Productivity Analysis:
Final Project
Gina Boling
BUSN 650
Davenport University
Research Focus
•Basic process improvement techniques will be utilized to define processes,
identify relationship between processes (USAFU, 2011)
•Six Sigma concepts related to process improvements will be used to structure the
analysis of procedures and provide a reputable methodology from which
objective conclusion may be drawn from the findings (Liebermann, 2011).
Research Question 1:
•Are we productive as a company? Determine productivity rate and measuring
our current position compared to U.S. Department of Labor Industry average 1.8
units output per hour
•H0 = 1.8
•H1 > 1.8
Research Focus
Research Question 2:
•Is there a relationship between employee experience and output? M1, employee
experience level 1-4 years, M2, employee experience 5 years +
H0 : M1 = M2 There is no difference between experience and output
H1 : M1 < M2 Inexperienced employees are less Productive than
Experienced employees
Level of Significance = 0.05, Lower tailed test
Prior Research
•Focus will be on prior research in processes management and analysis and
studies that are related to production and productivity
•Six Sigma is based on continuous improvement, optimization and problem
solving (Leibermann, 2011)
•How Six Sigma points of analysis and concepts apply to problem solving
Six Sigma Steps of value to research design:
•Define a plan of what issue is to be resolved and why
•Summarize key process components
•Review current processes/Process mapping
•Collect data/Measure
•Quantitative assessment of the situation
Prior Research
•Finding appropriate method of measure is difficult especially in a multi-machine
environment. (Braglia, Frosolini and Zammori 2007)
•Overall Equipment Effectiveness of a Manufacturing Line (OEEML) calculates plant
efficiency based on output of the line as a whole
•Losses included
•Equipment failures
•Set up time
•Idling / minor stoppages
•Reduced speed
•Reduced yield
•Quality defects/reworks
•Identifies bottlenecks and inefficiencies by machine
•Limitations of this study are that capacity usage and labor efficiency are not included.
Research Design
•Procedures
•Phase I – Process Analysis
•Phase II – Data Collection
•Phase III – Data Analysis
Research Design
Phase I
• Process Analysis
based on Six Sigma framework (Liebermann,
2011)
•Create Process Flow Diagrams
•Define targets for Analysis
•Define variables to be measured
•Collect Data
•Assess Baseline Capability
Research Design
Phase II
• Data
Collection
•Sample daily time sheets for 10 machine operators
•Administer employee survey to voluntary participants
•Calculate current production output using daily completed
parts list for time period 1 week
Research Design - Phase III
• Data Analysis
•Calculate Labor Productivity using U.S. Department of Labor measure
•Labor Productivity = (Output/Hours of Labor Input)
•Sample of machine time sheets will be used to determine output
•Perform correlation analysis relating to employee experience and output
•Variables : Dependent variable = output (Y), Independent variable = Years
experience (X)
•H0 : Slope = 0, The linear relationship between output and experience is not
significant
•H1 : Slope ≠ 0, The linear relationship between output and experience is
significant
•Level of Significance = 0.05, Two tailed test
Research Phase I
•Process Analysis
•Work process flow chart creation
•Interview Production manager to define stages of production and flow
of parts through the machining stages
•Targets for analysis
•Variables to be measured
•Estimate capacity by department
•Totals hours worked 1 week, Human Resources schedule
•Machine output – 7 days of production output lists
•Output by experience – Random sample of 10 machines daily
job sheets, Hire date of assigned operator to determine years
experience
Research Question I
• Labor
Productivity – Parts completed in 7 day time frame / number of labors hours
• US Department of Labor 1.8 Industry average (BLS.GOV, 2011)
•H0 < or = 1.8 We are not productive compared to industry average
•H1 > 1.8 We are productive compared to industry average
•Level of Significance 0.05, sample size 10, sample mean 8.89, standard deviation 5.3
•Upper tailed test , Critical value = 1.8331, Test Statistic = 4.23, P value 0.0011
•The test statistic falls outside of the upper critical value and the P value is less than the
level of significance, this leads to rejection of the null hypothesis. We conclude we are
productive compared to the U.S. Department of Labor industry average.
(Cooper & Schindler, 2011)
Research Question II
•Correlation analysis performed to compare daily output of 10 sample machine operators with
years on the job. This is a quantitative test. Y (dependent variable) = output, X (independent
variable) = Years experience
•Variables : Dependent variable = output (Y), Independent variable = Years experience
(X)
•H0 : Slope = 0, The linear relationship between output and experience is not significant
•H1 : Slope ≠ 0, The linear relationship between output and experience is significant
•Level of Significance = 0.05, Two tailed test
•R squared = 0.081, variance between x and y is only 8.1%
• P-value = .4243 exceeds level of significance 0.05, fail to reject null
hypothesis
• Conclusion: Insufficient evidence to conclude correlation.
(Cooper & Schindler, 2011)
Limitations
• Capacity was determined using estimates of production manager. Actual
capacity determination would be difficult to calculate as individual machines
vary in capability, types of parts being run my have longer or shorter cycle
time.
•Factors such as set up time, breakdowns, and supply shortages are not
currently accounted for
•Overall equipment effectiveness discussed in previous studies section would be
an ideal measure to calculate efficiency and bottlenecks in production but data
available currently does not allow for utilization of this method.
•A small time frame was used for this analysis and a larger time frame would
be beneficial to track potential issues.
Conclusions
•Basic production processes were defined and mapped
•Current Labor Productivity is higher than the National average as published by
the U.S. Department of Labor Statistics
•No correlation was found between worker output and experience
•We can conclude that currently, our manufacturing output is at a desirable level
• We can delve further into detailed productivity analysis or decide to look into
other areas as to why we are struggling to keep product on the shelves
•The employee survey can be reviewed to see if other areas may factor in that we
did not investigate in this exploratory study
References
Board of Governors of the Federal Reserve, System (2011). Industrial capacity
and Utilization – G. 17. Retrieved August 2, 2011 from
http://www.federalreserve.gov/releases/g17/current/
Cooper, D. & Schindler, P. (2011). Business Research Methods. New
York: McGraw-Hill Higher Education.
Jana, P., Narag, A., Knox, A. (2007). Measuring efficiency of a supply chain I.
Textile/Clothing Technology Corporation. Retrieved July 12, 2011 from
http://www.techexchange.com/thelibrary/measuring1.html
Lieberman, G. (2011). Apply six sigma for process improvement and problem
solving. Chemical Engineering Progress. New York. 107, 53-60.
United States Air Force University (2011). Handbook for basic process
improvement. Retrived July 10, 2011 from
http://www.au.af.mil/au/awc/awcgate/navy/bpi_manual/handbook.htm#W
hat is the new Handbook for Basic Process Improvement