an assessment of labor management standards for

25 years customizing restaurant labor management systems. This is the consulting and implementation experience with chain operators that we’ve drawn upon to document the state of labor management standards in the restaurant industry. This White Paper describes how new labor systems bring labor standards to life by putting them “in context” while making fixed, picture-in-time labor standards obsolete. Ponder the possibilities and enjoy!

AN ASSESSMENT OF LABOR MANAGEMENT STANDARDS FOR THE RESTAURANT INDUSTRY

Introducing a New Era of “Contextual” Labor Management

New Labor Systems Flex Staffing Targets to Accommodate Guest Preferences

In a restaurant world heaving with marketing promotions and guests demanding customized experiences, labor staffing targets must flex with differing guest preferences and company marketing programs. New labor systems now afford a way of personalizing a store's labor targets based on guest needs and marketing programs outside the restaurant managers' control.

In a "contextual" labor management system, the dynamics of labor targets change depending on who is in the store and how they wish to use the experience. Labor requirements expand and contract depending on differing seating arrival rates, party sizes, service requests, table turn durations, marketing promotions, menu item choices and bar sales mixes. Everyday managers must adjust their staffing deployment in real time based on customer decisions about how they want to use the experience, in other words, in the context of meeting their needs. Contextual labor systems, like the Deterministics Labor System, now offer operators credible labor guidelines that they can believe in and adhere to because they are based on a brand's standards and flex with the needs of each guest experience.

Deterministics White Paper Introducing a New Era of “Contextual” Labor Management

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The Case for Contextual Labor Management (CLM)

Restaurants are complex enterprises where the variables of combining customer service and food manufacturing under the same roof lead to labor staffing challenges. Throw into this mix the variability of demand factors displayed below and you can see the cocktail of customization managers face to effectively staff each day.

Demand Variability Factors

Macro environments that impact when and how many guests will patronize Store location - freestanding, office building, shopping strip, mall, airport, concession Seasonality Day of week Daypart Demographics - gender, age Purpose of visit - refuel, reward, special occasion, business occasion, neighborhood

occasion

Restaurant environments that impact how guests will use the experience Seasonal menu rotation Daily menu rotation Variable menu pricing Limited time offers - new menu items, item discounting, menu bundling Daily specials - early bird, happy hour Service offerings - bar, dining room, take out - all with differing labor economics and

differing service durations Menu offerings with differing labor economics - e.g. Salad X takes 1.5 minutes to

prepare while Salad Y takes 3.1 minutes Prep sub recipes - each food recipe requires a corresponding Labor Recipe™ to calculate

the amount of replenishment and the amount of work time based on menu item purchases

All these factors combine to affect the variability of business patterns that determine where, when, why and how the guest will use the restaurant experience. Each of these factors forms a kaleidoscope of operating environments that change the mix of guest arrival rates, party sizes, service steps, table turn durations, menu item choices and bar sales mixes that exhibit differing labor economics. This in turn impacts the number and duration of restaurant staff based on their contribution in the service-and-production-delivery chain on a shift by shift and minute by minute basis. All restaurant managers will tell you that no restaurant experience is the same. They will say "a guest is not a guest, a plate is not a plate, a check is not a check, a table is not a table and therefore...one restaurant's performance is not a good proxy for another."


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Metrics of Labor Management

To effectively plan and staff for business needs, restaurant companies have employed a variety of metrics to calculate required labor hours. The most common are a labor percent of sales, followed by productivity metrics such as sales/hour, guests/hour, checks/hour, plates/hour, and tables/hour. At a companywide level these can be important benchmark metrics for “C” level management and investors. However, they are not useful in day to day staffing decisions since they do not reflect the dynamic nature of marketing promotions and differing guest experiences in each restaurant, each day.

The Contextual Labor Management (CLM) Solution

Work Content and Work Context: Bringing Labor Standards to Life

Accurate labor management requires a combination of time studied work tasks and technology configured for each restaurant's operating environment. The value of CLM systems, like the Deterministics Labor System, comes from the accuracy of recipe based labor standards that can account for guest purchases (content) and guest behaviors (context). It also accounts for the complex interrelationships of a food manufacturing facility with layers of shared sub recipe prep work that supports final menu item execution in each cook station.

Contextual Labor Management reflects the labor dynamics of each store based on actual guest purchase patterns. For example, a CLM System can report the correct staffing of Host, Server, Cooks, Bus and Bartender accommodating the following scenarios.

Table 26 Party of five seated at 11:45am Ordered one round of alcoholic drinks Ordered three appetizers to share Ordered entrees after appetizers consumed Ordered second round of alcoholic drinks Two desserts ordered after entrees consumed Paid bill via credit card Departed 1:14 for a table turn time of 89 minutes Sales: $162.80 Server Hours: 1.5 Sales Per Labor Hour: $109

Table 31 Party of two seated at 12:23pm Ordered one round of non-alcoholic drinks and entrées in one Server trip Paid bill with cash left on table Departed 1:08 for a table turn time of 45 minutes Sales: $33.00 Server Hours: .75 Sales Per Labor Hour: $44

Needless to say, a CLM Report of required staffing is much different than a fixed Sales Per Labor Hour Report since the actual sales per labor hour was $109 for table 26 and $44 for table 31.


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Each of these tables were occupied for different meal durations and required differing amounts of time for service, food production, drink production and cleaning at different times of day by different staff. CLM Systems can report the correct staffing levels for all participants based on these two very different scenarios. The Components of a CLM System

The process illustrated below show the steps to setting up the CLM System and the components used to calculate required hours by employee position.

Define Employee Positions and Labor Drivers

The first step is to define what Employee Positions will be managed by the system. The next step is to identify transaction-level information obtained from the point-of-sale system that can be correlated with the work content of each position. Data that determines when and where labor is allocated, we refer to these as Labor Drivers. The “when” refers to labor driver data that is “time stamped.” It reflects the timing and sequence of guest requests that represent the work context of the meal experience. This is critical for accuracy because work is performed before the meal, during the meal, and after the meal is completed. The “where” refers to each driver connected to an Employee Position who performs work. In this process, an Employee Position may be connected to one or many Labor Drivers depending on the work. For example, Bus staffing may be driven by a single driver of departing guests, while Grill Cook staffing is driven by all grill orders on the menu – each a separate driver with its own work content. Examples of service and production drivers are as follows:

Service Labor Drivers

Host/Seaters – check open times backset five minutes

Waitlist Host staff – 85% dining room tables occupied

Servers - check open and check close time stamp denotes occupied tables per server at any point in time (validates staffing is within section size goals)

Server - order drink time stamp triggers production work and order delivery

Server - order appetizer/entrée/dessert time stamp triggers order delivery offset by throughput time

Bus - check close time stamp to trigger table bussing and resetting Production Labor Drivers

Food menu items are drivers for Line Cooks and Dishwashers

Food menu item sub recipes are drivers for Prep Cooks and Dishwashers

Beverage orders from the bar are drivers for Bartenders

Beverage orders by the Server are drivers for Servers


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Assign Labor Recipes

The third step is to measure all work content through time and motion studies of each position to establish standard work times for all tasks performed in the restaurant. We call these Labor RecipesTM because just as restaurants have standard food recipes with correct procedures to follow, so too should they have labor recipes that reflect the correct work sequence and quantity (in time) to meet their brand standards.

The following example details a food recipe and a labor recipe for a cheeseburger produced at the Grill Station. The colorized work elements identify prep sub recipes that also involve work content in support of the menu item. The work content of these recipes is summarized and the pro rata work time, or portion of the prep recipe time that goes into the menu item, is detailed to reflect the total work time required to produce a single cheeseburger.


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Apply Deployment Rules

With each Employee Position assigned Labor Drivers and Labor Recipes, Deployment Rules are applied to reflect brands standards in the context of how work is to be performed. These are perhaps the most challenging assumptions that CLM Systems can manage…the ability to configure labor drivers with employee positions and display when the work can be shared based on business volumes, position availability, work rules and physical constraints. For example, Host and Seater positions may be collapsed into a single position in slow periods as will Cooks in adjacent stations who can help each other out. Line Cooks slide up and back between the cookline and the prep kitchen depending upon business volumes and the time of day. However, Cooks working at opposite ends of the cookline cannot share work in the peak rush because the physical barriers of crossing other stations and the risk of leaving a station unattended is an unacceptable risk. Further examples of deployment rules include minimum staffing during slow periods for Hosts at the front door and Bartenders in the bar.

Analyze Staffing Performance by Position

With each CLM component linked, the CLM System can be forward looking and predict labor requirements for scheduling by simply forecasting the Labor Drivers based on historical purchase patterns. It can also generate after-the-fact reports of how much “ideal” labor should have been used based on actual guest purchases recorded through the point-of-sale from the prior day. This is contrasted with time attendance data of the “actual” numbers of employee positions on the clock throughout the day. A variance analysis contrasting Ideal vs. Actual Staffing gives management an expedient picture of their staffing performance based on, and this is key: when guests arrived, what and when orders are placed, how long they occupy tables in a section, and when they depart the restaurant. Logically this is performed by revenue center – reporting the bar separate from the dining room for example.


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Manager Interpretation

The next step for managers is to review data. The graphical illustration below shows previous day business patterns. This validates for management that the output from the labor recipes is correct. In other words, the shape of the graph matches their memory of the previous day business patterns in arriving guests, occupied tables, kitchen orders, bar orders and so forth. The Server data shows occupied tables in the dining room by fifteen minute segments. A Manager may remember that after the peak dinner rush from 5:30 to 7:15 there was a second push at 8:30. Note how quick and easy it is to interpret staffing performance with a graphical illustration. In this case, the manager can see she was overstaffed by one Server from 6:00 to 7:45. The calculation of Occupied Tables per Server in blue at the top of the graph indicates Server utilization reaching just 3.5 tables of the Server’s 4 table capacity.


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Allocation of Non Contextual Work

Full function CLM Systems have the ability to display when fixed work (set up / tear down) and prep work should be performed throughout the day. This is a good example of where the Deployment Rules play a role. If certain pre-opening fixed and prep work must be completed prior to opening the doors, the system will show the time staff must be scheduled for the work to be completed. In the graphic below, Back of House (BOH) fixed and prep work is displayed in brown with guest preference work in yellow. Note that three Cooks had to be scheduled in at 4:00 for pre-opening work, and that the kitchen was understaffed by one Cook from 6:30 to 7:30.


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Contextual Labor Management Framework

The CLM Framework provides language for describing the components of the system and distinction between contextual and non contextual work:

Aren’t Sales and Productivity Metrics Close Enough?

Some will argue that in the absence of a CLM solution, and with only point-of-sale metrics to work with, the metrics available will get them “close enough.” So we decided to measure if “close enough” is “good enough” and let the reader decide.

For purposes of this study we have used a hypothetical 100 unit full service restaurant chain. The analysis is based on actual data which has been modified to eliminate the impact of wage rate variances, menu pricing variances and any similarities to existing operations. Choosing Labor Metrics

Restaurant companies typically use labor percent of sales (Labor %) and productivity metrics segmented in “sales bands” for labor standards. Productivity metrics for the Front of House (FOH) include Sales per Labor Hour (SPLH) and Guests per Labor Hour (GPLH). Productivity metrics for the BOH include Sales per Labor Hour (SPLH) and Plates per Labor Hour (PPLH).


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Restaurants have relatively high fixed hour requirements for opening and closing activities, pre-preparation of products and general cleaning regardless of business volumes. As such, productivity of low volume stores is penalized with minimum fixed hours that make up a larger proportion of total work hours. By contrast, high volume stores exhibit higher productivity as fixed hours become a smaller proportion of total work hours. For this reason, companies will develop productivity standards in sales bands where the productivity metric is assumed to be stable. If all guest purchases and behaviors were stable this method would work. But the reality is that guest preference for customization results in divergent outcomes, both productivity and labor % within sales bands. This is due to differences in arrival rates, party size, table turn speed, order sequencing and timing, work intensity of menu items, sub-recipes and so on. To understand the degree of variability that guest preferences impact, we selected a group of stores in a similar sales band to compare the outcomes.

In the absence of a CLM System, restaurant companies must rely on historical performances from which to base their productivity models. These may be based on significant empirical data, cherry picked metrics by seasoned management, or a combination of both. In any case, the ability to choose standards that reflect the true potential of the brand is hard work and, in the absence of contextual information, an educated guess. We have the advantage of using contextual-based data to display the results of different stores. The variances can then be considered a “best case” scenario of outcomes over and above metrics chosen by a seasoned management team.

CLM versus Labor Metric Standards Comparisons

Daily sales by restaurant and recipe-based CLM hours are plotted below for a sample week of all company stores. The distribution of food and beverage sales and CLM hours reveal differences in staffing driven solely by differences in guest preferences and guest behaviors.


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Front of House Comparisons

For FOH comparisons, a band of stores sharing comparable daily sales within $200 of each other was selected. The group was made up of 18 stores ranging in daily food and beverage sales from $7,804 to $7,988. FOH positions for this company included Host, Server, Runner, Busser, Bartender and Take Out. In the chart below, daily food and beverage sales are plotted against CLM hours to reveal recipe driven variances in store required hours at nearly identical sales volumes.


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The chart below provides a view of FOH required hours and the corresponding labor % and productivity within the $200 sales band. The high, low and high/low variance for each metric show the variability of output and cost associated with each store’s performance.

Sample

Number

Total

Sales

FOH

CLM

Hours

FOH

Percent of

Sales

Sales per

Labor

Hour

Guests

per Labor

Hour

Average

Table

Turn

Average

Party Size

1 $7,804 126.9 6.58% $61 4.1 64 3.3

2 $7,814 106.0 5.49% $74 4.4 56 2.8

3 $7,827 115.8 5.98% $68 3.9 72 3.7

4 $7,838 100.5 5.19% $78 4.5 67 3.1

5 $7,841 111.8 5.76% $70 4.6 59 3.1

6 $7,854 102.0 5.25% $77 5.0 66 3.3

7 $7,892 114.9 5.89% $69 4.5 51 3.0

8 $7,897 105.5 5.40% $75 3.8 73 2.9

9 $7,924 121.9 6.22% $65 4.4 59 3.2

10 $7,924 112.9 5.76% $70 4.6 60 3.4

11 $7,960 127.9 6.50% $62 4.3 56 3.0

12 $7,962 115.8 5.88% $69 4.2 58 3.0

13 $7,968 120.3 6.11% $66 4.3 58 2.8

14 $7,969 114.9 5.83% $69 4.4 51 3.2

15 $7,973 106.6 5.41% $75 3.9 67 3.1

16 $7,974 113.0 5.73% $71 3.3 95 3.7

17 $7,978 111.5 5.65% $72 3.7 62 2.5

18 $7,988 122.9 6.22% $65 4.4 60 2.7

HIGH $7,988 127.9 6.58% $78 5.0 95 3.7

LOW $7,804 100.5 5.19% $61 3.3 51 2.5

VARIANCE $184 27.4 1.39% $17 1.7 44 1.2

VAR % 2.4% 27% 27% 27% 52% 87% 50%

FRONT OF HOUSE COMPARATIVE RESULTS


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Back of House Comparisons

For BOH comparisons, the different band of stores sharing comparable daily sales within $200 of each other was selected. The group was made up of 28 stores ranging in daily food sales from $6,400 to $6,591. BOH positions for this company included Line Cook, Prep Cook, Expeditor and Dishwasher. Food sales are plotted against required hours to reveal recipe-driven variances in store, required hours at nearly identical sales volumes.


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The chart below provides a view of BOH CLM hours and the corresponding labor % and productivity within the $200 sales band. The high, low and high/low variance for each metric show the variability of output and cost associated with each store’s performance.


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CLM Versus Labor Metric Standards Conclusion

It is clear from the CLM and metrics comparisons that a one-size-fits-all metric, even within bands of comparable sales volumes, will yield unreliable results in most cases. Labor standards such as labor % and productivity metrics are historical measures developed “top down”, in absence of more accurate methods. Time study-based CLM data that reflects guest preferences and a manager’s operating environment is a “bottom up” approach that presents an unambiguous and inarguable portrayal of staffing requirements.

Aligning Brand Standards with Labor Cost Goals We all understand that labor costs are a major component of restaurant company profitability. Under the CLM approach labor cost reflects the real cost of doing business according to established brand standards. To the extent that labor costs exceed an acceptable level, CLM provides the tools to understand and correct the root cause of inflated labor cost beyond the impact of wage rates. This way an intelligent review of all cause and effect labor variables that affect profitability can help the company redirect the outcomes in a way that are acceptable, and achievable, for all stakeholders. Deterministics CLM Services offers ways to manipulate the labor components of your brand standards to achieve an optimized labor cost. CLM Labor Standards Give You Control of Labor Cost Brand standards cost money. They also convey the quality of your operation. The labor intensity of your service structure and menu tie directly to the dollars you invest in creating satisfied guests. The outcome of this is a labor percent of sales metric that you can use in the budget process to manage a profitable enterprise. Sounds simple right? It’s not. The myriad of tradeoffs that exist between wage rates, sales values of guest purchases (work content), and the when, where and how guests make their purchases (work context), are factors to consider. Thankfully, a Contextual Labor Management (CLM) approach can help you separate what you can control (work content) from what you cannot control (work context) to determine what the exact labor cost should be. And if it’s too high, Contextual Labor Management provides you the tools to align brand operating standards with labor cost goals.


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Calculate Ideal Labor Cost The first place to start is to perform time studies of all work content. Just as you use weights and measures to calculate ideal food cost, so too should you use time and yield measures to calculate ideal labor cost.


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CLM Labor Standards Tell the Story

Unlike productivity metrics that are simple statements of output: sales or guests per labor hour for example, CLM labor recipes are contextual labor standards that tell a story of why a store achieved a particular labor cost. Was it due to bar sales mix, peak and shoulder arrival patterns, party sizes, table turn times, additional menu coursing of appetizers and desserts, or labor intensive entree selections? Such differences can explain why labor economics differ at identical sales ranges as discussed in the previous white paper. They can also explain why labor cost percent will vary across regions.

CLM Tools to Re-Engineer Brand Standards

As stated before, we can change work content but not work context. The labor intensity of menu items is work content that can vary considerably as demonstrated in the following graphic.


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Streamlining recipe work steps by the R&D Department is a common means of improving labor cost and throughput speed. Understanding areas of opportunity and deficiency to maximize kitchen throughput with the minimum of staff is a powerful objective using CLM Labor Recipes. Of course we always have to wear our marketing hat, so labor recipe intensity is crossed with market demand to prioritize items for improvement. Recipe engineering objectives can be prioritized based on the Portfolio Analysis summarized below. Each menu item is catalogued in one of four quadrants based on menu mix demand and hands-on work time.


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Improvement strategies are presented for four selected menu items as follows:

Strengths like the Baby Back Ribs present high demand and low work time – celebrate them!

Problems such as the hand crafted Potato Skins exhibit high demand and high work time – move into Strengths quadrant by streamlining work steps and/or eliminate work steps with a make or buy analysis

Opportunities like the Pork Pot Stickers experience low demand and low work time – move into Strengths quadrant with marketing tactics such as menu merchandising and other promotions

Challenges such as Spinach Con Queso exhibit low demand and high work time – consider removal from the menu

CLM Tools Forecast Marketing Impact on Operations Limited Time Offers (LTO) can have a detrimental effect on staff capacity if the operation is not prepared in advance for the new workload. Armed with menu mix adds and subtracts estimated by the Marketing Department; a projected menu mix can be exploded through forecasted arrivals to reveal the impact on line cook capacity. In this example where an LTO of new Grill menu items is planned, it is clear a team of three cooks will be required to execute the offer without compromising throughput times. If the maximum Grill station capacity is two Grill Cooks, this LTO would result in throughput delays and many unsatisfied guests.


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CLM Labor Recipes Represent Brand Standards

As we discussed in an early installment of our Labor Standards Assessment, Deterministics CLM System uses time measured Labor RecipesTM and point of sale transaction detail to calculate how much labor is required by position, by quarter hour. These become a quantified statement of brand standards. An advantage of CLM reporting is identifying where and when brand standards are compromised. In the example below, the frequency that stores comply with minimum staffing standards is plotted by time of day for the Host position. Senior management can drill down to unit level performances to evaluate compliance integrity.


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Another example of brand standard compromise is the extent to which management will under staff throughout the day. In the following graph of all company stores, the frequency of Server Peak Period Short Staff (PPSS) by day of week shows the extent to which stores did not have sufficient staff to handle business volumes. In this example, lunch was frequently understaffed.

Summary

CLM Systems give restaurant companies the keys to the kingdom of labor standards accuracy by basing staffing performance on how guests choose to use the experience. In an era of increasing customization, labor standards that reflect the customer experience will achieve a higher degree of buy-in from management. They also enable zero based modeling of labor cost for the company P&L. CLM Systems provide a new path forward for achieving staffing success for the manager, the customer, and the company.

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Deterministics is the leading provider of CLM based labor management consulting services and software solutions for the multiunit restaurant operator.

For more information, visit www.deterministics.com or call 1.800.322.4146.

© 2012 Deterministics, Inc. All rights reserved.

an assessment of labor management standards for

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