As we navigate a post-pandemic world, the retail industry is reinventing itself with artificial intelligence (AI) technology. The main objective? Obtain a more accurate forecast of consumer demand and resuscitate supply chains that have been shaken by capricious consumer purchasing patterns during the recent pandemic and related challenges. “just in time” inventory management. One such challenge is faulty inventory data, where inaccurate sales floor quantities of specific SKUs lead to complications such as inaccurate locations behind the scenes.
The pandemic has forced a sea change in consumer behavior, leading to the pitfalls of previous forecasting models that relied almost entirely on historical sales data alone. During the pandemic, consumer purchasing preferences have consistently and rapidly shifted between items such as cleaning supplies, home gym equipment and office clothing, and then towards services such as travel. Changing shopping habits have led to an overflow of unsold items in many retailers’ inventories, while demand for other items has soared out of control, worsening shortages. Streamlining these concerns is paramount for retail businesses looking to save time and money.
During peaks in 2020 and 2021, companies ordered inventory on a large scale to guard against supply chain disruptions, often leading to a surplus of unwanted goods and changes in consumer spending. According to the Census Bureau, as of August 2022, retailers held approximately $760 billion in inventory, a figure A jump of 30% compared to August 2019double the sales growth rate. Accurate demand forecasting is vital for profitability. Understanding customer behavior allows retailers to spot trends, make informed purchases, and design appropriate pricing and promotion strategies.
AI is coming to the retail scene
AI has become the knight in shining armor, offering the potential to improve inventory management with a variety of innovative strategies. Consider this fascinating use case: AI can examine consumer spending patterns to predict changes in the movement of specific merchandise across the sales floor. This intelligence encourages a more targeted approach to manual auditing, allowing staff to target specific areas that need attention rather than embarking on the herculean task of auditing the entire store.
Of course, not all solutions require a sophisticated camera network. In situations where manual efforts are required, machine learning algorithms can still provide valuable assistance. For example, rather than performing a tedious item-by-item audit of empty shelf slots, a retail employee could simply take a photo of each shelf section using their camera. Leveraging object recognition and deep learning capabilities, these photographs could be compared to a planogram to quickly identify missing items. However, the system is not without caveats. The success of this method relies largely on careful zoning, and difficulties can arise when estimating quantities if elements interfere with each other.
Retailers Implementing AI Inventory Management
Retailers such as Walmart, Walgreens and online fashion retailer ASOS have begun implementing advanced AI technology for retail inventory management, according to the Wall Street Journal. By leveraging parameters such as weather conditions and social media trends, AI-based algorithms can process a vast array of data and facilitate strategic decision-making regarding inventory placement. With this technological support, retailers aim to refine their long-standing practice of using internal historical sales data to forecast consumer demand. This would, in turn, help them stock their shelves with the right items at the right time, solving many years of difficult and costly inventory imbalances.
The complexities for retailers go beyond simply assessing demand. Changing consumer purchasing habits have made it increasingly difficult to determine where to place inventory. In our modern retail landscape, consumers expect fast fulfillment on purchases made online for home delivery or in-store pickup. Therefore, retailers must strategically plan where to store their goods for quick and efficient movement.
Additionally, previous forecasting tools were unable to adequately account for the impact of factors such as viral social media videos and local weather conditions on customer purchasing decisions. However, advances in AI and machine learning technologies have made it possible to incorporate this data into forecasting models.
Walmart, for example, has programmed its inventory management system to take into account weather forecasts and online search trends. Using this data, the retailer can then use AI to anticipate regional demand for specific products and allocate inventory accordingly.
Walgreens also leverages AI technology to forecast demand, using social media data and seasonal illness reports. The information gleaned is then used to position inventory near where consumers are expected to purchase those items.
For example, Rajnish Kapur, head of sourcing and supply chain, said that “last year, the company’s AI-based forecasting model helped predict regional and local trends during cough, cold and flu season so Walgreens can put over-the-counter products on shelves. . The model predicted higher fever rates and lower congestion and cough rates, leading the retailer to stock more pediatric fever reducers in areas where demand was highest.
Meanwhile, British company ASOS has started using AI to forecast demand for items such as T-shirts, jeans and dresses. By combining past sales, returns data, product popularity and trends, AI technology provides a granular and accurate forecast that was not possible before.
Macy’s improves its gross margin despite a drop in sales during the last quarter of 2023, due to tighter inventories. Its inventory was reduced 6% in the third quarter from a year earlier and 17% from 2019. Tony Spring, the next president and CEO, indicated that the retailer’s focus now shifts towards offering a greater variety of products rather than stockpiling. redundant elements.
“Today’s customer doesn’t want an endless shelf. They want the best aisle.
Tony Spring, future chairman and CEO of Macy’s, via the WSJ
In conclusion, as retailers adapt to the operational realities of a post-pandemic world, artificial intelligence is emerging as the secret weapon for predicting buyer demand, reducing inventory imbalances and optimizing supply chains. supply.