Answer:
22.5
Explanation:
According to the given situation, the computation of the number of kanbans is shown below:-
Number of kanbans needed = [(Demand × Lead time) + (Demand × Safety stock)] ÷ Kanban size
= (750 × 0.5) + (750 × 0.25) ÷ 25
= 22.5
Therefore for computing the number of Kanbans we simply applied the above formula by considering all items
Answer:
primary source of law, oral, and repetitive
Explanation:
In legal terms, custom and usage is a doctrine widely used in commercial law, since they are a long established practice which many courts consider unwritten laws. Laws are formal and always written, but custom and usage is not formal nor written.
Usage refers to the general repetition of a certain act, while custom refers to the rules that result from such repetitions and usage.
Such employment would fall outside the production possibilities curve as the values plotted on that curve would be the minimum unemployment levels. The usual figure to use is % unemployment so most likely the differing levels shown would be for unemployment ie 10% above the curve and say 5 % on the curve.
Answer:
c. credit to Additional Paid-in Capital
Explanation:
The journal entry to record the difference is shown below:
Cash A/c Dr $75 million
To Treasury stock A/c $70 million (1 million shares × $70 per share)
To Additional paid in capital - in excess of par $5 million
(Being the issuance of treasury stocks is reported and the amount remaining is credited to the additional paid-in capital account)
Answer:
Zone Of Tolerance.
Explanation:
In our daily meetings with our customers, it is very important to remember that customer behavior, wants, needs, expectations are very fluid, so they depend on an immediate context, and this applies here. It would be a mistake to think that customers have some consistent rule let’s say they’ll only wait 5 minutes, that they apply to every single interaction.
Also there are some desired, perfect point, but around that point is a zone of tolerance in which all values are acceptable. Also the larger that gap, the more likely the customer will be dissatisfied.
The chief strength of this is that it explains something the expectation models do not know why customers return to companies where the service is bad.
