Optimizing Inventory Management

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Table of Contents

• Introduction
• Problem Statement
• Data Sourcing
• Data Preparation
• Data Modeling
• Data Visualization and Analysis
• Shareable link

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Introduction

SellCheapy Retail is a chain of department stores that sells a wide range of products, including bikes and different components. Despite having a large customer base, the company has been struggling to increase sales in recent years. The management team is looking to use data analysis to understand customer spending patterns and make changes to their sales and marketing strategies to improve performance.

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Problem Statement

Based on the request that was made from the management team we are following the user stories that were defined to fulfill delivery and ensure that acceptance criteria’s were maintained throughout the project.

# As a (role)	I want (request / demand)	So that I (user value)	Acceptance Criteria
1 Sales Manager	To get a dashboard overview of internet sales	Can follow better which customers and products sells the best	A Power BI dashboard which updates data once a day
2 Sales Representative	A detailed overview of Internet Sales per Customers	Can follow up my customers that buys the most and who we can sell more to	A Power BI dashboard which allows me to filter data for each customer
3 Sales Representative	A detailed overview of Internet Sales per Products	Can follow up my Products that sells the most	A Power BI dashboard which allows me to filter data for each Product
4 Sales Manager	A dashboard overview of internet sales	Follow sales over time against budget	A Power BI dashboard with graphs and KPIs comparing against budget.

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Data Sourcing

• The data set used for this analysis was sourced from AdventureWorks sample databases
• The additional data set used for this analysis is available at: Sales Budget
• The query used to update the database can be found at Update Query

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Data Preparation

Data cleaning and transformation was done in Microsoft SQL Server and the data sets was loaded into Microsoft Power BI Desktop for modeling.

The data sets are split into 5 tables:

• DIM_Calender contains 8 columns and 1461 rows
• DIM_Customers contains 7 columns and 18484 rows
• DIM_Products contains 11 columns and 606 rows
• FACT_InternetSales contains 7 columns and 58168 rows
• FACT_Budget which has 2 columns and 18 rows of observation

Below are the SQL statements for cleansing and transforming necessary data.

DIM_Calender

SELECT 
  [DateKey], 
  [FullDateAlternateKey] AS Date, 
  [EnglishDayNameOfWeek] AS Day, 
  [EnglishMonthName] AS Month, 
  Left([EnglishMonthName], 3) AS MonthShort,  
  [MonthNumberOfYear] AS MonthNo, 
  [CalendarQuarter] AS Quarter, 
  [CalendarYear] AS Year --[CalendarSemester], 
FROM 
 [AdventureWorksDW2019].[dbo].[DimDate]
WHERE 
  CalendarYear >= 2019

DIM_Customers

SELECT 
  c.customerkey AS CustomerKey, 
  c.firstname AS [First Name], 
  c.lastname AS [Last Name], 
  c.firstname + ' ' + lastname AS [Full Name], 
  CASE c.gender WHEN 'M' THEN 'Male' WHEN 'F' THEN 'Female' END AS Gender,
  c.datefirstpurchase AS DateFirstPurchase, 
  g.city AS [Customer City]
FROM 
  [AdventureWorksDW2019].[dbo].[DimCustomer] as c
  LEFT JOIN dbo.dimgeography AS g ON g.geographykey = c.geographykey 
ORDER BY 
  CustomerKey ASC

DIM_Products

SELECT 
  p.[ProductKey], 
  p.[ProductAlternateKey] AS ProductItemCode, 
  p.[EnglishProductName] AS [Product Name], 
  ps.EnglishProductSubcategoryName AS [Sub Category], 
  pc.EnglishProductCategoryName AS [Product Category], 
  p.[Color] AS [Product Color], 
  p.[Size] AS [Product Size], 
  p.[ProductLine] AS [Product Line], 
  p.[ModelName] AS [Product Model Name], 
  p.[EnglishDescription] AS [Product Description], 
  ISNULL (p.Status, 'Outdated') AS [Product Status] 
FROM 
  [AdventureWorksDW2019].[dbo].[DimProduct] as p
  LEFT JOIN dbo.DimProductSubcategory AS ps ON ps.ProductSubcategoryKey = p.ProductSubcategoryKey 
  LEFT JOIN dbo.DimProductCategory AS pc ON ps.ProductCategoryKey = pc.ProductCategoryKey 
order by 
  p.ProductKey asc

FACT_InternetSales

SELECT 
  [ProductKey], 
  [OrderDateKey], 
  [DueDateKey], 
  [ShipDateKey], 
  [CustomerKey], 
  [SalesOrderNumber], 
  [SalesAmount] 
FROM 
  [AdventureWorksDW2019].[dbo].[FactInternetSales]
WHERE 
  LEFT (OrderDateKey, 4) >= YEAR(GETDATE()) -2
ORDER BY
  OrderDateKey ASC

The tabulation below shows the FACT_Budget table with its column names and their description:

Column Name	Description
Date	Represents the date budget was made
Budget	Represents the amount budgeted

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Data Modeling

After the dataset was cleaned and transformed, it was ready to be modeled(using Power BI Desktop).

• The DIM_Calender table was marked as the official date table in the dataset.
• A one-to-many (*:1) relationship was created between the Fact_InternetSales and the DIM_Calender tables
• A one-to-many (*:1) relationship was created between the Fact_InternetSales and the DIM_Customers tables
• A one-to-many (*:1) relationship was created between the Fact_InternetSales and the DIM_Products tables
• A one-to-many (*:1) relationship was created between the FACT_Budget and the DIM_Calender tables using
• The realtioships formed is shown in the data model below:

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Data Visualization and Analyis

Data visualization for the datasets was done in 3 folds using Microsoft Power BI Desktop.

Based on the business problem, the following report was prepared:

• The Sales Overview page: contains the sales vs budget KPI, sales by top 10 customers, sales by top 10 products, e.t.c
• The Customer Details page: contains customer specific information
• The Product Details page: contains product specific information

Figure 1 shows visualizations from Sales Overview page

Figure 1

Figure 2 shows visualizations from Customer Details page

Figure 2

Figure 3 shows visualizations from Product Details page

Figure 3

Measures used in this visualizations are:

• Budget = SUM ( FACT_Budget[Budget] )
• Sales = SUM ( FACT_InternetSales[SalesAmount] )
• Sales / Budget = [Sales] - [Budget]

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Shareable Link

You can interact with the report here:

View Report