Menetapkan ketergantungan untuk penjualan dan COGS

Txs Tools telah membangun prakiraan bulanan untuk laba kotor mereka. Ini akan bergantung pada ketergantungan untuk Penjualan dan COGS.

Tetapkan ketergantungan untuk sales dan cogs berdasarkan informasi berikut:

Ketergantungan penjualan sales_dep: Harga jual adalah harga bersih setelah komisi 1 USD. Komisi yang dibayarkan meningkat dari 1 USD per unit menjadi 2 USD per unit untuk setiap unit di atas 350 unit terjual.
Ketergantungan biaya cost_dep: Ketika penjualan per unit meningkat di atas 500 unit, jalur produksi tambahan perlu digunakan, sehingga biaya per unit di atas 500 meningkat sebesar 2 USD per unit.

Harga jual dasar per unit (base_sales_price) adalah 15 USD dan biaya dasar per unit (base_cost_price) adalah 7 USD.

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set the Sales Dependency
if sales >= 350:
    sales_dep = (350 * ____) + ((sales - 350) * (____ - 1))
else:
    ____ = sales * base_sales_price

# Print the results
print("The sales dependency is {} USD.".format(____))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, we will learn the basics of financial statements, with a specific focus on the income statement, which provides details on our sales, costs, and profits. We will learn how to calculate profitability metrics and finish off what we have learned by building our profit forecast for Tesla!

Exercise 1: Introduction to financial statements Exercise 2: Calculating gross profit Exercise 3: Calculating net profit Exercise 4: Elements within net profit & gross profit Exercise 5: Calculating sales & Cost of Goods Sold (COGS)Exercise 6: Calculating sales Exercise 7: Forecasting sales with a discount Exercise 8: Calculating COGS Exercise 9: Calculating the break-even point Exercise 10: Working with raw forecast datasets Exercise 11: Tesla income statement Exercise 12: Forecasting profit for Tesla

In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

Exercise 1: Introduction to the balance sheet Exercise 2: Calculating accounts receivable (debtors)Exercise 3: Bad debts Exercise 4: Calculating accounts payable (creditors)Exercise 5: Understanding accounts payable and receivable Exercise 6: Balance sheet efficiency ratios - Part 1 Exercise 7: Debtor days ratio Exercise 8: Days payable outstanding Exercise 9: Balance sheet efficiency ratios - Part 2 Exercise 10: Days in inventory and asset turnover ratio Exercise 11: Understanding ratios Exercise 12: Calculating balance sheet ratios for Ford Exercise 13: Forecasting the balance sheet for Ford

We have gotten a basic understanding of income statements and balance sheets. However, consolidating data for forecasting is complex, so in this chapter, we will look at some basic tools to help solve some of the complexities specifically relating to finance - working with dates and different financial periods, and formatting our raw data into the correct format for financial forecasting.

Exercise 1: Financial periods and how to work with them Exercise 2: Converting quarters into months Exercise 3: Merging months into quarters Exercise 4: The datetime library Exercise 5: Working with the datetime library Exercise 6: Converting date formats - simple Exercise 7: Converting date formats - explicit Exercise 8: Tips and tricks when working with datasets Exercise 9: Working with datasets - month totals Exercise 10: Working with datasets - combining datasets Exercise 11: Working with datasets - exporting data

In this chapter, we will be exploring two more aspects to creating a good forecast. First, we will look at assumptions, what drives them and what happens when an assumption changes? Next, we will look at variances, as a forecast is built at one point in time, but what happens when the actual results do not correspond to our forecast? We need to build a sensitive forecast that can be sensitive to changes in both assumptions and take into account variances, and this is what we will explore in this chapter.

Exercise 1: Membangun model prakiraan yang sensitif Exercise 2: Probabilitas berbobot Exercise 3: Sentimen pasar Exercise 4: Ketergantungan dan sensitivitas Exercise 5: Menetapkan ketergantungan untuk penjualan dan COGS

Latihan Saat Ini

Exercise 6: Membangun analisis sensitivitas untuk laba kotor Exercise 7: Menetapkan ketergantungan untuk beban Exercise 8: Bangun analisis sensitivitas untuk laba bersih Exercise 9: Bekerja dengan varians dalam prakiraan Exercise 10: Membangun proyeksi alternatif Exercise 11: Membangun analisis gap antar prakiraan Exercise 12: Menetapkan dependensi untuk Netflix Exercise 13: Menghitung prakiraan alternatif untuk Netflix