Users Guide Analytics

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A guide to using Workforce Analytics.

Kronos Workforce Central SuiteVersion 6

Workforce Analytics

Users Guide

Document Part Number: 4704297-001Document Revision: A


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The information in this document is subject to change without notice and should not be construed as a commitment

by Kronos Incorporated. Kronos Incorporated assumes no responsibility for any errors that may appear in this

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Document Revision History

Document Revision Product Version Release Date

A Workforce Central 6.1 May 2009


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Contents

About This Guide

Organization of this guide .............................................................................6

Workforce Analyticsdocuments ................................................................... 7

Chapter 1:Introduction toAnalytics

Overview .....................................................................................................10

AnalyticsTechnology .................................................................................. 11

Chapter 2: Multidimensional Analysis

Introduction to Analytics cubes ................................................................... 14

Using cube dimensions ................................................................................17

Using the Time Summary Daily and Time Summary Monthly cubes ........ 23

Analytic categories ................................................................................ 23

Measures used with analytic categories ................................................ 44

Using the Scorecard Daily and Scorecard Monthly cubes .......................... 47

Scorecard Daily cube ............................................................................ 47Scorecard Monthly cube ....................................................................... 61

Using the Time Detail Daily cube ...............................................................69

Metrics in the Time Detail Daily cube .................................................. 69

Index


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Contents

4


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About This Guide

This guide is for Kronoscustomers who use KronosWorkforce Analytics to

perform multidimensional data analysis to understand business issues.

This preface contains the following sections:

Organization of this guideon page 6

Workforce Analytics documentson page 7


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About This Guide

6 Kronos Incorporated

Organization of this guide

This guide contains the following chapters:

Chapter 1, Introduction to Analytics,on page 9

Chapter 2, Multidimensional Analysis,on page 13


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Workforce Analytics documents

Workforce Analytics Users Guide 7

Workforce Analytics documents

The following documentation is available to help you install, maintain, and use

the Workforce Analytics software:

Workforce Analytics Installation Guideprovides instructions on planning,

installing, and configuring the core Workforce Analytics product.

Workforce Central System Administrators GuideAnalyticsdescribes the

day-to-day system administration tasks that are performed through theWorkforce Analytics Manager utility, such as examining the log files of

Extract, Transform, and Load (ETL) processes, maintaining user security, and

mapping pay codes.

Workforce Analytics Users Guidedescribes the analytic data the core

Workforce Analytics product makes available, and the Analytic cubes that can

be accessed and manipulated with business intelligence (BI) tools that

perform multidimensional analysis.

Online Help for Workforce Analytics Manager is installed automatically with

the product. To access online Help: select Help > Workforce Analytics Help

from the menu bar.

Online Help for the Workforce Analytics Criteria Builder Web Parts is

installed automatically with the product. To access online Help: click the

down-arrow on the right side of the Web Part title bar, and select Help fromthe Web Part Menudrop-down list.

Release notes provide additional information about Workforce Analytics,

including a list of new features, resolved issues, and late-breaking changes.


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About This Guide

8 Kronos Incorporated


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Chapter 1

Introduction to Analytics

Analytics enables authorized users to perform multidimensional analytic querieson data derived from the timekeeping database.

This chapter contains the following sections:

Overviewon page 10

Analytics Technologyon page 11


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Chapter 1 Introduction to Analytics

10

Overview

Analyticsis a business intelligence software solution. It provides new insights

into your workforce based on data you are already collecting and the necessary

tools to monitor workforce performance and reduce costs.

Analytics is a comprehensive software solution that includes logic and routines to

pull data from the timekeeping product into an analytic database that is designed

specifically for reporting and analysis.It enables you to monitor workforce performance using dashboards, metrics and

key performance indicators (KPIs), analytic views, and custom reports. The

following table describes how to use Analytics to respond to some commonly

asked questions about managing your workforce:

Question How to use Analyticsto get an answer

Is the workforce as productive as

it could be?

Identify and compare the productivity of various shifts,

factories, regions, time periods, and so on.

How can I manage workforce

costs better?

Examine regular and overtime scheduling,

absenteeism and resources to determine the most

efficient way to work with what you have and save

money.

How much money am I losing

because of employee time clockabuse?

Determine whether employees are abusing the

rounding rules of data collection devices minutes ashift can add up to millions in costs a year.

Am I complying with

government regulations?

View information that indicates compliance with

regulations, such as FLSA, EEOC and FMLA.


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Analytics Technology

Users Guide 11

Analytics Technology

Analytics uses leading business intelligence (BI) technologies to provide a rich

reporting and analytic environment that gives managers at all levels in the

organization new insight into their workforce.

The architecture components included in Analytics vary by client, but generally

include the following:

Microsoft SharePoint Services provides a front-end portal where you can viewand interact with analytic data.

Cubes can be viewed and manipulated using Microsoft Excel 2007. If you prefer

to use BI tools such as Business Objects, COGNOS or Hyperion, integration is

provided through a Service Representative.


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Chapter 1 Introduction to Analytics

12


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Chapter 2

Multidimensional Analysis

Analytics enables users to view Workforce Central data using tools that supportmultidimensional analysis.

Multidimensional analysis groups data into two basic categories: data dimensions

and measurements (or facts). Multidimensional analysis enables you to view data

from a relational database in a more hierarchical form, and to combine multiple

categories of data into a single view.

To support multidimensional analysis, business intelligence (BI) applications

typically allow you to connect to a data source from which they may import datato populate a list of fields. You can drag and drop elements from this list to a

presentation tool, such as a pivot table or dashboard, to dynamically establish

relationships among the data that provide insight into the key performance

indicators (KPIs) of an enterprise. You can also alter these relationships, add

dimensions that supply further detail, and drill down to more specific levels

within data hierarchies to perform root-cause analysis.

The data source for these types of applications is an On Line AnalyticalProcessing (OLAP) cube.

This chapter describes the cubes provided in Analytics. It includes the following

sections:

Introduction to Analytics cubeson page 14

Using cube dimensionson page 17

Using the Time Summary Daily and Time Summary Monthly cubeson

page 23

Using the Scorecard Daily and Scorecard Monthly cubeson page 47

Using the Time Detail Daily cubeon page 69


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Chapter 2 Multidimensional Analysis

14

Introduction to Analytics cubes

Analytics provides the following cubes in the database within SQL Server

Analysis Services (SSAS). These cubes are compatible with any business

intelligence (BI) tool that supports the Multidimensional Expressions (MDX)

query language.

Cube Use

Time Summary Daily Ad-hoc analysis of employee-level data on a daily basis

within a hierarchy of metric categories: allows an

employee to be compared to a peer group or other

corporate group.

Time Summary Monthly Ad-hoc analysis of organizational-level metrics on amonthly basis within a hierarchy of metric categories:

allows interpretation of organizational performance and

drill down to the employee level.

Scorecard Daily Analysis of predefined employee-level key performance

indicators (KPIs) on a daily basis: allows an employee

to be compared to a peer group or other corporate

group.

Scorecard Monthly Analysis of predefined organizational-level KPIs on a

monthly basis: allows interpretation of organizational

performance and drill down to the employee level.

Time Detail Daily Analysis of payroll amounts and hours per employee,

date, pay period, pay code, job, organization,

supervisor, labor account, age, or tenure.


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Introduction to Analytics cubes

Users Guide 15

The structure of these cubes consists of the following elements:

Cube Elements Definition

Measures Fed (unprocessed data that comes directly from Workforce

Central ) or calculated data values that are initially viewed as

columns across the top of the cube.

The Time Summary Daily and Time Summary Monthly

cubes contain a set of measures, known asstandard(and

distinct) measures, that refer to data values relative to theirlocation within a hierarchy of analytic categories. Some

examples of standard measures are Paid hours per employee

and Overtime hours as a % of paid hours. Thestandard

measures in the Time Detail Daily cube are basic amount and

count metrics.

The Scorecard Daily and Scorecard Monthly cubes contain a

set of measures, known asscorecard(or core) measures, thatreturn KPI values that are prepopulated within the cube.

Some examples of scorecard measures are Paid hours total

and Total OT Cost.

Cube architects sometimes refer to measures asfacts. You are

more likely to see measures referred to as metricsor totals

within the context of analytical processing.

Dimensions Descriptive elements that allow you to analyze and filter data(such as, time of year or labor level).

Analytics cubes share many of the same dimensions.

However, the Analytics dimension is provided in the Time

Summary Daily and Time Summary Monthly cubes only.

Analytic categories Specific areas of analysis (such as, unscheduled overtime or

unexcused absenteeism) that are initially viewed as rows

down the side of the cube. Analytic categories are membersof the Analytics dimension and are imported as part of that

dimension by a cube data source.

Only the Time Summary Daily and Time Summary Monthly

cubes provide the Analytics dimension and analytic

categories.


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Like any traditional OLAP cube, the information derived from the intersection of

the selected measure(such as, hours per employee), multiple dimensions(such as,fourth quarter (Q4) and labor level 2), and, in the case of the Time Summary

cubes, analytic category(for example, absenteeism) is pre-calculated. Therefore,

analysis is easy to build within a selected business intelligence (BI) tool by using

drag and drop techniques, and results can be viewed almost immediately.

For example, the Time Summary Daily cube can be used to identify the absence

hours paid per employee in the 4thQuarter for all employees in Labor Level 1, as

shown in the following illustration:


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Using cube dimensions

Users Guide 17


The Analytics cubes provide the dimensions described in the following table.

With the exception of the Analytics dimension, all dimensions are available for

use with any cube.

Dimension

Visible

AttributesandHierarchy Definition

Age Age

Age Band

Age

Defined employee age ranges (for example, 18 to 20 and

21 to 35)

Analytics Level 01

Level 02

Level 03

Level 04

Level 05

Hierarchy of analytic categories, as described in Using

the Time Summary Daily and Time Summary Monthly

cubeson page 23. The Analytics dimension is availableonly in the Time Summary Daily and Time Summary

Monthly cubes.

Date Calender Month Calendar month; for example, September is September

1, 2009 through September 30, 2009.

Calendar Quarter Calendar quarter; for example, Q2is April 1, 2009

through June 30, 2009.

Calendar Year Calendar year; for example, 2009 is January 1, 2009

through December 31, 2009.

Date Day; for example, Aug1 is 12:00 a.m. August 1

through 11:59 p.m. August 1.

Day of Week Day of the week from Sunday to Saturday.

Fiscal Year First day of selected fiscal year through last day of fiscal

year, as defined by the administrator in Analytics

Manager.

Fiscal Month First day of selected fiscal month through last day of

fiscal month, as defined by the administrator in

Analytics Manager.


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Fiscal Quarter First day of selected fiscal quarter through last day of

fiscal quarter, as definedby the administrator in

Analytics Manager.

Relative Date Current calendar year or fiscal year as compared against

the preceding year or the preceding year minus one. The

Relative Date dimension must be used in combinationwith either the Fiscal or Calendar dimension (one on the

X axis and the other on Y).

Note: If you are using Microsoft Excel 2007 to display

Relative Date calculations in a pivot table, you must

perform the following steps:

1. Add the Relative Date dimension to the pivot table.

2. Right click on the pivot table and select PivotTableOptions.

3. Click the Displaytab.

4. Select Show calculated members from OLAP

Serverand click OK.

Calendar

Year

QuarterMonth

Date

Time frames within the calendar year, defined by year,

quarter, month, and date.

Fiscal

Year

Quarter

Month

Date

Time frames within the fiscal year, defined by fiscal

year, fiscal quarter, fiscal month, and date. For

information on how to set company-specific fiscal dates,

see the Workforce Analytics Installation Guide.

Dimension

Visible

Attributesand

Hierarchy Definition


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Users Guide 19

Employee Accrual Profile Employee accrual profile, as defined in Workforce

Central.

Base Wage Employee base wage, as defined in Workforce Central.

Device Group Employee device group, as defined in Workforce

Central.Employee ID Employee ID, as defined in Workforce Central.

Employee Name Employee name, as defined in Workforce Central.

Employee Status Employee status, as defined in Workforce Central.

Group Schedule Employee group schedule, as defined in Workforce

Central.

Home City Employee home city, as defined in Workforce Central.

Home Country Employee home country, as defined in Workforce

Central.

Home Labor Account Name Employee home labor account, as defined in Workforce

Central.

Home State Employee home state, as defined in Workforce Central.

Home Zip Code Employee home zip code, as defined in Workforce

Central.Pay Rule Employee pay rule, as defined in Workforce Central.

Work Rule Employee work rule, as defined in Workforce Central.

Employee

Last Name Initial

Employee Name

Employee last name, organized by the first initial of the

employee last name, as defined within Workforce

Central

EmployeeStatus Employee Status Type Employee status: Active, Inactive, Terminated, or NotApplicable. The Employee Status dimension is available

only in the Time Detail Daily cube.

Holiday Holiday

Holiday Name

Holidays, as defined in Workforce Central. The Holiday

dimension is available only in the Time Detail Daily

cube.

Dimension

Visible



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Job Job Code Job codes, as established within Workforce Central

Job Description Job descriptions, as established in Workforce Central.

Job Name Job names, as established in Workforce Central.

Labor Account

Type

Labor Account Type Name Labor account types: for example, home labor account

or transfer labor accountLabor Levels Labor Account 1 Name

Labor Account 2 Name




Labor Account 6 NameLabor Account 7 Name

Labor Level 1

Labor Level 2

Labor Level 3

Labor Level 4

Labor Level 5

Labor Level 6

Labor Level 7

Labor accounts, as defined in Workforce Central

Dimension

Visible



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Users Guide 21

Organization Level 02

Level 03

Level 04

Level 05

Level 06Level 07

Level 08

Level 09

Level 10

Level 11

Up to 10 levels of organization, as defined within

Workforce Central. Using this dimension, you can trace

metrics by drilling down from higher to lower levels in

the organizational hierarchy.

Note: Workforce Analytics rolls up data to a parent node

from its children (nodes at the next lower level). If theorganizational structure allows data to be logged against

the parent itself, as well as its children, the name of the

parent appears as one of its children, and is marked with

an asterisk(*). (English-speaking locales only)

For example, in the following organization, transactions

not logged against Bakery, Dairy, and Produce may be

logged against the store itself (Chelmsford MA*).

Transactions logged against all four children are rolled

up to their parent (Chelmsford MA).

Chelmsford MA

Bakery

Dairy

Produce

Chelmsford MA*Original

Currency

Original Currency Currency, as assigned to employees in Workforce

Central, organized by ISO currency codes. (The code

UNSrepresents employee amounts from Workforce

Central sources where there are employees who are not

assigned a currency type.)

Pay Code Pay Code

Pay CategoryPay Code Name

Pay codes, as established within Workforce Central and

mapped to analytics pay categories (Regular, Overtime,Other, Non Productive, Training, and Unknown). The

Pay Code dimension is available only in the Time Detail

Daily cube.

Pay Code Name Pay codes, as established within Workforce Central .

Dimension

Visible



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Pay Period Pay Period

Pay Period Time Name

Pay Period Date Range

Pay period as defined by name (current, next, other, and

previous) and date range. The Pay Period dimension is

available only in the Time Detail Daily cube.

Pay Rule Pay Rule Name Pay rules, as established within Workforce Central. The

Pay Rule dimension is available only in the Time DetailDaily cube

Reporting

Currency

Reporting Currency Currency, organized by ISO currency codes, to which

the original currency has been converted, according to

the conversion table established in Workforce Central.

The administrator configures the reporting currencies for

analytics cubes in the Analytics Manager utility. (The

code 000represents the original amount as stored in the

Analytics data mart. The code UNSrepresents employeeamounts from Workforce Central sources where there

are employees who are not assigned a currency type.)

If you do not use this dimension, amounts are displayed

in the default member currency.

Note: If you use the Reporting Currency dimension as a

filter, make sure that only one currency is selected.

Otherwise, the amount displayed will have no meaning.Supervisor Supervisor ID Supervisor ID, as established when employees are

assigned supervisors within Workforce Central.

Supervisor Name Supervisor name, organized by last name, as established

when employees are assigned supervisors within

Workforce Central.

Tenure Tenure

Tenure BandTenure Month

Defined tenure bands, such as years employed; for

example three to six months or one to three years.

Tenure Month Number of months of employment.

Dimension

Visible



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Using the Time Summary Daily and Time Summary Monthly cubes

Users Guide 23

Using the Time Summary Daily and Time Summary

Monthly cubes

In addition to the dimensions (described in Using cube dimensionson page 17)

that are defined for use with all Analytics cubes, the Time Summary Daily and

Time Summary Monthly cubes contain a special Analytics dimension that defines

a hierarchical structure of analytic categories. When combined with a set of

measures specifically defined for this use in a business intelligence tool (such as a

pivot table or dashboard), analytic categories provide a flexible and powerfulmechanism to drill down to more specific areas of problem analysis with a

minimal amount of effort.

When using analytics categories in multidimensional analysis, note the following:

The Time Summary Daily and Time Summary Monthly cubes provide the

same set of analytic categories

Analytic categories are members of the Analytics dimension. By dragging theAnalytics dimension into a business intelligence tool, you obtain access to the

analytics categories it contains.

Analytic categories are hierarchical in nature. Each category has a root, such

as All Punches. The root may have several children, such as Deficient

Punches and Non-Deficient Punches. These children may also have

children. For example, Start Late 0-5 min is the child of Start Late. (In

other words, Start Late is the parent of Start Late 0-5 min.) Start Lateitself is the child of Deficient In Punches, which is the child of Deficient

Punches, which is the child of All Punches. Because of the measures

defined for the categories understand this hierarchy, you can move up and

down this hierarchy by specifying the desired measure.

The dimensions described in Using cube dimensionson page 17are also

available for use with the Analytics dimension and analytic categories.

Analytic categories

Analytics defines the following analytic categories as members of the Analytics

dimension.


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Important: The definition of Core in Workforce Analytics differs from that in

Workforce Central. In Workforce Analytics, core pay codes recognize activities

that are associated with specific events in which hours are tracked: for example,

regular hours spent working or overtime. Depending upon the organization, non-

productive pay codes such as paid time off (PTO) and Family Medical Leave Act

(FMLA) may be considered core if their hours are being tracked.

In Workforce Central, the word is used to indicate ranges of time when employees

are expected to work and exceptions are flagged for any unworked time duringcore hours. Do not confuse the two uses of the term.

All Paid OT

The All Paid OT analytic category is a measurement of events in which

employees were paid for overtime.Analytics determines whether overtime is scheduled or unscheduled based on the

basic scheduler module in the timekeeping or scheduling products.

Scheduled overtime must have been scheduled in the context of total time, and the

overtime must have started and completed at the exact times it was scheduled to

begin and end.

Unscheduled overtime is any time worked that falls outside of the overtime

planned in the schedule. If the overtime does not start and end as scheduled,

Analytics counts it as unscheduled OT.

The metrics in this analytic category are used to identify how compliant the

workforce is with the schedule: both with respect to the composition of total

overtime hours and whether or not scheduled overtime was worked at the correct

time of day.

Note: The scheduling metrics provided by Analytics are derived from scheduling

data that has been maintained within the basic scheduler module of the

timekeeping product, or with the assistance of the scheduling products. If schedules

have not been established and maintained in these products, scheduling metrics, such

as those relating to absenteeism, deficient punches, and scheduled overtime, will

contain either no data or unreliable data.

h l d hl b


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Users Guide 25

Structure of the All Paid OT analytic category

The All Paid Overtime analytic category has the following hierarchical structure:

All Paid OT

Paid Scheduled OT

Paid Not Scheduled OT

Members of the All Paid Overtime analytic category

The All Paid Overtime analytic category contains the members described in the

following table:

All Absenteeism

Absenteeism is an event that occurs when an employee who is assigned to ashift,

patternorscheduledoes not record any punch. Absences can be excused or

unexcused.

Analytics determines whether an absence is excused or unexcused based on the

employee timecard and schedule items in the timekeeping and scheduling

products.

An absence defaults to unexcused until a manager manually approves it. When a

manager assigns the absence a pay code and attaches a comment to it, it becomesexcused.

Workforce Analytics does not extrapolate values for unexcused absences. When

there are no hours or dollars associated with the unexcused absence, the most

common situation, the unexcused absenteeism metrics will be presented as '0.0'.

Analytic Definition

All Paid OT >

Paid Scheduled OT Measurement of Overtime (OT) events in which employees were paid

for working the shift or schedule to which they were assigned.

Paid Not Scheduled OT Measurement of Overtime (OT) events in which employees were paid

for working a shift or schedule that they were not assigned to.

Ch t 2 M ltidi i l A l i


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In the rare event that there are associated hours or dollars, the metrics will present

that data.



timekeeping product, or with the assistance of the scheduling product, both part of

the product suite. If schedules have not been established and maintained in these

products, scheduling metrics, such as those relating to absenteeism, deficient

punches, and scheduled overtime, will contain either no data or unreliable data.

Structure of the All Absenteeism analytic category

The All Absenteeism analytic category has the following hierarchical structure:

All Absenteeism

Excused Absenteeism

Excused Absenteeism Reg Core

Excused Absenteeism OT Core

Excused Absenteeism Train Core

Excused Absenteeism Non Prod Core

Excused Absenteeism Oth Core

Excused Absenteeism Unknown Core

Unexcused Absenteeism

Unexcused Absenteeism Reg Core

Unexcused Absenteeism OT Core

Unexcused Absenteeism Train Core

Unexcused Absenteeism Non Prod Core

Unexcused Absenteeism Oth Core

Unexcused Absenteeism Unknown Core



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Users Guide 27

Members of the All Absenteeism analytic category

The All Absenteeism analytic category contains the members described in the

following table:

Analytic Definition

All Absenteeism > Excused Absenteeism

Excused Absenteeism

Reg Core

Missed or empty record punch activity flagged (by an employee's

manager) with a pay code mapped to the analytics Regpay category,and a comment for the duration of the shift.

Excused Absenteeism

OT Core


manager) with a pay code mapped to the analytics OTpay category, and

a comment for the duration of the shift.

Excused Absenteeism

Train Core


manager) with a pay code mapped to the analytics Trainpay

category), and a comment for the duration of the shift.Excused Absenteeism

Non Prod Core


manager) with a pay code mapped to the analytics Non Prod pay

category, and a comment for the duration of the shift.

Excused Absenteeism

Oth Core


manager) with a pay code mapped to the analytics Otherpay category,

and a comment for the duration of the shift.

Excused AbsenteeismUnknown Core

Missed or empty record punch activity flagged (by an employee'smanager) with a pay code mapped to the analytics Unknownpay

category, and a comment for the duration of the shift.

All Absenteeism > Unexcused Absenteeism

Unexcused

Absenteeism Reg Core

Missed or empty record punch activity, for a pay code mapped to the

analytics Regpay category, that is not flagged by the employees

manager with a pay code and comment. It becomes an exception (no

timecard item record in Exception tables in the timekeeping product) asan unexcused absence event.

Unexcused

Absenteeism OT Core


analytics OTpay category, that is not flagged by the employees


timecard item record in Exception tables in the timekeeping product) as

an unexcused absence event.



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All Paid Not Worked

The All Paid Not Worked analytic category represents time paid but not worked;

based on the rounding rule (from the timekeeping product) assigned. This is

tracked for both in-punches and out-punches. Data in this category helps

determine the impact of the current rounding rules.

For example:

Start Late - An employee started at 8:15 AM and the rounding rule rounds hispunch to 8:00 AM, then the 15-minute differential becomes Paid Not Worked.

Leave Early - An employee ended at 3:45 PM and the rounding rule rounds

his punch to 4:00 PM, then the 15-minute differential becomes Paid Not

Worked.

Unexcused

Absenteeism Train

Core


analytics Trainpay category, that is not flagged by the employees




Unexcused

Absenteeism Non Prod

Core


analytics Non Prodpay category, that is not flagged by the

employees manager with a pay code and comment. It becomes anexception (no timecard item record in Exception tables in the

timekeeping product) as an unexcused absence event.

Unexcused

Absenteeism Oth Core


analytics Otherpay category, that is not flagged by the employees




Unexcused

Absenteeism Unknown

Core


analytics Unknownpay category, that is not flagged by the employees




Analytic Definition



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Users Guide 29

Structure of the All Paid Not Worked analytic category

The All Paid Not Worked analytic category has the following hierarchical

structure:

All Paid Not Worked

Paid Not Worked In

Paid Not Worked Out

Members of the All Paid Not Worked analytic category

The All Paid Not Worked analytic category contains the members described in the

following table:

All Early Start / Late Leave 16 - 60 min

The All Early Start / Late Leave 16 - 60 min analytic category contains exceptions

that are defined within a punch event exception rule that may be categorized as

Deficient In/Out or Non-Deficient In/Out-punches.Exception rules identify shifts

that deviate from the expected pattern and are part of an employees work rule.

Analytic Definition

All Paid Not Worked >

Paid Not Worked In Measure of time paid but not worked, due to rounding rules and basedon in an punch.

Example: An employee is assigned to a pay rule that is set to round to

the nearest quarter hour, and the shift or schedule is 7 A.M to 4 P.M. If

the employees in-punch shows a time of 7:22 A.M, the in-punch will be

rounded to the nearest quarter hour (7:15 A.M and the employee will be

paid for the 7 minutes not worked.)

Paid Not Worked Out Measure of time paid yet not worked due to rounding rules and based onan out-punch.

Example:An employee is assigned to a pay rule that is set to round to

the nearest quarter hours, and the shift or schedule is 7 A.M. to 4 P.M. If

the employees out-punch shows a time of 3:53 P.M., the out-punch will

be rounded to the nearest quarter hours (4 P.M.) and the employee will

be paid for the 7 minutes not worked.



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Analytics compares actual punches to the schedule. For example, if an employee

is scheduled for 8:00 and punches in at 7:58, the punch is counted as an earlypunch.

A punch is early or late if it deviates from the schedule by even one minute. This

concept is only in Analytics. The timekeeping product has exception rules that are

similar, but there is not a one-to-one mapping.

Structure of the All Early Start / Late Leave 16 - 60 min analytic category

The All Early Start / Late Leave 16 - 60 min analytic category has the followinghierarchical structure:

All Early Start / Late Leave 16 - 60 min

Early Start 16 - 60 min

Late Leave 16 - 60 min

Members of the All Early Start / Late Leave 16 - 60 min analytic category

The All Early Start / Late Leave 16 - 60 min analytic category contains the

members described in the following table:

All Worked Not Paid

The All Worked Not Paid analytic category represents time worked but not paid,based on the rounding rule (from the timekeeping product) assigned. This is

tracked for both in-punches and out-punches. Data in this category helps

determine the impact of the current rounding rules.

For example:

Analytic Definition

All Early Start / Late Leave 16 - 60 min >

Early Start 16 - 60

minutes

Identifies an early shift or schedule that is 16 minutes to 60 minutes

before the shift or schedule begins.

Late Leave 16 - 60

minutes

Identifies a late shift or schedule that is 16 minutes to 60 minutes after

the shift or schedule ends.



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Start Early - An employee started at 7:45 AM and the rounding rule rounds his

punch to 8:00 AM, then the 15-minute differential becomes Worked Not Paid. Leave Late - Ex. Employee ended at 4:15 PM and the rounding rule rounds

his punch to 4:00 PM, then the 15-minute differential becomes Worked Not

Paid

The formula Analytics uses to calculate worked hours is as follows:

Worked Hours = Productive Hours + Nonproductive

Hours

Productive and nonproductive hours are determined by the mapping your system

administrator has established between pay codes from the timekeeping product

and Analytics pay categories.

Structure of the All Worked Not Paid analytic category

The All Worked Not Paid analytic category has the following hierarchical

structure:All Worked Not Paid

Worked Not Paid In

Worked Not Paid Out

Members of the All Worked Not Paid analytic category

The All Worked Not Paid analytic category contains the members described in thefollowing table:

Analytic Definition

All Worked Not Paid >

Worked Not Paid In Measure of time worked but not paid, due to rounding rules and based

on an in-punch.

Example: The employee in-punch is 8:08 A.M. The pay rule specifiesthat any in-punch 8 or more minutes after the quarter hour will be

rounded to the next quarter hour (Start Late / In Late). The in-punch will

be rounded to 8:15 A.M. and the employee will be docked to have

started at 8:15 A.M. and will lose one-quarter of his hourly wage. For

example, an employee who makes $22.00 an hour will only receive

$16.50 for that hour.



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All Punches

Analytics calculates nondeficient and deficient punches as follows:

Nondeficient punches are within the limits of the work rule that is assigned to

the employee(s). Nondeficient punches are calculated by analyzing the actual

punch event and comparing it to the worked time.

Deficient in-punches and deficient out-punches do not fall within the

classification of the work rule established that is assigned to the employee(s).

Deficient punches are calculated by analyzing the actual punch event and

comparing it to the worked time.

Analytics applies its own rounding rules. It uses the raw punch data and defines its

own thresholds to generate metrics related to early, late, deficient, and

nondeficient punches. (That is, the rounding rules from the timekeeping productare not applied against this data.) These thresholds are not configurable; they are

always defined as follows:

For in-punches:

All late punches are deficient.

A punch that is 0 to 15 minutes early is nondeficient.

A punch that is 16 to 60 minutes early is deficient.

A punch that is greater than 60 minutes early is considered unscheduled

overtime, because it may not necessarily be a deficient punch.

For out-punches:

All early punches are deficient.

Worked Not Paid Out Measure of time worked yet not paid due to rounding rules (based onout-punch).

Example: The employees shift (schedule) ends at 5:00 P.M.and the

employee punches out at 4:52 P.M. Based on the pay rule that is

assigned to the employee, the out-punch will be rounded to 4:45 P.M,

and the employee will lose 15 minutes of his hourly base wage (for

leaving early).

Analytic Definition



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A punch that is to 15 minutes late is nondeficient.

A punch that is 16 to 60 minutes late is deficient.

A punch that is greater than 60 minutes late is considered as unscheduled

overtime, because it may not necessarily be a deficient punch:

Notes:The scheduling metrics provided by Analytics are derived from scheduling data that

has been maintained within the basic scheduler module of the timekeeping

product, or with the assistance of the scheduling product, both part of the product

suite. If schedules have not been established and maintained in these products,

scheduling metrics, such as those relating to absenteeism, deficient punches, and

scheduled overtime, will contain either no data or unreliable data.

Punch Deficient or nondeficient

Start Early 0 to 15 minutes Nondeficient

Start Early 16 to 60 minutes DeficientStart Early greater than 60 minutes Nondeficienta

a. Analytics considers a punch deficient if a person punches in less than an hour priorto his scheduled time or punches out less than an hour after his scheduled time. Itassumes that, if the person punches in more than an hour early or punches outmore than an hour later, there is likely a business reason for his doing so (forexample, the employee was called in early or was asked to stay late).

Start Late 0 to 5 minutes Deficient

Start Late 6 to10 minutes Deficient

Start Late 11 to 15 minutes Deficient

Start Late greater than 15 minutes Deficient

Leave Late 0 to 15 minutes NondeficientLeave Late 16 to 60 minutes Deficient

Leave Late greater than 60 minutes Nondeficienta

Leave Early 0 to 5 minutes Deficient



Leave Early greater than 15 minutes Deficient



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Only Count metrics are relevant to the All Punches analytic category. You canapply Count metrics to the members of the category to obtain counts of deficient

and nondeficient events. To obtain the Amount and Hours associated with

deficient and nondeficient punches, apply those metrics to the All Paid Not

Worked and All Worked Not Paid categories.

Structure of the All Punches analytic category

The All Punches analytic category has the following hierarchical structure:

All Punches

Non-Deficient Punches

Start Early 0 - 15 min

Start Early GT 60 min

Leave Late 0 - 15 minLeave Late GT 60 min

Deficient Punches

Deficient - In Punches

Start Early 16-60 min

Start Late

Start Late 0 - 5 min

Start Late 6- 10 min

Start Late 11 - 15 min

Start Late GT 15 min

Deficient - Out Punches

Leave Late 16-60 min

Leave Early

Leave Early 0 - 5 min

Leave Early 6- 10 min

Leave Early 11 - 15 min



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Leave Early GT 15 min

Members of the All Punches analytic category

The All Punches analytic category contains the members described in the

following table:

Analytic Definition

All Punches > Non-Deficient Punches >

Start Early 0 - 15 min Identifies a start early shift or schedule that is 0 minutes to 15 minutes


Start Early Greater than

60 min

Identifies a start early shift or schedule that is more than 60 minutes


Leave Late 0 - 15 min Identifies a leave late shift or schedule that is 0 minutes to 15 minutes

after the shift or schedule ends.

Leave Late GT 60 min Identifies a leave late shift or schedule that is more than 60 minutes after

the shift or schedule ends.

All Punches > Deficient Punches > Deficient - In Punches >

Start Early 16 - 60 min Identifies a start early shift or schedule that is 16 to 60 minutes before the

shift or schedule begins.

All Punches > Deficient Punches > Deficient - In Punches > Start Late >

Start Late 0 - 5 min Identifies a start late shift or schedule that is 0 minutes to 5 minutes after

the shift or schedule begins.Start Late 6 - 10 min Identifies a start late shift or schedule that is 6 minutes to 10 minutes

after the shift or schedule begins.

Start Late 11 - 15 min Identifies the start late shift or schedule that is 11 to 15 minutes after the

shift or schedule begins.

Start Late GT 15 min Identifies the start late shift or schedule that is greater than 15 minutes

after the shift or schedule begins.

All Punches > Deficient Punches > Deficient - Out Punches >

Leave Late 16 - 60 min Identifies a leave late shift or schedule that is 16 minutes to 60 minutes

after the shift or schedule ends.

All Punches > Deficient Punches > Deficient - Out Punches > Leave Early >

Leave Early 0 - 5 min Identifies a leave early shift or schedule that is 0 minutes to 5 minutes

before the shift or schedule ends.



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All Paid

The All Paid analytic category represents the events in which employees were

paid.

Analytics calculates its labor utilization metrics as follows:

Worked Hours = Productive Hours + Nonproductive Hours

(Productive and nonproductive hours are determined by the mapping your

system administrator has established between pay codes from the timekeeping

product and Analytics pay categories.)

All Paid Scheduled Amount + All Paid Not Scheduled Amount = All Paid

Amount - Sum of Money Amounts

All Paid Hours = All Paid Scheduled Hours + All Paid Unscheduled Hours

Structure of the All Paid analytic category

The All Paid analytic category has the following hierarchical structure:

All Paid

All Paid Reg / OT / Train

Paid Reg / OT / Train (Worked)

Paid Reg

Paid OT

Paid Train

Paid Reg / OT / Train Money

Paid Reg Money

Leave Early 6 - 10 min Identifies a leave early shift or schedule that is 6 minutes to 10 minutes


Leave Early 11 - 15

min

Identifies a leave early shift or schedule that is 11 minutes to 15 minutes


Leave Early GT 15 min Identifies a leave early shift or schedule that is more than 15 minutes


Analytic Definition



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Paid OT Money

Paid Train Money

All Paid Non Prod / Other / Unk

Paid Other / Non Prod /Unk

Paid Non Prod

Paid Other

Paid UnknownPaid Other / Non Prod /Unk Money

Paid Non Prod Money

Paid Other Money

Paid Unknown Money

Members of the All Paid analytic category

The All Paid analytic category contains the members described in the following

table:

Analytic Definition

All Paid > All Paid Reg / OT / Train > Paid Reg / OT / Train (Worked) >

Paid Reg Contains the wage amount paid for regular hours worked.

Paid OT Contains the wage amount paid for overtime hours worked.

Paid Train Contains the wage amount paid for training hours worked.

All Paid > All Paid Reg / OT / Train > Paid Reg / OT / Train Money >

Paid Reg Money Contains direct money amounts associated with regular worked events.

Paid OT Money Contains direct money amounts associated with overtime worked events.

Paid Train Money Contains direct money amounts associated with training worked events.

All Paid > All Paid Non Prod / Other / Unk > Paid Non Prod / Other / Unk >

Paid Non Prod Contains the wage amount paid for nonproductive hours worked.

Paid Other Contains the wage amount paid for other hours worked.

Paid Unknown Contains the wage amount paid for unknown hours worked.



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All Scheduled

The All Scheduled analytic category represents the events in which employees

were scheduled to work.







Structure of the All Scheduled analytic category

The All Scheduled analytic category has the following hierarchical structure:

All Scheduled

Scheduled Reg / OT / Train

Scheduled Reg

Scheduled OT

Scheduled Train

Scheduled Non Prod / Other / UnkScheduled Non Prod

Scheduled Other

Scheduled Unknown

All Paid > All Paid Non Prod / Other / Unk > Paid Non Prod / Other / Unk Money >

Paid Non Prod Money Contains direct money amounts associated with nonproductive worked

events.

Paid Other Money Contains direct money amounts associated with other worked events.

Paid Unknown Money Contains direct money amounts associated with unknown worked events.

Analytic Definition



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Members of the All Scheduled analytic category

The All Scheduled analytic category contains the members described in thefollowing table:

All Paid Sched

The All Paid Sched analytic category represents the events in which employees

were paid for working the shift or schedule to which they were assigned.

Structure of the All Paid Sched analytic category

The All Paid Sched analytic category has the following hierarchical structure:

All Paid Sched

Paid Sched Reg / OT /Train

Paid Sched Reg

Paid Sched OT

Paid Sched TrainPaid Sched Non Prod / Other / Unk

Paid Sched Non Prod

Paid Sched Other

Paid Sched Unknown

Analytic Definition

All Scheduled > Scheduled Reg / OT / Train >

ScheduledReg Contains regular hours employees are scheduled to work.

ScheduledOT Contains overtime hours employees are scheduled to work.ScheduledTrain Contains training hours employees are scheduled to work.

All Scheduled > Scheduled Non Prod / Other / Unk >

ScheduledNon Prod Contains nonproductive hours employees are scheduled to work.

ScheduledOther Contains other hours employees are scheduled to work.

ScheduledUnknown Contains unknown hours employees are scheduled to work.



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data that has been maintained within the basic scheduler module of thetimekeeping product, or with the assistance of the scheduling product, both part of




Members of the All Paid Sched analytic category

The All Paid Sched analytic category contains the members described in the

following table:

All Paid Not Scheduled

The All Paid Not Scheduled analytic category represents the events in which

employees were paid for working a shift or schedule to which they were not

assigned.





Analytic Definition

All Paid Sched > Paid Sched Reg / OT / Train >

Paid SchedReg Wage paid to work the assigned scheduled regular shift or schedule.

Paid SchedOT Wage paid to work the assigned scheduled overtime shift or schedule.Paid SchedTrain Wage paid to work the assigned scheduled training shift or schedule.

All Paid Sched > Paid Sched Non Prod / Other / Unk >

Paid SchedNon Prod Wage paid to work the assigned scheduled nonproductive shift or

schedule.

Paid SchedOther Wage paid to work the assigned scheduled other shift or schedule.

Paid SchedUnknown Wage paid to work the assigned scheduled unknown shift or schedule.



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Structure of the All Paid Not Scheduled analytic category

The All Paid Not Scheduled analytic category has the following hierarchical

structure:

All Paid Not Scheduled

Paid Not Scheduled Reg / OT /TrainPaid Not Scheduled Reg

Paid Not Scheduled OT

Paid Not Scheduled Train

Paid Not Scheduled Non Prod / Other / Unk

Paid Not Scheduled Non Prod

Paid Not Scheduled Other

Paid Not Scheduled Unknown

Members of the All Paid Not Scheduled analytic category

The All Paid Not Scheduled analytic category contains the members

described in the following table:

Analytic Definition

All Paid Not Sched > Paid Not Sched Reg / OT / Train >

Paid Not Scheduled

Reg

Wage paid for working a shift or schedule that is not the assigned shift or

schedule.

Paid Not ScheduledOT Wage paid for working a shift or schedule that is not the assigned

overtime shift or schedule.Paid Not Scheduled

Train

Wage paid for working a shift or schedule that is not the assigned

training shift or schedule.

All Paid Not Sched > Paid Not Sched Non Prod / Other / Unk

Paid Not Scheduled

Non Prod

Wage paid for working a shift or schedule that is not the assigned

nonproductive shift or schedule.



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All Paid Non Core

The All Paid Non Core analytic category represents events based on how

employees are paid for non-core hours worked.

Structure of the All Paid Non Core analytic category

The All Paid Non Core analytic category has the following hierarchical structure:

All Paid Non Core

All Paid Reg / OT / Train Non Core

Paid Reg / OT / Train Non Core

Paid Reg Non Core

Paid OT Non Core

Paid Train Non Core

Paid Reg / OT / Train Non Core MoneyPaid Reg Non Core Money

Paid OT Non Core Money

Paid Train Non Core Money

All Paid Non Prod / Other / Unk Non Core

Paid Non Prod / Other / Unk Non Core

Paid Non Prod Non Core

Paid Other Non Core

Paid Unk Non Core

Paid Non Prod / Other / Unk Non Core Money

Paid Not Scheduled

Other

Wage paid for working a shift or schedule that is not the assigned other

shift or schedule.

Paid Not Scheduled

Unknown

Wage paid for working a shift or schedule that is not known.

Analytic Definition



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Paid Non Prod Non Core Money

Paid Other Non Core MoneyPaid Unk Non Core Money

Members of the All Paid Non Core analytic category

The All Paid Non Core analytic category contains the members described in the

following table:

Analytic Definition

All Paid Non Core > Paid Reg / OT / Train Non Core >

Paid Reg Non Core Premium amounts associated with regular non-core worked events, such

as shift premiums.

Paid OT Non Core Premium amounts associated with overtime non-core worked events,

such as shift amounts for per diems or commissions.

Paid Train Non Core Premium amounts associated with training non-core activities such as apremium for staying late for off-premises training.

All Paid Non Core > Paid Reg / OT / Train Non Core Money >

Paid Reg Non Core

Money

Direct money amounts associated with regular non-core worked events,

such as money amounts for per diems or commissions.

Paid OT Non Core

Money

Direct money amounts associated with overtime non-core worked

events, such as shift premiums, money amounts for per diems or

commissions.

Paid Train Non Core

Money

Direct money amounts associated with training non-core activities, such

as premium for staying late, or a money amount to pay for off-premises

training.

All Paid Non Core > Paid Non Prod / Other / Unk Non Core >

Paid Non Prod Non

Core

Premiums amounts associated with nonproductive time, such as time off

not taken, money amount rewards, or bonuses.

Paid Other Non Core Sites unique use for other premium and amounts paid.

Paid Unknown Non

Core

Applied when a new pay code is added to the configuration in the

timekeeping product. A warning is created during the nightly data

extract and load routine to indicate that the new pay code needs to be

assigned to a pay category in the Analytics configuration table.

All Paid Non Core > Paid Non Prod / Other / Unk Non Core Money >



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Measures used with analytic categories

The metrics available for use with analytics categories are measures defined

within the Time Summary Daily and Time Summary Monthly cubes specifically

to be used with analytic categories.

Note: Negative values can appear for hour and amount metrics in the Time

Summary Daily and Time Summary Monthly cubes when adjustment edits have

been performed in Workforce Central.

For example, if an employee mistakenly enters 12 hours instead of 10, the

manager may make an entry of -2 hours to adjust the hours down to 10. Similarly,

if an employee is given a bonus of $80 as a money amount, the manager may

make a subsequent entry of -$20 to adjust the money amount to $60.

The following table lists the key terms that are commonly used for these

measures:

Paid Non Prod NonCore Money

Direct money amounts associated with nonproductive non-core timesuch as time off not taken, money amount rewards, or bonuses.

Paid Other Non Core

Money

Sites unique use for other premiums and amounts paid.

Paid Unknown Non

Core Money

Applied when a new pay code is added to the configuration in the

timekeeping product. A warning is created during the nightly data

extract and load routine to indicate that the new pay code needs to be

assigned to a pay category in the Analytics configuration table.

Term Definition

Amount Amount of money paid to employees for events that occurred within the

analytic category.

Count Count of events that occurred within the analytic category.

Hours Hours associated with the events that occurred within the analytic category.

Paid All pay categories such as regular, overtime, training, nonproductive, and

other.

Analytic Definition



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The following measures are defined for the Time Summary Daily and Time

Summary Monthly cubes. The data provided by these measures is based on the

selected dimension(s) and analytic category.

Worked Pay categories associated with time on the job such as regular, overtime andtraining.

Involved The subset of employees that had an occurrence in the analytic category. For

example, if the analytic category is All Absenteeism, an employee is involved

if he or she has an absence event

Employee All active employees those who were paid any amount for any type of

activity. The "per Employee" metrics are averages based on the employee

population.The Per Employee metrics are provided to determine how extensive a

problem is at the individual level. They do not provide the actual metric

values per person in the organization. To obtain those metrics, use the

Employee dimension with the metric.

Parent Analytic category summary one level up. For example, Start Late is Parent to

Start Late 0 to 5 minutes.

Root Highest-level analytic category summary. For example, All Punches is Rootto Start Late 0 to 5 minutes.

Metric Definition

All Employee Count Distinct count of employees in the All Paid category of the Analytics

dimension. The value of All Employee Count remains the same as the All

Paid analytic category, regardless of the other dimensions that are applied.

For example, if you select employees whose last name begins with E from

the Employee dimension for Q1 of 2006 with a Tenure Band of 1 to 2 years,

All Employee Count returns the same total for each.

Amount Amount of money paid for events that occurred within this analytic category

Amount as % of Paid Amount paid for all events in this analytic category, as a percentage of the

active paid amounts

Amount as % of Parent Amount paid for all events in this analytic category, as a percentage of the

amount paid for its parent category

Term Definition



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Amount as % of Root Amount paid for all events in this analytic category as a percentage of the

amount paid for its root categoryAmount as % of

Worked

Amount paid for all events in this analytic category, as a percentage of the

worked amount

Amount per Employee Amount paid for all events in this analytic category per all active employees

Amount per Event Amount paid for all events in this analytic category

Amount per Hour Amount paid for hours of all events in this analytic category

Amount per Involved Amount paid to employees involved in the event in this analytic category

Count Count of events that occurred within this analytic category

Count as % of Paid Count of specific events in this analytic category, as a percentage of all paid

events for all employees

Count as % of Parent Count of specific events in this analytic category, as a percentage of specific

events in its parent category

Count as % of Root Count of specific events in this analytic category, as a percentage of specific

events in its root category.Count as % of Worked Count of specific events in this analytic category, as a percentage of all

worked events for all employees.

Count per Employee Count of specific events in this analytic category per all active employees

Count per Involved Count of specific events in this analytic category per all involved employees

Hours Hours associated with the events that occurred within this analytic category

Hours as % of Paid Hours of all events in this analytic category, as a percentage of paid hours for

all employees

Hours as % of Parent Hours of all events in this analytic category, as a percentage of hours of all

events in its parent category

Hours as % of Root Hours of all events in this analytic category, as a percentage of hours of all

events in its root category

Hours as % of Worked Hours of all events in this analytic category, as a percentage of all worked

hours for all employeesHours per Employee Hours of all events in this analytic category per all active employees

Hours per Event Average number of hours per event in this analytic category

Hours per Involved Hours of all events in this analytic category per all involved employees

Involved Employee

Count

Number of employees involved in an event in this analytic category

Using the Scorecard Daily and Scorecard Monthly cubes


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When used as a data source for such business intelligence tools as dashboards,

scorecards, and pivot tables, the Scorecard Daily and Scorecard Monthly cubes

contain a set of measures, known as scorecard measures (or metrics), that return

key performance indicator (KPI) values that are prepopulated within the cube.

The Scorecard Daily and Scorecard Monthly cubes each provide a different set of

scorecard metrics. These metrics exist as precalculated measures in the cube, and

thus provide an almost immediate response when they are deployed in a MicrosoftExcel 2007 view or a scorecard.

Most of the metrics discussed in this section can also be generated within the

defined hierarchy of analytic categories within the Time Summary Daily and

Time Summary Monthly cubes when measures that navigate within the hierarchy

are applied to the Analytics dimension. For example, the Excused absent events

as a % of total absent events metric can be reproduced by applying the Count as

% of Root metric to the Excused Absenteeism subcategory of the AllAbsenteeism analytic category. See Using the Time Summary Daily and Time

Summary Monthly cubeson page 23for a discussion of how to use analytic

categories in multidimensional analysis.

Scorecard Daily cube

The following table lists and defines each metric in the Scorecard Daily cube. The

definition of any metric is based on the selected dimension(s).

For more information on how Analytics calculates each metric in the table, see the

following sections:

Absenteeism - All Absenteeismon page 25

Labor utilization - All Paidon page 36

Scheduling - All Scheduledon page 38

Time paid not worked - All Paid Not Workedon page 28

Time worked not paid - All Worked Not Paidon page 30

Overtime - All Paid OTon page 24



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Punches - All Puncheson page 32

Metric Definition

Related Time

Summary

Cube Analytic

Category

Related

Time

Summary

Cube

Measure

Group Employee

Count

Distinct count of all employees that

have paid hours in the same group,

excluding the Employee dimension. For

example, if you select employees whose

last name begins with E from the

Employee dimension for Q1 of 2006

with a Tenure Band of 1 to 2 years, the

Group Employee Count returns all

employees that have paid hours in Q1 of

2006 and a Tenure Band of 1 to 2 years,

regardless of whether their last names

begin with E or not.

N/A N/A

Absenteeism

Absence events

Absent event total Total number of absence events

(excused and unexcused) for all

employees

All Absenteeism Count

Absent events as a

% of paid events

Percentage of all paid events that are

absence events (absent events / all paid

events)

All Absenteeism Count as % of

Paid

Absent events as a

% of worked events

Percentage of all worked events that are

absence events (absent events / all

worked events).

All Absenteeism Count as % of

Worked

Absent events peremployee

Average number of absence events peremployee (absent events / all employee

count)

All Absenteeism Count perEmployee

Excused events



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Excused absent

event count

Total number of absence events for all

employees

All Absenteeism

> Excused

Absenteeism

Count

Excused absentevents as a % of

total absent events

Percentage of all absence events that areexcused (excused absent events / all

absent events)

All Absenteeism> Excused

Absenteeism

Count as % ofRoot

Excused absent

events per employee

Average number of excused absence

events per involved employee (excused

absent events / absent employee count)

All Absenteeism

> Excused

Absenteeism

Count per

Involved

Unexcused events

Unexcused absentevent count

Total number of unexcused absenceevents

All Absenteeism> Unexcused

Absenteeism

Count

Unexcused absent

events as a % of

total absent events

Percentage of all absence events that are

unexcused (unexcused absent events /

absent events)

All Absenteeism

> Unexcused

Absenteeism

Count as % of

Root

Unexcused absent

events per employee

Average number of unexcused absence

events per involved employee

(unexcused absent events / absent

employee count)

All Absenteeism

> Unexcused

Absenteeism

Count per

Involved

Absence hours

Absent hours as a %

of paid hours

Percentage of all paid hours that are

absence hours (absent hours / all paid

hours)

All Absenteeism Hours as % of

Paid

Absent hours as a %of worked hours Percentage of all worked hours that areabsence hours (absent hours / all

worked hours)

All Absenteeism Hours as % ofWorked

Absent hours per

employee

Average number of absence hours per

employee (absent hours / all employee

count)

All Absenteeism Hours per

Employee

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure


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Paid regular hours

as a % of worked

hours

Percentage of worked hours that are

paid regular hours (paid regular hours /

all worked hours)

All Paid > All

Paid Reg / OT /

Train > Paid Reg

/ OT / Train

(Worked) > PaidReg

Hours as % of

Parent

Paid regular hours

per employee

Average number of paid regular hours

per employee (paid regular hours / paid

employee count)

All Paid > All

Paid Reg / OT /

Train > Paid Reg

/ OT / Train

(Worked) > Paid

Reg

Hours per

Involved

Labor cost

Paid Amount (Labor

Cost)

Total labor cost All Paid Amount

Paid amount per

employee

Average amount of money paid to each

employee (paid amount / all employee

count)

All Paid Amount per

Employee

Paid regular amountper employee

Average amount of money paid forregular hours per paid employee (paid

amount / paid employee count)

All Paid > AllPaid Reg / OT /

Train (Worked) >

Paid Reg

Amount perInvolved

Paid scheduled

amount per

employee

Average amount of money paid for

scheduled hours per paid employee

(paid scheduled amount / paid employee

count)

All Paid Sched Amount per

Involved

Scheduled and Paid Scheduled hours

Scheduled hours per

employee

Average number of scheduled hours per

scheduled employee (scheduled hours /

scheduled employee count)

All Scheduled Hours per

involved

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure



52/80

52

Scheduled hours

total

Total number of scheduled hours All Scheduled Hours

Scheduled hours as

a % of paidscheduled hours

Percentage of paid scheduled hours that

are scheduled hours (scheduled hours /all paid scheduled hours)

All Scheduled,

All Paid Sched

N/A

Paid scheduled

hours as a % of paid

hours

Percentage of paid hours that are paid

scheduled hours (paid scheduled hours /

all paid hours)

All Paid Sched Hours as % of

Paid

Paid scheduled

hours as a % of

worked hours

Percentage of worked hours that are

paid scheduled hours (paid scheduled

hours / all worked hours)

All Paid Sched Hours as % of

Worked

Paid scheduled

hours per employee

Average number of paid scheduled

hours per employee (paid scheduled

hours / paid scheduled employee count)

All Paid Sched Hours per

Involved

Time paid not worked

Time paid not

worked hours

Total number of time paid not worked

hours for all employees

All Paid Not

Worked

Hours

Time paid notworked hours as a %

of paid hours

Percentage of all paid hours that weretime paid not worked hours (time paid

not worked hours / all paid hours)

All Paid NotWorked

Hours as %Paid

Time paid not

worked hours as a %

of worked hours

Percentage of all worked hours that

were time paid but not worked hours

(time paid not worked hours / all

worked hours)

All Paid Not

Worked

Hours as %

Worked

Time paid not

worked hours per

employee

Average number of time paid not

worked hours per involved employee

(time paid not worked hours / involved

employee count)

All Paid Not

Worked

Hours per

Involved

Time paid not

worked amount

Total cost of paid not worked time for

all employees

All Paid Not

Worked

Amount

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure



53/80

Users Guide 53

Time paid not

worked amount per

employee

Average amount of money paid for time

paid not worked per involved employee

(time paid not worked amount / involved

employee count)

All Paid Not

Worked

Amount per

Involved

Worked hours

Worked hours per

employee

Average number of worked hours per

employee

N/A N/A

Worked amount per

employee

Average amount of money paid for

worked hours per employee

N/A N/A

Time worked not paid

Time worked notpaid hours as a % of

paid hours

Percentage of all paid hours that aretime worked not paid hours (time

worked not paid hours / all paid hours)

All Worked NotPaid

Hours as %Paid

Time worked not

paid hours as a % of

worked hours

Percentage of all worked hours that are

time worked not paid hours (time

worked not paid hours / worked hours)

All Worked Not

Paid

Hours as %

Worked

Time worked not

paid hours peremployee

Average number of time worked not

paid hours per involved employee (timenot worked paid hours / involved

employee count)

All Worked Not

Paid

Hours per

Involved

Time worked not

paid hours

Total number of worked not paid hours All Worked Not

Paid

Hours

Time worked not

paid amount

Total amount of money not paid for time

worked but not paid hours

All Worked Not

Paid

Amount

Time worked notpaid amount per

employee

Average amount of money not paid fortime worked but not paid per involved

employee (time worked not paid amount

/ involved employee count)

All Worked NotPaid

Amount perInvolved

Overtime

Overtime hours

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure



54/80

54

OT hours total Total paid overtime hours All Paid OT Hours

Overtime hours as a

% of paid hours


overtime hours (overtime hours / paid

hours)

All Paid OT Hours as % of

Paid

Overtime hours as a

% of worked hours


overtime hours (overtime hours /

worked hours)

All Paid OT Hours as % of

Worked

Overtime hours per

employee

Average number of overtime hours per

employee (overtime hours / all

employee count)

All Paid OT Hours per

Employee

Overtime costOvertime Cost Total cost of overtime All Paid OT Amount

Overtime cost as a

% of total labor cost

Cost of overtime hours as a percentage

of total cost (overtime cost / total labor

cost)

All Paid OT Amount as %

Paid

Overtime cost per

employee

Average overtime cost per employee

(overtime cost / all employee count)

All Paid OT Amount per

Employee

Scheduled overtime

Paid scheduled OT

hours

Total number of paid scheduled

overtime hours

All Paid Sched >

Paid Sched Reg /

OT / Train > Paid

Sched OT

Hours

Scheduled OT hours

as a % of paid hours

Percentage of paid hours that are

scheduled overtime hours (scheduled

overtime hours / paid hours)

All Paid OT >

Paid Scheduled

OT

Hours as % of

Paid

Scheduled OT hours

as a % of total OT

hours

Percentage of all paid overtime hours

that are scheduled overtime hours

(scheduled overtime hours / all overtime

hours)

All Paid OT >

Paid Scheduled

OT

Hours as % of

Root

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure


R l t d


55/80

Users Guide 55

Scheduled OT hours

as a % of worked

hours


scheduled overtime hours (scheduled

overtime hours / all worked hours)

All Paid OT >

Paid Scheduled

OT

Hours as % of

Worked

Scheduled OT hours

per employee

Average number of scheduled overtime

hours per employee (scheduled overtime

hours / all employee count)

All Paid OT >

Paid Scheduled

OT

Hours per

Employee

Paid scheduled OT

amount

Total mount of money paid for

scheduled overtime hours

All Paid Sched >

Paid Sched Reg /

OT / Train > Paid

Sched OT

Amount

Scheduled OT Cost Total cost of scheduled overtime All Paid OT >

Paid ScheduledOT

Amount

Scheduled OT cost

as a % of total labor

cost

Percentage of the total labor cost

contributed by scheduled overtime

(scheduled overtime cost / total labor

cost)

All Paid OT >

Paid Scheduled

OT

Amount as %

of Paid

Scheduled OT cost

per employee

Average cost of scheduled overtime per

employee (scheduled overtime cost / allemployee count)

All Paid OT >

Paid ScheduledOT

Amount per

Employee

Unscheduled overtime

Paid unscheduled

OT hours

Total number of paid unscheduled

overtime hours

All Paid OT >

Paid Non

Scheduled OT

Hours

Unscheduled OT

hours as a % of paidhours


unscheduled overtime hours(unscheduled overtime hours / all paid

hours)

All Paid OT >

Paid NonScheduled OT

Hours as % of

Paid

Metric Definition

Related TimeSummary

Cube Analytic

Category

Related

TimeSummary

Cube

Measure


Related


56/80

56

Unscheduled OT

hours as a % of total

OT hours

Percentage of all overtime hours that are

unscheduled overtime hours

(unscheduled overtime hours / all

overtime hours)

All Paid OT >

Paid Non

Scheduled OT

Hours as % of

Root

Unscheduled OT

hours as a % of

worked hours


uns

Users Guide Analytics

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