Halden Zimmermann evaluates an attendance improvement program at a major manufacturing plant in the United States. Based on factors such as years spent with the company, Halden uses mathematical equations and various management theories to propose a hypothesis and final verdict.

1. !
!
EXP 420 Project
!
Assessment of the
Attendance Improvement
Program at the Ford Motor
Company

2. I. EXECUTIVE SUMMARY !
A study was performed to review and analyze the increased absenteeism at the Buffalo
Stamping Plant (BSP) in Woodlawn, New York. A recent Detroit Headquarter communique
revealed an increased absenteeism rate among the hourly employees at BSP. The BSP Senior
Management subsequently decided to explore the underlying causes of the observed increase in
absences and to generate appropriate recommendations.
The Ford Motor Company instituted a union-approved Attendance Improvement Program
(AIP) in 1984 to improve employee attendance. This program specifically targeted employees that
have less than 10 years of service (See the AIP Guide in Appendix G).
In order to determine what the underlying causes may be for the increased absenteeism
rate, historical absenteeism data beginning July 2000 through October 2001, was reviewed for all
of the employees at the BSP plant. The hypothesis of this study is that employees with more than
10 years of service contribute to the increased controllable absences:
Ho: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service >= 0
Ha: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service < 0
Historically, Ford management believed employees with less than 10 years of service are
more likely to utilize the four occurrences (one occurrence may be up to 9 consecutive days or 72
hours of absence) that are allowed each rolling twelve month period before an employee enters the
AIP. This likelihood was based on the fact that employees with less than 10 years of service
received less vacation days than employees with more than 10 years of service. These facts
supported the enrollment of employees with less than 10 years of service in the AIP remedial
program to correct their absenteeism record. Employees with 10 or more years experience are not
contractually obligated to participate in AIP.
The results of the analyses revealed that the absenteeism rate of employees with greater
than 10 years of service were not statistically significant when compared to employees who have
less than 10 years of service. However, upon inspection of the data using time-series, there does
appear to be the potential for a Type II error, i.e. failing to reject the null or base-line hypothesis
when it is false (see Appendix E and F). There may be a need to inspect the data further to
determine whether employees are likely to “abuse” the allowed occurrences available to them, as
well as the potential that plant foremen are miscoding absences.
Given these observations, we recommend that select employees with more than 10 years
of service be admitted to the AIP (with union management approval). These employees are those
who are abusing their contractual rights such that they are chronically absent (more than 1000
hours of absenteeism). Additionally, the plant must retrain foremen regarding the absence codes
recording and evaluate the use of a "reward" program for all employees that demonstrate
consistent attendance and do not abuse the amount of occurrences that are allotted to them. !!!!!!!
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3. !!!!
II. INTRODUCTION !
The goal of this project is to examine and evaluate the expected controllable absenteeism
between employees with 10 years of service or less and employees with more than 10 years of
service at the Ford Motor Company Buffalo Stamping Plant in Woodlawn, NY. A recent report on
absenteeism released from the Ford Motor Company’s headquarters in Detroit, Michigan, shows
that BSP has experienced an increased rate of absenteeism. BSP management’s strategy is to
reduce controllable absenteeism and ultimately, the costs of absenteeism that contribute to the
unfavorable cost variances experienced by the plant. !
III. OBJECTIVE
The objective of the analysis is to review the available absenteeism data of all hourly
employees at the BSP. This data will be collected and compiled from two major systems: the
Timekeeping & Work Order System (TWOS) and the Plant On-Line Reporting System (POLR).
These systems collect the hourly attendance of each employee in the Ford Motor Company plants
by tracking the hours worked and/or the hours absent. Each hour (or partial hour) of absence is
assigned a code by the plant foremen.
Ford’s absence codes can be segregated into two categories (see Appendix A for a
complete list of codes and their definitions by category):
Controllable – absences which may be controlled by plant management, and
Contractual – absences dictated by the United Auto Workers Union labor contract
Different statistical methods as well as charts and graphs will be utilized to analyze the
available data to reveal trends and relationships. In this analysis we will test the hypotheses that:
Ho: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service >= 0
Ha: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service < 0
This hypothesis is established to statistically test whether the expected amount of
controllable hours of absence by employees with more than 10 years of service have more
controllable absence vs. those employees with less than 10 years of service.
We assume that this data will provide valuable information that will be used by BSP's
management.
!
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4. IV. DATA DESCRIPTION
This study included both controllable and contractual absenteeism data collected from July
2000 to October 2001. The coded data was compiled from Ford’s database of employees during
this sixteen-month period. The absenteeism data is compiled into two data sets: amount of
absence by employee by absence code, and amount of absence by absence code by month.
Within the employee data set, additional variables were formed to segregate the employee
sample into analyzable subsets. These variables were created as "dummy" variables with the
intent of homogenizing the employees within these subsets, and to determine what relationship, if
any, these variables have to the BSP absenteeism.
! V. DATA ANALYSIS PROCESS & METHODS
To achieve the objective of this project, the employee and monthly data were inspected
and "cleaned" such that the Overtime Refusal data was deleted. This omission will have no affect
on the outcome of this study as there were no observations during the sixteen-month period. !
There were 5 phases to the analysis process: !
Phase I:
Test the validity of the observed sample data. This was done by randomly selecting
individuals within the employee sample and validating the absenteeism hours with Human
Relations and their foremen. The employee sample appears to be consistent with the absenteeism
recorded for each employee. ! Phase II:
Calculate an employee's age on 10-31-2001, years of service on 10-31-2001, and age
on their hire date (rounded up to the nearest integer). A Y2K error was detected for
employees whose date of birth occurred prior to 1940. These errors were corrected before
completing these calculations. ! Phase III: !
Creat dummy variables to segregate the following employees:
▪ Employee Years of Service: 10 years or less, more than 10 years
▪ Employee Years of Service: less than 5 years, between 5 and 10 years, between 10 and
20 years and more than 20 years
▪ Employee Age: less than 20, between 20 and 30, between 30 and 40, between 40 and
50, and more than 50
▪ Employee Gender: Male or Female
▪ Employee Race: White, Black, Asian, Hispanic or North American Indian
▪ Employee Skill-Level: Unskilled, Skilled-Direct, Skilled-Indirect, Retired
▪ Employee Status: Active or Inactive (Retired/Terminated)
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5. Phase IV: !
Use MS-Excel and StatPro to calculate summary statistics and correlations among
all of the variables (see Appendix B). Used StatPro to create scatterplot charts of select
paired variables and their correlations, time-series charts of the absence codes, and side-by-
side box plots of Controllable and Contractual Absences with their respective
component absences. !
Phase V: !
Constructed and performed hypothesis tests of specific correlations and differences
of sample means to determine the statistical significance of these estimators. Upon
completion of each test a decision re the null hypothesis is provided at an appropriate
confidence level. !
VI. DATA ANALYSIS RESULTS
! Phase I:
The sample data was compiled and arranged so the employee subsets could be
analyzed statistically. The data was then arranged in a table format so that different subsets
could be analyzed against the Controllable and Contractual absenteeism codes. The
sample data is available on a diskette labeled EXP420 Project Data within a WinZip file titled
"Total Sample Data & Subsets" provided with this project report. ! Phase II:
Box plots of Controllable and Contractual Absence and their respective component
absence codes were constructed to determine the influence that particular absence codes
may have on the sample statistics (see Appendix C). The Controllable Absence box plot
reveals a significant amount of outliers associated with Medical Absences. Additionally, the
Contractual Absence box plot revealed a significant amount of outliers associated with
Temporary Lay-off Absences (TLO). These outliers in both the Controllable and Contractual
Absences indicate that there may be unique individuals that should be removed from the
statistical sample due to their circumstances or there is a high degree of miscoding
occurring that is distorting the outcome of the statistical analysis. ! Phase III:
Correlation of Sample Variables !
One particularly important correlation of Controllable Absence with the dummy
variable "10 Yr Split" was statistically analyzed with an initial 95% confidence level (see
Appendix D) using the following hypothesis test: !
Ho: Population correlation ρ = 0, where ρ is the sample correlation
Ha: Population correlation ρ ≠ 0
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6. !!
The outcome of this statistical analysis was that there was a significant statistical
correlation between these variables at a 99% confidence level. !
Hypothesis Testing !
Based on the result of the correlation analysis, the primary hypothesis test was
constructed of the difference of the means for the two groups of employees with
controllable absences: !
Ho: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service >= 0
Ha: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service < 0
The outcome of the hypothesis was to fail to reject the null hypothesis at a 95% confidence
level. ! To solidify our results, we performed an additional two-tailed hypothesis test
comparing the difference of the means of the same two groups in Phase IV using the
following hypothesis: !
Ho: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service = 0
Ha: Controllable Employee Absences of μ <=10 Years of Services - μ >10 Years of Service ≠ 0
The outcome of the hypothesis was to fail to reject the null hypothesis at a 95% confidence
level. Based on these statistical analyses, the data would suggest that employees with 10
years of service or less have a greater affect on the total amount of Controllable absences
and should continue to be subject to the AIP program. Additionally, the employees with
more than 10 years of service should not be subject to the AIP program. ! Phase IV: !
Upon failing to reject the null hypothesis above, there is greater probability of
committing a Type II error, i.e. failing to reject the null when it is in fact false. As a result,
further analysis may be warranted over all of the variables. !! VII. DISCUSSION !
Examination of the data leads us to conclude that controllable absence associated
with higher seniority individuals (greater than 10 years) are not relevant. Based on the data,
people with higher seniority do not have more controllable absence issues than lower
seniority individuals (less than 10 years). !
The probability of having a Type II error warrants additional analyses of the
remaining variables and their relationship with Controllable Absence.
!6

7. ! VIII. RECOMMENDATIONS !
Based on our analysis and findings it is recognized that there exists a problem with
absenteeism related to the following categories. !
These are the XXXXX number of recommendations that are proposed:
1. Institute the AIP Program across the plant.
2. Categorize employees as to “low,” “medium” or “high” risk for absenteeism based on the
data that is available. Calculate the Expected Absence of each employee and place them
into each of the XXXX risk categories
3. Provide incentives to those employees that do not abuse the absences.
4. Given that the absenteeism has hurt the profit maximizing business strategy of the
company this study recommends …….
5. Use a set of rules for hiring, discipline, and
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