Answer:
the answer is as follows
Explanation:
First of defining real wages is a cumbersome process. The living wage calculator developed by MIT professor Amy Glasemeier in 2004 eased the way a little but that too has it's issues.
The paper you are trying to write should start with this that how the idea of living wages is in itself difficult to be adopted as it is. Second the free market approach has been more successful in the economic history and a lot of evidence and data is available on that. The analysis that your paper will develop should outline the concerns that mainstream economists have regarding living wages and support it with some actual data.
The presentation would be rather easy after writing the paper. Which will include some graphs and data and some scholarly citations and it should work.
Answer:
Collaborative Planning, Forecasting and Replenishment
Explanation:
Based on the information provided within the question it can be said that the procedure they are following is known as Collaborative Planning, Forecasting and Replenishment (CPFR). This is a concept whose main focus is enhancing supply chain integration by emphasizing joint practices. Which is what is being done in this situation as companies begin to work closely together with their customers and/or suppliers.
Based on the labor cost, and output of the process, the multifactor productivity for the week is 3.06.
<h3>What is the multifactor productivity for Week 1?</h3>
This can be found by the formula:
= Cost in week 1 / Value of output in week 1
Cost in week 1:
= Labor + Material + Overheads
= 12,195 + 21,392 + 8,546
= $42,133
Value of product:
= 110 x 1,173 units
= $129,030
Multifactor productivity is:
= 129,030 / 42,133
= 3.06
Find out more on multifactor productivity at brainly.com/question/17550779.
Answer:
The percentage distribution is a statistical distribution of relative frequency, in which the relative frenquencies are percentages over the total number of data, that in this case is equal to 100%.
In order to create a percentage distribution chart, we group the data into classes, and then, we count the number of times the elements of the class appear in the sample, finally, we convert this number into a percentage.
