COGS berekenen

Cost of Goods Sold (COGS) wordt als volgt berekend:

\(COGS = Openings\,waarde\,van\,voorraad\) - \(sluitings\,waarde\,van\,voorraad\) + \(vaste\,kosten\,huidige\,periode\) + \(variabele\,kosten\,huidige\,periode\)

We gaan uit van geen begin- of eindbalansen: de voorraad wordt in dezelfde maand verkocht als waarin deze is geproduceerd.

Materiaalkosten om één T-shirt te produceren zijn 8 USD. Loonkosten zijn 2 USD per shirt. Omdat je alleen materialen en arbeid gebruikt om het shirt te maken, noemen we dit variabele kosten. De kosten om een machine te huren die deze shirts produceert, zijn 1300 USD per maand, ongeacht het aantal geproduceerde shirts. Dit is dus een voorbeeld van een vaste kost, omdat vaste kosten in COGS kosten zijn die direct aan het product gerelateerd zijn, maar worden gemaakt of je nu produceert of niet.

Vooraf geladen variabelen worden in de shell afgedrukt.

Deze oefening maakt deel uit van de cursus

Financiële forecasting in Python

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set the variables for fixed costs and variable costs
fixed_costs = ____ 
variable_costs_per_unit = ____ + labor_costs_per_unit

# Calculate the cogs for January and February
cogs_jan = (____ * variable_costs_per_unit) + ____
cogs_feb = ____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financiële forecasting in Python

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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: Introductie tot financiële overzichten Exercise 2: Brutowinst berekenen Exercise 3: Nettowinst berekenen Exercise 4: Onderdelen van nettowinst en brutowinst Exercise 5: Omzet en kostprijs van de omzet (COGS) berekenen Exercise 6: Omzet berekenen Exercise 7: Verkoopprognose met een korting Exercise 8: COGS berekenen

Huidige oefening

Exercise 9: Het break-evenpunt berekenen Exercise 10: Werken met ruwe forecast-gegevenssets Exercise 11: Winst-en-verliesrekening van Tesla Exercise 12: Winst voorspellen voor 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: 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