Retail Company Inventory Recommendations
Retail Company Inventory Recommendations
About this project
A C-Suite executive team of a nationwide retail company has watched their sales trend down over the past year. As a part of their overall strategy to remedy this issue, they are aiming to save money in the coming year by optimizing their inventory holdings. Their goal is to cut unnecessary inventory costs for products that are not performing well and maintain a healthy inventory level for top-performing products.
Key Performance Indicators For This Project
In-Stock Percentage represents the percentage of orders that can be fulfilled immediately due to adequate inventory of the product. Low or high percentages can negatively impact a company: Low = poor customer experience, High = tying up capital in unnecessary inventory costs.
The executive team is aiming for a healthy In-Stock Percentage of 97% which requires effective planning for product demand. Historical Sales, In-Stock Percentages, and Inventory Levels can help determine how to allocate capital to meet demand.
Key Project Steps
Cleaned Data In Power Query: Removed unneeded columns, adjusted data types, trimmed white space, and adjusted incorrect values.
Created Custom Date Lookup Table Using M Code: Created a list of dates, date number columns, date name columns, and a date scaffolding to provide users with an easy-to-use date navigation button system.
Created Data Model In Power BI: Connected item/category and date lookup tables to the central data table.
Created Dashboard In Power BI and PowerPoint: Created KPI cards, a dual axis line chart, categoryID/itemID slicers, and a date navigation button system. Then used color and other visual encodings to emphasize areas of focus.
Explored Dashboard for Trends and Insights: Began with a high-level overview and analyzed different time periods, categories, and items to identify key insights and trends that informed the creation of effective recommendations.
Forecasted for Ideal Inventory levels: Used mathematical formulas to predict ideal inventory levels in each category for the upcoming month using the following factors:
- Sales increase or decrease from 2018 to 2019
- Target in-stock% and last year’s in-stock% during Jan-Feb
- Last year’s inventory levels during Jan-Feb
Created Pages for Each Insight: Listed key insights/recommendations and accompanied them with various charts, using DAX and other Power BI tools.
Created Cover Page/Navigation Buttons: Included key project information and incorporated user-friendly buttons to enable easy navigation throughout the report.
