Inventory Management Challenges: From Chaos to Control with Winning Optimization Strategies

For many businesses, chaotic stockrooms and unpredictable supply chains are a frequent headache. Inability to meet customer demands on time as a result will not bode well for their reputation either. However, turning these turmoils into smooth operations isn’t impossible. 

This is where the concept of inventory management takes precedence to help production and supply chain leaders take back control, improve accuracy, and ensure timely deliveries to customers. In this article, we’ll explore some of the common inventory management challenges and winning strategies to turn those inventory headaches into streamlined success. 

Common Inventory Management Challenges

While effective inventory management is critical for business success, it’s not without its hurdles. Here are some of the common inventory management challenges you may face often:

Lack of real-time inventory visibility

Companies may struggle to accurately track stock levels across locations and sales channels when there’s limited real-time inventory visibility. This enhances the risk of stockouts or overstocking, leading to missed sales and increased holding costs. By 2025, approximately 77% of retailers plan to leverage real-time inventory visibility, powered by automation, sensors, and analytics, which could potentially make this a problem of the past. Source

Overstocking & understocking

When companies overstock, excess inventory takes up valuable warehouse space and in time, rendering them obsolete. And during stockouts, the business runs out of products, resulting in lost sales and dissatisfied customers. These are the damages overstocking and understocking can do. 

Poor demand forecasting

Determining the right inventory levels and safety stock to maintain can be a challenge when demand forecasts are not accurate. In such cases, stock availability would be limited for customers, leading to dissatisfaction and lost revenue. Rapid changes in customer preferences also disrupt demand forecasting, rendering them obsolete and subject to constant changes. 

Lack of integration with sales channels 

Most businesses today often sell their products through various sales channels such as online stores, social media platforms, and e-commerce platforms. Since each channel may have its own inventory tracking system, inconsistent updates and other discrepancies will exist. For instance, a product sold out on one platform, but the inventory is not updated on another, thereby resulting in cancelled orders and unsatisfied customers. 

Lack of expertise and poor communication

Demand forecasting, stock control, and replenishment rely on two key factors: Expertise and proper communication. Without them, inventory management and supply chain risks are almost inevitable. Unskilled personnel with poor communication can lead to misunderstandings and misaligned priorities, causing further inventory management problems. 

Warehouse inefficiencies

When it comes to streamlining inventory management tasks, storage solutions play a key role in ensuring inventory is kept safe and usable for the end user. However, poorly-designed warehouse layouts, lack of organization, and inadequate storage spaces can slow down packing and shipping processes. The end results are increased labor costs, delays in order fulfillment, and even added risks of inventory damage, theft, or misplacement. 

Inventory defects 

Expanding upon inventory defects, poor storage practices can expose them to damage, theft, or contamination. Furthermore, inaccurate record-keeping and tracking also lead to misplaced or defective products, making it difficult to maintain proper product quality before reaching customers. 

Inefficient tracking and ordering systems 

Inefficient or outdated tracking systems hinder a company’s ability to gain accurate, real-time visibility into inventory levels across storage units. This lack of information results in poor purchase decisions as market demand cannot be predicted.

Returned inventory flow

Inventory management problems extended beyond order processing as sellers often face significant hurdles in handling returned inventory. For instance, when a customer initiates a return and the product is not processed on time, it leads to an inventory loss. Sellable products must be restocked and classified as good inventory, while damaged goods should be given special attention. 

Real-World Example of an Inventory Management Challenge

To truly grasp the implications of inventory mismanagement, let’s dive into a case study highlighting how Tredence helped the world’s largest convenience retailer optimize inventory and enhance personalization:

Case Study

The client is one of the world’s largest convenience retailers, operating over 80,000 stores worldwide with an annual retail sales volume of $160 billion. 

Key challenges faced

• Inaccurate demand forecasts resulted in over 10% of inventory getting spoiled or unsold.
• Stockouts and overstocking led to lost sales in fuel and merchandise, impacting revenue opportunities.
• Their profit margins were impacted due to high inventory carrying costs, compounded by legacy system limitations and high tech debt. 
• Data silos and lack of a centralized platform hindered the aggregation and analysis of customer data, limiting personalization efforts. 
• Ineffective use of first and third party data reduced the efficacy of targeted marketing campaigns and media network ROI.

The solution // How we solved the above inventory management challenges?

The retailer partnered with Tredence to transform its data infrastructure through a unified data platform built on Databricks. The platform consolidated data from multiple systems and provided advanced analytics capabilities at scale. 

 The results

• The client witnessed an improvement in forecast accuracy by over 10%, amounting to significant cost savings of $45 million and 10% reduction in spoilage
• Real-time alerting systems prevented $318 million in lost sales from fuel and merchandise
• Optimized inventory reduced carrying costs by $98 million, boosting sales in the process
• Improved overall financial health after reduced tech debt and streamlined legacy tools
• Personalized marketing efforts drove a 14% increase in marketing ROI, promoting repeat customer visits as well

Strategies to Address Inventory Management Issues

Having explored common inventory management challenges, it’s time to turn our attention to practical AI and data-driven solutions that can help businesses overcome them for greater efficiency and profitability:

Regular stock auditing

For auditing smaller subsets of inventory, daily cycle counting can be done to match recorded inventory levels with physical quantities at hand. But what happens when you have to conduct a full inventory audit for larger quantities of stock?

Here, AI and advanced data analytics process vast amounts of sales, supply chain, and customer data for actionable insights on inventory management. Using technologies like barcode scanners, RFID tags, and IoT sensors, AI automates stock counting and reconciliation processes, updating inventory data and flagging discrepancies if any. 

AI-powered drones are also widely used to autonomously conduct inventory audits in warehouses or retail stores. Using advanced computer vision and machine learning algorithms, the drones capture and analyze real-time data and navigate inventory space efficiently. 

Improving demand forecasts

While traditional demand forecasting methods rely on historical data and basic statistical models, AI-driven demand forecasting is more suited for modern, dynamic markets. It integrates real-time data streams, advanced algorithms, and external factors like market trends, consumer behavior, and economic shifts. 

AI systems employ powerful algorithms like:

• Time series analysis: A demand forecasting method that identifies seasonal patterns, anomalies, and trends in historical data at regular intervals.
• Natural language processing (NLP): NLP algorithms analyze social media and other textual data types to gauge consumer sentiments.
• Deep learning: Deep learning models, such as neural or memory networks, process complex data and make highly accurate predictions. 
• Scenario analysis: Businesses can test what-if scenarios using machine learning models, preparing better for potential market shifts.

Just In Time inventory system

Just-In-Time’s lean inventory management approach is known to ensure raw materials and goods are received exactly when needed for production or sales. The general idea behind this model is to synchronize supply with actual demand to reduce inventory holding costs and reduce wastes. Being a demand-driven approach, its success depends on seamless supply chain coordination, where reliable suppliers deliver materials on time and in precise quantities. 

When high-quality workmanship can improve JIT systems, AI can do it even more. In the case of supply chain management, it analyzes supplier performance, reliability, and market conditions in real-time to anticipate potential risks and suggest contingency plans. AI simulates risk scenarios like supplier disruptions, geopolitical events, and natural disasters also to help companies better prepare for distribution efforts.  

FIFO and LIFO

Under FIFO (First-In, First-Out), companies sell the oldest stock first before selling newer stock. This approach is particularly applied to perishable goods, seasonal clothing or electronic devices, helping businesses calculate the cost of goods sold and assess remaining inventory value. 

On the other hand, LIFO (Last-In, First-Out), most recently purchased items are the first ones sold or used. Companies use the latest stock to calculate the cost of goods sold, while older inventory is reported as remaining inventory. This approach is commonly used for non-perishable items or for those that have low turnover rate as they’re least likely to cause any spoilage or obsolescence problems. 

Now when we look at AI’s integration in inventory management, the traditional distinction between FIFO and LIFO may be blurred. Since AI analyzes real-time data on demand for pricing adjustments, it influences the flow of goods, optimizing the use of FIFO and LIFO. Future strategies may incorporate sustainability metrics, further scrutinizing FIFO/LIFO inventory valuation. However, AI’s ability to quickly identify expired inventory and dispatch new stocks can help businesses manage inventory layers precisely, ensuring smooth operational flow and customer satisfaction across both FIFO and LIFO systems. 

Preventative control

For preventative control measures, AI systems implement reinforcement learning and rule-based optimization to automate inventory replenishment decisions. They dynamically adjust reorder quantities based on predicted demand and real-time consumption rates. 

The following technical workflows also aid in preventative inventory control measures:

• Data ingestion: Collects and imports both structured and unstructured data from POS systems, ERP, supplier feeds, and external APIs.
• Feature engineering: Selects and extracts new features like supplier lead time variability, seasonality indices, and real-time stock levels from raw data to analyze inventory patterns.
• Decision automation: Based on predefined rules and algorithms, AI models can be used here to automate decisions related to inventory levels. Key examples include automated order placements, supplier selection, price negotiations, and optimal stock level determination. 

AI-powered copilots

What if you could entrust core inventory management tasks to AI? This technology can act as your digital assistant, providing actionable recommendations, alerts, and reorder suggestions in real time through deep analysis of vast inventory data. Through insights obtained, your AI copilot could make certain decisions for you. 

The way it works is quite simple:

• Data integration: The AI system collects data from various sources like historical sales, supply chain, warehouse operations, seasonal trends, weather, etc.
• Pattern recognition: It leverages complex machine learning algorithms to identify recurring patterns like stockouts, seasonal demand spikes, and slow-moving items.
• Recommendations: Based on the insights obtained, the copilot generates alerts, also suggesting reorder adjustments and flagging excess inventory.
• Decision support: Receiving these insights via centralized dashboards or mobile apps, businesses can make data-driven decisions without bearing the burden of manual data crunching. 

Multi-location warehousing 

According to ValuTrack, approximately 73% of warehouses plan to implement mobile inventory management solutions. Source This solution offers businesses more control over inventory levels, orders, and asset movements across supply chains. But with advanced AI and ML capabilities, multi-location warehousing gets easier.

Advanced AI algorithms centralize inventory management across multiple warehouses and locations, providing a unified view of inventory levels and movements in real-time. They analyze vast datasets such as seasonal trends, historical sales, and external market factors. And with the insights obtained, businesses can optimize inventory allocation at each warehouse effectively. 

Implementing AI-driven tracking systems

AI-driven tracking systems use supervised and unsupervised ML models to analyze historical inventory data, identify demand patterns, and predict future stock requirements. They factor in variables like market trends and seasonality to optimize reorder points and safety stock levels. Inventory flows and anomalies such as unexpected drops in stock or theft are also continually monitored by AI, sending in automated alerts for rapid response. 

Computer vision algorithms, often deployed via cameras, are also used to monitor shelf-space utilization and automate visual stock counts. This reduces human errors and speeds up cycle counting processes. 

Supplier performance monitoring 

Given that supplier delays and quality issues are also common challenges in inventory management, AI-powered supplier evaluation systems play a significant role here. They aggregate data from ERP and purchase orders and use KPIs like defect rates, compliance, and delivery timeliness to score suppliers. 

Unstructured data such as supplier communications, customer reviews, and various multimedia files are also analyzed by NLP to benchmark suppliers against industry standards and detect early warning signs of supply chain disruptions. Procurements teams use this data to proactively eliminate risks and ensure supplier reliability. 

Tackle Inventory Management Challenges and Unlock Efficiency with Tredence

Inventory chaos doesn’t have to be your business’ story. Smart inventory management isn’t just about keeping shelves stocked, it’s also about building a strong, agile business that can still stand on any market storm. By leveraging advanced AI and ML-based tools, your path to fully controlling inventory is within your reach. But why walk the path alone? 

At Tredence, we offer the tech stack you need to optimize your organization’s inventory levels. Our on-shelf availability solutions help you identify out of stock risks and create automated out of stock alerts. Our supply chain control tower solution allows you to create persona-based dashboards to collect information, identify risks, and respond strategically. It also offers self-service capabilities to help you empower your supply chain executives with deep dive and root cause analysis. 

To know more about how we help you tackle inventory management issues, contact us today and we will assist you!

FAQs

1] How do I evaluate if my current inventory system is causing these challenges?

Check for frequent stockouts or overstocking, manual errors committed, or difficulties in tracking and forecasting inventory. These are key signs your system may need improvement.

2] How do I prioritize which inventory management challenge to fix first?

Focus on the issues that have the greatest impact on your business operations and customer satisfaction. You can use ABC analysis, a method that highlights high-value or fast-moving items that are critical to your revenue and service levels. 

3] How do inventory issues typically impact other departments like sales or finance?

Inventory issues like stockouts cause sales delays, which can lead to lost customers. Lost revenue complicates financial forecasting and disrupts cash flows, creating bottlenecks that affect the entire company’s performance. 

4] What are common inventory management techniques?

There are several techniques like ABC analysis (prioritizing items by value), Economic order quantity (EOQ), Just-In-Time (JIT), Material Requirements Planning (MRP), and multi-location warehousing, and even maintaining safety stocks to avoid shortages.

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Editorial Team
Tredence