Excel Function to Determine Optimal Price - excel

I need help creating an Excel function that will determine the optimal price-point for a product, given the required profit margin.
NET MARGIN = (PROFIT / PRICE) = 30%
PRICE = (COGS + Fixed Costs + Variable Costs)
COGS = $5.00
FIXED = $3.00
VARIABLE = (15% * PRICE)
The problem that I can't get my head around is that as the price goes up, the variable costs go up as well. So how do I automate the product pricing for a required margin of 30%?
Any help is appreciated.

Related

Weighted Average in Excel, 2 different results

Hi all,
I have something that I cannot explain.
Here is an explanation of the table
Weight
Price Increase in %
Old Price
New Price = ( Old Price * Price Increase ) + Old Price
Total Old Price = Old Price * Weight
Total New Price = New Price * Weight
When using SumProduct to calculate the weighted average I get
=SUMPRODUCT(A2:A6,B2:B6)/SUM(A2:A6) = 16.1072%
But a Finance guy told me that was wrong that I needed to calculate it like this
=SUM(F2:F6)/SUM(E2:E6)-1 = 16.2465%
1 - Why is it giving different results
2 - Who is right
Anyone able to shed some light on this would be greatly appreciated !

Profits calculation based on total margin w/variable costs

So I don't know if my title accurately described my needs, but here goes;
I sell items online. I need to figure out what I should set the Retail price at to come to a specific margin %.
Image of the Sheet I am using
In this example, I want to hit 20% margin. What formula (Excel) can I use to help me determine that? Keep in mind that the 'Amazon 15%' and the 'Returns 5%' will change when the Retail changes - therein lies my challenge.
This is more a mathematical question instead of an excel question.
But the answer is:
=(B2+E2)/(1-(0.15+0.05+0.2))
We divide the total of cost by 1 - the sum of the various percentages.
x = COGS + Shipping + 0.15x + 0.05x + 0.20x
x = COGS + Shipping + 0.40x
x - 0.40x = COGS + Shipping
0.60x = COGS + Shipping
x = (COGS + Shipping)/0.60

DAX. Problem with subtotals and grand totals

hope you are doing well and can help solve this puzzle in DAX for PowerBI and PowerPivot.
I'm having troubles with my measure in the subtotals and grand totals. My scene is the following:
I have 3 tables (I share a link below with a test file so you can see it and work there :robothappy:):
1) "Data" (where every register is a sold ticket from a bus company);
2) "Km" (where I have every possible track that the bus can do with their respective kilometer). Related to "Data";
3) and a "Calendar". Related to "Data".
In "Data" I have all the tickets sold from a period with their price, the track that the passenger bought and the departure time of that track.
Each track can have more than 1 departure time (we can call it a service) but only have a specific lenght in kilometers (their kilometers are specified in the "Km" table). 
Basically what I need is to calculate the revenue per kilometer for each service in a period (year, month, day).
The calculation should be, basically:
Sum of [Price] (each ticket sold in the period) / Sum of [Km] (of the period considerating the services with their respective kilometers)
I managed to calculate it for the day granularity with the following logic and measures:
Revenue = SUM(Data[Price])
Unique dates = DISTINCTCOUNT(Data[Date])
Revenue/Km = DIVIDE([Revenue]; SUM(Km[Km])*[Unique dates]; 0)
I created [Unique dates] to calculate it because I tried to managed the subtotals of track granularity taking into account that you can have more than 1 day with services within the period. For example:
For "Track 1" we have registered:
1 service on monday (lunes) at 5:00am.
Revenue = $1.140.
Km = 115.
Tickets = 6.
Revenue/Km = 1.140/115 = 9,91.
1 service on tuesday (martes) at 5:00am.
Revenue = $67.
Km = 115.
Tickets = 2.
Revenue/Km = 67/115 = 0,58.
"Subtotal Track 1" should be:
Revenue = 1.140 + 67 = 1.207.
Km = 115 + 115 = 230.
Tickets = 6 + 2 = 8.
Revenue/Km = 1.207/230 = 5,25.
So at that instance someone can think my formula worked, but the problem you can see it when I have more than 1 service per day, for example for Track 3. And also this impact in the grand total of march (marzo).
I understand that the problem is to calculate the correct kilometers for each track in each period. If you check the column "Sum[Km]" is also wrong.
Here is a table (excel file to download - tab "Goal") with the values that should appear: 
[goal] https://drive.google.com/file/d/1PMrc-IUnTz0354Ko6q3ZvkxEcnns1RFM/view?usp=sharing
[pbix sample file] https://drive.google.com/file/d/14NBM9a_Frib55fvL-2ybVMhxGXN5Vkf-/view?usp=sharing
Hope you can understand my problem. If you need more details please let me know.
Thank you very much in advance!!!
Andy.-
Delete "Sum of Km" - you should always write DAX measures instead.
Create a new measure for the km traveled:
Total Km =
SUMX (
SUMMARIZE (
Data,
Data[Track],
Data[Date],
Data[Time],
"Total_km", DISTINCT ( Data[Kilometers Column] )
),
[Total_km]
)
Then, change [Revenue/Km] measure:
Revenue/Km = DIVIDE([Revenue], [Total Km])
Result:
The measure correctly calculates km on both subtotal and total levels.
The way it works:
First, we use SUMMARIZE to group records by trips (where trip is a unique combination of track, date and time). Then, we add a column to the summary that contains km for each trip. Finally, we use SUMX to iterate the summary record by record, and sum up trip distances.
The solution should work, although I would recommend to give more thoughts to the data model design. You need to build a better star schema, or DAX will continue to be challenging. For example, I'd consider adding something like "Trip Id" to each record - it will be much easier to iterate over such ids instead of grouping records all the time. Also, more descriptive names can help make DAX clean (names like km[km] look a bit strange :)

Excel Formula, To Calcuate a maximum Weight based off a desired minimum profit (GP%)

So I am working on a spreadsheet for a Butchery I manage and have run into a problem.
First off back story: We do $20 packs for certain bulk products that have a min/max weight range.
The Goal is to be able to put in this spreadsheet the desired minimum GP% and from that get a maximum weight based off that minimum profit margin.
For example a Beef Steak that Costs $17.50 p/kilo Would be minimum of 680g (at a GP% of 30.30%) and a maximum weight of 790g (at a GP% of 20.50%)
I have been 'googling' all day, and banging my head on my desk (as well as experimenting with different formula's) I am starting to think I may have to resort to programming a macro to perform this but I would prefer to be able to achieve in a formula on the cell that way I can copy-paste easily down the spreadsheet.
If anyone has a solution or can put me on the right track would be Awesome.
I think the formula you are looking for is :
your selling price (=20$) / your mark up on cost
where your mark up is :
your cost per kilo / (1- your margin)
So for 20% expected GP it gives :
= 20 / (17.5 / (1-0.2))
= 20 / 21.875
= 0.914... kilos
Balance is then :
Revenue = 20$
Cost = 0.914 * 17.5 = 16
Margin = 4
Margin % = 20

Creating a Variable Curve from Linear Data

My end goal is to distribute items into different discounts for sales. For example I may have 1000 items in an up-to 20% off sale. I am trying to come up with a way to cluster more items towards a discount, say 15%. So the discount distribution is a curve, with the peak number of items around 15% and less items at the extreme low and high discounts.
I want this to work for many different sales- number of items included will change and the discount ranges will change. I may have 25,000 items in a 10-40% off sale.
Variables:
L = Low Discount
H = High Discount
S = Discount Spread (H - L)
I = Item Count
D = Discount Sread (S / I)
C = ClusterPeak, where I want the most items
All items are ranked and sorted. Using Excel, Excel VBA or FileMaker I am sure there is math thing I can throw at this to make work.
Using the first example of 1000 items in an up-to 20% off sale. (starting at 1%)
L = .01
H = .2
S = .19
I = 1000
D = .00019
C = .15
R = Current Row or Record.
To get linear, evenly distributed discounts, I know I can do this-
(R * S) + L or (1 * .00019) + .1
This gets me .01019, .01038... The 1000th item gets .2, or 20%. This works regardless of the discounts and items count. Yay!
But I am stumped on how to cluster more items around a discount in a smooth curve. I have tried throwing LOG and standard deviation at the problem in various uses but obivously it didn't work or I wouldn't be here.
Sorry if this is miscategorized or poorly explained. It is very hard explaning what you don't know. Is there a better name or description for what I am trying to accomplish?

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