Math question - Probably 5 grade level (1 Viewer)

[rodg12]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

 

[Drifter]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

This is secretive siloing for the purpose of secrecy and nothing else. I guarantee you that such a list already exists.

No wonder the stock is down 25% over the last year (I'm guessing who it is)

 

[Norman Paperman]

Sounds to me like you need to show the numbers as they are, and as someone else suggested, you should provide a variance report, if the numbers come out above the expected variance.

Management wants to know if there is a problem, where is the problem, why is there a problem, and how can you make them look good by fixing the problem.

From the limited description we have, it sounds like there is a problem, it is confined to 20-30 stores, and ___________ is why they have so many more tickets than expected.

ETA - And, here is what we can do to reduce the number of tickets (if appropriate).

 

[Kal El]

Since you already have the average for the twenty stores, remove the outlying store and average the remaining number of tickets over the 19 other stores.

 

[McGarnicle]

Since you already have the average for the twenty stores, remove the outlying store and average the remaining number of tickets over the 19 other stores.

That's how we would handle it. Then a short blurb explaining that this one store has a million tickets and the rest are okay. Sometimes numbers don't tell the whole story.

 

[Ilov80s]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

I think that is what he was trying to get it at with his "true average" concept.

 

[Notorious T.R.E.]

Hopefully you're really good at other stuff. Friday camel clutches or something.

 

[Short Corner]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

 

[The Commish]

I'm not trying to hide anything. It's actually a quite detailed report I present each month for upper management. The problem that occurs is that UM tends to focus on numbers that seem out of place.

We have over 450 stores nationwide. Some of these stores are huge while others are very small. If a small store has a lot of tickets, that's an issue. But if a large store has the same amount of tickets, it's not a problem.

Of the 450 some stores, there are usually about 20 to 30 stores that somehow mess up the stats. If you remove those 20 stores, our average falls right into the threshold that's allowed. But usually, one of the bigger stores (like a Manhattan or Los Angeles store that is extremely high volume) will produce large amounts of tickets. These throw off the average of the other 420 stores.

It's nothing major, and I have nothing to do with the actual tickets. I'm merely tracking data and compiling it into reports. But when I'm in the meetings and numbers seem to be off because a couple of stores are inflating the average, I tend to think there could be a better way.

Also, this place has kind of gone down hill a bit. I started off by saying I suck at math. And then people post in here "You suck at math!" This is what qualifies as good FFA posts nowadays? I know how old people feel when they look at young kids now.

If they really are that different, you should probably consider breaking them into "high volume" vs "low volume" and have a few slightly different stats with a tolerance level for each.

ETA: Issues like this happen when you're trying to dumb things down for management who wants to see a number and not have to think about it.

 

[TheIronSheik]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

I think somehow it got lost in everyone saying I'm terrible at math, but I actually said the weighted average in my original post. I just don't understand how they work. I've never done one before.

 

[McGarnicle]

The stores with lots of tickets are heavier than the ones without the tickets. If you can switch to paperless tickets, the stores will weigh the same.

 

[Drifter]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

I think somehow it got lost in everyone saying I'm terrible at math, but I actually said the weighted average in my original post. I just don't understand how they work. I've never done one before.

So a weighted average needs something to apply relative values to. That's where the revenue figures come in. Even if you don't want to do tiers or do a tickets per revenue metric, to do a weighted average you would still need a way to say that this stores value is 3x this other store's value in determining the weighted average.

 

[TheIronSheik]

I'm not trying to hide anything. It's actually a quite detailed report I present each month for upper management. The problem that occurs is that UM tends to focus on numbers that seem out of place.

We have over 450 stores nationwide. Some of these stores are huge while others are very small. If a small store has a lot of tickets, that's an issue. But if a large store has the same amount of tickets, it's not a problem.

Of the 450 some stores, there are usually about 20 to 30 stores that somehow mess up the stats. If you remove those 20 stores, our average falls right into the threshold that's allowed. But usually, one of the bigger stores (like a Manhattan or Los Angeles store that is extremely high volume) will produce large amounts of tickets. These throw off the average of the other 420 stores.

It's nothing major, and I have nothing to do with the actual tickets. I'm merely tracking data and compiling it into reports. But when I'm in the meetings and numbers seem to be off because a couple of stores are inflating the average, I tend to think there could be a better way.

Also, this place has kind of gone down hill a bit. I started off by saying I suck at math. And then people post in here "You suck at math!" This is what qualifies as good FFA posts nowadays? I know how old people feel when they look at young kids now.

If they really are that different, you should probably consider breaking them into "high volume" vs "low volume" and have a few slightly different stats with a tolerance level for each.

ETA: Issues like this happen when you're trying to dumb things down for management who wants to see a number and not have to think about it.

This pretty much sums it all up. Again, I know people are ragging on my math skills, and that's cool. I know I'm not good at the math. But I think they're missing the point here. It's not me that's the dumb one. The problem really comes down to I'm not smart enough to figure out how dumb people want to see their data.

 

[Drifter]

If revenue data is too sensitive could someone tell you the number of transactions a day? Could HR tell you the number of employees per store?

I guarantee you someone in your corporate office has all of those figures and looks at them regularly.

 

[Galileo]

So, it really isn't an average you need at all. Rather trying to give weight to particular stores in an average, it sounds to me like you would be better off calculating a frequency or rate of occurrence... # of tickets per transaction, # of tickets per some convenient revenue amount, # of tickets per whatever you deem to be a relevant characteristic that measures a store's volume of business...

 

[TheIronSheik]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

This is secretive siloing for the purpose of secrecy and nothing else. I guarantee you that such a list already exists.

No wonder the stock is down 25% over the last year (I'm guessing who it is)

Fairly sure we're not a publicly traded company.

 

[Drifter]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

This is secretive siloing for the purpose of secrecy and nothing else. I guarantee you that such a list already exists.

No wonder the stock is down 25% over the last year (I'm guessing who it is)

Fairly sure we're not a publicly traded company.

OK - I just looked at Philly based retail operations with 400-500 stores. Urban Outfitters fit the bill.

 

[TheIronSheik]

The reason I had said weighted average was because a friend of mine who is an Excel guy like myself were talking about reports. I had mentioned my problem and he likened it to this:

A company has 10 employees. The CEO and 9 people under him. Those 9 people make $15,000 a year. But the CEO makes 4 million a year. Besides being a crappy company to work for, it would be disingenuous to say that the average salary for a person working there is over $400k a year.

 

[Ilov80s]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

I think somehow it got lost in everyone saying I'm terrible at math, but I actually said the weighted average in my original post. I just don't understand how they work. I've never done one before.

So say an administrator is trying to evaluate a professor's teaching. Let's say she has 3 classes of Biology. Class A has 200 kids and the average grade is 70. Class B has 350 kids with an average of 60 and Class C has 100 kids with an average of 73. If you just added those numbers up and divide by 3, you get a mean score of 67.6% However, some of those groups represent more kids than other groups, they aren't equal in the amount of data that they include. So here is how you get a weighted average:

(200 * 70) + (350*60) + (100*73) / 200 + 350 + 100 which gives you a weighted average of 65%. That is a better representation of what the average student in one of the teacher's classes will score because it has accounted for the differences in size between the groups.

 

[TheIronSheik]

Yeah, a weighted average with the weight being sales volume seems to be the best solution for you, Sheik.

I think the people with access to the annual revenues are feeding you a line of bull about not having time to do it. A report that lists volume by store should take about a minute to make.

This is secretive siloing for the purpose of secrecy and nothing else. I guarantee you that such a list already exists.

No wonder the stock is down 25% over the last year (I'm guessing who it is)

Fairly sure we're not a publicly traded company.

OK - I just looked at Philly based retail operations with 400-500 stores. Urban Outfitters fit the bill.

I'm a former evil Big Oil guy. Not sure I fit the Urban Outfitters standard.

 

[The Commish]

I'm not trying to hide anything. It's actually a quite detailed report I present each month for upper management. The problem that occurs is that UM tends to focus on numbers that seem out of place.

We have over 450 stores nationwide. Some of these stores are huge while others are very small. If a small store has a lot of tickets, that's an issue. But if a large store has the same amount of tickets, it's not a problem.

Of the 450 some stores, there are usually about 20 to 30 stores that somehow mess up the stats. If you remove those 20 stores, our average falls right into the threshold that's allowed. But usually, one of the bigger stores (like a Manhattan or Los Angeles store that is extremely high volume) will produce large amounts of tickets. These throw off the average of the other 420 stores.

It's nothing major, and I have nothing to do with the actual tickets. I'm merely tracking data and compiling it into reports. But when I'm in the meetings and numbers seem to be off because a couple of stores are inflating the average, I tend to think there could be a better way.

Also, this place has kind of gone down hill a bit. I started off by saying I suck at math. And then people post in here "You suck at math!" This is what qualifies as good FFA posts nowadays? I know how old people feel when they look at young kids now.

If they really are that different, you should probably consider breaking them into "high volume" vs "low volume" and have a few slightly different stats with a tolerance level for each.

ETA: Issues like this happen when you're trying to dumb things down for management who wants to see a number and not have to think about it.

This pretty much sums it all up. Again, I know people are ragging on my math skills, and that's cool. I know I'm not good at the math. But I think they're missing the point here. It's not me that's the dumb one. The problem really comes down to I'm not smart enough to figure out how dumb people want to see their data.

You can't present it in a single number. Best you can do is be clear in description of several numbers whether they like it or not. You can either give them accurate information with more detail or misleading numbers for the sake of laziness on their part.

 

[Drifter]

The reason I had said weighted average was because a friend of mine who is an Excel guy like myself were talking about reports. I had mentioned my problem and he likened it to this:

A company has 10 employees. The CEO and 9 people under him. Those 9 people make $15,000 a year. But the CEO makes 4 million a year. Besides being a crappy company to work for, it would be disingenuous to say that the average salary for a person working there is over $400k a year.

That's where median would be a more telling statistic. Tiers would also work - Executives average X, Associates average Y

 

[bryhamm]

Weighting it on sales volume is not what you would want. That would make the problem worse as the store(s) with the high volume would pull the weighted average up, even if their rate was down.

Those who said to convert it to a rate (tickets per dollar of sales) are correct. This would show the best average.

 

[TheIronSheik]

I'm not trying to hide anything. It's actually a quite detailed report I present each month for upper management. The problem that occurs is that UM tends to focus on numbers that seem out of place.

We have over 450 stores nationwide. Some of these stores are huge while others are very small. If a small store has a lot of tickets, that's an issue. But if a large store has the same amount of tickets, it's not a problem.

Of the 450 some stores, there are usually about 20 to 30 stores that somehow mess up the stats. If you remove those 20 stores, our average falls right into the threshold that's allowed. But usually, one of the bigger stores (like a Manhattan or Los Angeles store that is extremely high volume) will produce large amounts of tickets. These throw off the average of the other 420 stores.

It's nothing major, and I have nothing to do with the actual tickets. I'm merely tracking data and compiling it into reports. But when I'm in the meetings and numbers seem to be off because a couple of stores are inflating the average, I tend to think there could be a better way.

Also, this place has kind of gone down hill a bit. I started off by saying I suck at math. And then people post in here "You suck at math!" This is what qualifies as good FFA posts nowadays? I know how old people feel when they look at young kids now.

If they really are that different, you should probably consider breaking them into "high volume" vs "low volume" and have a few slightly different stats with a tolerance level for each.

ETA: Issues like this happen when you're trying to dumb things down for management who wants to see a number and not have to think about it.

This pretty much sums it all up. Again, I know people are ragging on my math skills, and that's cool. I know I'm not good at the math. But I think they're missing the point here. It's not me that's the dumb one. The problem really comes down to I'm not smart enough to figure out how dumb people want to see their data.

You can't present it in a single number. Best you can do is be clear in description of several numbers whether they like it or not. You can either give them accurate information with more detail or misleading numbers for the sake of laziness on their part.

This is kind of my battle I face now. It's both hilarious and infuriating. One week, I'll present an awesome report and someone will say, "Is there a way we can show X on this?" Then the next week, that same person will say, "Is there a way we can show this report without X?"

Every week I'm adding and removing things that were suggested weeks earlier. I know my report was flawless, but in the past year, it's become muddled with useless information because people were taught to always make a suggestion in a meeting if you want to look good.

I stopped fighting the good cause because it was a losing battle. Now I just tell them "Sure. I can do that", as I watch my masterpiece get crayoned and scissored by amateurs.

 

[bryhamm]

Take his original "example":

An example would be (simplified):

There are 20 stores. 19 of those stores have 2 tickets in May. 1 of those stores has 1000 tickets in May. The average tickets each store had in May is almost 51 tickets. But obviously, this is not true. One store is affecting the average.

Let's say that the stores with 2 tickets had sales of 1,000 each and the store with 1,000 tickets had sales of 1,000,000. The weighted average would be 981 on sales. This is worse than the 52 he was originally stating. But, the big store has a ticket/sales ratio of 0.001 where as the other stores are at 0.002. The overall ratio would be 0.001

 

[The Commish]

I still think it's a bad idea to lump all the stores together simply because they are stores. Given what we know, even within the company they can be very different in their demographics.

 

[Ilov80s]

Take his original "example":

An example would be (simplified):

There are 20 stores. 19 of those stores have 2 tickets in May. 1 of those stores has 1000 tickets in May. The average tickets each store had in May is almost 51 tickets. But obviously, this is not true. One store is affecting the average.

Let's say that the stores with 2 tickets had sales of 1,000 each and the store with 1,000 tickets had sales of 1,000,000. The weighted average would be 981 on sales. This is worse than the 52 he was originally stating. But, the big store has a ticket/sales ratio of 0.001 where as the other stores are at 0.002. The overall ratio would be 0.001

Yeah you are right. I was thinking of weighting with a decimal, but that's just the ratio anyway and more work than it needs to be.

 

[TheIronSheik]

Also, just to clear up:

Tickets = Calls from the store to any type of help desk (Network, Facilities, Telecom, Computer, etc.)

The idea is to spot which stores are having issues by the amount of calls they are placing. The problem is that stores in Montana are being measured against stores in NYC. And stores that have new employees are being measured against staff that knows their way around certain issues. And stores that have brand new equipment are being measured against stores using computers with Y2K compliant stickers on them. Or stores on slower networks, like Alaska and Puerto Rico are being measured against stores in NYC or Chicago.

There are so many variables that the tier system really can't handle it. What I have suggested is breaking out each store into about 10 categories based on things I've mentioned above. Then rank each store into a tier for a single category. Then average those 10 categories out to get a total tier number. Then basing the average off of that weighting.

It's the weighting part of that scenario that still troubles me, though. I have a plan to try and do this, but it's more of a free time project. And I'd rather it didn't cut into my FFA time. So I'll see how it goes.

 

[Ilov80s]

Also, just to clear up:

Tickets = Calls from the store to any type of help desk (Network, Facilities, Telecom, Computer, etc.)

The idea is to spot which stores are having issues by the amount of calls they are placing. The problem is that stores in Montana are being measured against stores in NYC. And stores that have new employees are being measured against staff that knows their way around certain issues. And stores that have brand new equipment are being measured against stores using computers with Y2K compliant stickers on them. Or stores on slower networks, like Alaska and Puerto Rico are being measured against stores in NYC or Chicago.

There are so many variables that the tier system really can't handle it. What I have suggested is breaking out each store into about 10 categories based on things I've mentioned above. Then rank each store into a tier for a single category. Then average those 10 categories out to get a total tier number. Then basing the average off of that weighting.

It's the weighting part of that scenario that still troubles me, though. I have a plan to try and do this, but it's more of a free time project. And I'd rather it didn't cut into my FFA time. So I'll see how it goes.

It will get complicated with all those variables to factor in. Just know that it won't be perfect and you will have to make lots of choices.

 

[Drifter]

What about classifying the tickets themselves to make callouts easier. "High ticket counts in this ticket category are indicative of older equipment" or "High volume" or "Store startups"

 

[TheIronSheik]

What about classifying the tickets themselves to make callouts easier. "High ticket counts in this ticket category are indicative of older equipment" or "High volume" or "Store startups"

That's not always the case, though. I see what you're saying. It just seems like every way is going to lead to something not working ideally.

 

[rodg12]

Take his original "example":

An example would be (simplified):

There are 20 stores. 19 of those stores have 2 tickets in May. 1 of those stores has 1000 tickets in May. The average tickets each store had in May is almost 51 tickets. But obviously, this is not true. One store is affecting the average.

Let's say that the stores with 2 tickets had sales of 1,000 each and the store with 1,000 tickets had sales of 1,000,000. The weighted average would be 981 on sales. This is worse than the 52 he was originally stating. But, the big store has a ticket/sales ratio of 0.001 where as the other stores are at 0.002. The overall ratio would be 0.001

Yeah you are right. I was thinking of weighting with a decimal, but that's just the ratio anyway and more work than it needs to be.

 

[fred_1_15301]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

Say what now?

 

[Patriotsfatboy1]

Also, just to clear up:

Tickets = Calls from the store to any type of help desk (Network, Facilities, Telecom, Computer, etc.)

The idea is to spot which stores are having issues by the amount of calls they are placing. The problem is that stores in Montana are being measured against stores in NYC. And stores that have new employees are being measured against staff that knows their way around certain issues. And stores that have brand new equipment are being measured against stores using computers with Y2K compliant stickers on them. Or stores on slower networks, like Alaska and Puerto Rico are being measured against stores in NYC or Chicago.

There are so many variables that the tier system really can't handle it. What I have suggested is breaking out each store into about 10 categories based on things I've mentioned above. Then rank each store into a tier for a single category. Then average those 10 categories out to get a total tier number. Then basing the average off of that weighting.

It's the weighting part of that scenario that still troubles me, though. I have a plan to try and do this, but it's more of a free time project. And I'd rather it didn't cut into my FFA time. So I'll see how it goes.

For IT tickets, use a Pareto analysis and group by ticket type. In ITIL terms, it could be by category in your Service Catalog. Work on the top 3 issues.

Then remeasure and repeat. Focus on no more than 3 things.

 

[shadyridr]

I'm not trying to hide anything. It's actually a quite detailed report I present each month for upper management. The problem that occurs is that UM tends to focus on numbers that seem out of place.

We have over 450 stores nationwide. Some of these stores are huge while others are very small. If a small store has a lot of tickets, that's an issue. But if a large store has the same amount of tickets, it's not a problem.

Of the 450 some stores, there are usually about 20 to 30 stores that somehow mess up the stats. If you remove those 20 stores, our average falls right into the threshold that's allowed. But usually, one of the bigger stores (like a Manhattan or Los Angeles store that is extremely high volume) will produce large amounts of tickets. These throw off the average of the other 420 stores.

It's nothing major, and I have nothing to do with the actual tickets. I'm merely tracking data and compiling it into reports. But when I'm in the meetings and numbers seem to be off because a couple of stores are inflating the average, I tend to think there could be a better way.

Also, this place has kind of gone down hill a bit. I started off by saying I suck at math. And then people post in here "You suck at math!" This is what qualifies as good FFA posts nowadays? I know how old people feel when they look at young kids now.

tickets/employee or something like that. You would know better than us what the divisor should be.For example, im in disability insurance. We have inforce cases with over 150k lives and some with 500 lives. If we wanted to know the incidence of the block we wouldnt take 500 claims for the large case and 2 claims for the small case and average them out. That would mean our incidence is 251 claims per case. No. Instead we would divide total claims per 1000 lives. So the incidence for the large case would be 500/150 and the incidence for the small case would be 2/.5. So the incidence/1000 lives would be 3.33/1000 + 4/1000 with an average incidence of appx 3.67 claims / 1000 lives.

I think thats what you need. Hope that helps.

 

[acarey50]

Also, just to clear up:

Tickets = Calls from the store to any type of help desk (Network, Facilities, Telecom, Computer, etc.)

The idea is to spot which stores are having issues by the amount of calls they are placing. The problem is that stores in Montana are being measured against stores in NYC. And stores that have new employees are being measured against staff that knows their way around certain issues. And stores that have brand new equipment are being measured against stores using computers with Y2K compliant stickers on them. Or stores on slower networks, like Alaska and Puerto Rico are being measured against stores in NYC or Chicago.

There are so many variables that the tier system really can't handle it. What I have suggested is breaking out each store into about 10 categories based on things I've mentioned above. Then rank each store into a tier for a single category. Then average those 10 categories out to get a total tier number. Then basing the average off of that weighting.

It's the weighting part of that scenario that still troubles me, though. I have a plan to try and do this, but it's more of a free time project. And I'd rather it didn't cut into my FFA time. So I'll see how it goes.

Do you have a way to identify stores that have old vs. new equipment, new vs. experienced employees, network speed by store, etc.

It seems clear that there will not be a meaningful way to boil this down to one number, but, if that is what management wants, then give them what they want and also provide the meaningful information:

So, first number is the basic mean that it seems like they are asking for - Total Tickets / Total Stores

Then, if you have the data to categorize the stores, who made the call and the tpe of trouble ticket it is, you can do the actual analysis and provide some breakdown such as volume of tickets by category, average tickets by new employee vs. experienced (maybe group employees into categories such as less than 1 year on job, 1-3 years on job and 3+ years, you would know better than us what is a decent breakdown)

Do the same thing for the other possible slices - location of store, size of store, type of equipment, category of ticket.

You could put all of these views in an appendix of the report and do the analysis of those breakdowns to identify any glaring areas of concern - perhaps something jumps out like stores with older equipment have 4x the calls as stores with newer equipment. Then do some calculations - maybe you have data that indicates the cost per call - let's say the typical ticket costs you guys $100 in time for the IT help desk plus the time for the employee calling it in - calculate how much calls about older equipment are costing compared to newer equipment, and compare that to the cost of upgrading the equipment. For simplicity, let's say the difference is 5 calls per month and the equipment upgrade costs $2000. If it makes sense, make recomendation that equipment be upgraded at x cost ($2000), which would reduce support cost by x amount ($500/month), leading to break even in x months (4 months)and $Y additional savings ($6000) per year.

At least that is th sort of stuff I would do for my management..... if the data is available, which is usually the most difficult part, at least for the company I work for.

 

[Norman Paperman]

Also, just to clear up:

Tickets = Calls from the store to any type of help desk (Network, Facilities, Telecom, Computer, etc.)

The idea is to spot which stores are having issues by the amount of calls they are placing. The problem is that stores in Montana are being measured against stores in NYC. And stores that have new employees are being measured against staff that knows their way around certain issues. And stores that have brand new equipment are being measured against stores using computers with Y2K compliant stickers on them. Or stores on slower networks, like Alaska and Puerto Rico are being measured against stores in NYC or Chicago.

There are so many variables that the tier system really can't handle it. What I have suggested is breaking out each store into about 10 categories based on things I've mentioned above. Then rank each store into a tier for a single category. Then average those 10 categories out to get a total tier number. Then basing the average off of that weighting.

It's the weighting part of that scenario that still troubles me, though. I have a plan to try and do this, but it's more of a free time project. And I'd rather it didn't cut into my FFA time. So I'll see how it goes.

Just my :twocents:

But you should have a benchmark for each store - based on past experience - measure each store against the benchmark, and report on that, maybe as # of stores that met benchmark

The only reason reports should exist is to easily identify problems. If I am a manager here, I want to quickly see if/when a store or a region is having problems. Lets say a store is expected to have 5 tickets per month, and it hits 10 - I want to know why. It might be as simple as new employees or equipment getting older, but then I have information I can act on - or not.

When I was running a couple of departments we had a monthly variance report that hit a number of things based on budgets - my direct reports would have to explain any variance over 15% from budget to me, and I in turn would create a report to more senior managers. The higher up the food chain, the less granular the report. So my direct reports might have to explain why food/entertainment expenses were high, while I would only report if overall expenses exceeded budget.

So, I suppose just a long-winded way of saying don't create a report that does not help identify where things are going wrong (or right). If your overall tickets are in line with what you expect, but you have 3 stores that are terrible - someone needs to know that.

 

[tjnc09]

Maybe you want to use a box plot (box and whisker plot)

 

[Grace Under Pressure]

Just hire someone underneath you to build these reports, and take the credit for it.

Are you the Director of Operations? Build out a whole reporting suite, hire a team of analysts and ride that for a decade.

 

[Corporation]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

Say what now?

He probably meant that Sheik's word choice of "almost" is ambiguous, although that's probably not right either. Best word choice would be approximate, but the point is he was calling you out for a kinda dooshy reply to Sheik.

 

[fred_1_15301]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

Say what now?

He probably meant that Sheik's word choice of "almost" is ambiguous, although that's probably not right either. Best word choice would be approximate, but the point is he was calling you out for a kinda dooshy reply to Sheik.

51.9 is closer to 52 than it is to 51. Nothing ambiguous about that all (regardless of my dooshiness).

 

[32 Counter Pass]

http://www.purplemath.com/modules/meanmode.htm

HTH

 

[TheIronSheik]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

Say what now?

He probably meant that Sheik's word choice of "almost" is ambiguous, although that's probably not right either. Best word choice would be approximate, but the point is he was calling you out for a kinda dooshy reply to Sheik.

51.9 is closer to 52 than it is to 51. Nothing ambiguous about that all (regardless of my dooshiness).

I think the point was that the number had nothing to do with the question. It's on par with pointing out some one's grammar mistake. Like how you left out a comma after "Wow" in your first post and used way too many periods in your ellipsis.

 

[fred_1_15301]

By the way, the average was actually 51.9. Wow you really are bad at math........

That's almost 51.

In math, we tend to round up if the first digit after the decimal point is "5" and above.

It appears that "we" don't realize that the term "average" is ambiguous.

Say what now?

He probably meant that Sheik's word choice of "almost" is ambiguous, although that's probably not right either. Best word choice would be approximate, but the point is he was calling you out for a kinda dooshy reply to Sheik.

51.9 is closer to 52 than it is to 51. Nothing ambiguous about that all (regardless of my dooshiness).

I think the point was that the number had nothing to do with the question. It's on par with pointing out some one's grammar mistake. Like how you left out a comma after "Wow" in your first post and used way too many periods in your ellipsis.

I disagree with your analogy but I'll drop it because I'm a Sheik fan.

 

[Phil Elliott]

Just report that 20% of the stores have 80% of the tickets and be done.