Question Completion:
see Exhibit 4 attached.
Answer:
1. The largest and smallest divisions by net sales in 2017:
Largest divisions:
Fabric & Home care with 32%
Baby, Feminine & Family Care, 28%
Smallest divisions:
Beauty with 18%
Grooming, 11%
Healthcare, 11%
2. The one most important division in terms of the proportionate net earnings for the company is:
Fabric & Home Care
Explanation:
The two largest divisions generate 60% of the net sales of the company while the three smallest divisions generate only 40%. In terms of the proportionate net earnings for the company, the two largest divisions also generate 53% of the net earnings of the company, while the three smallest divisions generate 47%. The analysis shows that the company's financial sustenance is largely driven by the Fabric & Home Care division and the Baby, Feminine & Family Care division. Another up-and-coming division is the Beauty division, which generates 18% of the net sales and 20% of the net earnings.
Answer:
The preparation is presented below:
Explanation:
The preparation of the retained earnings statement for the year ended July 31, 2018 is presented below:
Cali Communications'
Retained Earning statement
For the year ended July 31, 2018
Beginning balance of retained earning $0
Add: Net income $5,150
Less: Cash Dividend paid -$0
Ending balance of retained earning $5,150
Answer:
Marley could not meet a rapid rise in demand
Explanation:
- A marketing penetration strategy means that a business deliberately reduces the product offered to the market. The purpose of setting a lower price is to entice consumers to buy the product, thereby creating demand for it.
- The penetration strategy discourages other companies from entering the market. Marketers who use this strategy want to establish a large market share for a product in a short period of time.
- Mary cannot implement a market entry strategy because of limited production capacity. This approach increases production demand in a short period of time. Mary cannot afford the increase in demand at the moment.
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
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