Key Takeaways
Balance is key to keeping cash flow and customer service healthy.
Overstock ties up capital.
Stockouts lose sales.
Tech aligns inventory with demand.
KPIs like turnover and GMROI guide.
Reviews keep balance dynamic.
Inventory balancing is a critical aspect of successful retail operations. In Part 1 of this series, we explored how unbalanced inventory can significantly impact a company's EBITDA, leading to increased holding costs, lost sales, and reduced profitability.
Now, in Part 2, we delve into the solutions that can help businesses achieve optimal inventory levels, improve efficiency, and boost their bottom line.
From advanced forecasting techniques to AI-powered inventory management systems, discover how to transform your inventory challenges into a source of competitive advantage.
Understanding the Problem of Unbalanced Inventory
Unbalanced inventory is a common issue faced by retailers, where excess inventory accumulates in some locations while other locations experience stockouts.
This imbalance can lead to lost sales, decreased customer satisfaction, and increased holding costs. The root causes of unbalanced inventory often include inaccurate demand forecasting, changes in consumer preferences, and shifts in the competitive landscape.
To address this issue, retailers need to implement effective inventory management processes that take into account historical sales data, supply chain efficiency, and customer demand. By doing so, they can ensure that inventory levels are optimized across all locations, reducing the risk of both overstocking and stockouts.
TOP-5 specifics of Fresh category Inventory Management in grocery retail
Inventory Balancing Strategies: a simple solution to a complex issue
The solution to the “sales vs economy” dilemma often arises in a company with a new and ambitious purchasing and supply chain director. This person has most likely already worked in systems businesses and has learned that inventory optimization and balancing lost sales and surpluses is easily achievable only when supply chains are fundamentally transformed and automated using advanced solutions.
As we move into 2025, implementing inventory balancing strategies is no longer optional—it’s a necessity for retailers aiming to optimize allocation and maintain availability while navigating dynamic market conditions.
Let's imagine a hypothetical grocery supermarket chain, Cranberry Market, with annual sales of $500 million and an inventory value of $179.8 million (20% of the company's assets). EBITDA for last year was $30 million or 6% of revenues.
Currently, the company has 20 people working on order generation, and they spend 50% of their time on it. They are assisted by an ERP system that builds forecasts based on historical data, while procurement specialists make manual adjustments based on upcoming promotions and seasonal trends.
Furthermore, let’s imagine that Linda, who previously worked for a supermarket chain as a purchasing director, takes over the Cranberry Market's supply chain department.
Linda’s trial period strategy will include implementing an automated inventory management system because the previous company used such software, and she has seen all its benefits. In addition, Linda is confident that the new system will allow her to show excellent results during the six-month trial period already. Still, it is now essential to convince the management of Cranberry Market of these benefits.
Leveraging Technology and Automation for Inventory Balancing
Linda believes technology-driven solutions are key to transforming Cranberry Market's supply chain. Modern inventory management systems provide real-time tracking, automate tasks like forecasting and replenishment, and offer data-driven insights.
AI and machine learning algorithms further enhance this process by accurately predicting demand, optimizing inventory levels, and minimizing shortages. Linda is confident that leveraging technology will showcase the system's value, streamline operations, and drive sustainable growth.
What opportunities will come with the dynamic inventory optimization solution?
Cloud solution
The inventory balancing process will be a critical strategy for optimizing stock levels across multiple locations, improving ROI, and integrating into regular supply planning for effective data utilization. With this in mind, Linda will undoubtedly choose a SaaS solution, as a cloud-based monthly subscription solution does not require costly servers and can be used as long as they are satisfied with the results. This, in turn, guarantees the vendor’s continued involvement in maximizing the short- and long-term benefits for the customer.
AI-based demand forecasting
The new system will provide automated short-, medium- and long-term sales forecasting for up to a year (which is especially important for products with long delivery times, such as those from China or India).
Sophisticated AI algorithms will consider most factors affecting customer demand at the SKU-store/channel-day level: sales history, seasonality, days of the week, trends, regional characteristics, upcoming holidays, promotions, etc. In addition, thanks to machine learning, such a system will continue to improve forecasts’ accuracy over time.
Fully automated replenishment system
An automated replenishment system will allow the company to generate and send orders to external suppliers or the warehouse centrally without the intervention of demand planners or store employees.
The dynamic inventory optimization solution will consider current balances, delivery lead time, in-transit quantities, product expiration dates, supplier terms, and schedules so that each item arrives at the right destination and in the correct quantity without creating overstock and ensuring optimal availability.
In addition, it will avoid human error and free up the staff involved in order generation for more critical strategic tasks by letting the system deal with routine tasks.
Efficient management of fresh stock
Unique algorithms will consider fluctuations in demand by day of the week, supplier conditions, packaging, and delivery times for perishable products. This will ensure maximum availability of fresh products daily, with minimal write-offs and markdowns at the end of the day/expiration date. In a large chain environment, this is an essential step towards lean and reducing waste and, therefore, improving operational efficiency.
Multi-echelon inventory allocation
The algorithms of the new system will also take into account all the peculiarities of goods movement and generate highly accurate forecasts for inventory management at both regional and central warehouses.
Demand for goods will be estimated for each store replenished through the DC. This will include the number of deliveries of goods by an external supplier to each store based on the DC delivery schedule, seasonality, promotions, and other factors, which will allow optimal and timely distribution of goods, regardless of the number of logistics links.
Automatic support for assortment rotation
Linda knows from previous experience that assortment rotation, even at 30% per year, is more effective when done automatically. When new SKUs are introduced, the system automatically predicts the first order depending on its specifics. The new SKU is a replacement or an analog of an existing one, or it is entirely new for the network.
This is the only way costly errors in assortment expansion and replacement can be avoided. In case of product withdrawal, the program will consider the supply chain structure to track inventory at all levels for efficient product withdrawal
Informative dashboard and deep analytics module
It is critical to keep a finger on the pulse of all key metrics to manage inventory on an ongoing basis effectively. Linda knows that, and she also knows that this requires having an informative dashboard at your fingertips, where you can see both operational and strategic KPIs in a few clicks.
It makes it possible to notice and eliminate deviations on time as well as to build an effective strategy for the future. The new system should also contain a powerful analytics module and a comprehensive reporting system for maximum visibility and problem analysis at all levels.
The Role of Historical Sales Data in Inventory Balancing Strategies
Historical sales data plays a crucial role in inventory balancing, as it provides valuable insights into customer demand patterns and trends. By analyzing historical sales data, retailers can identify which products are in high demand, which products are slow-moving, and which products are seasonal. This information can be used to optimize assortment matrices, adjust inventory levels, and improve forecasting accuracy.
However, relying solely on historical sales data can be limiting, as it does not account for changes in consumer preferences, market trends, and external factors that can impact demand. Therefore, it is essential to complement historical data analysis with other predictive tools and market insights to achieve a comprehensive understanding of customer demand.
Cross-Functional Collaboration for Inventory Balancing
Cross-functional collaboration is critical for effective inventory balancing, as it involves multiple departments and stakeholders working together to optimize stock levels and improve supply chain efficiency.
Retailers should foster collaboration between merchandising, supply chain, and sales teams to ensure that inventory decisions are aligned with business objectives and customer demand. This can involve sharing insights and data, aligning strategies, and working together to resolve inventory imbalances and other issues.
By working together, retailers can improve inventory management processes, reduce costs, and enhance customer satisfaction. Effective cross-functional collaboration ensures that all departments are on the same page and working towards common goals, ultimately leading to a more efficient and responsive supply chain.
What effects can we expect from the implementation of a dynamic inventory optimization solution?
Referring to the example of a hypothetical supermarket chain, Cranberry Market, we think it is fair to consider two variants of events and, accordingly, calculations.
Option 1
Linda joined the company at a time when the “sales first” approach had long been the basis of the business strategy. As a result, product availability averaged 96% with a turnover rate of 35 days (compared to 28 days for a direct competitor).
While in Linda’s previous experience, the solution implementation resulted in a 3% increase in sales, she realized that even with already high availability, a 1% increase in sales could be expected through better forecasting and distribution.
She set a turnover target of 30 days rather than 28 days because the system is “inertial,” and it will take time to eliminate excess inventory without incurring additional costs. Calculations showed this would yield an annualized EBITDA gain of $1,914 million (7.7%).
In fact, the expected annual increase in net income in this case would be about $1.55 million or 17%. Effective inventory management not only boosts sales but also plays a crucial role in enhancing customer satisfaction by ensuring timely product availability and personalized services.
Option 2
If Linda arrived when the company was in the “economy” mode and product availability was 92% with a turnover rate of 30 days, she expects to increase availability and thereby increase sales by 2% while maintaining and even slightly improving turnover by 5% to 28.5 days through the implementation of the new inventory management system.
Expected economic results:
EBITDA growth: $2.41 million (+9.6%)
Net profit growth: $1.75 million (+16%)
Bonus effect
Linda identified significant potential labor cost savings by automating Cranberry Market's replenishment process. Currently, 20 employees spend 50% of their time on orders, costing $900,000 annually.
Automation would reduce this to 5 full-time employees, costing $450,000. This freed-up time could be redirected to strategic tasks, improving overall efficiency.
Preliminary ROI calculations showed a compelling 500-600% return, strengthening Linda's case for investment in the new inventory management system.
In conclusion
As we can see, the financial and operational efficiency of a retail business can suffer from many and often non-obvious factors, such as unbalanced inventory.
The world of retail is too dynamic and the amount of data that needs to be analyzed is so large that the experience, intuition of employees, and relatively simple models in Excel or ERP are not enough to make optimal decisions. It is very difficult to find a balance without good methodology and sophisticated mathematical models in the age-old battle between the strategies of "salespeople" and "economists,".
Fortunately, today the role of the great equalizer in this matter is firmly occupied by AI or rather modern technological solutions based on artificial intelligence and machine learning. Such systems impartially make calculations and tactical decisions for people on the basis of complex algorithms, and also help managers to make important strategic decisions. These systems can simply “see” the key clues and patterns in the endless array of data that are ordinarily inaccessible to human eyes.
In addition, simple and approximate calculations show that even minimal effects of such a system give a significant increase in profit and allow you to recoup your investments in the first year of implementation. And in subsequent years, it provides opportunities for stable growth and expansion of the business, freeing up enormous amounts of money and fixing inefficient business processes.
What is inventory balancing?
Inventory balancing is the process of aligning inventory levels with demand to ensure optimal stock availability without excess or shortages. This involves accurate forecasting, efficient procurement, and streamlined operations.
What is inventory in balance?
Inventory is in balance when a business has the right amount of stock to meet customer demand without incurring excessive holding costs or experiencing stockouts. This optimal state maximizes efficiency and profitability.
How do you solve inventory balance?
Inventory balance is achieved through accurate demand forecasting, efficient inventory management systems, optimized ordering processes, and regular monitoring of stock levels.
How do you reconcile inventory balance?
Inventory balance reconciliation involves comparing physical inventory counts with recorded inventory data to identify discrepancies and make necessary adjustments. This ensures accurate inventory records and informs effective management strategies.
Have a question?
Have inquiries about retail automation or optimization? Talk to our expert for solutions!
Helen Kom
Inventory Optimization Product Director
