Complete question reads;
Katie, a salesperson for Sol Computers, is faced with an objection from one of her prospects. The prospect says that the graphics of the computers do not meet his requirements. Katie listens to the objection and makes it known to her prospect that she has received the message. According to the LAARC method, which of the following should Katie do next?
(A) Continue listening
(B) Assess the objection
(C) Ask confirmatory questions
(D) Respond to the objection
(E) Change the subject of the conversation
<u>Answer:</u>
<u>(B) Assess the objection</u>
<u>Explanation</u>:
Remember, the LAARC method an acronym fully means;
Step 1: LISTEN,
Step 2: ACKNOWLEDGE,
Step 3: ASSESS,
Step 4: RESPOND,
Step 5: CONFIRM.
So, at this point according to the LAARC method, Katie should access the objection by getting a sense of why the prospect feels the computer does not meet requirements. Her ultimate goal is to try and ensure a sale of the computer.
It allows workers to transfer, update, or share files within seconds.
Answer:
b. unskilled labor, skilled labor, and professionals
Explanation:
- As the people services include the semi and skilled based works done they include Lawn care, security guards, and skilled labor as plumbing and catering and those of the professionals include the accountant, lawyers, and the managing consultants.
Answer:
$6896551.7
Explanation:
Given the following :
Product R:
Selling price = $20
Variable cost = $6
Product S:
Selling price = $50
Variable cost = $30
Firm's fixed cost = $4, 000,000
Break-even point dollars = (Fixed cost /Contribution margin ratio)
Contribution margin : selling price - variable cost
Product R: $(20 - 6) = $14
Contribution margin ratio = ($14/$20) * 60% = 0.42
Product S: $(50 - 30) = $20
Contribution margin ratio = ($20/$50) * 40% = 0.16
Sum of contribution margin ratio for both products = (0.42 + 0.16) = 0.58
Break-even point dollars = (Fixed cost /sum of Contribution margin ratio)
= $4,000,000/0.58
= $6896551.7
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
brainly.com/question/28322914
#SPJ4
