An Integrated Labor-Management Systemfor Taco Bell

JACKIE HUETER Taco Bell Corporation17901 VonKarmanIrvine, California 92714-6212

WILLIAM SWART College of Engineering and TechnologyOld Dominion UniversityNorfolk, Virginia 23529-0236

Taco Bell Corporation has approximately 6,490 company-owned, licensed, and franchised locations in 50 states and agrowing international market. Worldwide yearly sales are ap-proximately $4.6 billion. In 1988, Taco Bell introduced six core-menu items for the reduced price of 59 cents and offered freedrink refills. Taco Bell has since continued to change and inno-vate. Its new strategy meant restructuring the business to be-come more efficient and cost-effective. To do this, the companyrelied on an integrated set of operations research models, in-cluding forecasting to predict customer arrivals, simulation todetermine the optimum labor required to provide desired cus-tomer service, and optimization to schedule and allocate crewmembers to minimize payroll. Through 1997, these modelshave saved over $53 million in labor costs.

Taco Bell was established in 1962 by national fast-food business. By the mid-Glenn Bell in Downey, Califorrua. By 1980s, it had achieved a successful steady-

the early 1970s, Taco Bell Corporation was state position in the fast-food marketplacea booming regional chain and had gained by delivering consistent food quality, ser-the food service industry's attention. vice, and value to its customers. As is typ-PepsiCo, Inc. purchased Taco Bell in 1978 ical in the industry, operating proceduresand transformed it into a dynamic, inter- and management principles evolved as a

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FORECASTING—APPLICATIONS

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consequence of a long process of trial anderror, based occasionally on adapting whatother quick-service companies were doingto the Taco Bell environment.

Taco Bell labor costs represent approxi-mately 30 percent of every sales dollar andare among the largest controllable costs.They are also among the most difficult tomanage because of the direct link that ex-ists between sales capacity and labor. Be-cause the product must be fresh whensold, it is not possible to produce largeamounts during periods of low demand tobe warehoused and sold during periods ofhigh demand, as is typical in most manu-facturing enterprises. Instead, the productmust be prepared virtually when it is or-dered. And since demand is highly vari-able and is concentrated during the mealperiods (52 percent of daily sales occurduring the three-hour lunch period from11:00 AM to 2:00 PM), determining howmany employees should be scheduled toperform what functions in the store at anygiven time is a complex and vexingproblem.

Because of the complexity of the labor-management problem described above, thetraditional approach to managing laborcosts is to establish a policy decision thatindicates what percentage of sales thecompany is willing to allocate to labor costin order to meet prescribed financial objec-tives. It is then up to those who managestore operations to develop rules and pro-cedures that seek to maximize the level ofservice and quality that can be deliveredwith the given amount of labor assignedto the store. Over time, these rules andprocedures evolved into a body of "tribalknowledge" that was applied manually at

every restaurant. In 1988, Taco Bell in-vested in an in-store computer network,which allowed it to develop moreadvanced-information solutions. The resultwas the field-automated-staffing program,FAST. FAST, like the traditional approach,was a top-down control system that allo-cated a percentage of sales to labor coststo meet prescribed financial targets. It stil'.told managers what to achieve, but nothow to do it, and managers still had torely on experience to do it well. If theamount of labor allocated to stores ap-pears insufficient to meet customer service

Labor costs representapproximately 30 percent ofevery sales dollar.

goals and maintain sales, intense debatesoccur between financial executives andoperations executives. Financial executivestypically resist increasing the labor-cost al-location, and operations executives typi-cally argue that sales are being lost be-cause they cannot assign enough labor tothe restaurants to get everything done.

In 1988, Taco Bell management decidedto move the company to a new plateau interms of market share, per-unit sales, andoperating profits. To do this it developedand implemented aggressive new strate-gies and tactics. One of the earliest andmost visible of the new strategies was"value meals." Subsequent strategies af-fected virtually all aspects of Taco Bell, in-cluding new store design and layout,products, equipment, and customer ser-vice with and without self-service bever-ages. However, before management couldmake any decisions to implement change,

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it had to know what impact this changewould have on labor cost and custonierservice. The fast-service food industry hadlong ago learned the lesson that AmericaOnline recently endured: Increasing de-mand without anticipating its effects onoperations can make for a very unpleasantexperience for all.

During the planning phase that pre-ceded the adoption of the new "valuestrategy" (value meals and associatedchanges to the system's stores), Taco Bell'smanagers realized they needed answers toquestions they had never before asked.They needed to know what impact opera-tional decisions would have on labor costsand speed of service. The existing laborsystem, FAST, was not designed to answersuch questions. It was designed to re-spond to questions whose answers couldbe inferred from the past. It could not beadapted to yield the information neededto support the many decisions that had tobe made in the new environment. Also, asthe company institutionalized new poli-cies, FAST would become inadequate be-cause the experience base upon whichoperations management had been success-ful in the past would no longer reflect tbecompany. As a result, Taco Bell began tolook for new ways to answer the manyquestions that accompanied each of theoperational decisions that managers hadto make to ensure that the value strategywould be successful.

Once it understood and accepted theneed for better and more timely informa-tion, management made a commitment tointroduce IBM-compatible personal com-puters in every Taco Bell store linked to acentralized mainframe system at the cor-

porate headquarters. This system, togetherwith a number of software modules to au-tomate management paperwork, collectand transmit transactional data to themainframes for financial control, and con-trol aspects of store operations, wasdubbed TACO (total automation of com-pany operations). By replacing FAST witha new, comprehensive, integrated labor-managenient system as an integral part ofTACO, the company could take advantageof and accelerate the deployment of thenew hardware and software to the field.Furthermore, with the new labor manage-ment system as a standard component of acomprehensive restaurant-managementsystem, Taco Bell and its franchisees andlicensees could manage labor in a consis-tent manner across the entire system. Theresults would be reflected in greater con-sistency of service and, hence, greater cus-tomer satisfaction and brand equity. Itcould also serve as one more tool to makeinvesting in a Taco Bell store more attrac-tive to prospective franchisees.Project Genesis

In Taco Bell's search for new ways toanswer the many questions that accompa-nied proposed operational decisions, oneindividual emerged as a champion for theuse of operations research as a means tosupport decision making in the new envi-ronment. This individual, Howard Frantz,manager of Taco Bell's newly establishedindustrial engineering function, convincedmanagement to fund the development of anew labor-management system (LMS). Thestudy was to be conducted under his lead-ership assisted by a newly formed laborsteering committee. This steering commit-tee consisted of Taco Bell's six zone vice

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presidents and represented all the com-pany's operations management. This com-mittee would (1) provide input and guid-ance regarding labor issues, (2) provideaccess to internal resources, and (3) reviewand evaluate results. The first directive ofthe comn:\ittee was that Howard Frantzseek the advice and counsel of the leadingauthority on the use of operations researchin the food service industry. This led to thehiring of Dr. William Swart as a strategicconsultant for the development of the newLMS. Dr. Swart had pioneered the use ofoperations research in the industry and re-ceived an Edelman finalist award in 1981for bis accomplishments. To complete theLMS development team, Howard Frantzhired one full-tinie industrial engineer, en-listed a job shopper, contracted for indus-trial engineering consulting services, andenlisted the services of data-entry and pro-gramming personnel. Howard Frantz andWilliam Swart held the project's kick-offmeeting with the steering committee onJanuary 11, 1991.

In its deliberations with the LMS devel-opment team, the steering committee iden-tified the following key objectives of TacoBell's new labor-management system:—Be responsive and economical;—Be able to predict labor requirementsthat minimize labor cost and meet all cor-porate standards for hospitality, quality,service, and cleanliness for any existing orplanned restaurant configuration;—Serve as an effective and efficient in-store labor-management tool to help thestore manager to plan and schedule;—Serve as an effective and efficient toolfor providing the labor required to achieveTaco Bell's financial targets;

—Provide timely feedback for controllinglabor cost at all levels; and—Have the inherent flexibiUty to evolve asTaco Bell evolves.

After appropriate deliberations amongall stakeholders in the study and a searchof the literature, we confirmed our earlierconclusion that these capabilities could beprovided only by a model-based labor-management system.The Restaurant as a System

As a first step, we developed a concep-tual system representation of a quick-service restaurant (Figure 1). The system ismade up of three interacting segments: thecustomer, the delivery, and the productionsubsystems. We do not consider a fourthsegment, the consumption subsystem (ordining area) because we focused primarilyon the other three.

The restaurant system and more tradi-tional manufacturing concerns are similarin many ways:—Each product on the menu requires awell-defined sequence of assembly andprocessing steps, including packaging.—Multiple products compete for access toscarce resources (for example, fryers).—The product line is a mix of standardproducts (for example, tacos) and custom-ized products (for example, "hold the hotsauce").

—Raw material, in process, and finishedproduct inventories must be maintainedand managed.—Distribution occurs through multiplechannels (walk-in, drive-through, homedelivery).—An order consists of a variety of prod-ucts that have to be assembled and pack-aged together.

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Order transmission

Order placement

Order delivery

Consumption

CUSTOMER SYSTEM

Sta

9

ng

F^oductOrderQueue

t^oduct

F^oduct

OrderQueue

OrderQueue

DELIVERYSYSTEM

PRODUCTIONSYSTEM

Figure 1: Representation of a quick-service establishment as a system, including its three sub-systems, and the flow of information between the customer and the production subsystems.

—Employee productivity and training arehighly correlated.

However, they also show some very sig-nificant differences:—The acceptable lead time between plac-ing an order and order delivery (speed ofservice) is only a few minutes (customerscannot wait a long time, in particular dur-ing lunch time in the work week, and theyexpect fast service).

—Finished product in inventory perishesin minutes (customers all like their foodfreshly prepared).—Demand can easily fluctuate by 1,000percent in a 30-minute interval (for exam-ple, between 11:30 AM and noon in busi-ness areas, stores can go from empty tocrowded).

—Ordering, production, and delivery takeplace under the same roof.—Employee and management turnover ishigh.Model Conceptualization

The total labor requirements in a restau-rant can be categorized into three types:fixed labor, variable food-preparation la-bor, and variable customer-service labor.Fixed labor is that which is required and islargely independent of the restaurant'sdaily sales volume. Such labor includes,for example, that which is required forcleaning between the closing of the restau-rant at night and its opening the nextmorning. Variable food-preparation laboris that which is required to meet the res-taurant's daily operating needs but is not

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instantaneously linked to customer vol-ume, such as, for example, preparing andrestocking ingredients on the productionline. Both of these categories of labor canbe scheduled at the discretion of the res-taurant manager (within limits). However,the third category of labor, variable cus-tomer labor, is that which is required tosatisfy a particular customer's order, suchas order taking, making change, produc-ing a custom product, and order delivery.There is little or no discretion as to whenthis work must be performed, and theavailability of this category of employeesmust match the highly variable flow ofcustomers to the restaurant. It also ac-counts for up to 80 percent of tbe restau-rant's labor requirements during the day.Because of the discretionary nature ofwhen the fixed labor and variable food-preparation labor can be performed,people bave used traditional time-studytechniques successfully to determine res-taurant requirements for these categories.However, determining variable customer-service labor requirements is much morecomplex.

Defining the Relationship Between Laborand Service Quality

Different customers order differentitems and combinations of items. From 20to 80 percent of these orders, dependingon region, require custom preparation (nohot sauce, extra hot sauce, and so forth).Consequently, few products are invento-ried and those that are can be held foronly a few n^inutes. As a result, customerarrivals drive the work to be performed inthe restaurant. The work to be performedin a quick service restaurant is commonlydefined as providing quality food with

good service in a clean environment, orQSC. Each of these three attributes is de-fined according to corporate standardsand can be measured by objective meth-ods, subjective methods, or a combinationof both.

Although Taco Bell sees the three attrib-utes as equally important, good service, asmeasured by speed of service, is most di-rectly related to revenues. The three-hourlunch period in Taco Bell stores accountsfor approximately 52 percent of dailysales. Within that period, the busiest hourcan account for 15 to 25 percent of dailysales. Most quick-service restaurants havcwaiting lines during this period. Conse-quently, speed of service determinesthroughput and hence sales. If service timedecreases, sales capacity increases and vic:eversa.

Taco Bell has and will continue to investheavily in strategies to improve producti^'-ity and speed of service, including radicalnew kitchen designs, more visible menuboards that promote quicker ordering, andall manners of automation, including ro-botics. However, speed of service dependson labor. Given that labor is deployed cor-rectly, additional labor means increasedsales capacity, faster service, and improvedquality and cleanliness. But it also meanshigher labor costs.

The crux of the labor-management prob-lem then hes in understanding tbe quanti-tative trade-off relationship between laborand speed of service, and speed of serviceand revenues. To our knowledge, the spe-cific relationship between speed of serviceand revenues has not been determined atTaco Bel! or at any other organization.However, from a pragmatic point of view.

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we [Swart and Donno 1981] have followedand defined the relationship in terms of thenumber of customers that are likely to balk(leave the line and the store) as a functionof speed of service. A study we performedassessed when customers would be likelyto leave a waiting line because they per-ceived waiting time as excessive. The re-sults indicated that customers in line per-ceive that waiting time is "a couple ofminutes" although actual waiting time canreach five minutes. After actual waitingtime exceeds five minutes, customer per-ception of waiting time increases exponen-tially (Figure 2). The higher the perceivedwaiting time is, the more likely a customeris to "balk." Further studies quantified thevariability of the waiting-time distribution.Using this probabilistic information, we de-termined that a three-minute average timein the queue (from arrival at the restaurantuntil food is delivered) ensures that only2.5 percent of customers that wait are likelyto balk. We discussed this finding with se-nior managers and they decided that, atthis stage of the process, achieving a ser-vice level of consistently delivering food tocustomers within an average of three min-

actual time (minutes)

Figure 2: Tlie relationship of perceived versusactual time spent in line by quick-service es-tablishment customers.

utes would be a significant improvement.Furthermore, they saw the resulting lowrisk of balking as acceptable. We agreed toreview the risks and consequei^ces againlater.Addressing Store ManagementRequirements

To satisfy one of the key objectives, thenew labor-management system had toserve as an efficient and effective in-storetabor-management tool. It had to help themanager plan shifts. That is, it had to de-termine who should begin work when andfor how long each day in such a mannerthat payroll is minimized. An integral partof that decision is planning when employ-ees should start and finish their allocatedshifts. To avoid the confusion of havingemployees begin or end their shifts at anytime, it is common to start and end shiftsat discrete points in time, such as on thehour, on the half hour, or on the quarterhour. The selection of start and end pointsis critical. For example, if they can startonly on the hour, then during lunch em-ployees could either start at 11:00 AM andhave little to do until noon or they couldstart at noon amidst the confusion of thebusiest time of the day. Consequently, it isdesirable for employees to begin and endtheir shifts in the smallest practical timeincrements (usually at 15-minute intervals,but no longer than 30 minutes) so that la-bor scheduled in the store can most closelyadhere to predicted demand. Scheduledlabor and labor demanded are not usuallythe same because employees, once broughtto the store to work, must be guaranteed aminimum shift, the length of which de-pends on state law and local laborconditions.

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To minimize payroll and dehver desiredcustomer service, store managers require atool that will help them to determinewhich employee should begin work andwhich employee should go home at everytime interval of the day for every day ofthe week. At the beginning of this study,this interval was 30 minutes because of re-strictions imposed by the in-store data-storage devices. Today, the company isovercoming this limitation and imple-menting a 15-minute time increment sys-temwide. In addition to the number of em-ployees required, such a tool must indicatewhere they must be positioned and howthey are to share the required duties. Andbefore it can determine that, it must knowhow many customer transactions will takeplace at the store during every 15-minuteinterval of every day of the week.

This means that the new labor manage-ment system needed three major compo-nents: (1) a forecasting model for predict-ing customer transactions; (2) a simulationmodel to translate customer transactionsto labor requirements; and (3) an integerprogramming model to schedule employ-ees to satisfy labor requirements and mini-mize payroll. These models must operatein concert while permitting the manager tointervene and make appropriate revisionsbefore implementing the results (Figure 3).The Forecasting Model

Customer transactions during a 15-min-ute interval are subject to many sources ofvariability, including but not limited totime of the day, day of the week, week ofthe month, and month of the year. Toeliminate as many sources of variability aspossible, we separated all customer-transaction data for all stores into a num-

ber of independent time series, each repre-senting the customer-transaction historycorresponding to a specific 15-minute in-terval during a specific day of the week.For example, the customer-transaction his-tory at a particular store for all Fridaysfrom 9:00 to 9:15 AM constituted the timeseries to be used to forecast customertransactions at that store for future Fridaysfrom 9:00 to 9:15 AM.

To produce the data on which to base aweekly staffing schedule, the forecastingprocedure must forecast next week's cus-tomer transactions for each 15-minute in-terval of every day of the week that therestaurant is open. It does this by forecast-ing the transaction time series associatedwith each 15-minute interval of the restau-rant's daily open time during the weekone period ahead. In an unpublishedstudy conducted by one of us (Swart), thecomplete array of forecasting methods in-cluded in Makridakis and Wheelwright's[1978] Sybill/Runner forecasting systemwas used to predict one-period-aheadsales (sales and transactions are directlycorrelated) for 30-minute interval data forall company-owned Burger King restau-rants. The forecasting procedure that mini-mized the average square deviation be-tween actual and predicted data was thenaive method (the projection is the sameas the last data point). With the under-standing provided by that experience, weset forth to determine the best forecastingprocedure to use for Taco Bell.

Although some argue that there is someevidence that trend and seasonal patternsmight exist in Taco Bell data, we could notextract a reliable database of multi-year,15-minute-interval customer transactions

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Forecasting

[VIodel

15-minute transactionprojections

[nteger Programming

staffing

requirements

Labor Schedule Which Delivers

Speed-of-Service

Figure 3: The structure of Taco Bell's labor-management system.

by day from the corporate databases be-cause the corporation had not collectedsuch data in the past. In cooperation withTaco Bell's information technology group,we identified 200 restaurants that had re-cently upgraded their telephone lines totake part in an effort to collect 15-minute-interval transaction data. We developedand implemented a rolling database con-taining six weeks of in-store and drive-through transaction data, the maximumthat restaurant in-store computers couldstore. We then conducted tests to deter-mine which applicable forecasting meth-ods would yield the least mean square de-viation. The methods tested were (1)moving averages (from one through sixperiods); (2) moving averages adjusted forlinear trend; and (3) moving averages ad-

justed for quadratic trend. Because thetime series consisted of only six datapoints, we did not consider using expo-nential smoothing. The results indicatedthat a six-week moving average was best.

Taco Bell incorporated this forecastingmethodology in each store's computer sys-tem. The computations are transparent tothe restaurant manager and do not tax in-store computer resources. The model com-pares weekly activity projections obtainedfrom the forecasting model to statisticalforecasting control limits that are continu-ously updated. The model also reoptim-izes the length of the moving-average-period when it declares the forecasts outof control, but this is also transparent tothe restaurant manager. The system pro-vides the manager with forecasts obtained

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from scientific approaches. The manager,however, has the authority to modify theforecast based upon known events. Ifthese events are foreseeable, the managercan assign a name to them and includethem in a special event database and use itto adjust future forecasts (Figure 4).The Simulation Model

A forecast of transaction levels providesinformation essential to the labor-scheduling process, provided that the rela-tionship between transactions and re-quired labor is known. Simulation hasproven effective in solving this particu-larly complex problem in the fast-food en-vironment [Swart and Donno 19811.

Taco Bell restaurants have continuouslyevolved since 1962. Consequently, there isno such thing as a standard Taco Bell res-taurant, and no single simulation modelcan provide answers that apply across thesystem. Thus we needed a simulationmodel that could be easily adapted to rep-

resent a variety of restaurant configura-tions. Management also expected that oncethe models were in use, it would use themto seek answers to a great number of ques-tions in a short time. Object-oriented simu-lation approaches are rapidly reconfigura-ble. Taco Bell selected MODSIM, anobject-oriented simulation language, as itsimulation modeling tool [Godward andSwart 19941.

To develop and use a simulation model,one must develop and collect quite a bit ofdata. Almost everything that takes place ina restaurant, from customer arrival pat-terns to the time it takes to wrap a taco,has to be translated into reliable and accu-rate data if the simulation model is to pre-dict actual behavior. For example, we col-lected interarrival times for in-store anddrive-through customers at a representa-tive sample of restaurants. Based on ap-plying the Kohnogorov-Smirnov test forgoodness of fit [Solomon 1982], we found

Data —-pForecasting

Model

statistical forecast

- Select New

Model

SpecialEvents

Database

Figure 4: Flow chart depicting the process used to transform a statistical iforecast into a finalforecast at Taco Bell.

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that there was no reason to reject the hy-pothesis that they followed an exponentialdistribution, regardless of the sales volumeof the particular store(s) from wliich weobtained the data.

In some cases, determining the nature ofthe data to use to populate the model be-came the subject of debate. For example,should one measure the time required toprepare a breakfast taco (or any otherproduct or process) by observation and in-clude observed statistical variability, orshould one obtain the time via a predeter-mined time standard method, such asMOST [Mishra 1982], which yields deter-ministic results? Taco Bell elected to collectdata for the variable customer-service la-bor by taking slow-motion videotapes oftrained employees working at a normalpace using acceptable procedures in actualrestaurants. We then analyzed these vid-eos and expressed the resulting standardsas an empirical frequency distribution toreflect the stochastic nature of the realworld.

Collecting all the data required to popu-late the simulation model became the ma-jor task we had in developing the LMS interms of time and effort. For example, wehad to conduct time studies and data anal-yses for every task that is part of prepar-ing every product on the menu board. Wehad to time every step of the process of in-teracting with the customer, including thetime to place an order, the time to makechange, and the time a customer took towalk from the cash register to the food-pickup area. We had to repeat this effort toaccount for each type of restaurant in thesystem, because the travel distance, andhence time, of the customer from the cash

register to the pickup area, and of the em-ployee from the preparation area to thefinished product area, varies. Thus thehours devoted to collecting the datagreatly exceeded those devoted tomodeling.

Once we had verified and validated themodel, we obtained the minimum laborrequirements to meet any level of activityby staffing the model with the minimumacceptable store complement (a policy de-cision at Taco Bell that takes into account

This system was dubbedTACO (total automation ofcompany operations).

safety as well as operational factors). Wethen increased simulated store-activity lev-els by increments until it became impossi-ble to keep average customer-waiting timebelow three minutes. At that time, we in-creased the staffing level by one, exploredthe logical assignment of duties, andadopted that assignment of duties that al-lowed for the maximum increase in storeactivity while maintaining service stan-dards. Thus the model also determinedthe optimal deployment for each staffinglevel to maximize throughput and there-fore service. We again increased the staff-ing level by one and incrementally in-creased the store-activity level, repeatingthe same process until additional staffingcould not accommodate the increased ac-tivity level while maintaining speed-of-service standards, or until it became physi-cally impossible to add additional employ-ees (Figure 5).

We captured the end result of the simu-lation process in a set of files containing

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HUETER, SWART

1.0Decrease if mean time in system is less than 3 minutes

time between arrivals

Object Oriented

Simulation Model

Staffing

•Number

•Positions

1

outputs

PerformanceMeasures

•System

•Mean timein system

•Mean timeat counter

•Utilization

•People

•Equipment

Increase if mean time in system is greater than 3 minutes

Figure 5: Flow chart depicting the iterative process used by Taco Bell to develop its staffingrequirements.

the minimum staffing levels and duty as-signments required to satisfy customer-service standards at each possible activitylevel. We tailored these files to each res-taurant type and adjusted these to refiectthe natural variabihty that exists in menumix and check averages at each restaurant.We use simulation to make these adjust-ments when warranted. For many situa-tions, however, we have developed adjust-ment factors by using various decisionrules and heuristic processes. These filesare used to generate data for the integerprogramming models.The Integer Programming Model

In a Taco Bell restaurant, the total laborrequirements consist of fixed labor, vari-able food-preparation labor, and variable

customer-service labor. We use simulationmodels to determine the amount of vari-able customer-service labor that is re-quired for any level of store activity. Weuse traditional time-study methods to cal-culate fixed labor requirements and ex-press them as labor hours per day per to-tal store-activity level (typically as a stepfunction, for example, x labor hours forsales to $v, x + a hours for sales between$1/ and $1/ + b, and so forth). The storemanager decides when this labor will beperformed—generally before the restau-rant opens or after it closes.

We also use traditional tinie-study meth-ods to calculate the variable food-preparation time and also express it as astep function of store-activity level. How-

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ever, unlike fixed labor, this labor must beperformed at various periods during theday. Ingredients must be restocked on theproduction line periodically (more oftenduring peak hours than during slackhours). Based upon projected store-activitylevel and such factors as the size of stor-age bins, the manager will know howmany times they are likely to need re-stocking during the lunch period. Conse-quently, the manager can predict whenthis type of labor must be performed.

In the first phase of developing a modelto schedule employees, we calculate thetotal labor requirements of a store for each15-minute interval during the day as thesum of fixed labor, variable food-preparation labor, and variable customer-service labor. The demand for total laborat any time interval must be satisfied bythe number of employees in the store dur-ing that interval, regardless of when theirshifts started. Thus, the solution to theemployee-scheduling problem, as definedfor Taco Bell, follows that described in de-tail by Godward and Swart [1994] and isan integer program with the decision vari-ables representing the number of employ-ees scheduled to begin their shifts at time tand the length of shifts s. The value of s isconstrained by the minimum and maxi-mum permissible shift lengths. The con-straints ensure that the total number ofemployees working at time t is equal to orgreater than the number required. Thisformulation can be transformed into anequivalent network formulation and Fordand Fulkerson's [1962] out of-kilter algo-rithm is the basis for the solution algo-rithm. The objective function to be mini-mized represents the total payroll for the

scheduling period and is obtained by put-ting the appropriate wage plus fringes infront of each integer variable.

Godward and Swart's model proved tobe satisfactory, but it did not account forthe fact that many stores have a limitednimiber of employees on the payroll. Con-sequently, we augmented the model to in-clude a constraint on the total number ofemployees that could be scheduled (for ex-ample, the sum of all variables had to beless than or equal to the number of em-ployees on the payroll and available towork during that scheduling period). Un-fortunately, this one constraint destroyedthe characteristic that allowed us to repre-sent the model as a flow network. Conse-quently, we would have to use more tradi-tional integer-programming algorithms toschedule employees. But the overhead andtime required by traditional solution algo-rithms made them infeasible to use withinthe Taco Bell environment. As an alterna-tive approach, we decided to use general-ized Lagrangean multipliers [Everett 1963]to eliminate this new constraint from theconstraint set. The out-of-kilter algorithmprovided the computational efficiency tosolve the reformulated problem efficientlyand within the store's computationalresources.

The solution to the above-formulatedemployee-scheduling problem providesthe manager with the set of shifts that willminimize payroll while satisfying all con-straints. Although Taco Bell has the capa-bility to automatically assign names to theshifts based on a number of criteria, storemanagers have preferred to do this manu-ally because they can and often do assigndesirable shifts as an internal reward or

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incentive system for store employees. Themanager enters employee names manuallyon the working screen created by the LMSafter reviewing the model outputs and, ifwarranted, modifying them to reflect spe-cial and unanticipated store, market, orpersonnel circumstances. The outputs ofthe LMS also provide the managers withinformation useful in supporting theirprofit-and-loss responsibilities.Model Implementation

We developed the individual modulesand integrated them into a cohesive labor-management system between 1990 and1992, implementing them to all companystores during 1993. Prior to implementingit systemwide, we prototyped the inte-grated system and tested it in a sample offive restaurants. Once we achieved satis-factory performance in the five restau-rants, we added an additional five restau-rants to the test group. After a five-monthtest period, Taco Bell decided to imple-ment the new LMS in all company restau-rants. At the same time, we developed ameasurement and validation process toquantify and document the benefits of thenew system.

To measure and validate the LMS, wecollected information on the use of laborfrom restaurants using the old FAST labor-management system (part of the manage-ment routine in restaurants). At the end ofeach week, ineinbers of Taco Bell'soperations-services group, assisted bymembers of the information-systems-technology group and the restaurant-systems-engineering group, retrieved theprevious week's sales data, and appliedthe new LMS process to those data just asa restaurant manager would. We then

compared the labor use obtained by thatprocess to that reported as actually used inthe restaurants. We aggregated the resultsand reported the difference as actual laborsavings. We used this process during thefive-store and the 10-store operations testdescribed above. After companywide im-plementation, we reversed the process. Wereported the labor actually used under thenew LMS system. We then applied the oldFAST system to actual data. We then re-corded the difference in labor used by thesimulated application of the old FAST sys-tem and the new LMS system. Since 1993,the LMS system has shown a cotisistentdifference of one less hour per day of la-bor than the FAST system.Implementation Cost

From January 1991 until the LMS wasfully implemented, Taco Bell budgeted$287,500 to support the labor consultant,the job shopper, the industrial engineeringconsultants, and the programmers work-ing on the project. Approximately $90,000was budgeted to support the simulationmodeling effort, $80,000 for the data col-Iecfion and development effort, and$117,500 for the combined tasks of devel-oping the forecasting model and develop-ing the integer-programming model. Inaddition, the industrial-engineering func-tion grew into the restaurant-systems-engineering department. Today, this de-partment has four individuals with train-ing in operations research or industrial en-gineering who devote approximately halfof their time to using the LMS to improv(^the efficiency and effecfiveness of the TacoBell system.Model Benefits

Between 1993 and 1996, the number of

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Taco Bell company and franchised restau-rants using the new LMS system increasedfrom t,298 to 3,954. Labor costs savedthrough the use of the LMS system wentfrom $3.51 miUion in 1993 to $16.4 millionin 1996 (Table 1). Licensed stores are out-side the scope of the LMS. Taco Bell li-censes Taco Bell express stores, which de-liver fast food beyond the traditional "fourwalls of a restaurant." They operate insuch nontraditional locations as dispens-ing carts, kiosks, and mobile units and donot use Taco Bell's in-store processingsystems.

The total savings in labor costs Taco Bellhas achieved by using the LMS instead ofthe old FAST system over the four-yearperiod, 1993-1996, are over $40.34 million.These cost savings will continue to accrueeach and every year in relation to thegrowth of company and franchised unitsin the Taco Bell system.Improving the Quality and Consistencyof Customer Service

The documented savings in labor costsare impressive. Other benefits may beeven more significant. As a result of thisstudy, Taco Bell now has a new, flexible,and adaptable methodology for determin-ing how much labor it actually needs ateach store and how to deploy that labor in

t

1993 1994 1995 1996

Company storesusing LMS 1298 2763 2785 2550

Franchise storesusing LMS 0 412 809 1404

Labor cost savings(millions) $3.51 $8.54 $11.89 $16.40

Table 1: Growth in stores using the LMS andthe corresponding yearly savings over FAST.

the store. Before, with the FAST system,the manager was told how many hours oflabor were allocated to the store. It wasthen his or her responsibility to decidehow to most effectively deploy this labor.This resulted in variability in the level ofservice, hospitality, quality, and cleanlinessamong stores. Such variability is one ofthe most significant sources of customerdissatisfaction in the quick-service foodindustry. Eliminating variability increasescustomers' satisfaction, which leads tomore frequent visits and hence to highersales.

Another major benefit from the newLMS system is that it functions as a toolmanagers can use to schedule labor. A re-view of labor-scheduling practices by oneof the authors revealed that, in a numberof quick-food companies, managerstended to schedule too little labor to meetcustomer service goals during the peakhours, and too much during nonpeakhours of the day. This was attributed toboth the difficulties in scheduling itselfand the manager's perception that every-body tended to work faster during peakhours and the manager could always per-sonally staff a position to resolve a bottle-neck in production. Failing to deliver ex-cellent service during peak periods canlower the sales curve for the entire dayand vice versa, or as the well-known say-ing goes, "If you can't meet your sales,they will decrease until you can." The newLMS system has diminished this problemin the Taco Bell system.Enhancing Productivity

Taco Bell also avoided costs by using theLMS to evaluate the impact on labor of acontinuous stream of ideas for improving

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HUETER, SWART

key processes. Prior to the LMS, stake-holders debated the merits of suggestionsfor improvement based on qualitative fac-tors. More often than not, the suggestionsadopted were those supported by thehighest ranking or most influential stake-holder. With the LMS, they could quantita-tively evaluate the cost benefits of newideas and make decisions on a rationalbasis for the first time.

By using the LMS to guide their effortsto reengineer work flow in the kitchen toeliminate non-value-added work, reniovebottlenecks, and balance workloads, exist-ing stores improved productivity by docu-menting savings of $13 million per year inlabor costs. Furthermore, the company hasused the LMS to determine optimal drive-through window and queue designs, tomeasure the benefits of alternative designsfor point-of-sales communication devices,and to develop manual procedures for op-timizing the man-machine performance ofnew technology investments.Enhancing the Return on Investment ofNew Buildings

The LMS system was critical in support-ing Taco Bell's decision to adopt the "K-minus" concept. Taco Bell removed thelarge traditional kitchens from its restau-rants and transformed them intoassembly-line areas for assembling prod-ucts using prepared ingredients. The oldkitchens had occupied 70 percent of thetraditional Taco Bell restaurant, leavingonly 30 percent for customer seating. To-day, these proportions are reversed. Sincethen, the LMS has been an essential designinstrument in Taco Bell's expansion to in-line locations in malls and other locationsthat offer limited space and promise high-

volume sales.Impact of New Equipment and MenuItems

The company decided to provide freedrink refills, which later led to self-servicedrink islands in restaurants, only after us-ing the LMS extensively to determine theconfiguration, location, and staffing strate-gies that would minimize labor costswhile maintaining customer-service stan-dards. Before considering any new equip-ment, management uses the LMS to quan-tify its impact on labor and speed ofservice.

In its value strategy, Taco Bell intro-duced six core-menu items for the reducedprice of 59 cents, and later expanded to athree-tier value menu of 59, 79, and 99cents. This provided the impetus for thecompany's development of the LMS. Inturn, the LMS was a crucial tool that al-lowed Taco Bell to achieve the operationalefficiencies and cost savings that helped itto increase sales and earnings at an aver-age of 20 percent or more each year be-tween 1993 and 1996.Designing the Future

Taco Bell is using the models of the LMSto develop labor-productivity pilot-teststores. Without it, the company could notassess the effectiveness and efficiencies ofnew technologies, including customer-activated touch screens (CATS), the fullyautomated taco maker, and the ergonomicworkstation. While the models do not gen-erate designs for the alternative technolo-gies that could be incorporated in futureTaco Bell restaurants, they can reliablypredict which technologies should be in-corporated based on labor cost and cus-tomer service.

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TACO BELL

ConclusionThis study took six years. The new LMS

has now has been fully deployed as an in-tegrated system in the entire Taco Bell sys-tem of company-owned stores. Almostfour years have elapsed since its initial im-plementation, during which time virtuallyeverybody has scrutinized and evaluatedthe performance of the system, from storemanagers to vice-presidents of operationsand the CEO; however, a system like thisis a living entity. Everybody in the corpo-ration has a stake in it and has sugges-tions, criticism, praise, and questions. Wemust continually address these commentsand make appropriate responses. In doingso, we continually improve the system andincrease its contributions to the Taco Bellsystem. What is emerging in Taco Bell isthe realization that there is another strat-egy for success, that of information andtechnology, which can take its place nextto the traditional strategies of finance,marketing, and operations to help the firmsucceed in this most competitive ofindustries.Acknowledgments

Over the years, many individuals havecontributed to the effort described in thispaper. The LMS is a tribute to the peopleof Taco Bell, who once again have demon-strated that the organization is only asgood as its people. We would like to rec-ognize the contributions of the followingtbrmer "Taco Bellers" who have left theirmark on this study: Howard Frantz, nowat In 'n Out; Mark Godward and TomWyczawski of Strategic Restaurant Engi-neering; and Jane Gannaway of FlagstarCorporation. Last, but not least, we wishto recognize the guidance and help pro-

vided by our Edelman coach, Russ Labe ofMerrill Lynch's private client group.ReferencesEverett, H. 1963, "Generalized Lagrangean

multiplier method for solving problems ofoptimum allocation of resources," OperationsResearch, Vol. 11, No. 3, pp. 399-^17.

Ford, L. R. and Fulkerson, D. R. 1962, Flows inNetworks, Princeton University Press, Prince-ton, New Jersey.

Godward, M. and Swart, W. 1994, "An objectoriented simulation model for determininglabor requirements at Taco Bell," Proceedingsof the 1994 Winter Simulation Conference, eds.Jeffrey D. Tew, S. Manivannan, Deborah A.Sadowski, and Andrew F. Seila.

Loucks, J. S. and Jacobs, F. R. 1991, "Tourscheduling and task assignment of a hetero-geneous work force," Decision Sciences, Vol.22, No. 4, pp. 719-738.

Makridakis, S. and Wheelwright, S. 1978, Inter-active Forecasting, Holden-Day, Inc., San Fran-cisco, California.

Mishra, D, 1982, "Computerized work mea-surement," in Handbook of Industrial Engineer-ing, ed. G. Salvendy, John Wiley and Sons,New York.

Solomon, S. L. 1982, Simulation of Waiting LineSystems, Prentice Hall, Inc., Englewood Cliffs,New Jersey.

Swart, W. and Donno, L. 1981, "Simulationmodeling improves operations, planning,and productivity of fast food restaurants,"Interfaces, Vol. 11, No. 6, pp. 35-47.

Joaquin Pelaez, Senior Vice President ofTechnology and Quality for Taco Bell, saidduring the Edelman Competition presen-tation of the paper: "This is not just a la-bor management system. It is an incredi-bly powerful and adaptive decision-making tool to help us reliably predict thepotential impact of new, strategic initia-tives. . . . To assess and avoid risk.... Ourability to deliver superior shareholdervalue . . . and attract investors is funda-n:\entally strengthened."

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