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Inventory management entails acquiring, storing, and processing products to meet customer demands quickly and efficiently. However, maintaining an optimal level of inventory is no easy task.
If businesses fail to strike a balance, they risk losing out on sales, customer retention, and customer satisfaction. Poor inventory management practices can lead to a major stock out.
These stockouts can be prevented if a company considers employing predictive analytics in retail. But before we go there, let’s dissect stockouts as a concept and how it can harm businesses.
The following scenario may be a little too relatable.
You set aside a $20-50 bill now and then, so you’ll have enough money when you shop for your wish list items. After a few months of waiting, you visit your favorite retail store only to find that the products you really wanted are out of stock. Disappointed, you leave the store and hope to find what you are looking for on the online store, but it’s unavailable there too.
At this point, you check other similar stores and websites and finally find what you are looking for. However, this process was time-consuming and inconvenient, despite getting what you wanted.
This is what happens when stock outs occur. Businesses that fail to forecast demand see a major drop in conversions, risk losing their loyal customers to competitors and struggle to keep afloat.
In the past, statistics have shown how poor inventory practices have contributed to a staggering loss in sales amounting to nearly $1 trillion.
As a crucial element of supply chain management, mitigating stock outs and other inventory inefficiencies is essential to business longevity and profitability.
Inventory is a retailer's valuable asset. While changing global supply chain conditions can be beyond our control, it can disrupt businesses and make way for discrepancies to creep in and dismantle business operations and stability.
Poor planning and forecasting can negatively impact business growth, profits, and clientele. As a business owner, you risk losing productivity, money, and customers if you aren't aware of how frequently, how long, how much, and when your inventory sells.
Here are some examples of large-scale retailer inventory management blunders:
In 2001, Nike sought help from demand planning software without adequate knowledge and testing. They ended up stocking low-selling products and not enough of their popular Air Jordans. It resulted in a loss in sales of $100 million.
In 2013, Walmart suffered a major out-of-stock issue. Though the stock was available in their warehouses, there was not enough staff on hand to stock the shelves. Had Walmart anticipated demand, they would not have had to lose $3 billion in sales.
Due to a poorly implemented supply chain system, Target failed to expand to Canada. It closed 124 stores in the first two years of operation, resulting in a $2 billion net loss overall. Canadians were appalled as pricing was 15% higher compared to US stores.
Although Target launched an e-commerce site, it failed to cater to Canada’s francophone population. Whenever they tried to shop online, they were redirected to the American store where they couldn’t select a language of their choice, nor could they justify the price increase.
This failed inventory control left some shelves overstocked, some bare, and consumers upset.
All of this can be avoided if predictive analytics is considered as a solution to mitigate them.
Predictive analytics is the process of making predictions about future business outcomes using historical data, machine learning, artificial intelligence, and statistical modeling. Businesses employ data-driven techniques to forecast inventory, manage resources, improve efficiency, increase revenue, optimize performance and productivity, and take appropriate action when needed.
Predictive analytics can go together with descriptive and prescriptive analytics to not only determine the course of a business with precision but can also ensure it stays aligned with growing and ever-changing market trends.
Data-driven processes combined with business intelligence lead to reliable and actionable insights. Brands can benefit from predictive analytics in many ways, such as:
Stocking inventory can be expensive, and stock outs can be detrimental. Brands can leverage predictive analytics to prioritize stock based on demand and profitability by adjusting pricing and offering discounts and promotions to different targeted consumers.
By using IoT in combination with predictive analytics, businesses can plan expenses ahead of time to prevent unnecessary maintenance costs, including reduced waste from damaged items from suppliers. Businesses can then increase downtime and extend the lifetime value of their assets.
Retailers can identify customers that spend the most and target them through proper marketing strategies to increase their profits. By configuring suggestive selling based on best-selling items, excess stock, and buyer trends, brands can target the right audience and improve customer segmentation.
By aggregating data from omnichannel commerce using predictive analytics, brands can forecast consumer behavior, anticipate market trends, optimize business processes, and predict periods in which stock outs are most likely to occur.
It can help them take preventative measures to improve overall supply chain management.
(Pro tip: For e-commerce companies aiming to enhance product design with data, embedded analytics can be a powerful tool.)
Here are some of the use cases and examples of predictive analytics case studies in retail and e-commerce:
Predictive analytics in business helps retailers avoid out-of-stock conditions by anticipating optimal inventory levels. Additionally, it helps create delivery routes that improve operational efficiency, reduce costs, and boost customer satisfaction.
Image Source: Carrefour
For instance, Carrefour uses data analytics to optimize inventory management. It collects data from warehouses, stores, and websites to reduce stock outages and excess stock and predict demand and supply.
According to McKinsey, 76% of consumers are most likely to purchase from a brand that offers personalized experiences. In e-commerce, predictive analytics and big data solutions have allowed brands to make connections between various data points. Brands that offer product recommendations to customers by analyzing behavioral patterns such as previous order history, shopping cart, and purchase frequency, create pleasant online shopping experiences.
Image Source: Macy’s
For instance, using predictive analytics, Macy's department store managed to increase sales by 10% in 3 months by sending personalized emails based on user data. This increase in sales for such a large retailer is substantial in less than three months. Also, by analyzing consumer purchases and browsing history, Amazon has generated up to 35% of sales through product recommendations.
With surveillance cameras and IoT sensors, brands can count the footfall at different times during the day, calculate time spent in queues, and see which products and discounts attract consumers the most. This data insight helps configure store layouts, opening hours, and staff schedules, leading to improved customer experiences and sales.
A national bakery chain in Pakistan relied on a manual, intuition-based process for estimating SKU demand. Branch managers were handling stock orders for the next day without any data-driven insights, resulting in lost sales and excessive waste.
To solve this, Data Pilot developed predictive models for the top 100 SKUs at the top 5 branches to accurately forecast demand. We also created a web-based application with dashboards and sheets to reflect the demand for each SKU from the model output. This model made it simpler for the managers and production personnel to prevent possible stockouts by minimizing the risk of human error and trusting the data to forecast demand.
The solution led to increased revenue, less waste, and reduced stockouts. The model was operating efficiently with an error rate for SKU demand prediction is 6. For instance, if the model predicts that the bakery will sell 25 cupcakes tomorrow, the actual sales will range between 19 and 31.
Many businesses today have seen remarkable improvements by employing predictive analytics. In a perfect utopian world, this would mean no problems whatsoever. Predictive analytics cannot solve every minuscule problem unless planned and implemented with proper knowledge and testing. It can, however, offer substantial help.
Data Pilot's predictive models take the guesswork out of supply chain management, forecasting growth, reducing uncertainty, and preventing volatility. Say goodbye to those late-night Excel sessions and the mystery of vanishing profits and customers.
Tap into data-driven insights with Data Pilot and watch your business thrive. Real-time, customizable inventory analytics are just a click away!
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