Okay, so you’ve had enough of wasting everyone’s time with building up labor targets that your Operators don’t agree with, that your Finance Director doesn’t budget to, and that your GM won’t even bother to look at. Time to take a hard look at whether or not you have enough reality built in so that that your results are actually usable. Or, put another way, are your results actionable in terms of “can we actually scheduled like this?” Or, "could we have actually operated like that?” If the answer is no, it could be time to go back to the drawing board.
So, how do you do it? Like any other analytical problem, I believe the right approach is to take it step by step and to not cut corners. At the risk of losing all of the Strategic Analysts who read this (and most of the Financial Analysts as well, these folks usually like their labor served up a little more abstractly, like, say, in terms of FTEs or productivity calculations), the first step is to actually understand the work being performed by employees working in the Job Class you are modeling. And this means you need to get a task list.
No, not a job description, I said a task list. Job descriptions are often too sanitized and too vague for analytical purposes and they do not have a listing of all the tasks associated with the performance of the work. Besides, in some cases if they actually put in a job description what the employee was expected to do while working then who would apply for the job in the first place? Seriously, take a look at what your Hotel Housekeeping staff has to deal with on a daily basis and then ask yourself if this is a gig you would be interested in. Here is the first thing Labor Analysts need to understand: there are people at your property who actually work for a living. You’re not one of them.
Building up labor standards without first understanding the actual work being performed is like dreaming of building up labor standards only to wake up finding yourself chewing on the corner of your pillow. So, while you are taking in knowledge of the actual work being performed you might as well understand what physical boundaries and timescales are in place which control (and sometimes limit), employees in their performance and delivery of the work. Put simply, here is the first step toward building actionable labor standards:
Identify all tasks associated with the Job Class and understand the physical plant that governs guest and employee interactions, including hours of operation.
A little reality never hurt anyone in analysis so step one should sound easy enough. Stay tuned for step two.
Showing posts with label Methodology. Show all posts
Showing posts with label Methodology. Show all posts
Tuesday, March 6, 2012
Wednesday, June 29, 2011
Using Table Limits to set Blackjack Utilization Targets
One of the questions I get most often from casino analysts is, what is the optimum table utilization (gaming spots being played), on Blackjack in order to achieve maximum profitability and labor efficiency? Note that the two goals can be somewhat opposed to each other, lower targeted utilization yields increased game pace and therefore has the potential for increased hold percentage (higher profit), higher utilization yields greater labor efficiency because there are potentially fewer tables open to satisfy guest demand (better labor margins). So what target utilization number hits the sweet spot?
While one can perform detailed analysis on this question by modeling hands per hour, shuffle time and frequency, number of decks, game rules, as well as other factors to determine the optimum spot utilization for a particular table operation, I usually start with a simpler approach – I use the actual hourly table limits in place by individual tables opened in order to determine how many spots should have been utilized.
Here are the targets I typically use as guidelines to determine optimum Blackjack utilization, note that these work no matter how many total spots are on the layout (five, six, or seven):
$5 dollar limit – 5 spots covered,
$10 dollar limit – 4 spots covered,
$15 dollar limit – 3 spots covered,
$25 dollar limit – 3 spots covered,
Anything over $25 dollars – 2 spots covered.
Obviously one would need to have hourly limit tracking in order to complete this type of analysis, if your Table Games Department has automated table tracking then this statistic should be readily available. If your department is still taking hourly manual counts then adjust your tracking input form to allow for the recording of table limits as well as spot counts. Keep in mind that the strategies discussed here are guidelines, a starting point for more detailed analysis. You may find that the optimum utilization during late night when business is winding down may be slightly lower.
Many well-respected industry table games experts have expanded on the impact of running too high a target utilization on Blackjack and I encourage you to investigate their analysis and recommendations while developing your own property’s strategy. In the meantime, try these simple guidelines as a starting point; you may be surprised at how close they come to optimum.
While one can perform detailed analysis on this question by modeling hands per hour, shuffle time and frequency, number of decks, game rules, as well as other factors to determine the optimum spot utilization for a particular table operation, I usually start with a simpler approach – I use the actual hourly table limits in place by individual tables opened in order to determine how many spots should have been utilized.
Here are the targets I typically use as guidelines to determine optimum Blackjack utilization, note that these work no matter how many total spots are on the layout (five, six, or seven):
$5 dollar limit – 5 spots covered,
$10 dollar limit – 4 spots covered,
$15 dollar limit – 3 spots covered,
$25 dollar limit – 3 spots covered,
Anything over $25 dollars – 2 spots covered.
Obviously one would need to have hourly limit tracking in order to complete this type of analysis, if your Table Games Department has automated table tracking then this statistic should be readily available. If your department is still taking hourly manual counts then adjust your tracking input form to allow for the recording of table limits as well as spot counts. Keep in mind that the strategies discussed here are guidelines, a starting point for more detailed analysis. You may find that the optimum utilization during late night when business is winding down may be slightly lower.
Many well-respected industry table games experts have expanded on the impact of running too high a target utilization on Blackjack and I encourage you to investigate their analysis and recommendations while developing your own property’s strategy. In the meantime, try these simple guidelines as a starting point; you may be surprised at how close they come to optimum.
Thursday, October 22, 2009
The Problem with (Actual) Volume Data
One of the common mistakes I encounter while working with Planning and Analysis Departments is with their frequent use (and abuse), of actual, system-reported volume data. Yes, actual volume is required to fuel trend forecasting models and to create labor standard reporting, but analysts should take care not to perpetuate negative business performance by using actual data from time periods when guest service fell short or when targeted revenues were not realized. In many cases I observe analysts indiscriminately feeding actual data to their models from time periods of good business performance as well as bad, the result being inaccurate volume forecasting and reduced operational buy-in of their labor standards.
Take for example the case of a large Hotel Front Desk Operation using actual hourly check-in volumes to drive Guest Service Agent staffing targets – and then using actual volume counts reported during an hour when Agent understaffing resulted in line queue timings which significantly exceeded established service targets. Without adjustment the actual data reported during this hour would potentially drive labor standards which would perpetuate the same inadequate staffing levels that yielded the poor guest service performance in the first place! To make matters worse, if understaffing at a Hotel Front Desk resulted in long wait times in one hour then the opportunity existed to “push” a portion of the actual reported volume to the next hour. A labor model driven by the raw volume occurring each hour would then not only erroneously report the staffing levels as being correct in the first hour, it would also incorrectly report labor requirements during the second hour as well.
The answer, of course, is to refine the information being fed to your labor and forecast models by first analyzing the actual data reported by systems against multiple guest service and financial feedback channels (via direct observation, Supervisor reporting, financial reporting, and available guest feedback mechanisms). Make the appropriate adjustments to the actual volume being fed to your models during time periods when understaffing had a direct negative influence on the amount of reported volumes, I would also suggest that you re-label your volume indicator’s name to include the term ‘Adjusted’ so as not to confuse your report users.
Refining actual data prior to feeding forecasting and labor standard models affords an analyst with the opportunity to include Operations, Finance, and Marketing in the process. Not only will your models potentially yield more accuracy, you may also see the buy-in of your results go through the roof as well!
Take for example the case of a large Hotel Front Desk Operation using actual hourly check-in volumes to drive Guest Service Agent staffing targets – and then using actual volume counts reported during an hour when Agent understaffing resulted in line queue timings which significantly exceeded established service targets. Without adjustment the actual data reported during this hour would potentially drive labor standards which would perpetuate the same inadequate staffing levels that yielded the poor guest service performance in the first place! To make matters worse, if understaffing at a Hotel Front Desk resulted in long wait times in one hour then the opportunity existed to “push” a portion of the actual reported volume to the next hour. A labor model driven by the raw volume occurring each hour would then not only erroneously report the staffing levels as being correct in the first hour, it would also incorrectly report labor requirements during the second hour as well.
The answer, of course, is to refine the information being fed to your labor and forecast models by first analyzing the actual data reported by systems against multiple guest service and financial feedback channels (via direct observation, Supervisor reporting, financial reporting, and available guest feedback mechanisms). Make the appropriate adjustments to the actual volume being fed to your models during time periods when understaffing had a direct negative influence on the amount of reported volumes, I would also suggest that you re-label your volume indicator’s name to include the term ‘Adjusted’ so as not to confuse your report users.
Refining actual data prior to feeding forecasting and labor standard models affords an analyst with the opportunity to include Operations, Finance, and Marketing in the process. Not only will your models potentially yield more accuracy, you may also see the buy-in of your results go through the roof as well!
Tuesday, September 22, 2009
All About Overtime, Part 1 - Basic Analysis
Overtime is probably the most often reviewed aspect of labor expense, partly because the overtime line tends to stick out on most P&Ls and partly because Property Leaders tend to jump on overtime as a way to engage in a larger discussion on operational efficiency. To aid in the understanding of overtime usage many Casinos and Hotels establish simplistic reporting thresholds for Job Class overtime hours as a percent of total hours worked (a 3% weekly threshold is common), exceed the established target and Operations will usually feel the heat. The questions usually go something like “was the overtime justified?” Or, “how can overtime expenses be reduced?”
In certain cases there are benefits to the operational use of overtime: overtime provides flexibility to match existing staff to variable guest demand and overtime coverage can be available on short notice, the staff working overtime requires no additional training to deploy and there are no additional wardrobe expenses associated (an especially important point with Job Classes like Casino Cocktail Servers). Finally, overtime can be seen as a morale-booster by some employees looking to supplement their income.
The problems associated with excessive overtime are also easily understood. Not only is overtime expensive, if prolonged it can reduce productivity, create employee morale issues and increase employee turnover. Perhaps most problematic is that some employees, after working prolonged periods of overtime will come to depend on the additional income and will be financially impacted if the opportunity for overtime is removed.
Overtime Work Rules
Understanding your local work rules regarding overtime is essential to producing an overtime analysis as a handful of states currently have work rules in place which supersedes Federal Guidelines. At the time of this writing California, Nevada, and Alaska all have 8 hour daily overtime rules while Colorado has a daily 12 hour overtime rule in place (the Federal rule is based on weekly hours worked). Some states have “rest” period rules in place which dictate the amount of employee time off between consecutive shifts, in Nevada, for example, an employee is eligible for overtime if their base pay is one and a half times the state-established minimum wage and if there is less than 16 hours between the end time and the start time of their work shifts on consecutive days. Note – Nevada employees can be scheduled for 10 hours per day for 4 calendar days per workweek without receiving overtime if mutually agreed to in advance.
Causes of Overtime
Excessive Overtime is almost always caused by one of three conditions:
1. The underlying structure of the Department or Job Class,
2. An incorrect understanding of the Department or Job Class operational conditions,
3. The scheduled availability of Department or Job Class staff.
I find it easier to understand the validity of overtime if I take all of three conditions into account in two different time contexts, first, the actual time of day in which the overtime occurred, and second, the actual duration of each overtime occurrence. Once I have completed my analysis using both time contexts a more accurate assessment of overtime validity can be reported to Senior Leadership.
Analyzing Overtime
Here are some strategies which may help you in preparing an analysis on the validity of overtime usage:
Structured Overtime tends to produce occurrences in durations of 30 minutes or less and is usually a byproduct of either clock management issues or gaps between job class shifts. Review employee historical pay records to identify patterns of potential clock abuse (and associated Supervisory issues), as well as the job class labor standards for shift end times and start times in order to reduce this type of overtime. Structured Overtime should be eliminated as quickly as possible and its trends should not be forecasted in future periods.
Operational Overtime tends to produce occurrences of less than 4 hours and is usually a by-product of some operational need to retain staff. Review the time frame in which these incidents occurred to determine if the overtime was a result of an overall Area Event or if it was due to a specific Property function, note the associated demand driver and review the Employee Schedules in use to determine if a re-deployment of staff can occur when similar circumstances are forecasted. Also note that in some cases Operational Overtime may be caused by intermittent family medical leave being taken during a scheduled shift.
Scheduled Overtime tends to produce occurrences of more than 4 hours and is usually a result of either a general lack of available staff or because a late call-off of a previously scheduled employee. Not enough available staff usually results in an overtime occurrence in the form of an additional day being scheduled, in the case of a call off; the overtime may be a result of an employee working a double shift. In either case a review of the Job Class standards for the same time period against actual staff available will give you a better understanding of whether or not the overtime was justified.
Understanding Incremental Overtime Expense
To complete an overtime analysis it will be necessary to understand the incremental cost of overtime over the use of straight time. For example, a 35% benefit load with 20 annual days of PTO and a time-and-a-half overtime rate would result in a 12% incremental overtime expense of overtime over the straight time rate. The use of Part Time Employees would reduce the coverage expense even more; however, an incremental analysis would need to be performed on the training and wardrobe costs associated with the use of greater headcount to deliver the same number of FTEs.
Next time out we will discuss strategies for effectively managing Overtime expense.
In certain cases there are benefits to the operational use of overtime: overtime provides flexibility to match existing staff to variable guest demand and overtime coverage can be available on short notice, the staff working overtime requires no additional training to deploy and there are no additional wardrobe expenses associated (an especially important point with Job Classes like Casino Cocktail Servers). Finally, overtime can be seen as a morale-booster by some employees looking to supplement their income.
The problems associated with excessive overtime are also easily understood. Not only is overtime expensive, if prolonged it can reduce productivity, create employee morale issues and increase employee turnover. Perhaps most problematic is that some employees, after working prolonged periods of overtime will come to depend on the additional income and will be financially impacted if the opportunity for overtime is removed.
Overtime Work Rules
Understanding your local work rules regarding overtime is essential to producing an overtime analysis as a handful of states currently have work rules in place which supersedes Federal Guidelines. At the time of this writing California, Nevada, and Alaska all have 8 hour daily overtime rules while Colorado has a daily 12 hour overtime rule in place (the Federal rule is based on weekly hours worked). Some states have “rest” period rules in place which dictate the amount of employee time off between consecutive shifts, in Nevada, for example, an employee is eligible for overtime if their base pay is one and a half times the state-established minimum wage and if there is less than 16 hours between the end time and the start time of their work shifts on consecutive days. Note – Nevada employees can be scheduled for 10 hours per day for 4 calendar days per workweek without receiving overtime if mutually agreed to in advance.
Causes of Overtime
Excessive Overtime is almost always caused by one of three conditions:
1. The underlying structure of the Department or Job Class,
2. An incorrect understanding of the Department or Job Class operational conditions,
3. The scheduled availability of Department or Job Class staff.
I find it easier to understand the validity of overtime if I take all of three conditions into account in two different time contexts, first, the actual time of day in which the overtime occurred, and second, the actual duration of each overtime occurrence. Once I have completed my analysis using both time contexts a more accurate assessment of overtime validity can be reported to Senior Leadership.
Analyzing Overtime
Here are some strategies which may help you in preparing an analysis on the validity of overtime usage:
Structured Overtime tends to produce occurrences in durations of 30 minutes or less and is usually a byproduct of either clock management issues or gaps between job class shifts. Review employee historical pay records to identify patterns of potential clock abuse (and associated Supervisory issues), as well as the job class labor standards for shift end times and start times in order to reduce this type of overtime. Structured Overtime should be eliminated as quickly as possible and its trends should not be forecasted in future periods.
Operational Overtime tends to produce occurrences of less than 4 hours and is usually a by-product of some operational need to retain staff. Review the time frame in which these incidents occurred to determine if the overtime was a result of an overall Area Event or if it was due to a specific Property function, note the associated demand driver and review the Employee Schedules in use to determine if a re-deployment of staff can occur when similar circumstances are forecasted. Also note that in some cases Operational Overtime may be caused by intermittent family medical leave being taken during a scheduled shift.
Scheduled Overtime tends to produce occurrences of more than 4 hours and is usually a result of either a general lack of available staff or because a late call-off of a previously scheduled employee. Not enough available staff usually results in an overtime occurrence in the form of an additional day being scheduled, in the case of a call off; the overtime may be a result of an employee working a double shift. In either case a review of the Job Class standards for the same time period against actual staff available will give you a better understanding of whether or not the overtime was justified.
Understanding Incremental Overtime Expense
To complete an overtime analysis it will be necessary to understand the incremental cost of overtime over the use of straight time. For example, a 35% benefit load with 20 annual days of PTO and a time-and-a-half overtime rate would result in a 12% incremental overtime expense of overtime over the straight time rate. The use of Part Time Employees would reduce the coverage expense even more; however, an incremental analysis would need to be performed on the training and wardrobe costs associated with the use of greater headcount to deliver the same number of FTEs.
Next time out we will discuss strategies for effectively managing Overtime expense.
Wednesday, August 26, 2009
Hotel Occupancy and Optimized GRA Staff Planning
Over the course of my casino career I have enjoyed the opportunity to work in almost every major U.S. Gaming Market, including Las Vegas, Atlantic City, Chicago, Reno / Tahoe, Tunica, and most places in between. The one major market I had never worked in, or, for that matter, had never even visited before, was Laughlin, Nevada. That all changed recently when I got a chance to work at what the locals call “The River” for a couple of weeks.
Coming into a new market is always interesting and Laughlin proved to be no exception. It quickly became apparent (at least to me), that the current economy has had a dramatic impact on area hotel room rates and has forced Laughlin to become somewhat of a “value” market (along with being somewhat of a seasonal market as well). I also noted that many Laughlin hotels had high (80%-90%), weekend occupancy rates but dramatically lower (40%-50%) mid-week counts.
There are some interesting labor analysis concepts that can be applied when hotel occupancy swings widely from day-to-day, particularly in the area of optimizing Hotel Housekeeping room credit targets and with the concept of “dragging” dirty rooms from heavy departure days to lighter departure days for cleaning. Volatile day-to-day arrival / departure swings create the need for differing credit targets for check-out and stay-over rooms. For example, a 16-credit per shift house standard results in approximately 24 minutes per checked out room, given that a normal 8-hour Guest Room Attendant (GRA), shift including a lunch, breaks, and cart stocking time. The time to clean a stay-over room, however, is usually 35% or so less than a check-out, which results in a credit target of approximately 24 credits per shift using the same GRA availability described above. Note – the credit targets and employee availability numbers used here are for illustration purposes only; you will need to carefully evaluate the time required to clean both check-out and a stay-over rooms according to your hotel's standard operating procedures and to ascertain the available GRA time per shift available to clean in order to develop accurate credit standards.
Once you have an approved credit targets for both check-out and stay-over rooms the normal methodology for determining the required number of daily GRA labor hours is to use the volume indicator Current Day Departures to drive your daily Check-Out credit standard and to use Prior Day Rooms Sold – Current Day Departures to drive your daily Stay-Over credit standard. Divide each room type total by their respective credit target and sum the two results to return required number of daily employee shifts required to clean the house.
If your Hotel has high weekend occupancy highlighted by heavy Friday arrivals and large numbers of Sunday departures then you may find that you have labor efficiency opportunities on Saturday due to the increased volume of stay-over rooms available to your GRA staff on this day in relation to scheduling.
The concept of dragging check-out rooms will add another labor analysis dimension to the mix - dragging rooms will potentially allow you to develop daily standards that will better “fit” employee schedules. Dragging rooms can also be effective in controlling overtime expense, for as most GRA schedules are loaded for heavy occupancy days, in some cases there are more total rooms to clean than staff available when the house is full. In Laughlin the heavy check in day was Friday and the big check out day was Sunday (while mid-week departure counts dropped dramatically). Spreading a percentage of Sunday check-out rooms to later in the week creates a more even workload while preparing for the next heavy check in day (in this case, the following Friday), and may better allow you to provide work for Part Time GRAs working mid-week.
To create a standard for dragging rooms first establish the number of rooms to be drug by reviewing the daily arrival patterns and then analyze employee schedules to determine which days will best accommodate the extra workload. Deduct the number of rooms to be drug from their normal departure day and add these totals to the departure counts for the target days. Remember to allow for some time in the current day for either House Persons or GRAs to remove trash from each room to be drug, and always make sure that enough clean rooms are ready for next day arrivals throughout the week.
A caveat on developing a drag rooms standard - you should look for seasonal / event trends that will potentially disrupt the normal daily arrival / departure patterns and make adjustments to your daily drag percentages as required.
I had a great time in Laughlin and had the opportunity to meet with some really good Casino Operators while there. If your hotel has similarly wide mid-week to weekend occupancy swings then the use of individual credit targets for both check-out and stay-over rooms will result in more accurate daily labor standards for your Housekeeping GRAs. The dragging of check-out rooms may smooth out your GRA work flow, provide additional opportunities for part-time staff, and result in less overtime expense for your Property.
Coming into a new market is always interesting and Laughlin proved to be no exception. It quickly became apparent (at least to me), that the current economy has had a dramatic impact on area hotel room rates and has forced Laughlin to become somewhat of a “value” market (along with being somewhat of a seasonal market as well). I also noted that many Laughlin hotels had high (80%-90%), weekend occupancy rates but dramatically lower (40%-50%) mid-week counts.
There are some interesting labor analysis concepts that can be applied when hotel occupancy swings widely from day-to-day, particularly in the area of optimizing Hotel Housekeeping room credit targets and with the concept of “dragging” dirty rooms from heavy departure days to lighter departure days for cleaning. Volatile day-to-day arrival / departure swings create the need for differing credit targets for check-out and stay-over rooms. For example, a 16-credit per shift house standard results in approximately 24 minutes per checked out room, given that a normal 8-hour Guest Room Attendant (GRA), shift including a lunch, breaks, and cart stocking time. The time to clean a stay-over room, however, is usually 35% or so less than a check-out, which results in a credit target of approximately 24 credits per shift using the same GRA availability described above. Note – the credit targets and employee availability numbers used here are for illustration purposes only; you will need to carefully evaluate the time required to clean both check-out and a stay-over rooms according to your hotel's standard operating procedures and to ascertain the available GRA time per shift available to clean in order to develop accurate credit standards.
Once you have an approved credit targets for both check-out and stay-over rooms the normal methodology for determining the required number of daily GRA labor hours is to use the volume indicator Current Day Departures to drive your daily Check-Out credit standard and to use Prior Day Rooms Sold – Current Day Departures to drive your daily Stay-Over credit standard. Divide each room type total by their respective credit target and sum the two results to return required number of daily employee shifts required to clean the house.
If your Hotel has high weekend occupancy highlighted by heavy Friday arrivals and large numbers of Sunday departures then you may find that you have labor efficiency opportunities on Saturday due to the increased volume of stay-over rooms available to your GRA staff on this day in relation to scheduling.
The concept of dragging check-out rooms will add another labor analysis dimension to the mix - dragging rooms will potentially allow you to develop daily standards that will better “fit” employee schedules. Dragging rooms can also be effective in controlling overtime expense, for as most GRA schedules are loaded for heavy occupancy days, in some cases there are more total rooms to clean than staff available when the house is full. In Laughlin the heavy check in day was Friday and the big check out day was Sunday (while mid-week departure counts dropped dramatically). Spreading a percentage of Sunday check-out rooms to later in the week creates a more even workload while preparing for the next heavy check in day (in this case, the following Friday), and may better allow you to provide work for Part Time GRAs working mid-week.
To create a standard for dragging rooms first establish the number of rooms to be drug by reviewing the daily arrival patterns and then analyze employee schedules to determine which days will best accommodate the extra workload. Deduct the number of rooms to be drug from their normal departure day and add these totals to the departure counts for the target days. Remember to allow for some time in the current day for either House Persons or GRAs to remove trash from each room to be drug, and always make sure that enough clean rooms are ready for next day arrivals throughout the week.
A caveat on developing a drag rooms standard - you should look for seasonal / event trends that will potentially disrupt the normal daily arrival / departure patterns and make adjustments to your daily drag percentages as required.
I had a great time in Laughlin and had the opportunity to meet with some really good Casino Operators while there. If your hotel has similarly wide mid-week to weekend occupancy swings then the use of individual credit targets for both check-out and stay-over rooms will result in more accurate daily labor standards for your Housekeeping GRAs. The dragging of check-out rooms may smooth out your GRA work flow, provide additional opportunities for part-time staff, and result in less overtime expense for your Property.
Monday, July 20, 2009
Table Games Labor Standards - Dealer Break Schedules
While establishing labor standards for a Table Game Department is a fairly straight-forward exercise, the entire process requires several steps – Dealer Coverage, Supervisor Coverage, and Tables Required by Game Type will all need to be created in order to produce and accurate overall Department labor standard. This time I will discuss establishing Dealer Coverage requirements, or, calculating a Dealer work-to-table ratio using break schedules.
Most Nevada and East Coast casinos utilize a 60 minute on - 20 minute off Dealer break schedule, a ratio commonly called a 60:20. Essentially, a 60:20 target is the same as covering 12 tables with 16 Dealers, by using this ratio the following standards can be used to calculated the number of Dealer labor hours per open table hour:
Blackjack, Carnival Tables - 1.33 Dealer hours per Table Hour.
Roulette - 1.33 Dealer hours per Table Hour + Racker (Mucker) coverage if Roulette Chippers are not installed.
Craps - Typically 3 X 1.33 (3.99) Dealer hours per open Table Hour + Box coverage if provided by Dealers (normally the Box Person is a Supervisor; however, some jurisdictions allow Dealers to set Box).
Most Midwestern casinos favor an 80 minute on - 20 minute off Dealer break schedule, a strategy which results in a bit more than 6% of increased labor efficiency along with higher Toke Rates for participating Dealers (essentially, using 15 Dealers to cover 12 tables). Using an 80:20 break standard the following ratios can be used to determine required Dealer labor by Table Hour:
Blackjack, Carnival Tables - 1.25 Dealer hours per Table Hour.
Roulette - 1.25 Dealer hours per Table Hour, again, + Racker (Mucker) coverage if required.
Craps - Typically 3 X 1.25 (3.75) Dealer hours per open Table Hour, again, + Box coverage if provided by Dealers.
Specialty games not listed here may require different Dealer coverage per open Table Hour. Establish Baccarat coverage based on game requirements (Mini-Bac versus Full Bac). Also, you may need to factor in fixed Toke Committee labor standards if members of the Committee perform their functions while on the clock.
Poker Dealer break schedules are normally a little different from Table Games targets, I have encountered many combinations or work-to-break ratios while working with different Properties. Determine the work-to-break ratio in use and calculate accordingly (some Card Room Dealers work a 120:30 break schedule, or, a 1.25 factor of required Dealer coverage per open Table Hour). Because in many cases Poker Dealer keep their own Tokes, break schedules can be even more aggressive than Table Games Dealers and at times I have observed the ratios going to 1.20 Dealer Hours per open Table Hour or more.
Multiplying open tables by game type using your break schedule calculations will yield the number of Dealer headcount required to cover. Check your results paying close attention to low-volume hours (particularly at closing), if you see the calculation returning a headcount target value slightly higher than your desired target (due to rounding), then adjust using a maximum Dealer headcount threshold.
Next time we will cover the second component of developing Table Games Labor Standards, that being the calculation of Supervisor Coverage requirements using Break Strings.
Most Nevada and East Coast casinos utilize a 60 minute on - 20 minute off Dealer break schedule, a ratio commonly called a 60:20. Essentially, a 60:20 target is the same as covering 12 tables with 16 Dealers, by using this ratio the following standards can be used to calculated the number of Dealer labor hours per open table hour:
Blackjack, Carnival Tables - 1.33 Dealer hours per Table Hour.
Roulette - 1.33 Dealer hours per Table Hour + Racker (Mucker) coverage if Roulette Chippers are not installed.
Craps - Typically 3 X 1.33 (3.99) Dealer hours per open Table Hour + Box coverage if provided by Dealers (normally the Box Person is a Supervisor; however, some jurisdictions allow Dealers to set Box).
Most Midwestern casinos favor an 80 minute on - 20 minute off Dealer break schedule, a strategy which results in a bit more than 6% of increased labor efficiency along with higher Toke Rates for participating Dealers (essentially, using 15 Dealers to cover 12 tables). Using an 80:20 break standard the following ratios can be used to determine required Dealer labor by Table Hour:
Blackjack, Carnival Tables - 1.25 Dealer hours per Table Hour.
Roulette - 1.25 Dealer hours per Table Hour, again, + Racker (Mucker) coverage if required.
Craps - Typically 3 X 1.25 (3.75) Dealer hours per open Table Hour, again, + Box coverage if provided by Dealers.
Specialty games not listed here may require different Dealer coverage per open Table Hour. Establish Baccarat coverage based on game requirements (Mini-Bac versus Full Bac). Also, you may need to factor in fixed Toke Committee labor standards if members of the Committee perform their functions while on the clock.
Poker Dealer break schedules are normally a little different from Table Games targets, I have encountered many combinations or work-to-break ratios while working with different Properties. Determine the work-to-break ratio in use and calculate accordingly (some Card Room Dealers work a 120:30 break schedule, or, a 1.25 factor of required Dealer coverage per open Table Hour). Because in many cases Poker Dealer keep their own Tokes, break schedules can be even more aggressive than Table Games Dealers and at times I have observed the ratios going to 1.20 Dealer Hours per open Table Hour or more.
Multiplying open tables by game type using your break schedule calculations will yield the number of Dealer headcount required to cover. Check your results paying close attention to low-volume hours (particularly at closing), if you see the calculation returning a headcount target value slightly higher than your desired target (due to rounding), then adjust using a maximum Dealer headcount threshold.
Next time we will cover the second component of developing Table Games Labor Standards, that being the calculation of Supervisor Coverage requirements using Break Strings.
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