The following appeared in a memo from a vice president of Quiot Manufacturing.

"During the past year, Quiot Manufacturing had 30 percent more on-the-job accidents than at the nearby Panoply Industries plant, where the work shifts are one hour shorter than ours. Experts say that significant contributing factors in many on-the-job accidents are fatigue and sleep deprivation among workers. Therefore, to reduce the number of on-the-job accidents at Quiot and thereby increase productivity, we should shorten each of our three work shifts by one hour so that employees will get adequate amounts of sleep."

Write a response in which you examine the stated and/or unstated assumptions of the argument. Be sure to explain how the argument depends on these assumptions and what the implications are for the argument if the assumptions prove unwarranted.

The vice president of Quiot Manufacturing (QM) recommends that the factory shorten each of its work shifts by one hour so as to reduce on-the-job accidents. Her claim mainly rests on the following two pieces of evidence. First, QM had 30% more on-the-job accidents last year than the nearby Panoply Industry (PI). Second, PI’s work shift is one hour shorter than QM’s. However, her argument is filled with assumptions and loopholes that seriously undermine the persuasiveness of her reasoning.

For starters, even is PI reports 30% fewer accidents than QM does, that does not imply that PI has a better safety record. The author unwarrantedly assumes that PI does not have a much smaller staff. True, the 30% difference would be a strong indicator of PI’s safer working environment, if PI has as many or more workers than QM does. However, if it later turns out that QM has twice as many as workers as PI does, then it is natural to expect that QM’s accidents are double to PI’s; hence, only having 30% fewer accidents would actually mean that PI is probably not as safe to work in as QM. In short, the author needs to provide specific data that compares the two factories with regard to the size of their work force, or else PI’s experience is probably not worth replicating.

Furthermore, by mentioning the different length of work shifts, the author clearly suggests that workers in QM are getting insufficient rest compared with those in PI. However, it is rash to assume that no other factor is in play which may make PI’s workers more exhausted. Perhaps their workers have no breaks during shifts, while QM’s have many; perhaps their products require intense physical and mental work to assemble, while thanks to QM’s automated assembly line, each worker at PI may take more shifts per week. Under these scenarios, PI’s workers would actually be the more tired ones, and PI’s superior safety record would have to be attributed to factors other than its work length. Thus, to corroborate her reasoning, the author needs to provide additional evidence to rule out other factors that might affect staff fatigue. Perhaps a direct medical test that examines workers’ average physical and mental status might provide more relevant data.

Provided that QM’s workers are indeed not as well rested as those from PI, and that QM’s safety record is inferior to PI’s, assuming a causal link between the two events is still contestable. True, if the two factories are not significantly different in their safety regulations, their workers’ abilities, or their equipment, then perhaps the assumption is plausible. However, maybe PI’s workers are better trained, and thus more skillful, their equipment is more advanced and frequently checked for safety, or their regulations are sounder and carried out more strictly. Any of the above possibilities, if true, would equally contribute to PI’s better safety record. Therefore, without ruling them out, she cannot attribute the record to fatigue, and thus her suggestion to reduce one hour per work shift will probably not affect QM’s future accident rate.

Now, granted that there is connection between accident rate and fatigue, apply the shortened work shift to each of QM’s three work shift is still contestable, since it is unclear if the three shifts had contributed equally to the past accidents. The author clearly assumes that the three shifts, need not be distinguished. However, it is quite possible that the evening shift, and especially the late night shift, if there is any, have been the major sources of fatigue, and thus of accidents, whereas the morning shift does not place excessive demand on workers’ concentration. Under this scenario, the adjustment does not have to be uniformly enforced throughout all the shifts. Accordingly, what the author needs to do is to investigate specifically the accident rate of each shift, and then to come up a more targeted solution.

In conclusion, many of the reasoning steps in this article are weak, and have unsupported assumptions. The author needs to provide further evidence to supplement all these missing links, or else her recommendation will remain doubtful.

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