage product ranges more efficiently. It also facilitates automated purchasing based on material needs and past purchases.

AI provides a big-picture view of customer and market demand patterns by combining historical data, real-time information, and market trends. Having AI-driven internal and external data sources enables predictive analytics that help businesses anticipate fluctuations in demand and adjust production schedules, inventory levels, and supply chain operations. 

4. Automated Restock

A common cause of production delays is not having parts or materials in inventory due to failure to restock them. ERP has been able to remedy that problem with Auto Purchasing software that can be programmed to know when to reorder parts and in what quantity. AI takes automated purchasing one step further by examining real-time tracking data to ensure inventory precision and reduce variances. It can also automate the procurement process by considering material necessities and past purchases, thus lessening dependence on human accuracy.

In addition to analyzing historical data, AI can incorporate seasonality, customer buying patterns, weather patterns, and other external factors to forecast expected demand for parts and materials with greater accuracy. This enables retailers to avoid stockouts and reorder materials based on reliable data from many different sources.

5. Improved Supplier Selection

AI will notably improve supplier selection by processing extensive data about various suppliers. It can evaluate several aspects, such as dependability, pricing, quality, and delivery schedules, for each supplier. This thorough evaluation can assist businesses in pinpointing the most appropriate suppliers, resulting in better supply chain productivity. Moreover, AI can aid in uncovering new suppliers or even new markets by analyzing external data. 

AI can also monitor supplier diversity metrics and scrutinize procurement trends. By recognizing patterns and predicting trends that might be challenging for humans to detect, AI can streamline procurement processes. Lastly, AI can track the source of raw materials and oversee supplier performance, further improving supply chain operations.

6. Demand Forecasting

For decades, managing inventory has been a reactive process. This was due primarily to relying on internal historical data and a lack of data from external resources. AI enables manufacturers to forecast demand more accurately by gathering and analyzing external data such as industry and product trends, global economics, and supply chain condition. It also improves forecasting accuracy by providing higher visibility of KPIs and automatically classifying SKUs to identify what’s needed to meet demand.

AI can also predict consumer demand for every product in a business by analyzing seasonality, pricing, promotions, and product lifecycles. It can combine supply, sales, finance, and marketing projections into a holistic view of demand across the entire enterprise.

7. Supply Chain Management

Supply chains can be disrupted by many different factors ranging from political or economic crises to supplier and transportation issues, regulatory changes, natural disasters and more. AI-powered ERP software helps optimize supply chain management by predicting supplier lead times, identifying bottlenecks, and optimizing logistics routes to reduce production lead times, lower costs, and increase customer satisfaction.

AI can also predict which products will be in demand and in what quantities, reducing strains on specific links of a manufacturer’s supply chain. AI even provides upgrades to important supply chain elements, such as improving warehouse supply and demand management, extending the longevity of transportation vehicles, and making the loading processes more efficient.

The Future of Manufacturing Belongs to ERP

The introduction of AI into ERP to support critical functions like inventory management is set to change manufacturing businesses forever. From optimizing stock levels and enhancing quality control to automating purchasing, improving demand forecasting, and refining supplier selection, ERP’s role in enabling AI’s capabilities cannot be overstated. With its ability to process vast amounts of data swiftly and accurately, AI offers unprecedented opportunities for manufacturers to simplify their operations, reduce costs, and improve efficiency. 

Furthermore, the predictive capabilities of AI will enable manufacturers to anticipate future trends and adapt accordingly, thereby gaining a competitive edge. As technology continues to evolve, the importance of AI in inventory management will only increase, making it an integral part of modern manufacturing. The transformative power of AI-integrated ERP not only promises enhanced operational efficiency but also paves the way for a more sustainable and profitable future in the manufacturing sector.

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