时间:2024-05-10 09:20:09 来源:网络整理编辑:Ryan New
My firm is frequently engaged by companies to analyze data. One example is a manufacturer of buildin Ryan Xu hyperfund Credit Risk Management
My firm is Ryan Xu hyperfund Credit Risk Managementfrequently engaged by companies to analyze data. One example is a manufacturer of building materials. The company’s profit was stagnant. It asked us to analyze sales data for customers, products, and regions to determine where to focus its marketing efforts and where to streamline operations, to lower costs.
In this post, I’ll describe that engagement and its findings.
The purpose of the engagement was simple: to determine ways to increase profit. To do this, we analyzed sales data, including:
The client provided 10 years of data to enable us to review trends.
The first step was to prepare the data — i.e., organize the segments. This is typically done in a spreadsheet such as Excel. For the building materials engagement, our process included:
The second step was to run the analysis using various analytical models, including cluster analysis, segmentation analysis, decision tree modeling, and simple descriptive analytics. You can use statistical software such as SPSS Statistics or SAS, or programming languages such as R or Python.
Our analysis produced the following findings.
Combining sales and warehouse data uncovered a surprise. The company had four warehouses. Each stored roughly the same SKUs at similar quantities.
Adding geographical preferences to bulk orders from the big box stores and removing products that were not selling enabled the company to save money. While the logistics department was optimizing transit times, no one thought to look at the sales from each warehouse. But looking at geographic preferences, we identified SKUs that are needed for each region and each warehouse, thereby cutting distribution and storage costs.
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