Determining a Style Changeover by Machine using PowerBI - powerbi

Question

So I have a table that has the output of all machines in a department with styles. For example:
|Machine| |Style| | QTY| |Time| |Date| etc...
1 001 100 8:00AM 5/21/19
2 001 200 8:05AM 5/21/19
1 001 100 9:00AM 5/21/19
1 004 100 10:00AM 5/21/19
2 001 200 9:05AM 5/21/19
I'm looking to see the amount of times a style is changed for a machine. So in this case, for Machine 1 it was one style change and for Machine 2 it was zero.
I've tried adapting some code to no avail; mainly because I'm having trouble understanding the logic and I can't really think of a good index to work with.
Here is what I got so far:
EarliestChange Over Index =
VAR Temp =
CALCULATE (
MAX ( Table[Index] ),
FILTER (
Table,
[Index] < EARLIER ( [Index] )
&& [Style] <> EARLIER ( [Style])
&& Table[Date] = today()-1
)
)
VAR Temp1 =
CALCULATE (
MIN ( [Index] ),
FILTER (
Table,
[Index] > EARLIER ( [Index] )
&& [Style] <> EARLIER ( [Style])
&& Table[Date] = today()-1
)
)
RETURN
IF ( [Index] > temp && OR ( [Index] < temp1, ISBLANK ( temp1 ) ), temp + 1, 0 )
I tried to restrict it to just one day so that I could evaluate the results so that portion can be dropped. I've tried two different indexes, one was the machine number and the other was the difference in time from today and the min date on the table. In a visual, I've been taking a distinct count of the EarliestChange Over Index and subtracted one since it didn't constitute a "change over."
EDIT:
Issue where multiple styles are logged at the same time causing false change overs.
|Machine| |Style| | QTY| |Time| |Date| etc...
1 001 100 8:00AM 5/21/19
1 001 100 9:00AM 5/21/19
1 004 100 10:00AM 5/21/19
1 004 100 10:00AM 5/21/19
1 004 100 10:00AM 5/21/19
In one department a time would never be duplicated. However, in another department (for whatever reason) might log 3 rolls at the same time. This would cause the equation to log 10:00am as 3 change overs. It might be a system glitch why there isn't unique time stamps per roll but this is the case unfortunately.

Answer 1

One way of doing it:
First, I modified your data as follows:
Added a record for Machine 1 at 11:00AM to capture a situation when a style reverts to the old one;
Added a column for Date-Time (simply Date + Time), to make life easier;
Named the table as "Data"
Measure:
Style Change Count
=
SUMX (
Data,
VAR Current_DateTime = Data[Date-Time]
VAR Current_Style = Data[Style]
VAR Previous_DateTime =
CALCULATE (
MAX ( Data[Date-Time] ),
FILTER ( ALLEXCEPT ( Data, Data[Machine] ), Data[Date-Time] < Current_DateTime )
)
VAR Previous_Style =
CALCULATE (
VALUES ( Data[Style] ),
FILTER ( ALLEXCEPT ( Data, Data[Machine] ), Data[Date-Time] = Previous_DateTime )
)
RETURN
IF ( Current_Style = Previous_Style || ISBLANK ( Previous_Style ), 0, 1 )
)
Result:
How it works:
We need to use SUMX to make sure that our subtotals and totals are correct;
SUMX iterates over Data table and for each record computes "Previous date-time", which is simply the max datetime less than the current datatime, per machine (hence ALLEXCEPT);
Then, we can calculate Previous Style, which is a style where date-time = previous date-time;
Finally, we compare current style and previous style. If they are not the same, we add 1;
In addition, I added a test for the starting condition - first occurrence of a machine, for which previous style does not exist yet. I did not treat such records as "style change". If you want to count initial records as style change, remove ISBLANK() part.