How HML Inventory Analysis Benefits to Industry Under AI Environment? Make Inventories Count - What, How, Where and Why!

Abstract

HML analysis is a tool used in inventory management to help classify inventory items and inventory control.

HML analysis is an inventory method that categorizes inventory based on a product’s unit price. This method classifies inventory into the following categories:

• (H) High Cost: Includes high unit value/cost products. Normally they are 10-15% of the total items and 60-70% of value.
• (M) Medium Cost: Includes average or medium unit value items. 20-25% of products fall into this category and 20-25% of value.
• (L) Low Cost: Includes items with low unit value. 60-70% of the products are usually low-cost and 10-15% of value.

Under this system, items are classified according to how fast they move or turnover.

AI enhances traditional inventory management systems by providing advanced analytics, automation, and real-time visibility. It enables businesses to overcome challenges, improve efficiency, and stay competitive in the dynamic marketplace.

Keywords:

HML Inventory, Inventory Management, Artificial Intelligence, Supply Chain Management, Inventory Forecasting, EoQ

Learning Outcomes

After undergoing this article you will be able to understand the following:

1. What's HML Inventory Analysis?

2. Why HML Inventory is used in industry?

3. What's the factors of HML Inventory?

4. How HML Inventory Benefits to Industry?

5. What's the process of implementing HML Inventory Systems?

6.  How Artificial Intelligence work on HML Inventory Management Systems?

7. Conclusions

8. FAQs

References 

1. What's HML Inventory Analysis?

HML analysis, an inventory classification technique, categorizes inventory items into three groups: high (H), medium (M), and low (L) based on their unit price.

To classify inventory items into H, M, and L categories, businesses typically establish three price ranges. Items with the highest unit prices fall into the H category, representing the most expensive items in the inventory. Items with medium unit prices are classified as M, while items with the lowest unit prices fall into the L category.

Rationale Behind Unit Price-Based Classification:

The rationale behind classifying inventory items based on unit price stems from the principle of Pareto optimality, also known as the 80/20 rule. This principle suggests that a relatively small percentage of items (typically 20%) account for a significant portion of the total inventory value (typically 80%). By focusing control efforts on the high-value items, businesses can minimize the risk of stockouts for critical components while reducing the administrative burden associated with managing low-value items.

Summarily, This method classifies inventory into the following categories:

• (H) High Cost: Includes high unit value/cost products. Normally they are 10-15% of the total items and 60-70% of value.
• (M) Medium Cost: Includes average or medium unit value items. 20-25% of products fall into this category and 20-25% of value.
• (L) Low Cost: Includes items with low unit value. 60-70% of the products are usually low-cost and 10-15% of value.

2. Why HML Inventory is used in industry?

This method for inventory writes or lists down products or items under the following classifications:

• High Cost — items with high unit value critical to an organization’s operations. For instance, the unavailability of these items can result in significant disruptions. Thus, businesses should have a constant supply of these items.
• Medium Cost — items with medium unit value. Unavailability will cause disruptions but aren’t immediately life-threatening.
• Low Cost — items with low unit value. They’re non-critical, so a lack of supply is inconvenient at most.

The HML analysis method is a valuable approach for categorizing inventory according to a product’s unit price. It is often used in materials management and inventory control for healthcare and medical supplies. 

As a result, it categorizes items or materials into High, Medium, and Low priority based on their impact on a business’ operations. This approach minimizes the effort and cost associated with low-priority items.

HML Inventory Management Systems analyzes inventory and  helps in your business by: 

• Establishing a proper warehouse layout;
• Reducing lead time in acquiring raw materials and sellable items;
• Implementing proper authorization;
• Accurate item classification for better cost management;
• Adequate management of dormant inventory items;
• Improving utilization of capital;
• Improving cash flow and profit margins, and
• Future identification of possible opportunities or losses.

So HML Inventory Management Systems is one of the best systemfor carrying out inventory management.

3. What's the factors of HML Inventory?

The following 6 Factors Affect Inventory Management, which are to be taken care of irrespective of the inventory system: 

• Financial Factors. Getting financials right is important, as every step of the process involves a great deal of financial risk. 
• Consumer Demand. 
• Suppliers.
• Product Type. 
• Managing tools and technology. 
• Lead Time.
• Improved Inventory Control 
• Forecasting/Projection.
• Barcoding & Scanning.
• Improved, Actionable Inventory Analysis.
• Configurability.
• Integration and Interfaces.
• On-Premise versus Software as a Service and Cloud system.

4. What's the process of implementing HML Inventory Systems?

This Inventory Analysis Method is a valuable approach for materials management, especially in healthcare settings. Here’s how to conduct an HML analysis:

1. Identify all the items or materials you want to analyze.

2. Gather data on each item, including its usage, impact on operations, and the consequences of unavailability.

3. Define the criteria for categorizing items into the HML categories.

4. Assign items to the appropriate categories.

5. Create a document to record item categories.

6. Prioritize your management and procurement efforts accordingly.

7. Regularly review and update the categorization as needed.

8. Incorporate the findings into your inventory management processes.

5. How HML Inventory Benefits to Industry?

Like other inventory management system HML Inventory Management Systems also has some Advantages .

The following are the advantages 

• Eliminates wastages in use of material.
• It reduces the risk of loss form fraud and theft.
• It helps in keeping perpetual inventory and other records to facilitate the preparation of accurate material reports management.
• To reduce the capital tied up in inventories.
• Continuous Improvement and Optimization of Inventory Management:
• Improved Inventory Control and Reduced Risk of Stockouts or Overstocks
• Enhanced Efficiency in Inventory Management Processes
• Better Decision-Making Regarding Inventory Levels and Purchasing Strategies
• Enhanced Inventory Visibility and Control
• Improved Inventory Turnover and Reduced Carrying Costs
• Improved Supply Chain Management and Collaboration

Hence, it can be summarised that HML analysis can provide many benefits for your inventory management, such as improving inventory performance and customer satisfaction, as well as increasing profitability. 

6. What's the Limitations of HML Inventory Management Systems?

HML analysis is not a perfect or comprehensive method of inventory classification. 

There are some limitations to be aware of, such as 

---ignoring other factors that may affect the inventory management decisions, like 

     # lead time and 

     # customer preferences.

HML Inventory relies on historical data.

HML Inventory Management may not reflect the current or future market conditions and demand patterns of each item. 

Finally, it is subjective and arbitrary in determining the criteria. 

 It is thresholds for assigning the items to the HML categories.

6.  How Artificial Intelligence work on HML Inventory Management Systems?

AI can be applied in various aspects of inventory management, including streamlining the entire process, using AI-based robotics in warehouses for increased productivity, optimizing stocking management and delivery, automating material procurement, and providing personalized customer experiences.

AI in demand forecasting in inventory management

AI uses real-time data to instantly forecast inventory requirements, reducing reliance on traditional methods that are time-consuming and prone to human error. AI-powered demand forecasting improves accuracy and reduces

AI manage safety stock levels

Yes, AI in safety stock management helps businesses dynamically adjust their stock levels to meet incoming demand across all fulfillment channels. It balances fulfillment costs against service demands to enhance customer experience, improve ROI, and encourage repeat purchases.

AI uses real-time data to instantly forecast inventory requirements, reducing reliance on traditional methods that are time-consuming and prone to human error. 

AI-powered demand forecasting improves accuracy and reduces supply chain errors, leading to optimized inventory replenishment plans.

7. Conclusions

HML analysis offers a multitude of benefits for businesses seeking to optimize their inventory management practices. By categorizing inventory items based on unit price, HML analysis provides a structured approach to inventory control, enabling businesses to make informed decisions regarding stock levels, purchasing strategies, and resource allocation.

8. FAQs 

Q. What's the role of AI in inventory management?

Ans : In today’s highly competitive business world, companies constantly look for ways to gain an edge and improve their operations. Artificial intelligence has emerged as a powerful ally in this quest, offering innovative solutions to longstanding inventory management challenges.

Artificial intelligence is not a single, monolithic technology but a spectrum of capabilities that enable machines to mimic human intelligence, including learning from data, making predictions, and automating tasks.

AI’s role in inventory management is multi-faceted, ranging from real-time data analysis to automated decision-making. This technology can process vast amounts of data quickly, accurately, and without fatigue, making it an invaluable tool for optimizing inventory.

AI’s application in inventory management extends to a wide range of industries, from retail and e-commerce to manufacturing and logistics:

Retail — AI-powered retail inventory systems can predict consumer demand, helping businesses maintain optimal stock levels and avoid overstock or stockouts.

Manufacturing — AI can enhance production planning by optimizing the supply of raw materials and components.

Logistics — AI can improve route optimization, leading to cost savings and timely deliveries.

Employing AI for inventory management in the abovementioned industries comes with many advantages, including:

Accuracy — AI can process vast datasets and make predictions with a high degree of accuracy, reducing forecasting errors and ensuring that the right products are in stock.

Real-time data analysis — AI systems can continuously monitor inventory levels, sales trends, and other relevant data in real time, allowing for proactive decision-making.

Automation — Routine inventory tasks, such as order placement and reordering, can be automated, allowing employees to focus on more strategic activities.

Cost reduction — Optimized inventory levels and streamlined operations can lead to significant cost savings for businesses.

Enhanced customer satisfaction — AI helps businesses meet customer demands by ensuring products are available when needed, leading to improved customer satisfaction.

As you can see, AI is not just a buzzword. It’s a transformative technology reshaping how businesses manage their inventory, ultimately driving efficiency and profitability.

References

Materials and Logistics Management by L. C. Jhamb

Handbook Of Materials Management by Gopalkrishnan

Introduction To Materials Management reference book by Tony Arnold

Materials & Logistics Management reference book By Dr. Kasande

Materials Management: Procedures, Text & Cases by A. K. Datta

Purchasing & Materials Management by Gopalakrishnan

Materials and Logistics Management reference book by Saroj Kumar