Membuat proyeksi neraca untuk Ford

Sekarang setelah kita memiliki rasio untuk Ford, manajemen ingin meningkatkan rasio-rasio tersebut untuk tahun buku 2018.

Ada satu rasio khusus yang ingin diperbaiki oleh manajemen—rasio hari piutang (debtor days), yang saat ini berada di 146 hari. Walau lebih baik daripada Nissan (244 hari), angka ini masih jauh lebih tinggi dibandingkan Toyota, pemimpin industri (31 hari).

Manajemen ingin menurunkan hari piutang menjadi di bawah 100 hari.

Variabel berikut sudah disiapkan:

filtered_balance_sheet berisi baris piutang (receivables) dari neraca.
ddays = 99 adalah target hari piutang.
sales = 156776 adalah total penjualan saat ini.

Rumus untuk menghitung hari piutang adalah: \(\dfrac{\text{closing balance debtors}}{\text{sales}} * 365\).

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate the forecasted sales 
f_sales = ____ * ____

# Solve for the forecasted debtors' ending balance
f_debtors_end = ____ * ____ / 365

print("If sales rise by 10% and the debtor days decrease to {:.0f} then the forecasted closing balance for debtors will be {:.0f}.".format(ddays, ____))

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: Pengantar neraca Exercise 2: Menghitung piutang usaha (debtors)Exercise 3: Piutang tak tertagih Exercise 4: Menghitung utang usaha (kreditur)Exercise 5: Memahami utang dagang dan piutang dagang Exercise 6: Rasio efisiensi neraca - Bagian 1 Exercise 7: Rasio hari piutang Exercise 8: Days payable outstanding Exercise 9: Rasio efisiensi neraca - Bagian 2 Exercise 10: Hari dalam persediaan dan rasio perputaran aset Exercise 11: Memahami rasio Exercise 12: Menghitung rasio neraca untuk Ford Exercise 13: Membuat proyeksi neraca untuk Ford

Latihan Saat Ini

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: Building sensitive forecast models Exercise 2: Weighted probability Exercise 3: Market sentiment Exercise 4: Dependencies and sensitivity Exercise 5: Assigning dependencies for sales and COGS Exercise 6: Building a sensitivity analysis for gross profit Exercise 7: Assigning dependencies for expenses Exercise 8: Build a sensitivity analysis for the net profit Exercise 9: Working with variances in the forecast Exercise 10: Building an alternate forecast Exercise 11: Building a gap analysis between forecasts Exercise 12: Setting dependencies for Netflix Exercise 13: Calculating an alternative forecast for Netflix