attached is the reading materials (7,8 and 13). please respond substantively to the instructions using the reading material to support claims.
discussion_2.docx
chapter_7.pdf
chapter_8.pdf
chapter_13.pdf
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Payroll Processing:
Pretend you are the V.P. of Human Resources. Your payroll manager wants to outsource day-today processing of pay checks to a third party to allocate more time to financial analysis. In at
least 250 words, describe at least three factors you would use to justify a decision to outsource or
not outsource payroll processing.
Support your claims with examples from required material and properly cite any references.
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HRIS Effectiveness Measures and HRM
Advice for HRIS Implementation
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HR Metrics and Workforce Analytics
Kevin D. Carlson and Michael J. Kavanagh
EDITORS’ NOTE
The capacity to manage is limited by the type and quality of data available to managers. Better
information about the expectations of customers, the actions of competitors, and the state of the
economy provides strong foundations for setting the strategic direction of organizations. Information
about levels of output, for example, numbers of defects and ef iciency of processes, positions line
managers to produce high-quality products in the right amounts at the right time to meet customer
needs. The same is true for the effective management of human capital in organizations. In “Future
Insights” the Society for Human Resources argues “The development of deeper levels of analysis to
monitor metric outcomes, identify trends, leverage positive outcomes and intervene in or mitigate
negative outcomes will lead to better overall human capital management” (Society for Human Resource
Management [SHRM], 2012, p. 6). As discussed in this chapter, effective approaches to the measurement
of human capital and its impact on organization processes enables both HRM professionals and line
managers to make better decisions about HRM programs, like recruitment, for example, to increasing
organizational effectiveness. This is accomplished by focusing on the development of systems of
workforce analytics and supporting HR metrics that meet the needs of organization decision makers.
This chapter offers a brief history of the efforts involved in the development of HR metrics and
workforce analytics and of how these efforts have been enhanced by the advent of integrated human
(http://content.thuzelearning.com/books/Kavanagh.5623.17.1/sections
resource
information
systems.1
/nav_60#rnote1) From benchmarking to operational experiments, the HRIS ield is rapidly evolving on
many fronts. These advances are changing how HR metrics and analytics are used in organizations, and,
subsequently, their impact on organization effectiveness. The use of HR metrics and workforce analytics
will help managers and organizations balance the costs and bene its consequences of decisions. These
cost-bene it analyses are covered in Chapter 8 (ch0008.xlink.html) .
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CHAPTER Objectives
After completing this chapter, you should be able to
• Discuss the factors that have led to increased organizational interest in HR metrics and
workforce analytics
• Discuss why the information from numeric systems like HR metrics and workforce analytics
may fail to generate a return on investment (ROI) unless they lead to improved better decision
making
• Discuss the difference between metrics and analytics
• Describe the limitations of the traditional HR metrics
• Discuss the historical role of benchmarking and its strengths and weakness today
• Discuss the roles that activities such as data mining, predictive analytics, and operational
experiments play in increasing organizational effectiveness
• Discuss the differences between metrics and analytics used to assess ef iciency, operational
effectiveness, and organizational realignment, and offer examples of each
• Describe which characteristics of HR metrics and workforce analytics are most likely to have an
organizational impact
HRIS IN ACTION
When Dan Hilbert arrived as manager of Employment Services at Valero Energy, he wasn’t quite
sure what he wanted or needed to do. Coming from a background in operations, he was used to
having information about the effectiveness of all current operations; yet, as he quickly learned,
these data were not available for HR operations and programs, nor were there systems in place to
generate them. He recognized the potential value of having even simple descriptive statistics about
the organization’s people, and its operations—to highlight potential opportunities and how
changes in these values could signal potential problems. However, since these data were not
currently available or easily developed, he created a small team, consisting of one HR staff member
who could help get access to data from the organization’s current systems and a graduate student
with a statistical background, who was hired as a part-time employee. The team’s assignment was
to collect data about the human capital in the organization in an effort to learn more about the
organization and its people, which Dan was now charged with supporting.
The team’s analysis highlighted a unique characteristic of the Valero workforce—all of its re inery
managers were at least 55 years old. This meant that these managers, each with long tenure in one
of the most critical positions for assuring operating success, would be eligible to retire in fewer
than 10 years. Further, given that these managers had all joined the company at roughly the same
time and had held these re inery manager positions for many years, the promotion pipeline for
succession to this position was limited. In other words, promising managers who had joined the
organization at lower managerial positions decided to leave the company when it was clear that
upward opportunities were limited.
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When Hilbert presented the results of this analysis and his conclusions to senior managers, they
were shocked. No one had considered this issue of the aging of re inery managers, and, likely,
management would not have become aware of the situation until the re inery managers began to
retire. By then, it would have been too late to act to get immediate replacements. Interestingly, as
Valero’s success increased and the stock price increased, the retirement age lowered, compounding
the problem. The pipeline of trained managers capable of illing these positions internally would
not have been suf icient to meet the demand created by the mass retirements, and the time to train
them as re inery managers was lengthy. As a result, the computation of relatively simple metrics
and analytics provided new insights on the current retirement status of employees. This data
allowed management to engage in the training and development needed to build internal bench
strength for this critical position prior to these managers retiring, likely saving the re inery
millions in salary expense and reduced re inery performance.
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Introduction2 (http://content.thuzelearning.com/books/Kavanagh.5623.17.1
/sections/nav_60#rnote2)
I have found that the largest single difference between a great HR department and an average one is the
use of metrics … bar none, there is nothing you can do to improve yours and your department’s
performance that exceeds the impact of using metrics.
John Sullivan (2003)
Human resources (HR) metrics and workforce analytics (http://content.thuzelearning.com/books
/Kavanagh.5623.17.1/sections/nav_140#glo272) have become a hot topic in organizations of all sizes.
Interest is rising, and organizations are reaching out to learn more about, useful metrics and analytics
and how they can use them to improve organizational effectiveness. Although the use of HR metrics and
workforce analytics is not new, various factors are driving increased interest. An important driver is the
widespread implementation of integrated HRISs. The adoption of these systems shifted what had been
primarily paper-and-pencil processes to electronic processes and, as a result, greatly increased the
capacity of organizations to access and examine transaction-level data. Today’s HRIS builds on the
capabilities of faster and more capable computers, improved connectivity through organizational
networks and the Internet, and the availability of user-friendly analytics software. These changes have
fundamentally altered the dynamics of human capital assessment in organizations, driving the
marginal cost of assessment lower while providing the potential for near real-time analysis and
distribution of information. These factors, combined with recent and growing interest in evidencebased management, account for the rapidly growing interest in HR metrics and workforce analytics.
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7.1 A Brief History of HR Metrics and Analytics
Systematic work on the development of measures to capture the effectiveness of an organization’s
employees can be traced as far back as the days of scienti ic management (Taylor, 1911) and industrial
and organizational psychology (Munsterberg, 1913). Methods of quantitative analysis and its use in
decision making were developed during the build-up of both men and maté riel occasioned by World
War II. Further study and development occurred during the great post-war industrial expansion in the
United States that continued into the 1970s. Many of the HR metrics used today were irst considered
and developed during this period (e.g., Hawk, 1967).
Widespread assessment of HR metrics did not occur until the pioneering work of Dr. Jac Fitz-enz and
the early benchmarking work he conducted through the Saratoga Institute. In 1984, Fitz-enz published
How to Measure Human Resources Management, currently in its third edition (Fitz-enz & Davidson,
2002), which is still a highly valued overview of many HR metrics and the formulas used to calculate
them. A set of 30 metrics were developed through the joint efforts of the Saratoga Institute and the
American Society for Personnel Administration (ASPA), the forerunner of the current Society for
Human Resource Management (SHRM). These metrics are listed in Table 7.1
(http://content.thuzelearning.com/books/Kavanagh.5623.17.1/sections/nav_52#tab7.1) . Initially, HR metrics
were primarily used to measure or audit aspects of HR programs and activities as described by Cascio
(1987) and Fitz-enz and Davidson (2002). Next, metrics began to be used to measure HR effectiveness.
SHRM has identi ied a number of metrics that organizations can use in this way. These metrics
comprise the HR Metrics Toolbox seen in Table 7.2 (http://content.thuzelearning.com/books
/Kavanagh.5623.17.1/sections/nav_52#tab7.2) (SHRM, 2010). For example, absence rate, a measure of the
extent to which employees are present each day to complete their work (Hollmann, 2002), can be
calculated as follows: [(# days absent in month) divided by (Avg. # of employees during mo.) times (#
of workdays)] times 100 (Hollmann, 2002; Kuzmits, 1979). Another useful metric is cost per hire,
which can be calculated as Cost per Hire (CPH) = the sum of external costs (recruiting) and internal
costs (training new employees) divided by the total number of starts in a time period (SHRM, 2010).
There are also more detailed approaches for the measuring and benchmarking of employees’ behaviors
such as turnover (Cascio, 2000), as well as for creating HR metrics for programs such as employee
assistance and work-life programs (Cascio, 2000).
Table 7.1 HR Metrics Toolkit (2010)
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Source: Adapted from Fitz-Enz, J. (1995) How to Measure Human Resources Management, 2nd
Edition. New York, NY: McGraw-Hill, Inc.
Kaplan and Norton’s (1996) introduction of the balanced scorecard (http://content.thuzelearning.com
/books/Kavanagh.5623.17.1/sections/nav_140#glo15) (see Chapter 10 (ch0010.xlink.html) ) further re ined
managers’ thinking about metrics. The balanced scorecard recognizes the limitations of organizations’
heavy reliance on inancial indicators of performance. Such measures focus on what has already
happened rather than providing managers information about what will happen. Balanced scorecards
focus on developing leading indicators of performance from several important perspectives, including
customer satisfaction, process effectiveness, and employee development, as well as inancial
performance. In addition, the thinking required to develop balanced scorecards help managers identify
sequences believed to lead to critical organizational outcomes.
About the same time, Huselid’s 1995 work on high-performance work systems demonstrated that the
systematic management of human resources was associated with signi icant differences in
organizational effectiveness. This work provided evidence that human resource management did
indeed have strategic potential. Becker, Huselid, and Ulrich (2001) helped bring these ideas together in
the HR scorecard, which highlights how the alignment of HR activities with both corporate strategy and
activity improve organizational outcomes.
Table 7.2 Measures in the Saratoga Institute/SHRM Human Resources Effectiveness Report
Revenue per Employee
Expense per Employee
Compensation as a Percentage of Revenue
Compensation as a Percentage of Expense
Bene it Cost as a Percentage of Revenue
Bene it Cost as a Percentage of Expense
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Bene it Cost as a Percentage of Compensation
Retiree Bene it Cost per Retiree
Retiree Bene it Cost as a Percentage of Expense
Hires as a Percentage of Total Employees
Cost of Hire
Time to Fill Jobs
Time to Start Jobs
HR Department Expense as a Percentage of Company Expense
HR Headcount Ratio—HR Employees: Company Employees
HR Department Expense per Company Employee
Supervisory Compensation Percentage
Workers’ Compensation Cost as a Percentage of Expense
Workers’ Compensation Cost per Employee
Workers’ Compensation Cost per Claim
Absence Rate
Involuntary Separation
Voluntary Separation
Voluntary Separation by Length of Service
Ratio of Offers Made to Acceptances
Source: Adapted from Fitz-enz, J. (1995). How to measure human resources management (2nd ed.). New York:
McGraw-Hill.
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7.2 Limitations of Traditional HR Metrics
Unfortunately, while the computing, communications, and software infrastructure supporting HR
metrics and analytics has undergone dramatic change since the late 1990s, the metrics themselves have
not. Current computing operations are capable of capturing data on a wide range of electronically
supported HR processes, extracting, analyzing, and then distributing that information in real time to
managers throughout the organization. However, currently popular HR metrics were developed before
current computing infrastructures existed. As a consequence, recognizing what data most
organizations could easily and inexpensively gather played an important role in identifying which
metrics could reasonably be included in benchmark studies. A quick perusal of the metrics listed in
Table 7.1 (http://content.thuzelearning.com/books/Kavanagh.5623.17.1/sections/nav_52#tab7.1) highlights
the early emphasis on readily available data, most of which came from accounting systems.
Consequently, these metrics emphasize costs or easily calculated counts (e.g., headcount, turnover) that
often serve as proxies for costs. Every managerial decision has cost and bene it consequences,
whether we recognize them or not. As a result, when metrics and analytics systems only provide
information about costs, they are of limited value to managers. If managers are only provided
information about costs, with little or no information about bene its, costs are likely to become the
primary driver of managerial decisions. This perpetuates the still-common perception of HR as a “cost
center.” Thus, information on bene its from a managerial decision must also be known in order to
conduct an estimated return on investment (ROI) for the decision.
A second limitation of early metrics efforts is that they tended to aggregate data to the level of the
organization. As such, they offer limited information that could be used to identify and diagnose withinorganization differences. Organizational turnover rates, for example, are heavily in luenced by the
turnover rate in the organization’s dominant job category, masking any differences in turnover rates for
jobs with fewer incumbents.
Finally, early efforts only provided data after events had occurred. This results in slow responses to
problems or opportunities. Because they provide data “after the fact,” these are described as “feedback”
metrics. Feedback metrics can be effectively used to signal problems, but they are suboptimal as a
primary source of data because they do not support real-time remedial action to minimize any negative
effects.
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7.3 Contemporary HR Metrics and Workforce Analytics
Using HR Metrics and Workforce Analytics
While benchmarking is still done, the ield of HR metrics and workforce analytics is expanding and
evolving. Workforce analytics has become an umbrella term that encompasses a wide range of activities
and processes.
HR Metrics (http://content.thuzelearning.com/books/Kavanagh.5623.17.1/sections
/nav_140#glo110)
There is a fundamental distinction between HR metrics and workforce analytics. HR metrics are data
(numbers) that re lect some descriptive detail about given processes or outcomes, for example, success
in recruiting new employees. In the domain of human resources, these re lect characteristics of the
organization’s HR programs and activities.
Workforce Analytics (http://content.thuzelearning.com/books/Kavanagh.5623.17.1
/sections/nav_140#glo272)
Workforce analytics refer to strategies for combining data elements into metrics and for examining
relationships or changes in metrics. Understanding these combinations is done to inform managers
about the current or changing state of human capital in an organization in a way that can impact
managerial decision making. The importance of this view is that the analytics an organization needs
depend on the problems and opportunities that currently face its managers. Understanding what
opportunities and problems managers face suggest relevant analyses that can support better decisions.
These analyses then determine what metrics the organization needs in order to compute these
analyses and how those metrics should be calculated.
Benchmarking (http://content.thuzelearning.com/books/Kavanagh.5623.17.1/sections
/nav_140#glo19)
The Saratoga Institute’s benchmarking (http://content.thuzelearning.com/books/Kavanagh.5623.17.1
/sections/nav_140#glo19) eff …
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