At least some degree of inventory record inaccuracy is inevitable in grocery retail. Yet the true cost of inventory inaccuracy often goes under the radar for many retailers, and the impact can have detrimental effects estate-wide.
On average, 60% of retail SKUs are estimated to be inaccurate1, resulting in overstocks and out-of-stocks that cost retailers a staggering $1.77 trillion globally2. This does not include the costs from factors such as theft, which is often a large cause of inventory record inaccuracies and significant losses.
Research shows that inventory accuracy reconciliation can deliver a sales increase of up to 8%3, demonstrating the benefits of optimized inventory management.
If your inventory management system solely relies on manual stock counts and updates, then human error is unavoidable. The most common culprits tend to happen in hand-offs between stores, stock replenishment processes, or even checkout errors and mis-scans.
Entry mishaps will inevitably cause discrepancies between recorded inventory and the volume of products actually available. It has been found that incorrect manual data entries can cost a business 30% of their revenues4. In 2023 out-of-stocks were estimated to have cost retailers a staggering $1.2 trillion2.
Unless you have an optimized markdown system in place, near-expiring products are highly likely to be thrown away as waste. According to the United Nations, one third of food produced globally is lost or wasted5.
To make matters worse, wasted products often don’t get recorded correctly. This not only impacts profit margins, but can also lead to significant inventory losses that fall through the cracks. In 2023, overstocks were estimated to have cost retailers $562 billion2 in losses. The true cost of wasted stock is not simply the lost sales value, but also the excess carrying costs, which research suggests has skyrocketed up to 30%6,7.
Delivery delays or products received in error can cause significant inventory errors. That is, when there are unrecorded changes to inventory records. Such supply chain disruptions are estimated to cost up to 10% of annual revenue8.
Many perpetual inventory systems automatically update records from the point of sale. However, this fails to take into account any disruptions that occur before the order reaches the warehouse, which is often recorded manually.
Products incorrectly stored, or placed on the wrong shelf on the shop floor, can lead to mismatched stock counts and gaps in aisles.
In addition to costing retailers $1.2 trillion in a single year overall2, out-of-stocks are estimated to result in a lost sale for the retailer one third of the time9. The result? Unhappy shoppers assuming the product they’re looking for is unavailable, when it could simply be in the wrong place - a significant missed sales opportunity.
Unaccounted loss due to theft and damage can also distort inventory accuracy, affecting stock counts and generating significant losses. Retail theft costs retailers $407 billion per year, with self-checkouts contributing more than $10 billion in lost profits10.
In 2021, retailers saw an average 26.5% increase in organized retail crime incidents when compared to the previous year11. Rates of such organized crime are now predicted to be even higher, likely contributing to even greater store losses if robust security measures aren’t in place.
The moral of the story? Inventory inaccuracies can happen anywhere, at any time. To combat the drivers of inaccuracies, a range of effective reconciliation processes are required. But these too can be costly unless a more comprehensive approach, such as using AI and machine learning, is leveraged. This increasingly advanced approach reduces incidences of manual errors by incorporating automated correction solutions.
Solutions that analyze inventory data can identify inventory discrepancies almost instantly. By continuously monitoring inventory records, these systems spot variances between recorded and actual stock levels using a range of data sources. Once a discrepancy is detected, the system can automatically adjust the records to reflect correct inventory levels, without any human intervention.
This not only helps reduce inventory inaccuracies and its downstream impact, but also boosts labor productivity. Store associates will no longer have to rely on manual audits, freeing up to 10% more of their time to serve customers and focus on higher-priority tasks.
Automatic corrections also optimize on-shelf availability, helping stores recover lost sales. Replenishment and forecasting are improved, with reduced waste as a result.
Cognitive technology that uses your existing data and is powered by AI and machine learning models analyzes key sales patterns, pinpoints specific trends and provides deep insights. These technologies process vast amounts of data to identify patterns and trends that might not be immediately apparent to human analysts.
The real magic lies in solutions that combine human expertise with AI and machine learning, to understand key trends and forecast future sales with greater accuracy. This aids in more precise inventory planning. Combining the nuanced understanding of human experts with the analytical power of AI and machine-learning results in a comprehensive approach to inventory management, smarter forecasting and replenishment strategies, with reduced waste overall.
Retail Insight’s InventoryInsight integrates human expertise with advanced AI and machine learning to enhance understanding of consumer behavior. This integration facilitates more precise forecasting and inventory management, to actively monitor inventory levels, identify discrepancies, and implement corrections autonomously. This ensures real-time inventory accuracy.
The system also provides comprehensive visibility across your business, allowing for informed decisions on reordering and supply chain management. It also enables strategic restocking based on product margins, which has been observed to improve restocking efficiency by 27%. This approach not only optimizes inventory but also supports better decision-making processes.
Find out more about how our InventoryInsight solution can help reduce inventory record inaccuracies and improve labor productivity in the process.
References
1 https://www.rila.org/focus-areas/asset-protection/total-retail-loss-report-2016
3 https://www.ecrloss.com/research/grow-sales-by-improving-inventory-records
4 https://nanonets.com/blog/manual-data-entry/
5 https://smith.queensu.ca/insight/content/To-Fight-Food-Waste,-Grocers-Turn-to-Analytics.php
6 https://algonomy.com/resources/guides/retail-demand-forecasting-in-grocery/
7 https://retailowner.com/Inventory/Costs-of-Excess-Inventory
9 https://www.linkedin.com/pulse/what-everybody-gets-wrong-store-inventory-accuracy-jeff-harrop/
10 https://www.thestreet.com/retail/retail-theft-expert-has-an-answer-for-walmart-target-and-kroger
11https://nrf.com/research/national-retail-security-survey-2021