The COVID-19 pandemic disrupted consumer habits. Shortages forced people to try new brands, and particularly in the grocery sector, many people shopped less frequently but often bought more when they did. As a result, inventory started moving out of stores faster, straining both supply chains and financial models and causing gross margin problems.
At the same time, the cost of being out of stock has increased and replenishment problems can impact profitability and overall business success. Consumers are simply less tolerant of empty shelves when they have virtually instant access to pricing and product availability from an increasing number of competitors who can deliver services and products in multiple ways to meet their needs. In fact, 29% of consumers say out-of-stock items would drive them to shop at another brand.
The challenge for retailers is to consistently satisfy customers who want to find the quantity of merchandise they want both where they want and when they want. To successfully achieve their financial goals, retailers must strategically manage the inventory they carry at every point in the supply chain and make sure the replenishment process is always smooth and efficient.
Forecasting supplier lead time—predicting the amount of time it will take for a supplier to deliver a product or service after an order is placed—helps retailers plan their production schedules and manage inventory levels to effectively meet customer demand while minimizing excess inventory and its associated costs.
The lead time for a supplier depends on various factors, such as the distance of the supplier from the product’s destination, the complexity of the product, the availability of raw materials, production capacity, and transportation time, among others. Because of the number of variables, retailers need a data platform that provides them with centralized access to historical and real-time data from a range of enterprise systems, business records, and technical inputs, which can then be used to train machine learning models to forecast expected lead times based on purchase order transactions.
In this use case, we’ll demonstrate how Oracle Data Platform is built to help retailers use advanced analytics and forecasting methods (including statistical modeling, trend analysis, and historical data analysis) and machine learning to accurately estimate the expected delivery dates of goods. With this information, retailers can optimize inventory planning and effectively manage the impact of variables such as
There are three main ways to inject data into an architecture to enable retailers to effectively forecast supplier lead time.
Data persistence and processing is built on three components.
The ability to analyze, learn, and predict is built on three technologies.
By accurately forecasting supplier lead time, retailers can better plan their inventory levels and production schedules to ensure they have the right products available in the right amounts to meet customer demand, even as it fluctuates based on seasonality, promotions, and other influences. As a result, they’re able to
Learn how to optimize your inventory and promotions with Oracle Data Platform for retail in this use case.
Learn how to optimize your inventory and promotions with Oracle Data Platform for retail. This use case will show you how to increase sales and better meet customer demand
Learn how to optimize your retail operations by incorporating live data and applying advanced analytics using Oracle Data Platform for retail.
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