Last Updated 28 Jan 2021

Putnam Work@Home Case

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Cost and Benefit Analysis of work@home One of the primary advantages of the work@home program is that it creates a cost advantage. Having employees work at home as opposed to in-facility is significantly less costly. According to Table A in the case, annual recurring costs for each individual working in-facility is around $10,650. That is over twice the recurring cost of the average work@home employee with an ISDN connection and over 20 times the annual recurring cost of a work@home employee with a cable modem connection.

Given the large quantity work@home employees at Putnam, this program provides a method of greatly reducing recurring costs in the short and long term. Additionally, the e-learning program costs less than half of what the traditional training process costs. It even better prepares employees for the job because the quality of the training is higher and individuals can complete the training at their own pace. Moreover, work@home employees feel that Putnam has made a sizeable investment in them, and feeling is supported by high productivity rates and decreased turnover.

The turnover rate among work@home employees is around 8% which is significantly lower than the Putnam average of 30%. By training employees for less and retaining them for longer, Putnam decreases both recruiting and training costs by a significant margin. Furthermore, the work@home program allows Putnam to expand their business into new areas without having to invest in additional real estate. And because the majority of these work@home employees are from rural areas where the cost of living is lower than locations near Putnam’s office facilities, Putnam can get away with paying work@home employees less than their in-facility counterparts.

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All these factors contribute to the low cost advantage that the work@home program creates. Because Putnam only allows high productivity workers the option of working at home, the work@home program can provide an incentive for employees to increase their productivity. According to a Putnam manager, anyone who is eligible to work from home and who wants to can work at home as long as he has higher than average productivity. Anyone at Putnam who desires to work from home will have the incentive to increase productivity above the mean so that they will have the option of working from home. However, this ncentive only applies to workers who have jobs that allow them to work from home. Also, many people like the social experience that the office brings and have no desire to work from home. One of the pitfalls of using this program as an incentive is that there is no reason for employees to produce anything higher than the company average. However, all things considered, this program does incentivize a select group of individuals to boost their productivity levels. Various costs arise from the work@home program as well. It takes a special type of person to succeed in a work@home position.

Employees must be willing to sacrifice the social aspect of work and must be good at solving problems on their own because immediate help cannot always be obtained. Unfortunately the workers who fit the work@home criteria do not necessarily bring about optimal production for Putnam. The most qualified and potentially productive candidates may find the work@home program to be unfulfilling. Consequently Putnam is forced to accept candidates who while still productive, may not produce optimally. In fact, overqualified candidates in Vermont and Mane tended to have higher turnover rates due to the unfulfilling nature of the work.

One of the other primary costs of the program is the communication barrier. By not being in-facility, work@home employees cannot as easily talk to co-workers or supervisors about work-related problems. Also they are not exposed to the culture and are unable to get as good of a sense of how the company operates compared to in-facility workers. Putnam has tried to mitigate these costs through the advent of the chat system and other communication methods, but the fact remains that communication is not as good as it is among in-facility workers. Finally, employee performance needs to be monitored a bit more closely with work@home.

Due to the lack of social pressure among co-workers to perform, employees could be tempted to shirk. But Putnam’s performance evaluation process has eliminated this problem, and in fact, work@home employees have been equally if not more productive than in-facility workers. Human Resources Policies Overall I think Putnam is doing a pretty good job with regards to its human resources policies in the work@home program. However, I feel a few changes could be made that could improve the program. According to some Putnam managers, monitoring an employee working at home is not significantly different from monitoring employees in-facility.

Rather, supervisors just have to monitor what’s going on “in different ways”. If it really isn’t much more costly or time consuming to monitor work@home employees as opposed to in-facility employees, I see no reason to offer work@home opportunities exclusively to high productivity employees. My recommendation is that Putnam rank workers on a relative scale in quintiles and assign each quintile a grade of A, B, C, D, or E with A workers being the top 20% and E workers the bottom 20%. Employees should not be made aware of their rankings.

Next, my recommendation is that Putnam select a sample of employees from each of the bottom three quintiles to work at home for a period of 6 months to a year. The reasoning for only using the bottom three quintiles is that the top two quintiles are already eligible to work at home. The purpose of the experiment is to determine from a cost standpoint whether or not it is advantageous to allow average and below average employees to work at home. Putnam should use the exact same evaluation process and compensation system with these employees. In other words, they should be treated no differently from the typical work@home employee.

Putnam should then compare the productivity numbers of the experimental work@home employees and compare them to their respective productivity numbers from when they worked in-facility. If there is not a huge discrepancy in their productivity, then it may be advantageous for Putnam to allow employees of average to below average productivities to participate in the work@home program. In fact, because overhead costs are so low for work@home employees compared to in-facility counterparts, it could still be advantageous from a cost standpoint for Putnam to allow these employees to work at home even if their productivities drop off a bit.

There are two major concerns I would have with employees in the bottom 3 quintiles working at home. One is that worker productivity will drop without direct monitoring. The second is that monitoring costs will spike due to the employees’ lack of motivation to do the job alone at home. If the increased costs of monitoring and the value of lost productivity do not exceed the difference in overhead cost between work@home and in-facility employees, then Putnam should definitely consider allowing more employees to work at home.

Doing so could decrease operating costs and increase profits in the long run. By performing this experiment Putnam can figure out how to optimally take advantage of its unique work@home program. The limitation of this is that it may not be possible to assign a dollar amount to the cost of increased supervisory monitoring or the value of lost productivity. In light of this, it may be difficult to determine any cost advantages from performing this experiment. With regards to employee evaluation and compensation, I believe Putnam is doing a more than adequate job.

By using both quantitative and subjective measures of performance like accuracy and call screening, Putnam keeps work@home employees on their toes and producing at a high level. Additionally, by offering bonuses tied to performance of up to 20% of base salary, Putnam does a solid job of aligning work@home employees interests with the company’s. The high level of productivity and low turnover rate among work@home employees is proof that these policies work. One other aspect of HR that could be improved is making a clear cut path of promotion from work@home employee up to a higher level position like manager or supervisor.

By establishing a clear path to a higher level job in the company, Putnam can inspire its work@home employees to work harder than ever. However, this could result in employee sabotage and decreased collaboration among work@home employees. Employees may refrain from helping each other out because they are all seeking the same promotion. Experimental work@home The first thing the travel agency should do is come up with a method of measuring employee performance. Without an accurate method of measuring performance, the experiment will not yield any meaningful results.

The travel agency ideally would find a quantitative measure of performance that helps predict the total profit or revenues of the firm. By finding a quantitative measure that drives revenues, the travel agency can be sure that their method of evaluation will tie closely into firm performance. For the sake of simplicity in this exercise, I will assume that the number of clients served is the quantitative measure that most closely measures firm profitability and employee productivity. The next step in performing this experiment would be to research the costs associated with having a call center employee work at home as opposed to in-facility.

If it is not any cheaper to have employees work at home, then there is no reason to even perform the experiment. This difference in cost is between work at home and in-facility employees will eventually determine whether or not a work at home program would be advantageous for the travel agency. The major cost would likely be installing the work phone in each employee’s house. There could be other costs in addition, however, like increased supervisory costs. Next, similar to my strategy for Putnam, I would rank all call center employees on a relative scale based on productivity and divide them into quartiles.

Then I would take a random selection of a given amount of employees from each productivity quartile. These randomly selected individuals would be the ones taking part in the work at home experiment. These individuals would work at home for a lengthy period of say 6 months to a year. The travel agency should heavily monitor their productivity during their time working at home, which in this case would be keeping track of clients served. At the end of the trial period of the work at home experiment, the travel agency should collect all the data regarding the participating individuals’ productivity.

Their productivity should be compared to each individual’s respective productivity in the 6 months to a year prior to the experiment. Also, to adjust for possible seasonal factors influencing productivity, the travel agency could compare each work at home employee’s productivity to other employees in the same quartile who work in facility. The main concern here should be that worker productivity could decrease to the point that it would not be cost effective for the travel agency to have employees work at home, in spite of the fact that it probably costs significantly less in overhead to have employees work at home.

If possible, the travel agency should attempt to assign dollar values to the additional costs of productivity loss and supervision from having employees work at home. If these additional costs are less than the difference in overhead cost between work at home and in-facility employees, then implementing a work at home program would probably be advantageous for the travel agency. There is a reason workers are ranked on a relative scale at the beginning of the experiment. Call center employees of different productivities may respond differently to working at home.

The highest productivity employees are probably the most intrinsically motivated, and thus we would expect to see not as large a drop off in their performance as employees in other quartiles. Based on the data collected at the end of the experiment, the travel agency could decide that it is only profitable to allow employees above a certain level of performance standard to work at home. The firm could then use this standard as a benchmark and incentive for employees to obtain in order to get the option of working at home.

Putnam Work@Home Case essay

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