Sem D Project

NIIT

2012

SPEEDSTER MOTOR CORPORATION

VENDOR MANAGEMENT SYSTEM

SURAMYA SHOME

SUKANYA SENGUPTA

HUZEFA KOTHALIA

SOURAV MITRA

1

Project Details

Subject Vendor Management System

Semester Semester D

Programme GNIIT [BSIM]

Subject Tutor Ms. Anuradha Saha

Name of the Students

Suramya Shome

Sukanya Sengupta

Huzefa Kothalia

Sourav Mitra

Student’s Registration Numbers

R110050100554

R110050100928

R110050100234

R110050100435 Declaration: All the material written in this assignment is my own and I have not used any material, content or information of others claiming them to be mine. Wherever materials have been used, proper citation has been done in the text. I am fully aware of the rules/regulations governing plagiarism. Should at any point of time my work be suspected/investigated and established to have been plagiarized, I am aware of the consequences. I have read the Student’s Handbook in details. There is a lot of inconsistency in the materials in the Project Book and the Word document provided to us, so we were compelled to follow the information given in the Project Book, as it is a written evidence of the said parameter.

2

Acknowledgement

We wish to acknowledge our gratitude to our Faculty Ms. Anuradha Saha for providing us an opportunity to undertake this project and for her valuable suggestions and guidance of each step in successful completion of this project. And a deep sense of gratitude is expressed to our parents, without their support and encouragement this project could not have been possible for us to complete so smoothly.

3

Table of Content

S/No. Topics Page No.

1. Introduction 4.

2. Case Study Summary 5.

3. Analysis Document 8.

4. Readiness Assessment Document 11.

5. Project Scope Of Speedster Motor Corporation 13.

6. Return On Investment (ROI) Calculations 14.

7. Evaluation Matrix 17.

8. Data Sources Analysis Document 18.

9. Logical Dimensional Model 29.

10. Logical Data Map 29.

11. Physical Model Format 31.

12. Bibliography 32.

4

Introduction

Speedster Motors Corp (SM Corp) is a pioneer and global leader in the field of automobile manufacturing since 1950. At present, the company has 1,13,000 employees across the globe. It has well-established manufacturing units in 10 countries. Its vehicles are sold in 10 countries. The main focus of SM Corp. is to constantly improve their manufacturing processes for making quality products, generating growth, and ensuring a healthy working atmosphere for all the employees. The company’s Chief Executive Officer (CEO), John Thomas Edgar Jr., explains, “Quality management is our first priority”. The company has well-established information systems for maintaining and monitoring its core and support business processes. All processes for manufacturing automobiles and its associated accessories are in line with the 8D Processes used in the manufacturing industry. The annual turnover of the company in the previous year was over $15 billion. The sales were boosted due to a strong economy in the operating countries. As a result, the company sold approximately 1.0 million cars globally, which was a 5 percent increase from previous year’s sales and second highest total sales in the company’s history. The SM Corp. has global alliances with automobile-related industry giants. Some of the alliances include: Mitzatchi Heavy Industries Ltd: This organization is one of the major suppliers of assembly line machinery required by SM Corp. for manufacturing automotives. Mitzatchi manufactures equipment such as vertical door assembly (assemblies), fully automatic spring making machines, CNC spring forming machinery, wire-straightening and cutting machines. Infuji Motors Ltd. and Ho-Chan Motor Corp. of Japan: These organizations supply several customized components required for manufacturing cars by SM Corp. The components include lambo doors, oil coolers, grille series, vertical door kits and lambo style hinge kits. Continental Heavy Industries Ltd: This organization supplies chassis control systems, electronic air suspension systems, and sensors to SM Corp. Lambart Corporation: This organization supplies electronic components and hydraulic brakes to SM Corp.

5

Case Study Summary

Overview

The CEO, John Thomas Edgar Jr, and the board of directors jointly take the company’s strategic decisions. A Vice President, who reports to the CEO, heads each department of the company. SM Corp. comprises the following departments:

� SM Global Manufacturing

� SM Global Materials

� SM Global Sales and Marketing

� SM Global InfoTech

� SM Global Engineering

� SM Global Finance and Accounting

� SM Global Central Quality Assurance (QA)

SM Global Materials Department (purchase) SM Corp is divided into four regions. The SM Global Materials department manages and monitors the material requirements and planning across all regions. The SM

6

Global Materials department is the nucleus of a strong Vendor Management System (VMS). The department is further divided into Regional Materials Department (RMD) for each manufacturing unit. The RMD plans work orders and purchase requisitions for all its manufacturing units and their associated storage warehouses. The RMD suggests appropriate rescheduling of the current material plan by taking into account lead-times for required components for an individual manufacturing unit falling within its territory. Shortcomings

The existing MIS have the following shortcomings:

� Inability to obtain consolidated reports: Due to the varied nature of the existing data management systems, (Excel, SQL, Access), individual MIS’s have created silos of information. Therefore, consolidation of data for report generation is difficult.

� Inability to access reports in real-time: The management at the head office or the management at other locations cannot access the reports generated by the MIS of one manufacturing unit in real-time. This is because every unit is organized as separate business or functional unit that doesn’t communicate or share data with another. The management is unable to take effective and timely business decisions.

� Unavailability of archived data for analysis: The Company has over the years amassed huge volumes of data. This data is archived in offline mass storage devices such as magnetic tapes. Every year the company consolidates its data according to the Key Performance Indicators (KPI’s) to generate reports for profitability analysis. The management compares the current results with that of the previous years. Such analysis gives the management insight into the profitability of its existing processes.

At times, the existing KPI’s used for calculating the profitability analysis report are different from the ones used in the past. This leads to problems in consolidation of previous reports with the existing ones. In such scenarios, the data used to calculate the previous years has to be restored from the offline storage devices.

The task of restoring data involves:

Incurring infrastructure costs to install additional high-performance, high-speed, and high-capacity servers to store this data.

Identifying and extracting relevant data from these newly restored systems.

Running complex algorithms on this data to generate reports as per the current KPI’s.

Such complex and time-consuming activities are required because of the inability of the existing IMS to provide the necessary historical data.

7

� Non-scalability of existing systems: The existing MIS’s are not versatile enough to offer advanced database management features, such as XML support, Data Transformation Services (DTS), and data mining. The existing systems do not allow the management to make predictions about the future or to do trend pattern analysis.

Solution

Implementation of the Business Intelligence solution that solves the above problems.

Hardware

Two Hewlett Packard ProLiant DL500 Class Servers for data warehouse. The ProLiant DL500s are 6-Core processor, Intel Xeon 7400 series servers with 16GB of standard RAM, and 256GB of maximum memory. One of the servers will be used to create the warehouse and cubes. The second server will be the read-only server allowing the new data to be reprocessed without impacting the users.

One Hewlett Packard LH4 class servers for the OLAP server. The LH4s include two processor with 500 MHz Pentium III Xeon Servers having 1 to 2 GB of RAM. The OLAP cubes can be built on the ProLiant DL500 and, then, restored to these smaller OLAP servers to free up the build server.

Software

Microsoft SQL Server 2008 software is used for providing the infrastructure for Data Warehousing solution Microsoft SQL Server 2008 is an RDBMS that will help your organization manage any data, any place, and any time. It will enable you to store data from structured, semi-structured, and unstructured documents, such as images and rich media, directly within the database.

SAS Enterprise Intelligence Platform (EIP) – tools and services for developing and hosting the Business Intelligence solution. SAS is the market leader in End-to-End Business Intelligence and Business Analytics solutions.

The Operating System for the BI solution: UNIX server (4 processors).

8

ANALYSIS DOCUMENT

Company Name Speedster Motors Corp. (SM Corp)

Name of the Data Warehouse Project Vendor Management System(VMS)

Document Version 1

Starting Month of the Project 4th April,2012

Duration of the Data Warehouse Project 6 Months

Business Overview

Established in 1950, Speedster Motor Corp., is a automobile manufacturing company, is a pioneer and global leader in automobile industry for more than 50 years. The company has well-established manufacturing units in 10 countries and its vehicles are sold in 33 countries. It has strength of 113,000 employees across the globe.

Business Objective

To constantly improve manufacturing processes for making quality products for its customers and generating growth and ensuring a healthy working environment for all the employees

Planned Budget for the Data Warehouse Project Short Term Budget of $550,000

Architectural Approach Data Mart/Bottom-up Approach

Business Requirement

The top management of Speedster Motor Corp. Wants to analyze their chain profitability as another new initiative to improve the performance and health statistics of the company. In other words, currently, the management is unable to segregate the profitable storage warehouses from the least profitable ones. Identifying the profitable storage warehouses will enable the management to: Critical Business Problems 1) Individual IMS have created SILOS of information 2) Management cannot access the reports generated by

9

the IMS of one manufacturing unit in real time 3) Unavailability of archived data for analysis 4) Cannot predict future requirements Analytic Requirement 1) Gross Margin for Every product category wise as well as sub category wise across all storage warehouse 2) Compare gross margin for all products in the existing storage warehouses 3) Segregate profitable storage warehouses and optimize inventory levels

End Users

Expected number of end-users of the data warehouse is 700 but, currently, the data warehouse can support 500 end users.

CEO, Vice Presidents, Regional Materials Managers, Business Analyst, Database Administrators, and Data Entry Operators.

The end users are located at different locations and in different time zones.

End User Training Training on data warehouse usage is required for the end users.

Specific Reports used by the End Users

Gross Margin Return on Investment (GMROI) for each warehouse on the basis of Vendor profitability and Vendor reliability.

Analysis of Reports

For vendor profitability, focus is on the average discounted cost price. This value is available for all the vendors who have supplied the same product that year. The vendor with the lowest average discounted cost price for that product is the most profitable while the vendor with the highest value is the least profitable.

For vendor reliability, focus is on the average lead times. This value is available for all the vendors who have supplied products that year. The lower the average lead time, higher is the reliability of the vendor and higher the average lead time, lower is the vendor reliability.

10

Data Sources

Data source 1: Excel

Data source 2: Excel

Data source 3: Access

Data Availability No issues in accessing data from the year 1985 onwards. Data prior to 1985 not available.

System Requirement

Two Hewlett Packard ProLiant DL500 Class Servers for data warehouse. The ProLiant DL500s are 6-Core processor, Intel Xeon 7400 series servers with 16GB of standard RAM, and 256GB of maximum memory. One of the servers will be used to create the warehouse and cubes. The second server will be the read-only server allowing the new data to be reprocessed without impacting the users.

One Hewlett Packard LH4 class servers for the OLAP server. The LH4s include 2-processor with 500 MHz Pentium III Xeon Servers having 1 to 2 GB of RAM. The OLAP cubes can be built on the LXR 8000 and, then, restored to these smaller OLAP servers to free up the build server to process the next month’s data.

Resource Requirement

Five developers/administrators for developing the BI solution

From the second year, two developers /administrators required to maintain the solution

Success Criteria of the data warehouse project

Access to more than 10 years of historical data for analysis

End users training will not take much time for the data warehouse project

Considerable support can be obtained from the business sponsor.

11

READINESS ASSESSMENT DOCUMENT

Readiness Factors Low Readiness Factor High Readiness Factor

Business Sponsor

Lacks leadership skills. Considerable hold over the company and the business

partners.

Not readily available to the data warehouse Project team for support and information.

Readily available o Team.

Slow decision maker. Quick and decisive to resolve issue.

Uncertain and User. Active, vocal and supporter.

Unrealistic Business expectations.

Realistic business expectations.

Unaware of the benefits of a data Warehouse.

Highly aware of the benefits of a data warehouse.

Business Urge Of a Business Sponsor

No inclination towards analysis of data.

Success of the enterprise depends on analysis

information and reports.

May have constraints in funding the data warehouse project.

May have some constraints in funding the data warehouse

project.

Does not possess a well-defined project vision.

Possesses a well-defined project vision.

12

Readiness Factors Low Readiness Factor High Readiness Factor

Joint Venture of stakeholders

Business sponsor hires external consultant without IS

knowledge.

Business sponsor and IS interact regularly with each

other.

No confidence in the existing reporting system. So, he will not

be able to envision the capabilities of a data warehouse.

Strong confidence in reporting environment. So he will be able

to appreciate of a data warehouse.

Slow IS response to ad requests. Quick IS response to ad hoc requests.

Technical Feasibility

IT infrastructure requires up gradation of technology. IT infrastructure is strong.

Lack of resources. Experienced resources are available.

Data cannot be easily accessed. Data is easily accessed.

Present Analytic Situation

Decision making of the management is based on instinct.

Decision-making of the management is based on facts.

Business users do not use data and current reports to analyze

business patterns.

Business users access data and reports to analyze and predict

business patterns.

Business users may not be technology savvy.

Business users are technology savvy.

No transparency of information across department and business

units in an enterprise.

Transparency of information exists across departments and

business in an enterprise.

13

PROJECT SCOPE OF SPEEDSTER MOTOR CORPORATION

Project Background

History of the Enterprise

Speedster Motors Corp (SM Corp) is a pioneer and global leader in the field of automobile manufacturing since 1950. At present, the company has 1,13,000 employees across the globe.

Objective of the Enterprise

The main focus of SM Corp. is to constantly improve their manufacturing processes for making quality products, generating growth and ensuring a healthy working environment of all employees.

Geographical Scope It has a well established manufacturing unit in 10 countries and its vehicles are sold in 10 countries.

The Business Problems faced

Inability to obtain consolidated reports.

Inability to access reports in real-time.

Unavailability of archived data for analysis.

Non-scalability of existing system.

Phase 1 Scope Definition (Based on requirements gathered from SM Corp)

"The following scope have been defined:

- Detailed information of Inventory Management System.

- 700 business user will use the data mart.

- The bottom-up architectural approach have being identified.

- The project timeline is 6 months."

Success Factors of Phase I

The success factors are as follows:

- A single source to support the Vendor Management system.

-Timely generation of report for analysis.

14

RETURN ON INVESTMENT (ROI) CALCULATIONS

To estimate the expected profitability of the data warehouse project, SM Corp used the following four financial measures:

� Net Present Value (NPV)

� Payback Period

� Return on Investment (ROI)

SM Corp. Cost Estimates for the BI Solution For Vendor Management System

Year 0 Year 1 Year 2 Year 3

Hardware $150,000 -- -- --

Software $150,000 $30,000 $30,000 $30,000

Resources & Maintenance $250,000 $100,000 $100,000 $100,000

Total $550,000 $130,000 $130,000 $130,000

SM Corp. Cost Estimates Without the BI Solution For Vendor Management System

Year 0 Year 1 Year 2 Year 3 Resources & Maintenance $575,990 $575,990 $575,990 $575,990

Total $575,990 $575,990 $575,990 $575,990

SM Corp. Net Savings

Year 0 Year 1 Year 2 Year 3 Costs without BI Solution $575,990 $575,990 $575,990 $575,990

Costs with BI Solution $550,000 $130,000 $130,000 $130,000

Net Savings $25,990 $445,990 $445,990 $445,990

15

SM Corp. Net Present Value (NPV)


Net Savings P.A - $445,990 $445,990 $445,990

NPV Formula - $445,990*1 / ( 1 + 0.08 )

$445,990*1 / [( 1 + 0.08 )* ( 1 +

0.08 )* ( 1 + 0.08 )]

$445,990*1 / [( 1 + 0.08 )* ( 1 + 0.08 )* ( 1 + 0.08 )* ( 1 +

0.08 )]

Discounted Net Savings at 8% - $412,953 $354,041 $327,815

Total Discounted Net Savings at 8%

$1,094,809


Costs with BI Solution $550,000 $130,000 $130,000 $130,000

Costs without BI Solution $575,990 $575,990 $575,990 $575,990

Net Savings $25,990 $445,990 $445,990 $445,990

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

SM Corp. Net Savings

16

SM Corp. Payback Period (Years) Total Discounted Net Savings at 8% $1,094,809

Number of Years in Time Horizon 3

Average Total Discounted Net Savings at 8% $445,990

Initial Investment in BI Project $500,000

Payback Period Formula $500,000/$445,990

Payback Period in Years 1.12

SM Corp Return on Investment (ROI)

Total Discounted Net Savings at 8% $1,094,809

Initial Investment in BI Project $500,000

ROI Formula ($1,094,809/$500,000)*100

ROI 218%

17

EVALUATION MATRIX

Criteria Bottom-Up Top-Down

Weight Value Weight Value Integration of Data 3 3 3 3 Structure of Data 2 3 2 3

Scalable 3 3 3 3 Limits Impact of

Technology Change

0 2 3 3

Time to Deliver 3 3 3 1 Cost to Deploy 3 3 3 3 Cost to Operate 3 2 3 3

Weighted Average 38/7=5.4 36/7=5.14

18

DATA SOURCES ANALYSIS DOCUMENT

The following data sources are analyzed to create the data sources analysis document:

� MS Excel Sources: From Year 1985 to 1989

� MS Excel Sources: From Year 1990 to 1994

� MS Access Sources: From Year 1995 to 1999

Excel Sheet (.xls) For 1985-1989

Database Name Table Name Column Name Data Type

Data Discrepancies

1985_1989_US_Cannon.xls

Inventory Ordered(Sheet) Product_name Text None Inventory Ordered(Sheet) Vendor_name Text None Inventory Ordered(Sheet) Date Ordered Date None Inventory Ordered(Sheet) Lead Time Number None Inventory Ordered(Sheet) Quantity Ordered Number None Inventory Ordered(Sheet)

Quantity Received Number None

Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) Quantity Deficit Number None Inventory Ordered(Sheet) Discount Currency None Inventory Ordered(Sheet)

After discount Price Currency None

Product(Sheet) product_name Text None Product(Sheet) Category Text None

Product(Sheet) Subcategory Text None Product(Sheet) Unit Text None Product(Sheet) Qty_per_unit Text None Product(Sheet) UnitCostPrice Currency None Product(Sheet) UnitSellingPrice Currency None Product(Sheet) Reorder Level Number None Product(Sheet) ReOrderQty Number None Product(Sheet) QtyonHand Number None Products_Vendors(Sheet) Product_name Text None Products_Vendors(Sheet) vendor_name Text None Products_Vendors(Sheet)

Unit_Selling_Price Currency None

19

Products_Vendors(Sheet) Discount Number None Products_Vendors(Sheet) DesiredLeadTime Number None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None



Data Discrepancies

1985_1989_US_Kansas.xls

Inventory Ordered(Sheet) Product_name Text None Inventory Ordered(Sheet) Vendor_name Text None Inventory Ordered(Sheet) Date Ordered Date None Inventory Ordered(Sheet) Lead Time Number None Inventory Ordered(Sheet) Quantity Ordered Number None Inventory Ordered(Sheet)

Quantity Received Number None

Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) QDeficitDeficite Number None Inventory Ordered(Sheet) Discount Currency None Inventory Ordered(Sheet)

After discount Price Currency None

Product(Sheet) product_name Text None Product(Sheet) Category Text None

Product(Sheet) Subcategory Text None Product(Sheet) Unit Text None Product(Sheet) Qty_per_unit Text None Product(Sheet) UnitCostPrice Currency None Product(Sheet) UnitSellingPrice Currency None Product(Sheet) Reorder Level Number None Product(Sheet) ReOrderQty Number None Product(Sheet) QtyonHand Number None Products_Vendors(Sheet) Product_name Text None Products_Vendors(Sheet) vendor_name Text None Products_Vendors(Shee Unit_Selling_Pric Currency None

20

t) e Products_Vendors(Sheet) Discount Number None Products_Vendors(Sheet) DesiredLeadTime Number None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None

Excel Sheet (.xls) For 1990-1994 Database Name Table Name Column Name Data Type Data Discrepancies Retailer_Vendor.xls Vendors(Sheet) Vendor_name Text None

Vendors(Sheet) Vendor_address Text None Vendors(Sheet) Vendor_phone Number None Vendors(Sheet) Vendor_state Text Vendors(Sheet) vendor_country Text None


Database Name Table Name Column Name Data Type Data Discrepancies

WarehouseAichi.xls

Inventory Issued(Sheet) Invoice Text

Some invoice numbers are missing.

Inventory Ordered(Sheet) Lead-time Number None Inventory Ordered(Sheet) QtyOrdered Number None Inventory Ordered(Sheet) QtyReceived Number None Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) QtyDeficit Number None Inventory Ordered(Sheet) Discount Number None Inventory Ordered(Sheet)

After_Discount_Price Currency None

Product(Sheet) Category Text None TransactionQOH(Sheet) ClosingQOH Number None TransactionQOH(Sheet) OpeningQOH Number None TransactionQOH(Sheet) product_name Text None TransactionQOH(Sh Transacted_Date Date None

21

eet) TransactionQOH(Sheet) Issued_to Text None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None



WarehouseApen.xls

Inventory Ordered(Sheet) Invoice Text None Inventory Ordered(Sheet) product_name Text None Inventory Ordered(Sheet) qty_issued Number None Inventory Ordered(Sheet) Cost Price Currency None Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) QtyDeficit Number None Inventory Ordered(Sheet) Discount Number None Inventory Ordered(Sheet)


Product(Sheet) Category Text None Product(Sheet) subcategory Text None Product(Sheet) Product_name Text None Product(Sheet) product_model Text None Product(Sheet) unit Text None Product(Sheet) Qty_per_unit Text Few values are missing. Product(Sheet) Unit_cost_price Currency None Product(Sheet) Unit_Selling_Price Currency None Product(Sheet) Re-Order Level Number None Product(Sheet) ReOrderQty Number None Product(Sheet) Qty-on-Hand Number None

Product(Sheet) Max Level Number It does not contain any values.

Product(Sheet) Min Level Number It does not contain any values.

Products_Vendors(Sheet) Product_name Text None Products_Vendors(Sheet) vendor_name Text None Products_Vendors(Sh Unit_Selling_Price Currency None

22

eet) Products_Vendors(Sheet) Discount Number None Products_Vendors(Sheet) DesiredLeadTime Number None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None



WarehouseCannon.xls

Inventory Ordered(Sheet) Invoice Text None Inventory Ordered(Sheet) QtyReceived Number None Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) QtyDeficit Number None Inventory Ordered(Sheet) Discount Number None Inventory Ordered(Sheet)


Product(Sheet) Category Text None Product(Sheet) subcategory Text None Product(Sheet) Product_name Text None Product(Sheet) product_model Text None Product(Sheet) unit Text None

Product(Sheet) Qty_per_unit Text It contains few null values.

Product(Sheet) Unit_cost_price Currency None Product(Sheet) Unit_Selling_Price Currency None Product(Sheet) Re-Order Level Number None Product(Sheet) ReOrderQty Number None Product(Sheet) Qty-on-Hand Number None

Product(Sheet) Max Level Number It does not contain any value.

Product(Sheet) Min Level Number It does not contain any value.

Products_Vendors(Sheet) Product_name Text None Products_Vendors(Sheet) vendor_name Text None Products_Vendors(Sheet) Unit_Selling_Price Currency None

23

Products_Vendors(Sheet) Discount Number None Products_Vendors(Sheet) DesiredLeadTime Number None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None



WarehouseKansas.xls

Inventory Ordered(Sheet) Invoice Text None Inventory Ordered(Sheet) product_name Text None Inventory Ordered(Sheet) qty_issued Number None Inventory Ordered(Sheet) QtyReceived Number None Inventory Ordered(Sheet) Unit_Cost_Price Currency None Inventory Ordered(Sheet) QtyDeficit Number None Inventory Ordered(Sheet) Discount Number None Inventory Ordered(Sheet)


Product(Sheet) Category Text None Product(Sheet) subcategory Text None Product(Sheet) Product_name Text None Product(Sheet) product_model Text None Product(Sheet) unit Text None

Product(Sheet) Qty_per_unit Text It contains few null values.

Product(Sheet) Unit_cost_price Currency None Product(Sheet) Unit_Selling_Price Currency None Product(Sheet) Re-Order Level Number None Product(Sheet) ReOrderQty Number None Product(Sheet) Qty-on-Hand Number None

Product(Sheet) Max Level Number It does not contain any value.

Product(Sheet) Min Level Number It does not contain any value.

Products_Vendors(Sheet) Product_name Text None Vendors(Sheet) vendor_name Text None Vendors(Sheet) vendor_address Text None

24

Vendors(Sheet) vendor_phone Text None Vendors(Sheet) vendor_state Text None Vendors(Sheet) vendor_country Text None

Access (.mdb)


Data Discrepancies

Access_WarehouseAichi.mdb

Inventory Ordered Product_id Text None

Inventory Ordered retailer_id Text None Inventory Ordered Warehouse_id Text None Inventory Ordered qtyissued Number None Inventory Ordered QtyDeficit Number None Inventory Ordered Discount Number None

Inventory Ordered After_Discount_Price Currency None

ProductCategory productcategory_id Text None

ProductCategory productcategoryname Text None

ProductDetails Product_id Text None ProductDetails productname Text None

ProductDetails productsubcategory_id Text None

ProductDetails unit Text None ProductDetails Qtyperunit Text None ProductDetails Unitcostprice Currency None ProductDetails UnitSellingPrice Currency None ProductDetails ReOrderLevel Number None ProductDetails ReOrderQty Number None Products_Vendors DesiredLeadTime Number None ProductSubCategory

productsubcategory_id Text None

ProductSubCategory productcategory_id Text None ProductSubCategory subcategoryname Text None Retailers retailer_id Text None Retailers retailername Text None Retailers retaileraddress Text None Retailers retailercity Text None Retailers retailerstate Text None Retailers retailercountry Text None Retailers retailerphone Text None Retailers retailerZip Text None Vendor vendor_id Text None Vendor AccountNumber Text None

25

Vendor credit rating Number None Vendor active flag Number None Vendor vendor_address Text None Warehouse Warehouse Zip Number None Warehouse Warehouse Country Text None Warehouse Warehouse Region Text None Warehouse Warehouse Phone Text None

Access (.mdb)


Data Discrepancies

Access_WarehouseApen.mdb

Inventory Ordered Product_id Text

Inventory Ordered retailer_id Text Inventory Ordered Warehouse_id Text Inventory Ordered qtyissued Number Inventory Ordered date issue Date Inventory Ordered Issued By Text Inventory Ordered Discount Number

Inventory Ordered After_Discount_Price Currency

ProductCategory productcategory_id Text

ProductCategory productcategoryname Text

ProductDetails Product_id Text ProductDetails productname Text

ProductDetails productsubcategory_id Text

ProductDetails unit Text ProductDetails Qtyperunit Text ProductDetails Unitcostprice Currency ProductDetails UnitSellingPrice Currency ProductDetails ReOrderLevel Number ProductDetails ReOrderQty Number ProductDetails QtyonHand Number Product Forecast Product_id Text Product Forecast For year Text Product Forecast ForQuarter Text ProductForecast expected_volume Text Products_Vendors Product_id Text Retailers retailercountry Text Retailers retailerphone Text Retailers retailerZip Text Vendor vendor_id Text Vendor AccountNumber Text Vendor creditrating Number Vendor activeflag Number

26

Vendor vendoraddress Text Vendor vendorcountry Text Warehouse WarehouseZip Number Warehouse WarehouseCountry Text Warehouse WarehouseRegion Text Warehouse WarehousePhone Text

Access (.mdb)


Data Discrepancies

Access_WarehouseCannon.mdb


Inventory Ordered retailer_id Text Inventory Ordered Warehouse_id Text ProductDetails unit Text ProductDetails Qtyperunit Text ProductDetails Unitcostprice Currency ProductDetails UnitSellingPrice Currency ProductDetails ReOrderLevel Number ProductDetails ReOrderQty Number ProductDetails QtyonHand Number ProductForecast product_id Text ProductForecast Foryear Text ProductForecast ForQuarter Text ProductForecast expected_volume Text Products_Vendors Product_id Text Products_Vendors vendor_id Text Products_Vendors Unit_Selling_Price Currency Products_Vendors Discount Number Products_Vendors DesiredLeadTime Number ProductSubCategory

productsubcategory_id Text

ProductSubCategory productcategory_id Text ProductSubCategory subcategoryname Text Retailers retailer_id Text Retailers retailername Text Retailers retaileraddress Text Retailers retailercity Text Retailers retailerstate Text Retailers retailercountry Text Vendor vendoraddress Text Vendor vendorcountry Text Vendor vendorstate Text Vendor vendorcity Text Vendor vendorphone Text Vendor vendorname Text

27

Vendor VendorZip Text Warehouse Warehouse_id Text Warehouse WarehouseName Text Warehouse WarehouseAddress Text Warehouse WarehouseCity Text Warehouse WarehouseZip Number Warehouse WarehouseCountry Text Warehouse WarehouseRegion Text Warehouse WarehousePhone Text

Access (.mdb)


Data Discrepancies

Access_WarehouseKansas.mdb


Inventory Ordered retailer_id Text Inventory Ordered Warehouse_id Text Inventory Ordered qtyissued Number Inventory Ordered dateissue Date ProductDetails productname Text

ProductDetails productsubcategory_id Text

ProductDetails unit Text ProductDetails Qtyperunit Text ProductDetails Unitcostprice Currency ProductDetails UnitSellingPrice Currency ProductDetails ReOrderLevel Number ProductDetails ReOrderQty Number ProductDetails QtyonHand Number ProductForecast product_id Text ProductForecast Foryear Text ProductForecast ForQuarter Text ProductForecast expected_volume Text Products_Vendors Product_id Text Products_Vendors vendor_id Text Products_Vendors Unit_Selling_Price Currency Products_Vendors Discount Number Products_Vendors DesiredLeadTime Number ProductSubCategory

productsubcategory_id Text

TransactionQOH OpeningQOH Number TransactionQOH ClosingQOH Number TransactionQOH Transacted_Date Date TransactionQOH Issued_to Text Vendor vendor_id Text Vendor AccountNumber Text Vendor creditrating Number

28

Vendor activeflag Number Vendor vendoraddress Text Vendor vendorcountry Text Vendor vendorstate Text Vendor vendorcity Text Vendor vendorphone Text Vendor vendorname Text Vendor VendorZip Text Warehouse Warehouse_id Text Warehouse WarehouseName Text Warehouse WarehouseAddress Text Warehouse WarehouseCity Text Warehouse WarehouseZip Number Warehouse WarehouseCountry Text Warehouse WarehouseRegion Text Warehouse WarehousePhone Text

29

LOGICAL DIMENSIONAL MODEL

LOGICAL DATA MAP

Database Name

Table Name Column Name

Data Type

Database Name

Table Name

Column Name

Data Type

Speedster Motor Corporation

Inventory_Ordered_Fact

Product_Id int NA NA NA NA

Region varchar(50)

Region Text

Phone_No varchar(20)

Phone_No Text

Product_Key int NA NA NA NA Vendor_id varchar

(20) Excel, Access

Product Product_Id Text

Vendor Management

System

Warehouse

Product

RegionDate & Time

Unit Price

30

Product_Name varchar(50)

Product_Name

Text

Unit varchar(20)

Unit Text

QtyPerUnit varchar(20)

QtyPerUnit

Text

UnitCostPrice money UnitCostPrice

Currency

Product_Dim UnitSellingPrice

money UnitSellingPrice

Currency

SubCategory_Key

int NA NA NA NA

SubCategory_Id

varchar(20)

Access

ProductSubCategory

SubCategory_Id

Text

SubCategory_Name

varchar(50)

SubCategory_Name

Text

Time_Key int NA NA NA NA Warehouse_K

ey int NA NA NA NA

Category_Key int NA NA NA NA SubCategory_

Key int NA NA NA NA

Product_Key int NA NA NA NA Category_Di

m Quantity_Received

int NA NA

Monthly_QuantityOnHand

int QOH int

Turns_Per_Month

int NA NA NA NA

SubCategory_Dim

GrossProfitMargin

money NA NA NA NA

GrossMargin money NA NA NA NA GMROI number

(38,6) NA NA NA NA

Quantity_Ordered_Fact

GMROI Per Product Per Month

number(38,6)

NA NA NA NA

31

PHYSICAL MODEL FORMAT

Table Name/ Column Name

Data Type Null Allowed

Primary Key

Description

Date_Dim Date_Key int No 1 Surrogate key Year int No Calendar year as

number Quarter Varchar(14) No Quarter as text such as

1st Quarter. Month varchar(15) No Month name as text. Warehouse_Dim Warehouse_key int No 1 Surrogate key Warehouse_Country varchar(10) No Name of the country. Warehouse_Id varchar(10) No Natural key. Phone_No varchar(20) No

Phone number of the warehouse.

Product_Dim Product_Key int No 1 Surrogate key Product_Id varchar(20) No

Contains the product id.

Product_Name varchar(50) No Name of the product. Unit varchar(20) No

Measurable quantity of the product in units sold.

QuantityOrdered int No Number of peace. Quantity Received int No Number of peace. Lead Time int No Days allowed.. After_Discount_Price int No Currency Vendor_Dim Vendor_Key int No 1 Surrogate key Vendor_Id varchar(20) Vendor_Name Varhar(50) No

Name of the Vendor of the product.

Region_Dim Region_Key int No Surrogate key. Region_Id varchar(20) No Contains the region id. Region_Name varchar(50) No

Region name of the product.

Vendor_Fact Time_Key int No

1 Foreign key reference to Time dimension.

32

Turns_Per_Month int No

Total profit.

Average_Discounted_Price money No

Vendor Profitability

Average_Lead_Time int No Vendor Reliability GMROI number(38,6) No

Turns_Per_Month * Gross Margin

GMROI Per Product Per Month

number(38,6) No

Gross Margin * per unit.

33

BIBLIOGRAPHY

Student Project Book Modeling and Designing a Data Warehouse Student Guide.

Sem D Project

Documents

Transcript of Sem D Project