This document discusses techniques for manipulating large data sets, including:
1. Using subqueries to copy data between tables, update rows based on other tables, and delete rows matching conditions in other tables.
2. Performing different types of multitable INSERT statements like unconditional, conditional ALL, conditional FIRST, and pivoting to insert data into multiple tables.
3. Using the MERGE statement to conditionally insert or update rows by matching conditions between tables.
4. Tracking changes to data over time using flashback queries to view past versions of rows.
This document discusses best practices for capturing and logging multiple errors in a single row. It recommends using binary operations to represent whether each validation check passes (0) or fails (1). These binary values can then be combined and logged as a single decimal error code. An example is provided where checks for product weight, name, size and date are represented by the binary number 1011, which equals the decimal error code 11 when logged. The document outlines how this approach can be implemented in a TMAP transformation, using ternary operations and bitwise logic to set variable values representing each check. It also explains how the logged error code can later be decrypted to identify the specific failed validation.
This document contains a practice exam for the Oracle Database: SQL Fundamentals I certification with 15 multiple choice questions. The questions cover topics like SQL queries, joins, functions, aggregates and more. Correct answers are provided for each question to help students practice and prepare for the certification exam.
This document discusses multiple-column subqueries in SQL. It describes how multiple-column subqueries can return multiple columns that can then be compared pairwise or nonpairwise to columns in the outer query. Examples are provided of multiple-column subqueries used to return order items that match the product number and quantity from another order. The document also discusses how subqueries can be used in the FROM clause and how null values are handled in subqueries.
The document introduces three fundamental concepts for tuning SQL queries: join order, join method, and access method. It uses an example query joining sales, products, and customers tables to illustrate how changing the join order can significantly impact performance. The best join order typically lists tables in order of selectivity of their predicates. Statistics and hints can help the optimizer determine optimal join order. Breaking a query into multiple statements is another way to control join order.
If SQL is the universal language of data, why do we author our most important data applications (metrics, analytics, business intelligence) in languages other than SQL? Multidimensional databases and languages such as MDX, DAX and Tableau LOD solve these problems but introduce others: they require specialized knowledge, complicate the data pipeline and don’t integrate well. Is it possible to define and query business intelligence models in SQL?
Apache Calcite has extended SQL to support measures, evaluation context, and multidimensional expressions. With these concepts you can define data models that contain measures, use them in queries, and define new measures in queries.
A talk given by Julian Hyde to Apache Calcite's virtual meetup, 2023-03-15.
Company segmentation - an approach with RCasper Crause
We classify companies based on how their stocks trade using their daily stock returns (percentage movement from one day to the next). This analysis will help your organization determine which companies are related to each other (competitors and have similar attributes).
This document discusses using Ruby to perform multidimensional data analysis on relational databases. It introduces Mondrian, an open-source OLAP engine that allows for multidimensional analysis on top of SQL databases using the MDX query language. A new Ruby gem called mondrian-olap will integrate Mondrian and provide a Ruby DSL and ActiveRecord-like query interface for defining OLAP schemas and performing analytical queries on relational data in a simpler way than SQL. Examples show how to write multidimensional queries in MDX and the Ruby interface to analyze sales data across dimensions like time, products, and customers.
This document discusses techniques for manipulating large data sets, including:
1. Using subqueries to copy data between tables, update rows based on other tables, and delete rows matching conditions in other tables.
2. Performing different types of multitable INSERT statements like unconditional, conditional ALL, conditional FIRST, and pivoting to insert data into multiple tables.
3. Using the MERGE statement to conditionally insert or update rows by matching conditions between tables.
4. Tracking changes to data over time using flashback queries to view past versions of rows.
This document discusses best practices for capturing and logging multiple errors in a single row. It recommends using binary operations to represent whether each validation check passes (0) or fails (1). These binary values can then be combined and logged as a single decimal error code. An example is provided where checks for product weight, name, size and date are represented by the binary number 1011, which equals the decimal error code 11 when logged. The document outlines how this approach can be implemented in a TMAP transformation, using ternary operations and bitwise logic to set variable values representing each check. It also explains how the logged error code can later be decrypted to identify the specific failed validation.
This document contains a practice exam for the Oracle Database: SQL Fundamentals I certification with 15 multiple choice questions. The questions cover topics like SQL queries, joins, functions, aggregates and more. Correct answers are provided for each question to help students practice and prepare for the certification exam.
This document discusses multiple-column subqueries in SQL. It describes how multiple-column subqueries can return multiple columns that can then be compared pairwise or nonpairwise to columns in the outer query. Examples are provided of multiple-column subqueries used to return order items that match the product number and quantity from another order. The document also discusses how subqueries can be used in the FROM clause and how null values are handled in subqueries.
The document introduces three fundamental concepts for tuning SQL queries: join order, join method, and access method. It uses an example query joining sales, products, and customers tables to illustrate how changing the join order can significantly impact performance. The best join order typically lists tables in order of selectivity of their predicates. Statistics and hints can help the optimizer determine optimal join order. Breaking a query into multiple statements is another way to control join order.
If SQL is the universal language of data, why do we author our most important data applications (metrics, analytics, business intelligence) in languages other than SQL? Multidimensional databases and languages such as MDX, DAX and Tableau LOD solve these problems but introduce others: they require specialized knowledge, complicate the data pipeline and don’t integrate well. Is it possible to define and query business intelligence models in SQL?
Apache Calcite has extended SQL to support measures, evaluation context, and multidimensional expressions. With these concepts you can define data models that contain measures, use them in queries, and define new measures in queries.
A talk given by Julian Hyde to Apache Calcite's virtual meetup, 2023-03-15.
Company segmentation - an approach with RCasper Crause
We classify companies based on how their stocks trade using their daily stock returns (percentage movement from one day to the next). This analysis will help your organization determine which companies are related to each other (competitors and have similar attributes).
This document discusses using Ruby to perform multidimensional data analysis on relational databases. It introduces Mondrian, an open-source OLAP engine that allows for multidimensional analysis on top of SQL databases using the MDX query language. A new Ruby gem called mondrian-olap will integrate Mondrian and provide a Ruby DSL and ActiveRecord-like query interface for defining OLAP schemas and performing analytical queries on relational data in a simpler way than SQL. Examples show how to write multidimensional queries in MDX and the Ruby interface to analyze sales data across dimensions like time, products, and customers.
This document discusses multidimensional data models and cube operations. It introduces key concepts like facts and measures, dimensions and hierarchies. It describes star and snowflake schemas for structuring multidimensional data in a relational database. The document also covers cube operations like roll-up, drill-down, slice and dice that allow interactive analysis of aggregated data across multiple dimensions.
Project Management CaseYou are working for a large, apparel desi.docxbriancrawford30935
Project Management Case
You are working for a large, apparel design and manufacturing company, Trillo Apparel Company (TAC), headquartered in Albuquerque, New Mexico. TAC employs around 3000 people and has remained profitable through tough economic times. The operations are divided into 4 districts; District 1 – North, District 2 – South, District 3 – West and District 4 – East. The company sets strategic goals at the beginning of each year and operates with priorities to reach those goals.Trillo Apparel Company Current Year Priorities
Increase Sales and Distribution in the East
Improve Product Quality
Improve Production in District 4
Increase Brand Recognition
Increase RevenuesCompany Details
Company Name: Trillo Apparel Company (TAC)
Company Type: Apparel design and production
Company Size: 3000 employees
Position
# Employees
Owner/CEO
1
Vice President
4
Chief Operating Officer
1
Chief Financial Officer
1
Chief Information Officer
1
IT Department
38
District Manager
4
Sales Team
30
Accountant
12
Administrative Assistant
7
Order Fullfilment
45
Customer Service
57
Designer
24
Project Manager
10
Maintenance
25
Operations
2500
Shipping Department
240
Total Employees
3000
Products: Various Apparel
Corporate Location: Albuquerque, New MexicoTAC Organization Chart
District 4 Production Warehouse Move Project Details
The business has expanded considerably over the past few years and District 4 in the East has outgrown its current production facility. Because of this growth the executives want to expand the current facility, moving the whole facility 10 miles away. The location selected has enough room for the production and the shipping department. However, the current warehouse needs some renovation to accommodate the district’s operational needs.
The VP of Operations estimates the production and shipping warehouse move for District 4 will provide room required to generate the additional $1 million/year product revenues to meet the current demand due to the expanded production capacity. Daily production generates $50,000 revenue so a week of downtime will cost $250,000 in lost revenues.
The move must be completed in 4 months.
Mileage between the old and new facilities is 10 miles.
Bids have been received from contractors to build out the new office space and production floor and have signed contracts for work as follows:
Activity
Company Providing Services
Total Contract
Supplies
Time Needed
Pack, move and unpack production equipment
City Equipment Movers
$150,000
n/a
5 Days
Move non-production equipment and materials
Express Moving Company
$125,000
n/a
5 Days
Framing
East Side Framing & Drywall
$121,000
$125,000
15 Days
Electrical
Sparks Electrical
$18,000
$12,000
10 Days
Plumbing
Waterworks Plumbing
$15,000
$13,000
10 Days
Drywall
East Side Framing & Drywall
$121,000
$18,000
15 Days
Finish Work
Woodcraft Carpentry
$115,000
$15,000
15 Days
Build work benches for production floor
Student Workers Carpentry
$112,000
$110,000
15 Days
Product.
This document discusses goal seek and sensitivity analysis in Excel. It provides examples of using goal seek to find unknown values that satisfy a desired result. Sensitivity analysis determines how changes in input values affect the output. The document explains how to perform one-variable and two-variable sensitivity analysis in Excel using data tables. Examples are provided to illustrate goal seek and sensitivity analysis for business scenarios like determining break-even points and analyzing profit impacts.
Creating the Target Structure in OWB: Creating dimensions in OWB,
The Time dimension, Creating a Time dimension with the Time Dimension
Wizard, The Product dimension, Product Attributes (attribute type),Product
Levels, Product Hierarchy (highest to lowest),Creating the Product
dimension with the New Dimension Wizard, The Store dimension, Store
Attributes (attribute type), data type and size, and (Identifier),Store Levels,
Store Hierarchy (highest to lowest),Creating the Store dimension with the
New Dimension Wizard, Creating a cube in OWB, Creating a cube with the
wizard, Using the Data Object Editor
The document provides examples of SQL queries and solutions to interview questions related to SQL queries on Oracle databases. It includes queries to find products with continuously increasing sales, products with no sales, products whose sales decreased in 2012 vs 2011, the top product sold each year, and total sales of each product. Tables called PRODUCTS and SALES are created with sample data on products and sales to demonstrate the example queries.
First of all, I like you to notice that I have set Starting WIP .docxvoversbyobersby
First of all, I like you to notice that I have set "Starting WIP" = 4, "mean daily production rate" = 3.5 units per day, and "Maximum Variation around mean" = 2.5 units on all operations in what we call a "Base Model" representing the game played for 20 days (i.e., 4 weeks, 5 days a week). The base model also assumes monthly demand of 70 units (3.5x20days, 4 weeks, 5 days a week). Next, I want you to note the most important feature of this model i.e., it reports results for a single user like you (we call it "1Run") as well as results averaged over 1,000 runs (as if a class of 1,000 students' results are averaged). Now,
1. Click "F9" key few times and observe Average Efficiencies in Panels C and D. Do you see a pattern in the Average Efficiencies of the various operations in Panel C? and D? Describe the pattern you see. In your opinion what causes this pattern?
2. Click "F9" key few times and observe how the measures in Panel E change for “One Run” and “1,000 Runs”. Explain the difference in the way the two sets of numbers change.
3. Finally, Copy and “Paste Special” (using the “values” option) your results from Panel E for “1000 Runs” in the “Base Model” column cells E6 through E18 in the "Scenario Output" worksheet in the Excel model. Explain how your "Base Model" results might be generalized for a more complex production system where parts flow from machine to machine in a wide variety of patterns or routings.
4
From: Dr. Mahesh Gupta Student ID: __________________
To: 401 Online Students (Do not print your name anywhere)
Subject: Managing a Business - An Excel-Based Dice Game
Purpose: The purpose of this fun and interactive game is to sharpen your understanding of core Operations Management and Theory of Constraints concepts using an Excel-based variant of The Dice Game which Alex played with the boy scouts in Chapter 13 of The Goal.
Story Line: A very close friend of yours, Mr. Herbie, had a great idea and developed a very successful product while pursuing his undergraduate degree in engineering school. Following an advice from engineering school, Herbie decided to open a company, The XYZ Company and made an investment of $50,000 in a production system consisting of 5 processes. His initial thinking was that he will run a single shift operation – 5 days per week, 4 weeks a month i.e., 20 days per month. Each process will have mean daily production capacity of 3.5 units, and the production system should be able to produce 70 units per month (a very definite market demand estimate). Herbie expected mean daily production of 3.5 units to vary + 2.5 units i.e., ranging between 6 and 1 (similar to the fair roll of a dice) due to various sources of variation e.g., machine break down, bad quality raw material, and worker morale. Because of interdependencies among processes and variation, he expected work-in-progress (WIP) to form in front of each process. (Note: Thus, Mean, Variation and WIP levels at each process can be man ...
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
Building a semantic/metrics layer using CalciteJulian Hyde
A semantic layer, also known as a metrics layer, lies between business users and the database, and lets those users compose queries in the concepts that they understand. It also governs access to the data, manages data transformations, and can tune the database by defining materializations.
Like many new ideas, the semantic layer is a distillation and evolution of many old ideas, such as query languages, multidimensional OLAP, and query federation.
In this talk, we describe the features we are adding to Calcite to define business views, query measures, and optimize performance.
A talk given at Community over Code, the annual conference of the Apache Software Foundation, in Halifax, NS, on 9th October, 2023.
Simplifying SQL with CTE's and windowing functionsClayton Groom
Too busy to learn the new capabilities of SQL Server? This session will cover several of the new features of the T-SQL language, specifically Common Table Expressions (CTE's) and Windowing Functions. This will be an code-heavy session with examples hat you can readily leverage in your solutions.
The focus will be on techniques to shape and manipulate your data for easier consumption by your application, and to leverage your SQL Server to avoid writing code in your application.
A basic to intermediate understanding of T-SQL is required.
The document discusses labor markets under conditions of perfect competition for goods and labor. It analyzes how a firm's demand for labor (derived demand) is determined by the marginal revenue product of labor curve. This curve can shift due to changes in productivity or price of the good produced. An increase in either factor causes the curve to shift right and increases labor demand. The firm hires workers up to the point where the wage rate equals marginal revenue product. The document also examines how the firm responds to changes in the market wage rate, hiring more workers if wages decrease and fewer workers if they increase.
The document discusses labor markets under conditions of perfect competition for goods and labor. It analyzes how a firm's demand for labor (derived demand) is determined by the marginal revenue product of labor curve. This curve can shift due to changes in productivity or price of the good produced. An increase in either factor causes the curve to shift right and increases labor demand. The firm hires workers up to the point where the wage rate equals marginal revenue product. The document also examines how the firm responds to changes in the market wage rate, hiring more workers if wages decrease and fewer workers if they increase.
Solved Practice questions for Microsoft Querying Data with Transact-SQL 70-76...KarenMiner
Are you searching for solved questions for Microsoft Querying Data with Transact-SQL 70-761. You also need to pass it in first attempt but It is difficult to pass Microsoft 70-761 for most of the students. You can make it easier with the help of fravo Microsoft 70-761 Querying Data with Transact-SQL Exam dumps. Get complete version here:
https://www.fravo.com/70-761-exams.html
Minimums and maximums optimization Problem by excelMostafa Ashour
Target Company produces three product types - pin, lock, and coil. It aims to maximize profits each month given resource constraints. In January, the optimal solution was found using Excel Solver. In subsequent months, additional constraints were added regarding minimum/maximum production amounts and fixed costs for equipment. The optimal solution was updated each time to reflect the new constraints and maximize profits within the available resources.
Optimization with minimums and maximums capacity excelMostafa Ashour
Target Company produces three product types - pin, lock, and coil. It aims to maximize profits each month given resource constraints. In January, the optimal solution was found using Excel Solver. In subsequent months, additional constraints were added regarding minimum/maximum production amounts and fixed costs for equipment. The optimal solution was updated each time to reflect the new constraints and maximize profits within the available resources.
This document provides an overview of four methods for project analysis and decision making: regression analysis, sensitivity analysis, Monte Carlo simulations, and decision trees. Regression analysis uses past data to forecast future trends through mathematical modeling. Sensitivity analysis evaluates how changes to variables impact outcomes like net present value. Monte Carlo simulations model projects probabilistically by assigning distributions to variables and running simulations. Decision trees visually represent decisions, consequences, probabilities, and opportunities to break down complex situations. Examples are provided for each method.
This project focused on creating data frames, filtering data, grouping data, merging, and displaying data. Furthermore, it also includes creating new columns in which specific conditions can be applied. The data is used to solve business problems within a superstore.
The first problem statement is determining the prizes taken from the Top 5 products from the Mobiles & Tablet Category. Second, the data is processed to fulfill the requirement to check whether there is a decrease in the sales of the Others Category in 2022. The task also requires the display of the top 20 products that have the highest decrease. Third, I utilize the data to process the Customer ID and Registered Data of the consumers who have checked out but have not yet made payment. Fourth, the data is sorted and analyzed to compare the average daily sales on the weekends and those on the weekdays in the time range of 3 months.
Optimization with minimums and maximums capacity sasMostafa Ashour
Target Company produces three products - pin, lock, and coil. In January, the optimal solution is to produce 75 pins, 35 locks, and no coils for a maximum profit of $145,000. In March, with updated constraints of minimum 10 and maximum 40 units of each type, the optimal solution is to produce 10 pins, 40 locks, and 10 coils for a maximum profit of $145,000. In April, with added fixed costs for equipment of $10,000 for pins, $5,000 for locks, and $1,000 for coils, the optimal solution is to produce 75 pins, 35 locks, and 0 coils for a maximum profit of $159,000.
This document provides 35 Excel tips to help save time when working with spreadsheets. It covers functions and tools like SUM, VLOOKUP, conditional formatting, and data tables that allow automating calculations and analyses. Step-by-step examples demonstrate how to use each tool to summarize and manipulate data in Excel.
This document provides a summary of 35 Excel tips to help save time when working with spreadsheets. It outlines various functions and commands in Excel like SUMIF, VLOOKUP, conditional formatting and more. Exercises are provided for each tip to allow users to practice the skills. The target audience is business analysts and associates who can use these tips to work more efficiently in Excel.
This document provides a summary of 35 Excel tips intended to save time for business analysts and associates. It covers functions and tools for splitting windows, hiding/unhiding rows and columns, sorting data, using formulas like IF, SUM, COUNT, and VLOOKUP, as well as formatting, filtering, and protecting worksheets. Exercises are provided for each tip to help users practice and learn when and how to apply each function.
This document discusses multidimensional data models and cube operations. It introduces key concepts like facts and measures, dimensions and hierarchies. It describes star and snowflake schemas for structuring multidimensional data in a relational database. The document also covers cube operations like roll-up, drill-down, slice and dice that allow interactive analysis of aggregated data across multiple dimensions.
Project Management CaseYou are working for a large, apparel desi.docxbriancrawford30935
Project Management Case
You are working for a large, apparel design and manufacturing company, Trillo Apparel Company (TAC), headquartered in Albuquerque, New Mexico. TAC employs around 3000 people and has remained profitable through tough economic times. The operations are divided into 4 districts; District 1 – North, District 2 – South, District 3 – West and District 4 – East. The company sets strategic goals at the beginning of each year and operates with priorities to reach those goals.Trillo Apparel Company Current Year Priorities
Increase Sales and Distribution in the East
Improve Product Quality
Improve Production in District 4
Increase Brand Recognition
Increase RevenuesCompany Details
Company Name: Trillo Apparel Company (TAC)
Company Type: Apparel design and production
Company Size: 3000 employees
Position
# Employees
Owner/CEO
1
Vice President
4
Chief Operating Officer
1
Chief Financial Officer
1
Chief Information Officer
1
IT Department
38
District Manager
4
Sales Team
30
Accountant
12
Administrative Assistant
7
Order Fullfilment
45
Customer Service
57
Designer
24
Project Manager
10
Maintenance
25
Operations
2500
Shipping Department
240
Total Employees
3000
Products: Various Apparel
Corporate Location: Albuquerque, New MexicoTAC Organization Chart
District 4 Production Warehouse Move Project Details
The business has expanded considerably over the past few years and District 4 in the East has outgrown its current production facility. Because of this growth the executives want to expand the current facility, moving the whole facility 10 miles away. The location selected has enough room for the production and the shipping department. However, the current warehouse needs some renovation to accommodate the district’s operational needs.
The VP of Operations estimates the production and shipping warehouse move for District 4 will provide room required to generate the additional $1 million/year product revenues to meet the current demand due to the expanded production capacity. Daily production generates $50,000 revenue so a week of downtime will cost $250,000 in lost revenues.
The move must be completed in 4 months.
Mileage between the old and new facilities is 10 miles.
Bids have been received from contractors to build out the new office space and production floor and have signed contracts for work as follows:
Activity
Company Providing Services
Total Contract
Supplies
Time Needed
Pack, move and unpack production equipment
City Equipment Movers
$150,000
n/a
5 Days
Move non-production equipment and materials
Express Moving Company
$125,000
n/a
5 Days
Framing
East Side Framing & Drywall
$121,000
$125,000
15 Days
Electrical
Sparks Electrical
$18,000
$12,000
10 Days
Plumbing
Waterworks Plumbing
$15,000
$13,000
10 Days
Drywall
East Side Framing & Drywall
$121,000
$18,000
15 Days
Finish Work
Woodcraft Carpentry
$115,000
$15,000
15 Days
Build work benches for production floor
Student Workers Carpentry
$112,000
$110,000
15 Days
Product.
This document discusses goal seek and sensitivity analysis in Excel. It provides examples of using goal seek to find unknown values that satisfy a desired result. Sensitivity analysis determines how changes in input values affect the output. The document explains how to perform one-variable and two-variable sensitivity analysis in Excel using data tables. Examples are provided to illustrate goal seek and sensitivity analysis for business scenarios like determining break-even points and analyzing profit impacts.
Creating the Target Structure in OWB: Creating dimensions in OWB,
The Time dimension, Creating a Time dimension with the Time Dimension
Wizard, The Product dimension, Product Attributes (attribute type),Product
Levels, Product Hierarchy (highest to lowest),Creating the Product
dimension with the New Dimension Wizard, The Store dimension, Store
Attributes (attribute type), data type and size, and (Identifier),Store Levels,
Store Hierarchy (highest to lowest),Creating the Store dimension with the
New Dimension Wizard, Creating a cube in OWB, Creating a cube with the
wizard, Using the Data Object Editor
The document provides examples of SQL queries and solutions to interview questions related to SQL queries on Oracle databases. It includes queries to find products with continuously increasing sales, products with no sales, products whose sales decreased in 2012 vs 2011, the top product sold each year, and total sales of each product. Tables called PRODUCTS and SALES are created with sample data on products and sales to demonstrate the example queries.
First of all, I like you to notice that I have set Starting WIP .docxvoversbyobersby
First of all, I like you to notice that I have set "Starting WIP" = 4, "mean daily production rate" = 3.5 units per day, and "Maximum Variation around mean" = 2.5 units on all operations in what we call a "Base Model" representing the game played for 20 days (i.e., 4 weeks, 5 days a week). The base model also assumes monthly demand of 70 units (3.5x20days, 4 weeks, 5 days a week). Next, I want you to note the most important feature of this model i.e., it reports results for a single user like you (we call it "1Run") as well as results averaged over 1,000 runs (as if a class of 1,000 students' results are averaged). Now,
1. Click "F9" key few times and observe Average Efficiencies in Panels C and D. Do you see a pattern in the Average Efficiencies of the various operations in Panel C? and D? Describe the pattern you see. In your opinion what causes this pattern?
2. Click "F9" key few times and observe how the measures in Panel E change for “One Run” and “1,000 Runs”. Explain the difference in the way the two sets of numbers change.
3. Finally, Copy and “Paste Special” (using the “values” option) your results from Panel E for “1000 Runs” in the “Base Model” column cells E6 through E18 in the "Scenario Output" worksheet in the Excel model. Explain how your "Base Model" results might be generalized for a more complex production system where parts flow from machine to machine in a wide variety of patterns or routings.
4
From: Dr. Mahesh Gupta Student ID: __________________
To: 401 Online Students (Do not print your name anywhere)
Subject: Managing a Business - An Excel-Based Dice Game
Purpose: The purpose of this fun and interactive game is to sharpen your understanding of core Operations Management and Theory of Constraints concepts using an Excel-based variant of The Dice Game which Alex played with the boy scouts in Chapter 13 of The Goal.
Story Line: A very close friend of yours, Mr. Herbie, had a great idea and developed a very successful product while pursuing his undergraduate degree in engineering school. Following an advice from engineering school, Herbie decided to open a company, The XYZ Company and made an investment of $50,000 in a production system consisting of 5 processes. His initial thinking was that he will run a single shift operation – 5 days per week, 4 weeks a month i.e., 20 days per month. Each process will have mean daily production capacity of 3.5 units, and the production system should be able to produce 70 units per month (a very definite market demand estimate). Herbie expected mean daily production of 3.5 units to vary + 2.5 units i.e., ranging between 6 and 1 (similar to the fair roll of a dice) due to various sources of variation e.g., machine break down, bad quality raw material, and worker morale. Because of interdependencies among processes and variation, he expected work-in-progress (WIP) to form in front of each process. (Note: Thus, Mean, Variation and WIP levels at each process can be man ...
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
Building a semantic/metrics layer using CalciteJulian Hyde
A semantic layer, also known as a metrics layer, lies between business users and the database, and lets those users compose queries in the concepts that they understand. It also governs access to the data, manages data transformations, and can tune the database by defining materializations.
Like many new ideas, the semantic layer is a distillation and evolution of many old ideas, such as query languages, multidimensional OLAP, and query federation.
In this talk, we describe the features we are adding to Calcite to define business views, query measures, and optimize performance.
A talk given at Community over Code, the annual conference of the Apache Software Foundation, in Halifax, NS, on 9th October, 2023.
Simplifying SQL with CTE's and windowing functionsClayton Groom
Too busy to learn the new capabilities of SQL Server? This session will cover several of the new features of the T-SQL language, specifically Common Table Expressions (CTE's) and Windowing Functions. This will be an code-heavy session with examples hat you can readily leverage in your solutions.
The focus will be on techniques to shape and manipulate your data for easier consumption by your application, and to leverage your SQL Server to avoid writing code in your application.
A basic to intermediate understanding of T-SQL is required.
The document discusses labor markets under conditions of perfect competition for goods and labor. It analyzes how a firm's demand for labor (derived demand) is determined by the marginal revenue product of labor curve. This curve can shift due to changes in productivity or price of the good produced. An increase in either factor causes the curve to shift right and increases labor demand. The firm hires workers up to the point where the wage rate equals marginal revenue product. The document also examines how the firm responds to changes in the market wage rate, hiring more workers if wages decrease and fewer workers if they increase.
The document discusses labor markets under conditions of perfect competition for goods and labor. It analyzes how a firm's demand for labor (derived demand) is determined by the marginal revenue product of labor curve. This curve can shift due to changes in productivity or price of the good produced. An increase in either factor causes the curve to shift right and increases labor demand. The firm hires workers up to the point where the wage rate equals marginal revenue product. The document also examines how the firm responds to changes in the market wage rate, hiring more workers if wages decrease and fewer workers if they increase.
Solved Practice questions for Microsoft Querying Data with Transact-SQL 70-76...KarenMiner
Are you searching for solved questions for Microsoft Querying Data with Transact-SQL 70-761. You also need to pass it in first attempt but It is difficult to pass Microsoft 70-761 for most of the students. You can make it easier with the help of fravo Microsoft 70-761 Querying Data with Transact-SQL Exam dumps. Get complete version here:
https://www.fravo.com/70-761-exams.html
Minimums and maximums optimization Problem by excelMostafa Ashour
Target Company produces three product types - pin, lock, and coil. It aims to maximize profits each month given resource constraints. In January, the optimal solution was found using Excel Solver. In subsequent months, additional constraints were added regarding minimum/maximum production amounts and fixed costs for equipment. The optimal solution was updated each time to reflect the new constraints and maximize profits within the available resources.
Optimization with minimums and maximums capacity excelMostafa Ashour
Target Company produces three product types - pin, lock, and coil. It aims to maximize profits each month given resource constraints. In January, the optimal solution was found using Excel Solver. In subsequent months, additional constraints were added regarding minimum/maximum production amounts and fixed costs for equipment. The optimal solution was updated each time to reflect the new constraints and maximize profits within the available resources.
This document provides an overview of four methods for project analysis and decision making: regression analysis, sensitivity analysis, Monte Carlo simulations, and decision trees. Regression analysis uses past data to forecast future trends through mathematical modeling. Sensitivity analysis evaluates how changes to variables impact outcomes like net present value. Monte Carlo simulations model projects probabilistically by assigning distributions to variables and running simulations. Decision trees visually represent decisions, consequences, probabilities, and opportunities to break down complex situations. Examples are provided for each method.
This project focused on creating data frames, filtering data, grouping data, merging, and displaying data. Furthermore, it also includes creating new columns in which specific conditions can be applied. The data is used to solve business problems within a superstore.
The first problem statement is determining the prizes taken from the Top 5 products from the Mobiles & Tablet Category. Second, the data is processed to fulfill the requirement to check whether there is a decrease in the sales of the Others Category in 2022. The task also requires the display of the top 20 products that have the highest decrease. Third, I utilize the data to process the Customer ID and Registered Data of the consumers who have checked out but have not yet made payment. Fourth, the data is sorted and analyzed to compare the average daily sales on the weekends and those on the weekdays in the time range of 3 months.
Optimization with minimums and maximums capacity sasMostafa Ashour
Target Company produces three products - pin, lock, and coil. In January, the optimal solution is to produce 75 pins, 35 locks, and no coils for a maximum profit of $145,000. In March, with updated constraints of minimum 10 and maximum 40 units of each type, the optimal solution is to produce 10 pins, 40 locks, and 10 coils for a maximum profit of $145,000. In April, with added fixed costs for equipment of $10,000 for pins, $5,000 for locks, and $1,000 for coils, the optimal solution is to produce 75 pins, 35 locks, and 0 coils for a maximum profit of $159,000.
This document provides 35 Excel tips to help save time when working with spreadsheets. It covers functions and tools like SUM, VLOOKUP, conditional formatting, and data tables that allow automating calculations and analyses. Step-by-step examples demonstrate how to use each tool to summarize and manipulate data in Excel.
This document provides a summary of 35 Excel tips to help save time when working with spreadsheets. It outlines various functions and commands in Excel like SUMIF, VLOOKUP, conditional formatting and more. Exercises are provided for each tip to allow users to practice the skills. The target audience is business analysts and associates who can use these tips to work more efficiently in Excel.
This document provides a summary of 35 Excel tips intended to save time for business analysts and associates. It covers functions and tools for splitting windows, hiding/unhiding rows and columns, sorting data, using formulas like IF, SUM, COUNT, and VLOOKUP, as well as formatting, filtering, and protecting worksheets. Exercises are provided for each tip to help users practice and learn when and how to apply each function.
Similar to Procedure rol against retail raw data (20)
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
2. Define the Term “ Norms”
In store Requirement Level of a said
commodity for a particular period of time (say 1
month) By example, If we wish to sale a
particular item /Footwear 15 pair by the next 45
days , then our requirement level is 15 pair for
45 days The average item movement for per day
will be =15/45 And for it 1 month ( . And for 1
month it will =15/45*30 comes 10 pair . Our
requirement for the product according my plan
wise will be 10 pair monthly we wish to sale. Its
also define level of inventory which triggers an
action to replenish that particular inventory stock.
3. Necessary of make “
Norms”
continuous review inventory system in which
the level of inventory is monitored at all times
and a fixed quantity is ordered each time the
inventory level reaches a
specific reorder point.
Control for the non moving stock s, New
Arrival Article like all the category are under
control that they can perform it accordingly
4. Factor should be consider for Norms
1 . Norms will be set by past sales records(let
it be last 90days or 60days) in item wise
2.Scope of supply from wear house
3.If any seasonal requirement(like special
requirement of Durga Puja, Diwali, EID)
3.Consider the stock keeping unit(SKU)
capacity /display capacity
4. Consider the stock room /go down capacity
5. PROCESS OF MAKING WORKING
FILE (STEP 1)
GO ITEM WISE SALE-STOCK ROL IN RMS
PACKAGE
SELECT THE DATE LAST 90 DAYS/60 DAYS FROM
TODAY
RMS > REPORT > SALE REGISTER
Generate the Excel sheet
it is you raw file for data sheet.
Remove unnecessary row and Colum (Item
name, size,mc3,mc2,mc1 ,mrp,discount,bill
amount,rol,transit,MC ,BRAND)
(N.B : STEP WISE MAY ARE FOLLOW THE
DIFFERENT STEP , SO PLEASE MAKE SURE THE
BASIC THING WILL SAME)
6. WE HAVE ONLY 3 COLUM NOW REMAIN IN EXCEL SHEET , ITEM
CODE (11 DIGIT), SALE QTY AND STOCK
MAKE A EXTRA COLUM IN BETWEEN ITEM CODE 11 AND SALE QTY
(MAKE THE NEW COL NAME ITEM 7)
Item code 11 ITEM 7 SALE STOCK
10200710250 1020075 1 1
(WE WISH TO WORK WITH PERTICULR PRODUCTWITH A PERTICULAR
COLOUR , NOT NESSARY THE MUTATION WISE/MFRC )
ITEM 11 DENOTES EVERY SINGLE PRODUCT WITH PERTICULAR
MANUFACTURER/VENDOR WITH A SINGLE BATCH OF PRODUCTION.
(BY EG . PRIMARY VENDOR CODE(102)+ARTICLE /STYLE
CODE(007)+SECONDARY VENDOR CODE(102)+COLOUE CODE(5)+MUTATION
CODE(0))
ITEM 7 DENOTES EVERY SINGLE PRODUCT LINE (BY EG PRIMARY
VENDOR+STYLE CODE+COLOUR CODE)
7. STEP 3
NOW SELECT first COLUM of item 7
Write the formula =LEFT(A2,6)&MID(A2,10,1)
“LEFT” AND “MID” all are predefine funtion of excel that you can use any number of times
for a perticular work . The syntax of the “LEFT” function is:
LEFT( text, [num_chars] ) like LEFT(A2 ,6) similarly (BY EG HERE TEXT is
10200710250 (A2 BY DEFAULT (FIRST 6 DIGIT DENOTES >PRIMARY VENDOR
CODE+ ARTICLE CODE)
The syntax of the “MID” function is:
MID( text, start_num, num_chars ) (by eg MID(A2,10,1) (IT DENOTES THE
COLOUR CODE)
Now item 7 comes alike 1012132(with vendor code ,article code and colour code )
and why we convert this 11 digit to 7 digit make it understand the sale fig and
stock fig (within a particular period) comes to me and it give a pictorial view of
the product status.
Item code item 7 sale quantity stock
10121326820 1012132 0 1
10121326822 -1 2
Item Code Item 7 Sale QuantityStock
10200710250 1020075 1 0
10200710250 1020075 1 0
10200710250 1020075 2 0
10200710250 1020075 1 1
10200710250 1020075 2 0
8. see the below pic .once first item 7 come you just drug it
horizontally it will come all the rows are below. Select the all
whole excel sheet make it copy the total item >paste special
>values>ok .
Item code Item 7 Sale
quantity
stock
10121326820 1012132 0 1
10121326822 1012132 -1 2
10121326822 1012132 0 1
10200710250 1020075 0 1
10200710250 1000075 0 3
(the way
item 7 will
be free from
its hold
formula)
9. Step 4
Remove the item code 11/item 11 col. Hence it comes
Item 7 sale qty & stock . Now select the whole col. and go to insert >
pivot table
7 Sale Stock
1020075 1 0
1020075 1 0
1020075 2 0
1020075 1 1
10. INCERT A PIVOT
(Take a pictorial view of total sale and
closing stock for every item 7)
Click any single cell inside the data set.
On the Insert tab, click PivotTable.
The following dialog box appears. Excel automatically selects the data for you. The
default location for a new pivot table is New Worksheet.
3. Click OK.
11. Drag fields
The PivotTable field list appears. To get the total amount exported of each product, drag the
following fields to the different areas.
1. Product Field/ITEM 7 to the Row Labels area.
2. Amount Field/stock AND SALE to the Values area.
Count of stock converted to sum of stock
Similarly count of sale converted to sum of sale. (pivot table field list looks like similar)
12. Step 5
Again do it copy the whole item > paste
special > by value >ok
Now what you got item 7 wise total sale and
stock
Arrange your file ,it likes
Item 7 stock sale
1012132 4 -1
1020075 10 0
1020079 10 0
1020086 6 1
7 Sale Stock
1012132 -1 3
1020075 0 11
1020079 0 10
1020086 1 6
1033238 64 7
1033239 0 0
1033245 0 1
1033251 49 3
1033259 18 4
1060075 0 15
1060076 6 12
1060086 10 10
13. Step 6
Insert several row in between item 7 and stock or sale qty
Give the name as per mention below successively
Mrp, mc( merchandise category), brand, discount, season catalogue ,
season year , existing norms . And always keep hand the latest item
master ,running and discount list.
.7 MRP MC BRAND DISCOUNT SEASON CAT SEASON YEAR Sum of Sale Sum of Stock EXSISTING Norms
1012132 -1 3
1020075 0 11
1020079 0 10
1020086 1 6
1033238 64 7
1033239 0 0
1033245 0 1
1033251 49 3
14. Use of “ vlookup” funtion
vlookup or “value look up funtion . When the VLOOKUP function is called, Excel searches for
a lookup value in the leftmost column of a section of your spreadsheet called the table
array. The function returns another value in the same row, defined by the column index
number.
Purpose :Lookup a value in a table by matching on the first column
( For us we are currently working on single excell sheet that contents Item 7 , sale
qty, stock qty. Now we have to take a look for every item 7 belongs to which MC and
Brand. For that we a help from Item master file .
Concept of Variable and its value
For this context we have to the variable , that is “ An object which have certain
range of values “ By eg we from Sunday to Saturday we are take everyday
different different breakfast , someday paratha, someday luchi etc . Then Day is the
variable (its renge from Sunday to Saturday) and the menu of breakfast is the
values of the variable.
Similarly, for us Item 7 is the variable and sale qty and stock qty is its value.
So every excel funtion we used(vlookup or left) everything generate against the
variable.
=VLOOKUP(Value you want to look up, range where you want to lookup the value, the column
number in the range containing the return value, Exact Match or Approximate Match – indicated as
0/FALSE or 1/TRUE).
15. Application procedure for Vlookup
NOW OPEN THE BOTH EXCELL FILE ITEM
MASTER AND WORKING EXCELL FILE
SUCCESSCVELY.
FOR MRP COL WRITE THE FORMULA
=VLOOKUP(SELECT THE FIRST ITEM 7 , GO TO
ITEM MASTER FILE > SELECT THE ITEM 7 AND
SELECT ENTIRE COLUME UPTO MRP AT ITEM
MASTER, WRITE DOWN THE ROW NUMBER,
TRUE)
IN GENERAL = VLOOKUP(LOOKUP
VALUE,TABLE ARRAY,COL INDEX
NUMBER,RANGE LOOKUP)
HERE ITS COMES
=VLOOKUP(A2,'[Item Master
Running_07.06.2017_CONCIZE (1).xlsb]Item
Master'!$E:$O,11,0)
A2 DENOTES THE FIRST ITEM 7 ,
,'[Item Master Running_07.06.2017_CONCIZE
(1).xlsb]Item Master'!$E:$O = TAKE
ATOMATICALLY WHEN YOU CHOOSE TEXT
FROM ITEM MASTER
11= UPTO WHITCH COLUME YOU WANT A
RETURN VALUE (MRP STAND FOR 11 TH COL
FROM ITEM 7 AT ITEM MASTER)
17. THE SAME PROCEDURE FILLED UP EVERY SINGLE COL UME OF MC
,BRAND , CAT YEAR , CAT SEASON ECT , FOR SEASON YEAR ,
CATALOUGE YOU HAVE A HAVE ITEM RUNNING LIST . FOR DISCOUNT
LIST YOU HAVE TO CHECK THE DISCOUNTED LIST
Row Labels MRP MC BRAND DISCOUNT SEASON CAT SEASON YEAR Sum of Sale Sum of Stock EXSISTING Norms
1012132 899 GENTS SP.SHOE PRO -1 3
1020075 1299 LADIES CHAPPAL SOFT TOUCH 0 11
1020079 1299 LADIES CHAPPAL SOFT TOUCH 0 10
1020086 1199 LADIES CHAPPAL SOFT TOUCH 1 6
1033238 375 LADIES CHAPPAL KHADIMS 64 7
1033239 375 LADIES CHAPPAL KHADIMS 0 0
1033245 325 LADIES CHAPPAL KHADIMS 0 1
1033251 399 LADIES CHAPPAL CLEO 49 3
1033259 399 LADIES CHAPPAL CLEO 18 4
1060075 349 LADIES CHAPPAL KHADIMS 0 15
1060076 349 LADIES CHAPPAL KHADIMS 6 12
18. TAKE A COMBINED OF MC AND BRAND ( MAKE A NEW COLUME OF
MC+BRAND AFTER THE MC AND BRAND COL) TAKE THE FIRST COL
OF MC+BRAND WRITE= TRIM(MC FIRST COL SAY C2)&TRIM(BRAND
FIRST COL SAY D2) DRAG IT AT FULL ROW BY MOUSE CURSOR
Row Labels MRP MC BRAND MC+BRAND SEASON CAT SEASON YEARSum of Sale Sum of Stock EXSISTING Norms
1012132 899 GENTS SP.SHOE PRO GENTS SP.SHOEPRO 0 0 -1 3
1020075 1299 LADIES CHAPPAL SOFT TOUCH LADIES CHAPPALSOFT TOUCH AUTUMN/WINTER 2016 0 11
1020079 1299 LADIES CHAPPAL SOFT TOUCH LADIES CHAPPALSOFT TOUCH AUTUMN/WINTER 2016 0 10
1020086 1199 LADIES CHAPPAL SOFT TOUCH LADIES CHAPPALSOFT TOUCH AUTUMN/WINTER 2016 1 6
1033238 375 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS SPRING/SUMMER 2014 64 7
1033239 375 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS SPRING/SUMMER 2014 0 0
1033245 325 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS AUTUMN/WINTER 2015 0 1
1033251 399 LADIES CHAPPAL CLEO LADIES CHAPPALCLEO SPRING/SUMMER 2016 49 3
1033259 399 LADIES CHAPPAL CLEO LADIES CHAPPALCLEO SPRING/SUMMER 2016 18 4
1060075 349 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS AUTUMN/WINTER 2016 0 15
1060076 349 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS AUTUMN/WINTER 2016 6 12
1060086 379 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS AUTUMN/WINTER 2016 10 10
1060088 379 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS AUTUMN/WINTER 2016 8 10
1061265 499 LADIES CHAPPAL KHADIMS LADIES CHAPPALKHADIMS SPRING/SUMMER 2015 6 0
1061275 499 LADIES CHAPPAL CLEO LADIES CHAPPALCLEO SPRING/SUMMER 2015 1 0
1061279 499 LADIES CHAPPAL CLEO LADIES CHAPPALCLEO SPRING/SUMMER 2015 4 0
19. Step 9
Select the col sale > go to sort & filter > custom sort>do it value wise largest to
smallest >ok
Then go MC –Brand , go to sort & filter > custom sort >do in order A to Z >ok.
It shows you descending order sale arrangement under a particular MC –Brand.
SEE THE MC BRAND COL AND SALE FIG , YOU CAN UNDERSTAND THE DECENDING
ORDER OF ARRGEMENT
Row Labels MRP MC BRAND MC+BRAND SEASON CAT SEASON YEAR Sum of Sale Sum of Stock EXSISTING Norms
4840043 650 BELT BRITISH WALKERS BELTBRITISH WALKERS 6 0
1771436 475 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2014 39 21
1770846 299 BELT KHADIMS BELTKHADIMS 0 0 36 18
4840054 699 BELT KHADIMS BELTKHADIMS 25 17
1770830 399 BELT KHADIMS BELTKHADIMS 0 0 21 23
1771700 399 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 18 18
1771824 699 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 17 19
1771870 999 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 16 26
7131096 299 BELT KHADIMS BELTKHADIMS 16 8
1771734 550 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 15 14
7130570 199 BELT KHADIMS BELTKHADIMS 14 21
7131146 350 BELT KHADIMS BELTKHADIMS 12 0
1770016 189 BELT KHADIMS BELTKHADIMS 0 0 11 25
7131135 350 BELT KHADIMS BELTKHADIMS 11 1
1771816 449 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 10 26
20. Apply “sumif “ funtion
The SUMIF function is a worksheet function that adds all numbers in a range of cells
based on one criteria.
By eg here we are interested to see each of the mc brand wise sale and their
internal contribution/performemce.
The syntax for the SUMIF function in Microsoft Excel is:
SUMIF( range, criteria, [sum_range] )
IT DENOTES THAT UNDER EACH MC+BRAD WHAT IS THE TOTAL FIGURE OF SALE
QTY . WE ARE INTERESTED TO TAKE SALE CONTRIBUTION FOR EACH MC+BRAND
UNDER MEASURE THE PERFORMANCE OF EACH MC+BRAND . BY EG IF A SCHOOL
HAVE CLASS LKG TO CLASS 10 ,AND EVERY CLASS HAVE A,B ,C 3 DIFFERENT
SECTION. SO SUPPOSE (CLASS 5 , SECTION A) IS A COMBINATION I WANT TAKE A
LOOK PERFORMANCE OF PERTICULAR STUDENT OF (CLASS 5, SEC A) ON THE
BASIS OF WHOLE CLASS.
SAME THING HAPPEN HERE ALSO.
EACH MC+BRAND WHAT IS THE SALE CONTRIBUTION OF EACH MEMBER.
MAKE A EXTRA COLUME NAMED AS CONTIBUTION %
21. Working with sumif
Write the first col of sale contribution
=SALE/SUMIF(MCBRAND 1ST COL: MCBRAND LAST
COL,MCBRAND 1ST COL, SALE FIRST COL:SALE LAST
COL)*100
LIKE FOR ME
=SALE(H2)/SUMIF(E2:E1843,E2,H2:H1843)*100
SELECT “MCBRAND 1ST COL: MCBRAND LAST COL”,
SALE FIRST COL:SALE LAST COL BOTH ARE SELECT
INDIVIALLY AND PRESS f4 FOR FIXED THE FORMULA)
DRUG IT HORIZENTALLY , DO AGAIN COPY , PASTE
SPECIAL .
22. NOW WHY WE DO IT , PLEASE TAKE A LOOK YOU HAVE A CLEAR IDEA
ABOUT THE MOVEMENT OF % OF YOUR MC+BRAND WISE EACH OF
THE ITEM.LIKE 1771436 BELT MEMBER OF (BELT+KHADIMS) HAVE
SALE CONTRIBUTION OF 11 % AMONG ITS SAME KIND OF MEMBER.
Row Labels MRP MC BRAND MC+BRAND SEASON CAT SEASON YEAR Sale CONT% Stock EXSISTING Norms
4840043 650 BELT BRITISH WALKERS BELTBRITISH WALKERS 6 100 0
1771436 475 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2014 39 11 21
1770846 299 BELT KHADIMS BELTKHADIMS 0 0 36 12 18
4840054 699 BELT KHADIMS BELTKHADIMS 25 9 17
1770830 399 BELT KHADIMS BELTKHADIMS 0 0 21 8 23
1771700 399 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 18 8 18
1771824 699 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 17 8 19
1771870 999 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 16 8 26
7131096 299 BELT KHADIMS BELTKHADIMS 16 9 8
1771734 550 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 15 9 14
7130570 199 BELT KHADIMS BELTKHADIMS 14 10 21
7131146 350 BELT KHADIMS BELTKHADIMS 12 9 0
23. YOUR SALE CONTRIBUTION COMES . NOW
WHAT IT GIVES, IT GIVES THE CLASS WISE
(IN A PERTICULAR DISPLAY PANEL , LIKE
GENTS SHOE ,KHADIMS ) SALE
CONTRIBUTION OF EACH MEMBER.
IF TOTAL LIST OF PRODUCT GIVE A
UNIVERSE THEN ITS MC DEFINE THE ONE
GROUP . AND ONE BRAND IN A PERTICULAR
BRAND DEFINE AS A CLASS UNDER THE
GROUP. WE GOT GROUP WISE PICTORIAL
VIEW.
24. STEP 11
MAKE ANOTHER COL NAMED cf (CUMULATIVE
FREQUENCY) MORE THAN TYPE
THE CUMULATIVE FREQUENCY STANDS FOR
THE DEFINE THE RANK OF A MEMBER OF A
PERTICULAR CLASS . BY EXAMPLE IN A
CLASS THE BOY WHO GOT THE TOP MARKS
IN ALL SUBJECT LIKE 850/OUT OF 900 , HE
HOLD THE RANK 1.
SAME LIKE HERE THE PRODUNT HOLDS THE
TOP SALE /HIGHEST SALE DEFINE THE TOP
RANK OR LEAST /LOWER CUMULATIVE
FREQUENCY.
25. HERE CUMULATIVE OF A PERTICLUAR
PRODUCT DEFINES THE PRODUCT WHOS
SALES CONTRIBUTION RANK STANDS FOR.
IF 4880024 ARTCLE HOLD TOP SALE
CONTRIBUTION AMONG ITS CLASS OF
BRITISH WALKERS, GENTS CHAPPAL SAY
SALE QTY 50 PICS LAST 90 DAYS ITS HOLD
THE LEAST CUMULATIVE FREQUENCY
BCAUSE ITS CLASS RANK IS TOP.
26. MAKE FORMULA OF cf
MAKE A COL NAME CF
WRITE THE SECOND COL MAKE THE “IF” FUNTION.
IT IS A FUNTION GIVES THE CONDITIONAL SUM , UNDER SOME
RETRICTIONS . AND HERE IT IS
=IF IS FUNTION OF SUM DENOTES THE CONDITIONAL SUM.
NOW APPLY THE FUNTION
=IF(MCBRAND 2ND ROW=mcbrand(1ST HEADER ROW),CF FIRST HEADER
ROW+SALE CONTRIBUTION 2ND ROW ,SALE CONTRIBUTION 2 ND ROW)
=IF(E2=E1,I2+J1,I2)
ITS GIVE THE FIG DRAG IT HORIZENTALLY MAKE COPY PASTE SPECIAL .
(REMEMBER THAT CUMULATION FREQUENCY FOR A PERTICULAR ITEM
NOT STAND FOR SALE PERFORMANCE , IT STAND FOR HOW MANY
MEMBER OR ITEM ARE MORE THAT THAT CATAGOGY IN TERMS OF
PERFORMANCE LEVEL IN ONE WORDS ITS GIVE THE RANK)
27. Row Labels MRP MC BRAND MC+BRAND SEASON CAT SEASON YEAR Sale CONT% CF Stock EXSISTINGNorms
4840043 650 BELT BRITISHWALKERS BELTBRITISHWALKERS 6 100 100 0
1771436 475 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2014 39 11 11 21
1770846 299 BELT KHADIMS BELTKHADIMS 0 0 36 12 23 18
4840054 699 BELT KHADIMS BELTKHADIMS 25 9 32 17
1770830 399 BELT KHADIMS BELTKHADIMS 0 0 21 8 40 23
1771700 399 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 18 8 48 18
1771824 699 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 17 8 56 19
1771870 999 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 16 8 65 26
7131096 299 BELT KHADIMS BELTKHADIMS 16 9 74 8
1771734 550 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 15 9 83 14
28. Step 12
Define the rank /category (A, B,C)
If cf value 1-50 its give A Cat
If cf value 51-80 its give B cat
If cf value 81-rest its give C cat.
It is already define that cat A have good
sale /very good sale for its community
For cat B it have good sale/average sale
For cat C its have poor sale/average sale.
29. NOW TAKE THE EXSISTING NORMS FILE TO TO
COMPARE THE WITH THE EXSISTING SALE
NOW FOR THE PACKAGE YOU JUST GO THROUGH
REPORTS>STOCK TRAIL> CLICK THE STOCK VS NORMS
BOTTOM > GENERATE THE FILE IN EXCELL SHEET .
IT IS YOUR ITEM WISE NORMS, YOU DO THE SAME THING IN
7 DIGIT AND DO IT PIVOT TABLE TO MAKE THE AND GET THE
7 DIGIT NORMS .
NOW BOTH THE FILE NORMS FILE AND MAIN WORKING FILE
YOU HAVE TO VLOOKUP TO DRAG THE FIG OF NORMS TO THE
WORKING FILE
NOW YOU CAN COMPARE WITH EXSISTING NORMS , WITH
SALE PERFORMANCE AND CLOSING STOCK LEVEL.
MAKE THE NECESSARY CHANGES . EVERY CHAGES YOU HAVE
GO GIVE A REASON BEHIND IT.
30. WHILE DOING OR MODIFY YOUR NORMS/REQUIREMENT LEVEL REMEMBER FOOT
FOOTWEAR CATEGORY MINIMUM NORMS WILL BE 6 AND MAXIMUM 24
FOR SOCKS ITS MINIMUN 12 TO 18.
FOR BELT ITS 12
FOR WALLET (GENTS) ITS 6 . YOU MAKE SPARATE COLUME NAME YOUR VIEW
AFTER THE EXSISTING NORMS COL FOR MODIFY
Row Labels MRP MC BRAND MC+BRAND SEASON CAT SEASON YEAR Sale CONT% CF Stock EXSISTING Norms
4840043 650 BELT BRITISH WALKERS BELTBRITISH WALKERS 6 100 100 0 24
1771436 475 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2014 39 11 11 21 24
1770846 299 BELT KHADIMS BELTKHADIMS 0 0 36 12 23 18 24
4840054 699 BELT KHADIMS BELTKHADIMS 25 9 32 17 12
1770830 399 BELT KHADIMS BELTKHADIMS 0 0 21 8 40 23 12
1771700 399 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 18 8 48 18 12
1771824 699 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 17 8 56 19 12
1771870 999 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2016 16 8 65 26 12
7131096 299 BELT KHADIMS BELTKHADIMS 16 9 74 8 12
1771734 550 BELT KHADIMS BELTKHADIMS AUTUMN/WINTER 2015 15 9 83 14 12
31. Ready for the make final Norms
modification
Your file are ready for Norms modification
. Its called are working file.
The procedure will be vary from person to
person ,but the main concept will same.
Please suggest if anyone any addition or
any mistake have done the whole
process.. Pls advice…..