Summary
Jeans, Inc. is one of several textile manufacturing facilities reporting to a large international corporation. The 35 year-old company is located in a small, Midwestern city with low unemployment, and where the majority of available employment requires a low or semi-low level of skill. Like other manufacturing facilities in the area, Jeans, Inc. suffers from an extremely high employee turnover rate. Leadership estimates that factors related to turnover (retraining, not meeting production goals, etc.) cost the company $100,000 per year. The company leadership decided to hire a professor and group of graduate students from the local university to remedy this debilitating performance problem.
Initial problem statement
Jeans, Inc. reported an average turnover rate of nearly 90%. This includes all functions in the facility, but mostly concerns operator trainees and operators / operator trainers. The high turnover caused the company significant financial loss, as well as a regularly disruptive atmosphere caused by trainees leaving and the shortage of labor to replace them. Research into current literature revealed that the most common measure used to reverse turnover in instances with similar characteristics were incentive-based, and were likely to yield poor results. The ad-hoc performance team led by the university professor concluded that turnover is a multifaceted problem, requiring a holistic solution.
A cultural problem presented itself as a potential barrier to success in this case, as the factory workers were completely unfamiliar with and wary of the performance team. A significant amount of attention and effort was therefore directed toward building and monitoring rapport and project morale.
Performance analysis tools, analysis and results
The performance team believed that taking a qualitative approach to data gathering was necessary to gather more meaningful data and because literature revealed a positive correlation between using a quantitative approach and mitigated success in similar HPI projects. Finally, this made sense because members of the performance team were more skilled in qualitative research. Thus, the analysis phase required many hundreds of hours of meetings, formal and informal interviews, observations, more literature research and complex data coding and analysis to arrive at a result.
In addition, the performance team reviewed HR employee data and metrics, examining turnover trends. It was found that turnover was highest at day 21 of employment, before then end of the training phase. Also of interest is the fact that turnover decrease slightly during the pre-analysis phase, indicating a positive correlation between turnover and employee perceptions of their employer taking action to address it.
Cause analysis tools, analysis and results
The same qualitative methods were used to analyze the causes of high turnover: continued formal and informal interviews, observations, research, data coding and analysis.
The analysis revealed organizational factors contributing to turnover, such as supervisors having unclear job responsibilities, disjointed communication between management and employees (partially due to language barrier). It was also determined according to the data that training for supervisors, operator-trainers and operator trainees was inconsistent and inadequate. Each of these groups had a skill and knowledge deficit greatly undermining their chances of success. For the operator-trainees especially, this resulted in disproportionately high turnover rates. Lastly, toward the end of the analysis phase, “persistent and pervasive” rumors of the facility shutting down affected turnover, and the ultimate success of the intervention.
Critique
This is a very interesting case namely because of the people involved in it. One cannot understate the difficulty of introducing university academics to interview observe an uneducated, unskilled worker population with a language barrier. The performance team put forth, as noted even by some the workers they interviewed, and exhaustive and exhausting effort to gather rich qualitative data. Many questions come to mind concerning the methodology used in this case. The first is this: how do we know that previous, similar interventions were unsuccessful due to the quality of the data (quantitative vs qualitative)? The only evidence to suggest this is coincidental, according to the case.
Could it be that faulty reasoning of the very first assumption made cost the performance team a success? Looking back at the results of the cause analysis, it is clear that new hire orientation and training, as well as supervisor training lacks rigor and substance. Furthermore, both populations stated they felt inadequately trained and equipped to do their jobs, and upon observation of training, it appears obvious why. Surprisingly, this does not appear in the Lessons Learned. Finally, could the performance team have arrived at the same result, with far less effort and expense, by examining the exit interviews of leavers, validating job descriptions and allocation of responsibilities, and creating a job aid and/or training for supervisors and operator-trainers?
It is interesting to note the approach taken by academics, as opposed to performance professionals who work in a business context. This performance improvement project ended up being a steamship in a puddle. The analysis itself was so massive that it undermined progress and ultimately may have even contributed to the facility closing. A lesson here is that performance consultants must also perform. In this case, the performance consultants were extremely productive, and yet, left no accomplishment behind.
Jeans, Inc. is one of several textile manufacturing facilities reporting to a large international corporation. The 35 year-old company is located in a small, Midwestern city with low unemployment, and where the majority of available employment requires a low or semi-low level of skill. Like other manufacturing facilities in the area, Jeans, Inc. suffers from an extremely high employee turnover rate. Leadership estimates that factors related to turnover (retraining, not meeting production goals, etc.) cost the company $100,000 per year. The company leadership decided to hire a professor and group of graduate students from the local university to remedy this debilitating performance problem.
Initial problem statement
Jeans, Inc. reported an average turnover rate of nearly 90%. This includes all functions in the facility, but mostly concerns operator trainees and operators / operator trainers. The high turnover caused the company significant financial loss, as well as a regularly disruptive atmosphere caused by trainees leaving and the shortage of labor to replace them. Research into current literature revealed that the most common measure used to reverse turnover in instances with similar characteristics were incentive-based, and were likely to yield poor results. The ad-hoc performance team led by the university professor concluded that turnover is a multifaceted problem, requiring a holistic solution.
A cultural problem presented itself as a potential barrier to success in this case, as the factory workers were completely unfamiliar with and wary of the performance team. A significant amount of attention and effort was therefore directed toward building and monitoring rapport and project morale.
Performance analysis tools, analysis and results
The performance team believed that taking a qualitative approach to data gathering was necessary to gather more meaningful data and because literature revealed a positive correlation between using a quantitative approach and mitigated success in similar HPI projects. Finally, this made sense because members of the performance team were more skilled in qualitative research. Thus, the analysis phase required many hundreds of hours of meetings, formal and informal interviews, observations, more literature research and complex data coding and analysis to arrive at a result.
In addition, the performance team reviewed HR employee data and metrics, examining turnover trends. It was found that turnover was highest at day 21 of employment, before then end of the training phase. Also of interest is the fact that turnover decrease slightly during the pre-analysis phase, indicating a positive correlation between turnover and employee perceptions of their employer taking action to address it.
Cause analysis tools, analysis and results
The same qualitative methods were used to analyze the causes of high turnover: continued formal and informal interviews, observations, research, data coding and analysis.
The analysis revealed organizational factors contributing to turnover, such as supervisors having unclear job responsibilities, disjointed communication between management and employees (partially due to language barrier). It was also determined according to the data that training for supervisors, operator-trainers and operator trainees was inconsistent and inadequate. Each of these groups had a skill and knowledge deficit greatly undermining their chances of success. For the operator-trainees especially, this resulted in disproportionately high turnover rates. Lastly, toward the end of the analysis phase, “persistent and pervasive” rumors of the facility shutting down affected turnover, and the ultimate success of the intervention.
Critique
This is a very interesting case namely because of the people involved in it. One cannot understate the difficulty of introducing university academics to interview observe an uneducated, unskilled worker population with a language barrier. The performance team put forth, as noted even by some the workers they interviewed, and exhaustive and exhausting effort to gather rich qualitative data. Many questions come to mind concerning the methodology used in this case. The first is this: how do we know that previous, similar interventions were unsuccessful due to the quality of the data (quantitative vs qualitative)? The only evidence to suggest this is coincidental, according to the case.
Could it be that faulty reasoning of the very first assumption made cost the performance team a success? Looking back at the results of the cause analysis, it is clear that new hire orientation and training, as well as supervisor training lacks rigor and substance. Furthermore, both populations stated they felt inadequately trained and equipped to do their jobs, and upon observation of training, it appears obvious why. Surprisingly, this does not appear in the Lessons Learned. Finally, could the performance team have arrived at the same result, with far less effort and expense, by examining the exit interviews of leavers, validating job descriptions and allocation of responsibilities, and creating a job aid and/or training for supervisors and operator-trainers?
It is interesting to note the approach taken by academics, as opposed to performance professionals who work in a business context. This performance improvement project ended up being a steamship in a puddle. The analysis itself was so massive that it undermined progress and ultimately may have even contributed to the facility closing. A lesson here is that performance consultants must also perform. In this case, the performance consultants were extremely productive, and yet, left no accomplishment behind.