users guide analytics
TRANSCRIPT
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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
manual. This document or any part thereof may not be reproduced in any form without the written permission of
Kronos Incorporated. All rights reserved. Copyright 2009.
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Published by Kronos Incorporated
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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
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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
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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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Chapter 2 Multidimensional Analysis
16
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
Using cube dimensions
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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Chapter 2 Multidimensional Analysis
18
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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Using cube dimensions
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
AttributesandHierarchy Definition
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Chapter 2 Multidimensional Analysis
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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 3 Name
Labor Account 4 Name
Labor Account 5 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
AttributesandHierarchy Definition
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Using cube dimensions
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
AttributesandHierarchy Definition
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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
AttributesandHierarchy Definition
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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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Chapter 2 Multidimensional Analysis
24
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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Using the Time Summary Daily and Time Summary Monthly cubes
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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Chapter 2 Multidimensional Analysis
26
In the rare event that there are associated hours or dollars, the metrics will present
that data.
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 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
Using the Time Summary Daily and Time Summary Monthly cubes
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Using the Time Summary Daily and Time Summary Monthly cubes
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
Missed or empty record punch activity flagged (by an employee's
manager) with a pay code mapped to the analytics OTpay category, and
a comment for the duration of the shift.
Excused Absenteeism
Train Core
Missed or empty record punch activity flagged (by an employee's
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
Missed or empty record punch activity flagged (by an employee's
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
Missed or empty record punch activity flagged (by an employee's
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
Missed or empty record punch activity, for a pay code mapped to the
analytics OTpay 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) 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
Missed or empty record punch activity, for a pay code mapped to the
analytics Trainpay 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) as
an unexcused absence event.
Unexcused
Absenteeism Non Prod
Core
Missed or empty record punch activity, for a pay code mapped to the
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
Missed or empty record punch activity, for a pay code mapped to the
analytics Otherpay 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) as
an unexcused absence event.
Unexcused
Absenteeism Unknown
Core
Missed or empty record punch activity, for a pay code mapped to the
analytics Unknownpay 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) as
an unexcused absence event.
Analytic Definition
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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 6 to 10 minutes Deficient
Leave Early 11 to 15 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
before the shift or schedule begins.
Start Early Greater than
60 min
Identifies a start early shift or schedule that is more than 60 minutes
before the shift or schedule begins.
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
before the shift or schedule ends.
Leave Early 11 - 15
min
Identifies a leave early shift or schedule that is 11 minutes to 15 minutes
before the shift or schedule ends.
Leave Early GT 15 min Identifies a leave early shift or schedule that is more than 15 minutes
before the shift or schedule ends.
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.
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 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 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.
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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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Note: The scheduling metrics provided by Analytics are derived from scheduling
data that has been maintained within the basic scheduler module of thetimekeeping 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.
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.
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 product, both part of
the product suite. If schedules have not been established and maintained in these
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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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 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
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Using the Scorecard Daily and Scorecard Monthly cubes
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
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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
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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
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OT hours total Total paid overtime hours All Paid OT Hours
Overtime hours as a
% of paid hours
Percentage of all paid hours that are
overtime hours (overtime hours / paid
hours)
All Paid OT Hours as % of
Paid
Overtime hours as a
% of worked hours
Percentage of all worked hours that are
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
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Scheduled OT hours
as a % of worked
hours
Percentage of all worked hours that are
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
Percentage of all paid hours that are
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
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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
Percentage of all worked hours that are
uns