Productivity Analysis:Productivity Analysis:Final ProjectFinal Project

Gina BolingBUSN 650Davenport University

Research FocusResearch 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 FocusResearch 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 outputH1 : M1 < M2 Inexperienced employees are less Productive than

Experienced employees

Level of Significance = 0.05, Lower tailed test

Prior ResearchPrior 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 ResearchPrior 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 DesignResearch Design

•Procedures•Phase I – Process Analysis•Phase II – Data Collection•Phase III – Data Analysis

• 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

• 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 IIIResearch 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 IResearch Phase 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)

•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)

• 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.

ConclusionsConclusions•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

ReferencesReferences

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#What is the new Handbook for Basic Process Improvement