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In this paper, I will examine the data collection methods used at work and how they are used. Then choose a problem in our business to discuss and identify a method to collect this data. Lastly, identify two critical variables whose relationship is critical for our company’s success. Also, identify a moderator that changes the dynamics of the two critical variables. I see right away that my company uses industrial and organizational psychology concepts in their management tool bag. The employees at Altria should know data is constantly being collected and they are the primary date source, but the use of the data to address opportunities has been mixed.
I work for Philip Morris USA (PMUSA), but the parent company is Altria owns many companies. The Altria Human Resources Department initiates and approves all data collections company wide. PMUSA also, has a communication group that works hand in hand with the parent company to execute and coordinated data collections. This is a luxury that PMUSA has over other operating companies.
My favorite company data collection tool is the Glint Surveys. We expect to get at least one survey a year. We completed the Glint Survey late 2018 then the company made some significate changes to the organization. So, the 2019 Glint Survey was intended to gauge how employees are doing after the recent organizational changes. The survey, in my opinion, was flawed. Management changes the questions for salaried operating companies, salaried services companies, salaried headquarters and hourly employees. The results were not representative of what I expect from talking to piers. The questions were around diversity compliance, corporate direction, process compliance, and recognition. The survey summary always compares our answer with answers from other companies. This comparison gives senior leaders further insight on the direction we are headed good or bad.
Although observational data collection is not as formally announced, the company actively uses observation. Safety uses this tool to monitor safe behaviors and logs the data as safe or unsafe. Of course, if bad behaviors are identified we all get to re-do safety training. This usually is prompted by an incident. The factory equipment is operated and repaired by hourly employees and they are constantly observed for efficiency improvements through improving processes. Maybe a little short of scientific, but the company does collect data through observation.
Like surveys, we collect data through ‘how are you feeling’ kiosks. The communications group can pose one question and push them to all the kiosk locations. The employee can hit the ‘happy’ button or ‘sad’ button or do nothing at all. After the organizational changes the message was, ‘are you happy today’. The questions are always simple and not much critical thinking involved. The employees collecting data measure the amount of responses and the answers. The communication group stays clear of political questions and other hot topics. The data is used to gain employees mindset whether it abruptly changes, responses pick up, or slow way down.
PMUSA is a converter. We don’t market, sell, distribute, or any other corporate function. We have two large objectives to manage. The fist is keeping conversion costs low or within budget. Production is the other key objective, where we must hit our target. If production is up then cost will drop, but with tightening margins cost are watched closely. The problem is we are not great at controlling costs. The company is regulated by the FDA and external pressures are constantly changing our manufacturing plan. The factory is trying to hit the original operating budgeted production number and internal news of curtailment days coming because of over production. Operators and maintenance are of the opinion that because we don’t need the production, they will slow down the machines. The company has over capacity capability, so they turned on more machines. This whole situation translates to higher costs of running more machines that budgeted.
In this experiment we should use both qualitative and quantitative data. Gathering information from machine (system) generated data to isolate specific operators or machines, observation, and interviews. I did not intend to have three collection points, but we use all of them in operations. We have regular planned meetings with operators, so conversations and information sharing are a normal part of the business. There are supervisors, process managers, area managers all watching individual performance, machine performance and organizational performance.
The independent variables for this analysis would be production target, information sharing, external pressures, and tenure with company. Production targets can get manipulated during the year but gets compounded by information sharing. Information sharing could be just a rumor or valid information taken out of context. Same I true for external pressures like new or changing regulations. Tenured and non-tenured employees may react differently. Like in the Hawthorne Study where the group set their own target, we may be seeing the same phenomenon.
The dependent variables for this analysis would be cost reduction, machine efficiency, and reduction in production capacity. Machine efficiency in relation to operators setting their machines to target instead of making their own target. Reduction in capacity is a stretch, but if we can reduce the dependence on more machines to do the same work, we could save money and space. We are a converter so costs should be our focal point.
The two most important variables are production target and cost. The moderator that changes this relationship is target incentives for meeting goal. Incentives have been custom designed shirts that say, ‘I meet goal’ or maybe a little catchier. This incentive was meant for employees who like this type of recognition. The employees that are just trying to stay under the radar have something else as motivation. The production data collected per module (machine), per bay (eight bays), and per shift (three shifts) will be displayed locally and on the company network. I personally will work for the shirt, but no one wants to negatively stick out in a crowd.
In summary, I talked about the data collection that Altria and PMUSA uses to gain knowledge of employee and organizational health. Surveys are the biggest tool, but observation is more frequently used. Operators creating their own production target numbers which increased cost of conversion. They could have changed the target numbers as a form of job security, tipped off that production was not needed, or some other reason. Cost of conversion and production target are our critical variables with incentives as the moderators. In our case, there is a positive and negative incentive. Industrial and organizational psychology concepts are in play at my company and I look forward to finding these hidden relationships.
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