Data science and analytics have entirely transformed the face of inventory management and retail. Studies show that the retail analytics market will grow by 18% between 2019 and 2025.
Big Data Analytics in Retail
The application of data-driven implements used for knowing business patterns and performance is known as big data and analytics. Data science and analytics in retail are using business analytics approaches in the retail industry.
Retailers apply data science, analytics, and business intelligence to gather meaningful insights for making well-informed decisions, optimizing inventory, maximizing operational efficiency, and enhancing customer experience and sales. The information that prominent data analyst generates assists retailers to
- Understand consumer behavior and buying tendencies.
- Recognize location-based and seasonal patterns.
- Find the right target audience.
- Compare customer preferences.
This shows the significance and function of big data in retail, but let us concentrate on inventory optimization by prominent data analysts.
Present State of Inventory Management Analytics
The inventory management industry is expected to reach $3.82 billion by 2028 and is growing massively. The current scenario in inventory management demands more than automation in business operations and accurate inventory for gaining a competitive advantage over other businesses. Retailers can make decisions for better profitability and productivity through results obtained from data mining, smart data discovery, and analytics.
Effective inventory optimization software can analyze various factors such as massive datasets of past sales data, seasonality trends, and lead times and then forecast future demand. In addition, prominent data analysts can provide insights into product performance, location and channel performance, customer preferences, and demand fluctuations. With data science and analytics, you can get answers to questions like:
- How is stock replenishment optimized?
- What quantities of inventory are necessary for meeting demand while maintaining low safety stock levels.
- How can cross-selling be achieved to enhance the performance of slow-moving goods?
- How can product recalls be minimized?
As it is essential to understand the application of data analytics in inventory management, it is equally important to understand how data science and analytics make the inventory management process seamless? Mentioned below are some of the processes:
- Diagnostics Analytics – This model explains why the customers left a bad review and the item’s stock out.
- Descriptive Analytics – It provides retailers an overview of how the inventory has performed, the trail of movement of items, and the speed of replenishment.
- Prescriptive Analytics – It assists retail businesses in making adjustments in predicting changes in supply shocks, consumer emotion, and demand.
- Predictive Analytics – It examines historical inventory management data to predict trends and consumer behavior.
Benefits of Data Science and Analytics for Inventory Management
Determining ways to improve inventory management is the main motive of any retailer. The adoption of data science and analytics makes it convenient and enhances inventory management:
- Demand Prediction – Big data in the retail space is used for forecasting demand for optimizing inventory. Retailers can achieve productive inventory management by anticipating customer behavior. Demand fluctuations inevitably occur for every product, and customers show varying buying habits. When retailers cannot understand patterns, they overstock the inventory with slow-moving goods with no space for what is in demand. With data analytics, companies can get accurate insights into what they should stock the inventory with at a particular time of the year. It saves them from acquiring goods from suppliers at the last minute at increased prices.
- Prevent Out-of-stock Situations – Retailers need to prevent stockouts as they leave customers unsatisfied and shift to their competitors to buy goods. Big data analysts can assist in measuring lead times, the number of days required for an item to reach the inventory after a replenishment order has been placed. Merging the sales data with the lead time can help to predict the right level of safety stock and inform retailers about the time when they need to place a replenishment order.
- Replenishment Optimization – Retailers initially used to manually check inventory and then form an estimate of the number of goods that should be ordered depending on guesswork. With integrated data analytics systems, you can examine sales trends, the speed at which a product becomes out of stock, etc. With all this information, you can make well-informed replenishment decisions, keep the fast-selling goods on the most frequented shelves, and move the slow-moving stock elsewhere.
- Quick Order Fulfillment – Retail business data can help speed up the order fulfillment. Orders can be placed in the closest inventory locations for fast delivery and reduced shipping expenses. Retailers can decide the inventory location where a particular item should be stored based on the delivery timeline. Moreover, an efficient data analytics system can help employees exact a product’s location for easier picking and shipping.
- Minimized Costs – Big data analysts can help minimize the inventory cost by reducing overstocking and maintaining an optimal inventory level. You need to bear additional expenses for excess inventory items, which involve the cost of storage, heating, and refrigerating the products. Stocking the inventory with in-demand products is essential, but it is equally important to reduce overstocking and locking up money in dead inventory. Inventory costs include logistics and warehouse, material handling, capital, insurance and storage, and risk-holding costs.
Wrapping Up
Retailers can efficiently manage inventory with data science and analytics insights. It not only allows retailers to improve their brand reputation by fulfilling orders fast, thereby contributing to customer satisfaction, but also reduces the excess inventory costs and helps to manage space productively. Big data analysts can provide retailers with real-time insights into the inventory, allowing them to make informed decisions.
