Human resource planning involves three main steps: 1) forecasting future labor demand and supply needs, 2) identifying any gaps between current resources and future needs, and 3) developing strategies to address surpluses or shortages. Forecasting methods include analyzing trends, indicators, simulations, and turnover data to predict staffing requirements and availability. Comparing demand and supply forecasts reveals where additional hiring or layoffs may be needed to achieve organizational goals.
Human resource planning involves three main steps:
1) Forecasting future labor demand and supply to determine if surpluses or shortages will exist. This includes analyzing trends, indicators, and turnover.
2) Determining current and future labor supply through tools like succession planning, skills inventories, transition matrices, and personnel ratios.
3) Comparing forecasts to identify needed changes in recruiting, training, or layoffs to align the workforce with strategic goals. The goal is having the right people in the right jobs at the right time.
The document appears to be a cover page for an operations report submitted by John Haller for a class numbered UST 459 dated January 31, 2013. It provides the name of the author, the course number, and submission date in a brief header but does not include any other details about the report contents or topic.
Human resource planning involves forecasting future labor demand and supply to determine if surpluses or shortages will exist. The process includes:
- Forecasting demand using methods like trend analysis, indicators, and simulations
- Forecasting supply by assessing succession plans, skills inventories, market analyses, and personnel ratios
- Creating transition matrices to analyze employee movement between roles over time
- Comparing forecasts to determine if recruitment or layoffs are needed to achieve organizational goals
AGENDA:
Introductions and Company Overviews
Stein Mart’s Business Objectives
Leveraging the IBM Cloud and QueBIT FrameWORQ to maximize time to value
Stein Mart’s Reporting Solution
Results Achieved
Managing data with an RDBMS is probably one of the key IT resources for roughly 40 years. Splitting up logical data structures into physical ones also known as partitioning is a key ingredient. The attached presentation demonstrates examples based on the recently released Oracle 12.2 database.
Update as of May 31st 2017: You might ask yourself: is this any practical? Yes it is. We are currently creating a bit of code to complement the example.
This document provides examples and practice questions for calculating different measures of central tendency including the arithmetic mean, median, mode, and geometric mean. It includes sample data sets and solutions for calculating these values from both grouped and ungrouped frequency distributions. Measures are calculated for data including wages, earnings, travel times, output levels, profits, and inflation rates. Advantages and disadvantages of each measure are also discussed.
Human resource planning involves three main steps:
1) Forecasting future labor demand and supply to determine if surpluses or shortages will exist. This includes analyzing trends, indicators, and turnover.
2) Determining current and future labor supply through tools like succession planning, skills inventories, transition matrices, and personnel ratios.
3) Comparing forecasts to identify needed changes in recruiting, training, or layoffs to align the workforce with strategic goals. The goal is having the right people in the right jobs at the right time.
The document appears to be a cover page for an operations report submitted by John Haller for a class numbered UST 459 dated January 31, 2013. It provides the name of the author, the course number, and submission date in a brief header but does not include any other details about the report contents or topic.
Human resource planning involves forecasting future labor demand and supply to determine if surpluses or shortages will exist. The process includes:
- Forecasting demand using methods like trend analysis, indicators, and simulations
- Forecasting supply by assessing succession plans, skills inventories, market analyses, and personnel ratios
- Creating transition matrices to analyze employee movement between roles over time
- Comparing forecasts to determine if recruitment or layoffs are needed to achieve organizational goals
AGENDA:
Introductions and Company Overviews
Stein Mart’s Business Objectives
Leveraging the IBM Cloud and QueBIT FrameWORQ to maximize time to value
Stein Mart’s Reporting Solution
Results Achieved
Managing data with an RDBMS is probably one of the key IT resources for roughly 40 years. Splitting up logical data structures into physical ones also known as partitioning is a key ingredient. The attached presentation demonstrates examples based on the recently released Oracle 12.2 database.
Update as of May 31st 2017: You might ask yourself: is this any practical? Yes it is. We are currently creating a bit of code to complement the example.
This document provides examples and practice questions for calculating different measures of central tendency including the arithmetic mean, median, mode, and geometric mean. It includes sample data sets and solutions for calculating these values from both grouped and ungrouped frequency distributions. Measures are calculated for data including wages, earnings, travel times, output levels, profits, and inflation rates. Advantages and disadvantages of each measure are also discussed.
The document discusses workforce optimization and utilization planning for a company. It analyzes the current workforce distribution, capacity, skills, and team and employee utilization rates. Issues like underutilized resources, delayed projects, and high costs are identified. Competitor analysis shows the company has lower utilization rates. Future talent requirements and department-wise resource allocation are presented. Training needs and plans are proposed to address optimization concerns and improve employee efficiency.
The document provides an overview of execution plans in SQL Server and what aspects to examine. It discusses the key stages in the execution process including parsing, optimization, and execution. Specific operators like nested loops, hash matches, and parallelism are also covered. The document is intended to help beginners understand execution plans and what aspects provide useful information for troubleshooting query performance issues.
Mike lawell executionplansformeremortals_2015mlawell
This document provides a beginner's introduction to execution plans in SQL Server. It covers basic concepts like execution steps, operators like nested loops, merge and hash joins. It also discusses cardinality estimation, parallelism and reading execution plans. The overall goal is to explain execution plans at a high level for those new to the topic.
This document provides an overview and examples of advanced SQL concepts such as subqueries, analytical functions, and hierarchical queries in Oracle databases. It begins with a brief outline of topics to be covered and then delves into detailed explanations and examples of different types of subqueries like correlated, nested, and inline views. It also demonstrates the use of analytical functions with ordered and partitioned windows as well as set operators.
The document discusses pattern matching and summarizing employee data by department. It provides examples of using SQL to concatenate employee names grouped by department, including older techniques using MODEL clause, CONNECT BY, and XMLTRANSFORM, as well as newer techniques using LISTAGG. It also discusses challenges in summarizing data and provides an example of analyzing customer transaction data to identify customers meeting growth criteria over single and multiple days.
The document describes the marketing intelligence services of Eleventy Group, including their large consumer databases containing demographic, behavioral and lifestyle information. They offer profiling, predictive modeling, and data enhancement services to help clients better target, segment, and communicate with audiences. Case studies show how these services helped clients improve targeting, test different audience characteristics, and enhance fundraising appeals.
Understand the Query Plan to Optimize Performance with EXPLAIN and EXPLAIN AN...EDB
What do you do, when you have to deal with poor database and query performance in PostgreSQL and there is no one around to help? Let us introduce you to important commands in PostgreSQL - EXPLAIN and EXPLAIN ANALYZE. Knowing how to use these 'tools' will help you identify query performance bottlenecks and opportunities. It provides a query plan detailing what approach the planner took to execute the statement provided.
Attend this webinar to learn:
- What are EXPLAIN and EXPLAIN ANALYZE in PostgreSQL?
- How do they help?
- Know planner tuning parameters
Week 2 Individual Assignment 2: Quantitative Analysis of Credit -
Solution
s
This assignment is based on the data we used during our two live sessions, but it has been updated to include a splitting variable (credit2.xlsx). In the spreadsheet under the tab “Data," you will find data
pertaining to 1,000 personal loan accounts. The tab “Data Dictionary” contains a description of what the various variables mean.
As a part of a new credit application, the company collects information about the applicant. The company then decides an amount of the credit extended (the variable CREDIT_EXTENDED). For these 1,000 accounts, we also have information on how profitable each account turned out to be (the variable NPV). A negative value indicates a net loss, and this typically happens when the debtor defaults on his/her payments.
The goal in this assignment is to investigate how one can use this data to better manage the bank's credit extension program. Specifically, our goal is to develop a classification model to classify a new credit account as “profitable” or “not profitable." Secondly we want to compare its performance in the context of decision support to a linear regression model that predicts NPV directly.
Please answer all the questions. Supply supporting documentation and show calculations as
needed. Please submit a single, well-formatted PDF or Word file. The instructor should not need to go searching for your answers! In addition, please upload an Excel file with your model outputs – the file will not be graded, but will help the instructor give you feedback, if your model differs substantially from the solutions.
For extra assistance, you may want to access the tutorials located on the course resource center page.Data Preparation
The data preparation repeats the steps from the live session:
a) The goal is to predict whether or not a new credit will result in a profitable account. Create a new variable to use as the dependent variable.
b) Create dummy variables for all categorical variables with more than 2 values (or if you prefer, you can sort your variables into numerical and categorical when you run the model).
c) Split the data into 2 parts using the splitting variable that has been added to the data set. This is to ensure a more balanced split between the validation and training samples. Note that Analytic Solver Data Mining only allows 50 columns in the analysis, so leave out your base dummies (if you created them) when partitioning. After the data partition, you should have 666 rows in your training data and 334 in your validation data.
The Assignment
1. Applying Logistic Regression
If one fits a Logistic Regression Model using all the independent variables, one observes a) a gap in the classification performance between the training data and the validation data, and b) very
high p-values for some of the variables. The performance gap between the training and validation may be a sign of overfitting, and the high p-values may b ...
Know more on the benefits of investing in ICICI Prudential Quant Fund:
● Limited Human Intervention to avoid any biases.
● Diversification across various sectors, styles and businesses.
● Systematic approach of investing by combining investing experience and avoiding human error.
● Passive Investing through a model using a combination of factors.
● Team with prior experience in managing quantitative models for asset allocation.
Pattern Matching with SQL - APEX World Rotterdam 2019Connor McDonald
The document appears to be a presentation on pattern matching in SQL. It includes examples of different techniques for grouping and aggregating employee names by department over time, from older procedural approaches to newer analytic functions. It also discusses challenges that remain and provides a hypothetical example of analyzing customer transaction data to identify periods of fast or slow growth.
Copart is a provider of online vehicle auction and remarketing services in the United States, Canada and United Kingdom. It provides vehicle sellers with a range of services to process and sell savage and clean title vehicles using its virtual auction technology. The challenge was to analyze their data and come up with useful recommendations for their business. We performed the following three analysis on the given dataset primarily using SAS, R and excel:
1. Time series analysis to forecast their revenue for the next year.
2. Copart's revenue model is built around both seller and buyer fees. We developed an optimization model to arrive at the optimum value of buyer fees at the given rate of seller fees, to achieve the target revenue in the future years.
3. We computed an alignment metric to help Copart balance their yard alignment across the different states in the USA to meet the rising demand for auto auction vehicles and at the same time maximize their profit.
The document describes several SQL experiments conducted to create and populate tables, apply constraints, modify schemas, and perform queries. Key points:
1) Tables were created for departments and employees, with data inserted. Describe commands showed the schemas.
2) More tables were created, drop and delete commands were used, and select queries with and without where clauses were run.
3) Schemas were altered by adding columns and modifying data types. Update commands modified existing data.
4) Primary keys, foreign keys, unique constraints and other constraints were applied to newly created tables in various experiments.
5) Select queries used aggregate functions, arithmetic operators, sorting, and nested queries. Joins were performed
This document provides instructions for updating resource details in the Sypris Resource Pool. It outlines the following key steps:
1. Select the resources to update and open them in MS Project Professional.
2. For each resource, make changes only to the Max Units, Skill Code, Standard Rate, and Employee Number fields.
3. Save the changes and check the resources back into the Sypris server.
Latin America Tour 2019 - 10 great sql featuresConnor McDonald
By expanding our knowledge of SQL facilities, we can let all the boring work be handled via SQL rather than a lot of middle-tier code, and we can get performance benefits as an added bonus. Here are some SQL techniques to solve problems that would otherwise require a lot of complex coding, freeing up your time to focus on the delivery of great applications.
The document describes a toolkit for recovering data from MySQL databases using InnoDB tables. It provides an overview of the tools in the toolkit, including stream_parser to parse raw data files into InnoDB pages, sys_parser to recover table structures from those pages, and c_parser and recover_dictionary tools to extract and load table records and reconstruct the InnoDB data dictionary. It demonstrates dropping a sample table and then using the toolkit to recover the table structure and records from the raw data files.
Data Exploration with Apache Drill: Day 2Charles Givre
Study after study shows that data scientists and analysts spend between 50% and 90% of their time preparing their data for analysis. Using Drill, you can dramatically reduce the time it takes to go from raw data to insight. This course will show you how.
The course material for this presentation are available at https://github.com/cgivre/data-exploration-with-apache-drill
This document provides financial and stock performance data for a company over several years and quarters. It also includes the company's current stock price, the algorithm's indicated price range, and purchase recommendations based on the price range. Additional sections give historical and sector-specific data on stock price rise probabilities to support the analysis.
This document outlines the key concepts and formulas in business statistics across 5 units. Unit 1 covers definitions of statistics, primary and secondary data collection methods, questionnaires, tabulation, classification and scheduling. Unit 2 covers measures of central tendency. Unit 3 covers measures of dispersion. Unit 4 covers correlation and regression. Unit 5 covers index numbers, time series analysis, seasonal and cyclical variations. It also provides example problems for each unit to practice applying the statistical concepts.
The document discusses workforce optimization and utilization planning for a company. It analyzes the current workforce distribution, capacity, skills, and team and employee utilization rates. Issues like underutilized resources, delayed projects, and high costs are identified. Competitor analysis shows the company has lower utilization rates. Future talent requirements and department-wise resource allocation are presented. Training needs and plans are proposed to address optimization concerns and improve employee efficiency.
The document provides an overview of execution plans in SQL Server and what aspects to examine. It discusses the key stages in the execution process including parsing, optimization, and execution. Specific operators like nested loops, hash matches, and parallelism are also covered. The document is intended to help beginners understand execution plans and what aspects provide useful information for troubleshooting query performance issues.
Mike lawell executionplansformeremortals_2015mlawell
This document provides a beginner's introduction to execution plans in SQL Server. It covers basic concepts like execution steps, operators like nested loops, merge and hash joins. It also discusses cardinality estimation, parallelism and reading execution plans. The overall goal is to explain execution plans at a high level for those new to the topic.
This document provides an overview and examples of advanced SQL concepts such as subqueries, analytical functions, and hierarchical queries in Oracle databases. It begins with a brief outline of topics to be covered and then delves into detailed explanations and examples of different types of subqueries like correlated, nested, and inline views. It also demonstrates the use of analytical functions with ordered and partitioned windows as well as set operators.
The document discusses pattern matching and summarizing employee data by department. It provides examples of using SQL to concatenate employee names grouped by department, including older techniques using MODEL clause, CONNECT BY, and XMLTRANSFORM, as well as newer techniques using LISTAGG. It also discusses challenges in summarizing data and provides an example of analyzing customer transaction data to identify customers meeting growth criteria over single and multiple days.
The document describes the marketing intelligence services of Eleventy Group, including their large consumer databases containing demographic, behavioral and lifestyle information. They offer profiling, predictive modeling, and data enhancement services to help clients better target, segment, and communicate with audiences. Case studies show how these services helped clients improve targeting, test different audience characteristics, and enhance fundraising appeals.
Understand the Query Plan to Optimize Performance with EXPLAIN and EXPLAIN AN...EDB
What do you do, when you have to deal with poor database and query performance in PostgreSQL and there is no one around to help? Let us introduce you to important commands in PostgreSQL - EXPLAIN and EXPLAIN ANALYZE. Knowing how to use these 'tools' will help you identify query performance bottlenecks and opportunities. It provides a query plan detailing what approach the planner took to execute the statement provided.
Attend this webinar to learn:
- What are EXPLAIN and EXPLAIN ANALYZE in PostgreSQL?
- How do they help?
- Know planner tuning parameters
Week 2 Individual Assignment 2: Quantitative Analysis of Credit -
Solution
s
This assignment is based on the data we used during our two live sessions, but it has been updated to include a splitting variable (credit2.xlsx). In the spreadsheet under the tab “Data," you will find data
pertaining to 1,000 personal loan accounts. The tab “Data Dictionary” contains a description of what the various variables mean.
As a part of a new credit application, the company collects information about the applicant. The company then decides an amount of the credit extended (the variable CREDIT_EXTENDED). For these 1,000 accounts, we also have information on how profitable each account turned out to be (the variable NPV). A negative value indicates a net loss, and this typically happens when the debtor defaults on his/her payments.
The goal in this assignment is to investigate how one can use this data to better manage the bank's credit extension program. Specifically, our goal is to develop a classification model to classify a new credit account as “profitable” or “not profitable." Secondly we want to compare its performance in the context of decision support to a linear regression model that predicts NPV directly.
Please answer all the questions. Supply supporting documentation and show calculations as
needed. Please submit a single, well-formatted PDF or Word file. The instructor should not need to go searching for your answers! In addition, please upload an Excel file with your model outputs – the file will not be graded, but will help the instructor give you feedback, if your model differs substantially from the solutions.
For extra assistance, you may want to access the tutorials located on the course resource center page.Data Preparation
The data preparation repeats the steps from the live session:
a) The goal is to predict whether or not a new credit will result in a profitable account. Create a new variable to use as the dependent variable.
b) Create dummy variables for all categorical variables with more than 2 values (or if you prefer, you can sort your variables into numerical and categorical when you run the model).
c) Split the data into 2 parts using the splitting variable that has been added to the data set. This is to ensure a more balanced split between the validation and training samples. Note that Analytic Solver Data Mining only allows 50 columns in the analysis, so leave out your base dummies (if you created them) when partitioning. After the data partition, you should have 666 rows in your training data and 334 in your validation data.
The Assignment
1. Applying Logistic Regression
If one fits a Logistic Regression Model using all the independent variables, one observes a) a gap in the classification performance between the training data and the validation data, and b) very
high p-values for some of the variables. The performance gap between the training and validation may be a sign of overfitting, and the high p-values may b ...
Know more on the benefits of investing in ICICI Prudential Quant Fund:
● Limited Human Intervention to avoid any biases.
● Diversification across various sectors, styles and businesses.
● Systematic approach of investing by combining investing experience and avoiding human error.
● Passive Investing through a model using a combination of factors.
● Team with prior experience in managing quantitative models for asset allocation.
Pattern Matching with SQL - APEX World Rotterdam 2019Connor McDonald
The document appears to be a presentation on pattern matching in SQL. It includes examples of different techniques for grouping and aggregating employee names by department over time, from older procedural approaches to newer analytic functions. It also discusses challenges that remain and provides a hypothetical example of analyzing customer transaction data to identify periods of fast or slow growth.
Copart is a provider of online vehicle auction and remarketing services in the United States, Canada and United Kingdom. It provides vehicle sellers with a range of services to process and sell savage and clean title vehicles using its virtual auction technology. The challenge was to analyze their data and come up with useful recommendations for their business. We performed the following three analysis on the given dataset primarily using SAS, R and excel:
1. Time series analysis to forecast their revenue for the next year.
2. Copart's revenue model is built around both seller and buyer fees. We developed an optimization model to arrive at the optimum value of buyer fees at the given rate of seller fees, to achieve the target revenue in the future years.
3. We computed an alignment metric to help Copart balance their yard alignment across the different states in the USA to meet the rising demand for auto auction vehicles and at the same time maximize their profit.
The document describes several SQL experiments conducted to create and populate tables, apply constraints, modify schemas, and perform queries. Key points:
1) Tables were created for departments and employees, with data inserted. Describe commands showed the schemas.
2) More tables were created, drop and delete commands were used, and select queries with and without where clauses were run.
3) Schemas were altered by adding columns and modifying data types. Update commands modified existing data.
4) Primary keys, foreign keys, unique constraints and other constraints were applied to newly created tables in various experiments.
5) Select queries used aggregate functions, arithmetic operators, sorting, and nested queries. Joins were performed
This document provides instructions for updating resource details in the Sypris Resource Pool. It outlines the following key steps:
1. Select the resources to update and open them in MS Project Professional.
2. For each resource, make changes only to the Max Units, Skill Code, Standard Rate, and Employee Number fields.
3. Save the changes and check the resources back into the Sypris server.
Latin America Tour 2019 - 10 great sql featuresConnor McDonald
By expanding our knowledge of SQL facilities, we can let all the boring work be handled via SQL rather than a lot of middle-tier code, and we can get performance benefits as an added bonus. Here are some SQL techniques to solve problems that would otherwise require a lot of complex coding, freeing up your time to focus on the delivery of great applications.
The document describes a toolkit for recovering data from MySQL databases using InnoDB tables. It provides an overview of the tools in the toolkit, including stream_parser to parse raw data files into InnoDB pages, sys_parser to recover table structures from those pages, and c_parser and recover_dictionary tools to extract and load table records and reconstruct the InnoDB data dictionary. It demonstrates dropping a sample table and then using the toolkit to recover the table structure and records from the raw data files.
Data Exploration with Apache Drill: Day 2Charles Givre
Study after study shows that data scientists and analysts spend between 50% and 90% of their time preparing their data for analysis. Using Drill, you can dramatically reduce the time it takes to go from raw data to insight. This course will show you how.
The course material for this presentation are available at https://github.com/cgivre/data-exploration-with-apache-drill
This document provides financial and stock performance data for a company over several years and quarters. It also includes the company's current stock price, the algorithm's indicated price range, and purchase recommendations based on the price range. Additional sections give historical and sector-specific data on stock price rise probabilities to support the analysis.
This document outlines the key concepts and formulas in business statistics across 5 units. Unit 1 covers definitions of statistics, primary and secondary data collection methods, questionnaires, tabulation, classification and scheduling. Unit 2 covers measures of central tendency. Unit 3 covers measures of dispersion. Unit 4 covers correlation and regression. Unit 5 covers index numbers, time series analysis, seasonal and cyclical variations. It also provides example problems for each unit to practice applying the statistical concepts.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
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.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
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.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
1. The Process of Human Resource Planning
• Organizations need to do human resource
planning so they can meet business objectives
and gain a competitive advantage over
competitors.
– Human resource planning compares the present state
of the organization with its goals for the future
– Then identifies what changes it must make in its
human resources to meet those goals
3. Human Resource Forecasting
• HR Forecasting attempts There are three major
to determine the supply steps to forecasting:
and demand for various
types of human resources, 1. Forecasting the demand
and to predict areas for labor
within the organization 2. Determining labor
where there will be labor supply
shortages or surpluses. 3. Determining labor
surpluses and shortages
5. Forecasting the Demand for Labor
Trend Analysis
• Constructing and applying statistical models that predict
labor demand for the next year, given relatively objective
statistics from the previous year.
Leading Indicators
• Objective measures that accurately predict future labor
demand.
7. SIMULATION MODEL/REGRESSION FORECAST
TARGET STORES STAFFING FORECAST
MODEL
Y = 8 + .0011(X1) + .00004(X2) + .02(X3)
Y = Number of employees needed to staff the store
X1 = Square feet of sales space
X2 = Population of metropolitan area
X3 = Projected annual disposable income in millions of dollars
Y = 8 + .0011(50,000sq ft) + .00004(150,000popul) + .00000002($850 million)
Y = 8 + 55 + 6 + 17
Y = 86 employees needed at this store
9. Determining Labor Supply
Predicting Worker Flows and Availabilities
• Succession or Replacement Charts
Who has been groomed/developed and is ready for promotion right NOW?
• Human Resource Information Systems (HRIS)
An employee database that can be searched when vacancies occur.
• Transition Matrices (Markov Analysis)
A chart that lists job categories held in one period and shows the proportion of
employees in each of those job categories in a future period.
It answers two questions:
1. “Where did people in each job category go?”
2. “Where did people now in each job category come from?
• Personnel / Yield Ratios
How much work will it take to recruit one new accountant?
10. SUCCESSION PLANNING
REPLACEMENT CHART
FOR EXECUTIVE POSITIONS
POSITION REPLACEMENT CARDS
FOR EACH INDIVIDUAL POSITION
------------------------------------------------------------------------
POSITION WESTERN DIVISION SALES MANAGER
DANIEL BEALER Western Division Sales Mgr Outstanding Ready Now
PRESENT PROMOTION
POSSIBLE CANDIDATES CURRENT POSITION PERFORMANCE POTENTIAL
SHARON GREEN Western Oregon Sales Manager Outstanding
Ready Now
GEORGE WEI N. California Sales Manager Outstanding
Needs Training
HARRY SHOW Idaho/Utah Sales Manager Satisfactory Needs Training
TRAVIS WOOD Seattle Area Sales Manager Satisfactory Questionable
-------------------------------------------------------------------------
11. HUMAN RESOURCE INFORMATION SYSTEMS
(HRIS)
PERSONAL DATA
Age, Gender, Dependents, Marital status, etc
EDUCATION & SKILLS
Degrees earned, Licenses, Certifications
Languages spoken, Specialty skills
Ability/knowledge to operate specific machines/equipment/software
JOB HISTORY
Job Titles held, Location in Company, Time in each position, etc.
Performance appraisals, Promotions received, Training & Development
MEMBERSHIPS & ACHIEVEMENTS
Professional Associations, Recognition and Notable accomplishments
PREFERENCES & INTERESTS
Career goals, Types of positions sought
Geographic preferences
CAPACITY FOR GROWTH
Potential for advancement, upward mobility and growth in the company
14. MARKOV ANALYSIS – 2
(Captures effects of internal transfers)
(Start = 3500) A TRANSITION MATRIX
FROM/ TO: TOP MID LOW SKILLED ASSY EXIT
TOP 100 .80 .02 .18
MID 200 .10 .76 .04 .10
LOW 600 .06 .78 .01 .15
SKILL 600 .01 .84 .15
ASSY 2000 .05 .88 .07
---------------------------------------------------------
END YR WITH: 100 190 482 610 1760 [358 left]
NEED RECRUITS ? 0 10 118 240* 368 tot
NEED LAYOFFS ? (10)* (10) tot
KEEP STABLE 100 200 600 600 2000 = 3500 Tot
15. MARKOV ANALYSIS – 3
(Anticipates Changes in Employment Levels)
Employment needs are changing. We need a 10% increase in skilled workers
(660), and a 15% decrease in assembly workers (1700) by year’s end.
-------------------------------------------------------
(Start = 3500) A TRANSITION MATRIX
FROM/ TO: TOP MID LOW SKILLED ASSY EXIT
TOP 100 .80 .02 .18
MID 200 .10 .76 .04 .10
LOW 600 .06 .78 .01 .15
SKILL 600 .01 .84 .15
ASSY 2000 .05 .88 .07
---------------------------------------------------------
END YR WITH: 100 190 482 610 1760 [358 left]
NEED RECRUITS ? 0 10 118 50*
NEED LAYOFFS ? (60)*
NEW LEVELS 100 200 600 600 1700 = 3260 tot
16. Determining Labor Surplus or Shortage
• Based on the forecasts for labor demand and
supply, the planner can compare the figures to
determine whether there will be a shortage or
surplus of labor for each job category.
• Determining expected shortages and surpluses
allows the organization to plan how to address
these challenges.
17. PERSONNEL / YIELD RATIOS
Past experience has developed these yield ratios for recruiting a Cost Accountant:
FOR EVERY 12 APPLICATIONS RECEIVED, ONLY 1 LOOKS
PROMISING ENOUGH TO INVITE FOR AN INTERVIEW
OF EVERY 5 PERSONS INTERVIEWED, ONLY 1 IS ACTUALLY
OFFERED A POSITION IN THE ORGANIZATION
OF EVERY 3 JOB OFFERS MADE, ONLY 2 ACCEPT THE POSITION
OF EVERY 10 NEW WORKERS WHO BEGIN THE TRAINING
PROGRAM, ONLY 9 SUCCESSFULLY COMPLETE THE PROGRAM
THUS: 100 APPLICATIONS MUST BE RECEIVED, so that
8.33 JOB INTERVIEWS CAN BE HELD, so that
1.67 JOB OFFERS CAN BE MADE, and
1.11 PEOPLE MUST BE TRAINED, so that we get
ONE NEW COST ACCOUNTANT!!!
Editor's Notes
Trends and events that affect the economy also create opportunities and problems in obtaining human resources. To prepare for and respond to these challenges, organizations engage in human resource planning – defined in Chapter 1 as identifying the numbers and types of employees the organization will require to meet its objectives.
Figure 5.1 shows the human resource planning process. The process consists of three stages: Forecasting Goal setting and strategic planning Program implementation and evaluation
The first step in human resource planning is forecasting. The primary goal is to predict which areas of the organization will experience labor shortages or surpluses.
Usually an organization forecasts demand for specific job categories or skill areas. After identifying the relevant job categories or skills, the planner investigates the likely demand for each. The planner must forecast whether the need for people with the necessary skills and experience will increase or decrease. There are several ways of making such forecasts.
Once a company has forecast the demand for labor, it needs an indication of the firm’s labor supply.
Table 5.1 is an example of a transitional matrix. Matrices such as this one are extremely useful for charting historical trends in the company’s labor supply.
Issues related to a labor surplus or shortage can pose serious challenges for the organization.