Big Data & Data Analysis
Improving Inventory Management with Big Data
Background:
A large retailer was facing issues with their inventory management. Despite having a large amount of data about customer buying behavior, sales and stock levels, the company was struggling to optimize their inventory and were losing money due to stockouts and overstocking.
Challenges
Data Quality: Ensuring that the data being used is accurate, complete, and consistent can be a major challenge. This can include dealing with missing or duplicate data, or dealing with data that is not in a format that can be easily analyzed.
Data Volume: Handling the sheer volume of data can be a major challenge, especially when it comes to storing and processing the data. This can require significant computational power and can also result in delays and inefficiencies in analysis.
Data Variety: Dealing with different types of data, such as structured and unstructured data, can be a major challenge. It can be difficult to integrate data from different sources and ensure that it can be easily analyzed.
Data Security: Securing big data can be a major challenge, especially when dealing with sensitive information. It’s important to ensure that data is protected from unauthorized access and that proper safeguards are in place to prevent data breaches.
Data Governance: Ensuring that data is being used ethically, legally, and in compliance with regulations can be a major challenge. This can include dealing with issues such as data privacy and data retention.
Analytical skill set: Having the necessary analytical skills and expertise to work with big data can be a major challenge. This can include dealing with the need to analyze and interpret large and complex data sets, as well as the need to use advanced analytical techniques such as machine learning or deep learning.
Scalability: When dealing with large amount of data, it can be a challenge to scale the analytical or processing infrastructure to handle the volume and velocity of data
Time constraints: Dealing with big data often requires extensive time for cleaning, pre-processing and analyzing the data, and the need to act on the insights quickly before the data becomes stale, which can be a major challenge.
Solutions
Aforcex worked with the retailer to analyze the large amount of data they had collected on customer buying behavior, sales, and stock levels. Aforcex used advanced machine learning techniques to build a predictive model that could forecast demand and optimize inventory levels.
The model was integrated into the company’s existing systems, allowing them to make real-time decisions about inventory levels. Aforcex also helped the company implement a new inventory management system that could automatically adjust stock levels based on the predictions made by the model.
- The retailer was able to significantly reduce stock outs and overstocking, resulting in an increase of 8% in their sales.
- The company was able to save cost and increase their profitability by reducing their inventory carrying costs by 15%
- The retailer improved customer satisfaction and reduced customer complaints by ensuring products were available when customers wanted to purchase them.
Results
Hassan Ali
Asad Khan
Technologies
Python data science stack, Tesseract
Time Period
Weeks
Website
