Expanding Operations in Fast-Food Industry under Uncertain Market Conditions

Transcription

1 International Journal of Trade, Eonomis and Finane, Vol., No., June, X Expanding Operations in Fast-Food Industry under Unertain Market Conditions Seyhan Sipahi, Senior Member, IACSIT Abstrat In the last two years global eonomi risis has devastated almost all business setors. However, the fast food setor is a one of the industries that has managed to be saved from the risis. The industry is experiening phenomenal growth that has been stimulated by the onseutive inreases of onsumer onsumption and apital investment in many ountries. Investments into the setor reate ever growing furious ompetition expanding in the global markets. In this study, a simulation optimization spreadsheet model was proposed for the deision of expanding operations in the fast-food industry. The model was presented via a hypothetial illustrative example and was aimed to determine optimum number of stores and their loations onsidering unertain market onditions. The problem was solved by the use of RISKOptimizer optimization software, whih has been reently introdued and is a simultaneous appliation of the Monte Carlo simulation and geneti algorithm. Index Terms Deision Making, Fast-Food Industry, Simulation Optimization, Unertainty. I. INTRODUCTION Reent global eonomial risis, originated in USA, has had a rippling effet on almost all finanial setors in many ountries. Surprisingly, the fast food industry has not been adversely affeted; on the ontrary, it has shown signs of growing faster, reating more fiere ompetition. The main reason may be preautious spending of the food dollars on heaper fast food than on expensive luxury restaurants, fueled by the attrative pries offered by the advertisement ampaigns of the fast food ompanies. For instane, China, whih is the fifth largest fast food onsuming nation in the world, has been experiening phenomenal growth in the fast food industry; with the ompounded annual growth rates of the market rossing 2%. Moreover, with the fast emerging middle lass population and surging disposable inome, it is expeted that the industry will ontinue to grow at a pae in oming years [2]. In UK, the number of outlets selling food on-the-go has surged during the reession. Among the UK's biggest fast-food hains, Subway grew its number of restaurants by 2.9 per ent to 734 in 2009, Domino's inreased its outlets by 9.8 per ent to 20, and Eat expanded its estate by 7.8 per ent to 8, aording to the Loal Data Company's survey of 70 town enters. In the 0 biggest ities, fast-food outlets soared by 8.2 per ent to,4 premises, with London, Manusript reeived Deember, Shool of Business, Istanbul University. IU Isletme Fak. Avilar, Istanbul, Turkey (phone: ; fax: ; sipahi@istanbul.edu.tr). 74 Edinburgh and Glasgow leading the way []. In US, the fast food industry has reported signifiant growth despite being severely affeted by the eonomi turmoil. Aording to US Fast Food Market Outlook 200, fast food is an important segment of the restaurant industry and growth of this segment is outpaing the growth of overall restaurant industry [30]. Aording to PriewaterhouseCoopers, fast food is proving to be reession-proof in emerging markets. Beause the fast-food setor works from a franhise model, expansion into emerging markets has been rapid and omparatively low-risk in finanial terms. In spite of eonomi reession, firms like Subway, MDonalds, KFC and Domino's Pizza have opened new stores in the emerging markets in the reent times [4]. Subsequently, many food ompanies are trying to inrease their profits by pushing into new markets in the global sale. Fast food restaurant site seletion problem have been analyzed in several studies. In Melaniphy s study [7], priniples of fast food restaurant site seletion were emphasized. In the study, geography, sales size and trends, market size, type of loation, aessibility, topography, visibility, adjaent uses, ompetition and demographis were onsidered loation riteria. Timor and Sipahi [22] surveyed fast food restaurant site seletion riteria and they analyzed their relative weights using Analyti Hierarhy Proess method. Aording to their study, ost, loation, visibility, traffi pattern, ompetition, areas future and physial harateristis were found the most important fators. In another study, Lorentz [3] analyzed the loation deision riteria and their relative weights in the ontext of food manufaturing internationalization into emerging markets. The study demonstrated that eonomi ativity potential, onsumption potential, available aquisition targets, adequate supply of raw materials, ompetitive situation, favorable level of input osts, and supply hain management readiness are the most important fators in the loation deision. In their study, Hurvitz et al. [23] examined potential assoiations between demographi fators, a novel transportation variable and fast-food restaurant density in King County at the ensus trat level. Their study demonstrated that fast food restaurant density was signifiantly assoiated in regression models with low median household inome and high arterial road density. Differing from previous studies, the purpose of this study is to propose an alternative spreadsheet optimization model for the problem of expanding operations in multiple loations in the fast-food industry. The model aims to determine the optimum number of new stores in various loations onsidering unertainty in the market onditions. The model

2 International Journal of Trade, Eonomis and Finane, Vol., No., June, X was presented and solved by RISKOptimizer optimization tool, whih suessfully ombines geneti algorithm with Monte Carlo simulation. II. SIMULATION OPTIMIZATION APPLICATIONS IN SERVICE SECTOR The simulation optimization an be defined as the proess of finding the best input variables among all possibilities without expliitly evaluating eah of the possibilities [33]. Overview of methodologies and appliations was onduted by Carson and Maria [33] and Azadivar []. In the reent years there is a growing interest in the use of simulation optimization models in the servie setor. For instane, in their study, Huthison and Hill [0] investigated ways to redue the ost and magnitude of air traffi delays. They used the SIMMOD air traffi simulation to model the problem in order to determine a set of ontrol measures that ahieve the best system performane. The model was a onstrained stohasti optimization problem with nonlinear objetive funtion and nonlinear stohasti onstraints. They used the simultaneous perturbation stohasti approximation algorithm. In another study, Su and Yao [2] onduted a field study in a Shanghai publi hospital to investigate the hospital s registration proess and olleted the data to determine the elapsed time between the inter-arrival time and every proedure's servie time. By using MedModel software, they set up a simulation model based on the realisti workflow and the operational data. The optimized registration proess was able to shorten the average yle time from 7.24 minutes to 3. minutes. Gosavi, Ozkaya and Kahraman [] developed a model-free simulation-based optimization model to solve seat-alloation problem arising in airlines. Their model was designed to aommodate a number of realisti assumptions for real-world airline systems. Dzeng and Lee [27] proposed a model searhing the optimal development shedule of resort projets by integrating simulation and a polyploidy geneti algorithm. The model prioritized the value that eah amenity brought to the projet, and onstruted the development shedule based on net present values. In their study, Dittman and Hesford [9] ombined optimization with Monte Carlo simulation to model an investment deision by means of a ase study of a hotel onsidering onverting a portion of its inventory to allergy-friendly rooms. Klassen, and Yoogalingam [9] used a simulation optimization approah to determine optimal rules for a stohasti appointment sheduling problem in an outpatient health are servie. Their model allowed for the onsideration of more variables and fators than did prior studies. RISKOptimizer is one of the ommerially available tools for modeling and solving simulation-based optimization problems. It uses a proprietary set of geneti algorithms to searh for optimum solutions to a problem, along with probability distributions and simulation to handle the unertainty in the model [2]. While the optimization is in progress, it runs a Monte Carlo simulation for eah trial solution and finds the ombination of deision variables that provide the best simulation results. In the last ten years, RiskOptimizer has been used in several studies espeially in finane and manufaturing. Table summarizes remarkable studies that RiskOptimizer was utilized. Ref. # TABLE LITERATURE OF RISKOPTIMIZER STUDIES Journal/Publiation Name Year Author(s) Problem Definition [29] J of Revenue and Priing Man S. Kumar; T. Ressler; M. Ahrens 7 Seletion of an optimal produt onfiguration for a new business venture [2] Expert Systems with Appl F. C. Yuan Appliation of real options to value expansion investment [] J of Business Case Studies 2008 D. F. Togo Risk Analysis for apital budgeting J. Sounderpandian, S. Prasad, Optimizing order quantity when raw material suppliers of a [] Omega 2008 M. Madan global supply hain are situated in developing ountries Cornell Hotel and Restaurant Simulation optimization for investment deision for a hotel [9] 2007 D. A. Dittman; J. W. Hesford Administration Quarterly onsidering the onverting a portion of its inventory to [8] Eology Letters 2007 J. C. Svenning; F. Skov Estimating migration rate probability distribution funtions [2] Journal of Applied Statistis 2007 M. Evans; R. E. Johnston Optimizing prodution sheduling model [8] Journal of Engineering Design 200 D. Laey; C. Steele [3] Omega 200 C. Chandra, M. Everson, J. Grabisa [20] Probl. and Perspetives in Man M. A. G. Dias; M. A. Rivera Modeling of dynami hedging Augmenting design of experiments for optimization and robustifiation Determining the overall business value of flexible manufaturing systems [7] Information and Software 2004 D. Greer, G. Ruhe Software release planning Teh J of the Operational Res. What strategy should a football (soer, in Amerian [4] 2003 C. Hope Soiety parlane)lub adopt when deiding whether to sak its manager [24] Soial Siene & Mediine 2003 P. Sendi, M. J. Al Cost effetiveness analysis under unertainty [28] Logistis Information Man. 200 [3] Western Agriultural Eonomis Assoiation Annual Meeting 2000 III. PROBLEM DEFINITION R. L. Nersesian; M.D. Troutt; G. J. Weinroth W. A. Wilson; B. L. Dahl; D. D. Johnson The hypothetial problem presented is based on Ragsdale Maximizing quality performane in terms of a ost-benefit analysis Evaluating ost/risk tradeoffs of three alternative prourement strategies []. Suppose that the franhisee of an international fast-food ompany is planning to expand its operations to more outlets in the nation for the oming year. The ompany operates

3 International Journal of Trade, Eonomis and Finane, Vol., No., June, X three different types of stores as lunh ounters, eat-in stores and stand alone stores. It has identified several loations for eah type of new stores. There are two ategories of osts assoiated with the new stores; setup ost and annual operation and maintenane ost. The ompany has made three operating ost versus annual return on investment estimates for eah of the prospetive site; a minimum possible value, a most likely value, and a maximum value. For eah type of store, ost and return on investment estimates in dollars and full-time employees needed for alternative sites are given in Tables 2 through 4. The ompany wants to keep the total setup ost under $4,000,000 and the annual operating ost under $,000,000. It aims to open minimum two and maximum twenty outlets for eah type of stores. Also it does not want to open more than three stores in the same loation. Even though the ompany does not have any limits or onstraint on the number of employees, it is preferable to employ about minimum of 20 employees in total. The objetive of the problem is to determine the number of stores to be opened in eah site to maximize the total revenue of the new stores due to limited setup and operating budget. TABLE 2. DATA FOR LUNCH COUNTER STORE ALTERNATIVES Alternative Sites Lunh Counter A B C D Employees Needed Annual Op. Cost (Worst) $80,000 $8,000 $70,000 $9,000 Annual Op. Cost (Most $0,000 $7,000 $0,000 $80,000 Likely) Annual Op. Cost (Best) $0,000 $0,000 $0,00 $,000 Costs $0,000 $80,000 $0,000 $200,000 Annual Return $0,000 $7,000 $0,000 $90,000 (Worst) Annual Return (Most $8,000 $00,000 $90,000 $0,000 Likely) Annual Return (Best) $00,000 $,000 $3,000 $44,000 TABLE 3 DATA FOR EAT-IN STORE ALTERNATIVES Alternative Sites Eat-In Stores E F G H I J Employees Needed Annual Operating Cost (Worst) $80,000 $70,000 $,000 $7,000 $90,000 $,000 Annual Operating Cost (Most Likely) $,000 $0,000 $4,000 $0,000 $7,000 $,000 Annual Operating Cost (Best) $,000 $4,000 $38,000 $2,000 $,000 $42,000 Costs $27,000 $200,000 $0,000 $20,000 $300,000 $20,000 Annual Return (Worst) $9,000 $7,000 $80,000 $90,000 $00,000 $90,000 Annual Return (Most Likely) $2,000 $9,000 $00,000 $20,000 $0,000 $2,000 Annual Return (Best) $4,000 $30,000 $20,000 $40,000 $4,000 $0,000 TABLE 4 DATA FOR STAND ALONE STORE ALTERNATIVES Alternative Sites Stand Alone Stores K L M N O Employees Needed Annual Operating Cost (Worst) $90,000 $7,000 $9,000 $00,000 $98,000 Annual Operating Cost (Most Likely) $80,000 $70,000 $80,000 $8,000 $7,000 Annual Operating Cost (Best) $,000 $3,000 $72,000 $80,000 $4,000 Costs $40,000 $420,000 $480,000 $480,000 $42,000 Annual Return (Worst) $0,000 $0,000 $,000 $0,000 $20,000 Annual Return (Most Likely) $7,000 $0,000 $80,000 $200,000 $0,000 Annual Return (Best) $9,000 $7,000 $90,000 $220,000 $78,000 IV. METHOD AND FINDINGS When the annual operating ost and expeted annual return on investment value for eah new store are deterministi, a solution may be obtained by the use of integer linear programming. However, in this problem, annual operating ost and annual return values are probabilisti. For modeling and solving the problem as a linear model, the annual operating osts and annual returns of eah alternative site must be deterministi values. In the Table, for alulating the expeted values of annual operating osts and annual returns, worst values, most likely values and best values were weighted by 2%, 0% and 2% respetively. Considering the Table, the integer linear programming model of the problem an be expressed as follows: Maximize R x = Subjet to: 4 e= Re y e Rs zs () 7

4 4 = 4 = O x Sx 4 = e= e= 2 x 2 2 y= International Journal of Trade, Eonomis and Finane, Vol., No., June, X Oe ye Oszs,000, 000 Se ye Sszs 4,000, ye 20 zs 20 0 x 3 for =,2, y e 3 for e =,2,... 0 z s 3 for s =,2,... x = integer for =,2,...4 y e = integer for e =,2,... TABLE INTEGER LINEAR SPREADSHEET MODEL OF THE PROBLEM z s = integer for s =,2,... R, R e, R s = Annual returns of lunh ounter, eat-in stores and stand alone stores respetively O, O e, O s = Operating osts of lunh ounter, eat-in stores and stand alone stores respetively S, S e, S s = Setup osts of lunh ounter, eat-in stores and stand alone stores respetively x, y e, z s = Number of lunh ounter stores, eat-in stores and stand alone stores respetively to be opened in the orresponding alternative site The problem was solved by Mirosoft Exel Solver, whih is a powerful spreadsheet tool for solving linear and some nonlinear models. The optimum solution was presented in the Table. In the Table, number of stores to be opened in alternative sites for eah type of operation was defined as deision variable of the problem. Total labor, total operating ost, total setup ost and total annual return were alulated in the range I:I4. The formulas of the integer linear spreadsheet model were presented in the Table. Inspeting the Table, it an be seen that the total revenue was found as $,930,000. Number of lunh ounter stores, eat-in stores and stand alone stores were found as 3,, and 2 respetively. The total number of employees is 29, whih is exeeding 20. TABLE FORMULAS OF THE INTEGER LINEAR SPREADSHEET MODEL CELL FORMULA B4 =80000*0,20000*0,00000*0,2 F7 H4 G2 I I2 I3 I4 =SUM(B7:E7) =SUM(B4:G4) =SUM(B2:F2) = SUMPRODUCT($B$7:$E$7,B3:E3) SUMPRODUCT ($B$4:$G$4,B0:G0) SUMPRODUCT ($B$2:$F$2,B7:F7) = SUMPRODUCT ($B$7:$E$7,B4:E4)SUMPRODUCT ($B$4:$G$4,B:G) SUMPRODUCT ($B$2:$F$2,B8:F8) = SUMPRODUCT ($B$7:$E$7,B:E)SUMPRODUCT ($B$4:$G$4,B2:G2) SUMPRODUCT ($B$2:$F$2,B9:F9) = SUMPRODUCT ($B$7:$E$7,B:E)SUMPRODUCT ($B$4:$G$4,B3:G3) SUMPRODUCT ($B$2:$F$2,B20:F20) Even though the problem an be modeled as an integer 77 linear model by the use of expeted values of operating osts and annual returns, it would be more realisti to onsider the probability distribution of operating osts and annual returns.

5 International Journal of Trade, Eonomis and Finane, Vol., No., June, X However, the spreadsheet model would be non-deterministi. For modeling unertainty and solving the and RiskOptimizer.0, powerful Mirosoft Exel spreadsheet tools by Palisade Corporation were ombined. The RiskOptimizer spreadsheet model of the problem was presented in the Table 7. TABLE 7 RISKOPTIMIZER SPREADSHEET MODEL OF THE PROBLEM TABLE 8 RISKOPTIMIZER MODEL SETTINGS RISKOptimizer Model Optimization Goal Maximum Constraints Type Cell to Optimize =I4 2 <= F7 <= 20 Hard Statisti to Optimize Mean 2 <= H4 <= 20 Hard Adjustable Cell Ranges: 2 <= G2 <= 20 Hard 0 <= B7:E7 <= 3 Integer 0 <= I2 <= Hard 0 <= B4:G4 <= 3 Integer 0 <= I3 <= Hard 0 <= B2:F2 <= 3 Integer At the RiskOptimizer model, number of iterations and total simulation number were set as 000. It means that during the optimization proess 000 trials were made. Furthermore, sine all onstraints were defined as hard (Table 8), only trials that satisfy all onstraints and optimality onditions were taken into aount during the simulation. The summary of optimization results were presented in the Table 9. The optimal value of total annual revenue was found as $,720,93. Optimum solution was evaluated at 438th simulation. For lunh ounter stores, opening three stores in the site A was found optimal. For eat-in stores, opening two stores in the site F, one store in the site G, and three eat-in stores in the site J was found as the best solution. Also opening three stand-alone stores in the site K and two stand-alone stores in the site L was optimal. As an be seen in Table 7, all onstraints and optimality onditions were satisfied. TABLE 9 RISKOPTIMIZER MODEL RESULTS Total Simulations 000 Number of Iterations 000 Valid Simulations 42 Best Value Found $,720,93 Best Simulation Number 438 V. CONCLUSION Admittedly, the unfavorable eonomi onditions are the major driver in business deisions in many industries suh as fast-food business. For the ompanies projeting to expand their operations to more outlets, it is definitely essential to be onerned with the unertain market onditions while making the best deisions. The expeted hanges in operating osts and annual return in investments should be srutinized. When problem data is deterministi, this type of deision problems an be modeled and solved easily by the use of linear integer programming. However, when the problem annot be solved by linear models due to unertain fators or stohasti variables, the RISKOptimizer spreadsheet tool an provide aurate solutions by using probability distribution funtions and ombining the Monte Carlo simulation with geneti algorithm-based optimization. REFERENCES [] A. Gosavi, E. Ozkaya, and A.F. Kahraman, Simulation optimization for revenue management of airlines with anellations and overbooking, OR Spetrum, vol. 29, 2007, pp [2] Business Wire, (2008, Otober 7) Researh and Markets: China's Fast Food Industry is Expeted to Grow at a CAGR of around 2%, Available: estaurants-fast/9709-.html [3] C. Chandra, M. Everson, and J. Grabis, Evaluation of enterprise-level benefits of manufaturing flexibility, Omega, vol. 33, 200, pp

6 International Journal of Trade, Eonomis and Finane, Vol., No., June, X [4] C. Hope, When should you sak a football manager? Results from a simple model applied to the English Premiership, Journal of the Operational Researh Soiety, vol. 4, 2003, pp [] C. T. Ragsdale. Managerial Deision Making. Thomson South-Western Publishing, 2008, p [] D. F. Togo, Sunset Company: Risk analysis for apital budgeting using simulation and binary linear programming, Journal of Business Case Studies, vol. 4(3), Marh 2008, pp [7] D. Greer, G. Ruhe, Software release planning: an evolutionary and iterative approah, Information and Software Tehnology, vol. 4, 2004, pp [8] D. Laey, and C. Steele, The use of dimensional analysis to augment design of experiments for optimization and robustifiation, Journal of Engineering Design, vol. 7(), January 200, pp. 73. [9] D.A. Dittman, and J.W. Hesford, A simulation-based optimization approah for investment deisions: A ase study of pure allergy-friendly rooms, Cornell Hotel and Restaurant Administration Quarterly, vol. 48(), 2007, pp [0] D.W. Huthison, and S.D. Hill. Simulation optimization of airline delay with onstraints and multiple objetives, Unertainty Modeling and Analysis, ISUMA Fourth International Symposium on, 2003, pp [] F. Azadivar, Simulation Optimization Methodologies, Proeedings of the 999 Winter Simulation Conferene, 999, pp [2] F.C. Yuan, Simulation optimization mehanism for expansion strategy using real option theory, Expert Systems with Appliations, vol. 3, 2009, pp [3] H. Lorenz, Prodution loations for the internationalising food industry: ase study from Russia, British Food Journal, vol. 0( 3), 2008, pp [4] InfoGrok, Fast food setor to expand in emerging markets in 2009?, (2009, 22 Otober), Available: /predition-onsumer/fast-food-setor-to-expand-in-emerging-market s-in-2009.html [] J. Sounderpandian, S. 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Johnston, Stohasti modelling of times to temperature for furnaes supplying titanium blooms to a rolling mill at TIMET, Journal of Applied Statistis, vol. 34( 4), May 2007, pp [22] M. Timor, S. Sipahi, Fast-food restaurant site seletion fator evaluation by the analyti hierarhy proess, The Business Review, Cambridge, vol. 4(), Summer 200, pp. -7. [23] P. M. Hurvitz, A.V. Moudon, C. D Rehm, L. C. Streihert, and A. Drewnowski, Arterial roads and area soioeonomi status are preditors of fast food restaurant density in King County, WA, International Journal of Behavioral Nutrition and Physial Ativity, 2009, doi:0.8/ , pp. -4. [24] P. Sendi, M. J. Al, Revisiting the deision rule of ost effetiveness analysis under ertainty and unertainty, Soial Siene & Mediine, vol. 7, 2003, pp [2] Palisade Corporation (2009, Marh ), RiskOptimizer: Optimization with simulation for spreadsheets, Available: [2] Q. Su, and X.Yao, Simulation and optimization of the hospital registration proess using MedModel, Servie Operations and Logistis, and Informatis, SOLI '0, IEEE International Conferene on, 200, pp [27] R. Dzeng, and H.Y. Lee, Optimizing the development shedule of resort projets by integrating simulation and geneti algorithm, International Journal of Projet Management, vol. 2, 2007, pp [28] R. L Nersesian, M. D. Troutt, and G. J. Weinroth, Utilizing RiskOptimizer to manage quality performane, Logistis Information Management, vol. 4(3), 200, pp [29] S. Kumar, T. Ressler, and M. Ahrens, Deision support model based on risk return tradeoff for examining viability of a business venture, Journal of Revenue and Priing Management, vol. 8, 2009, pp [30] US Fast Food Market Outlook 200, (2009, Deember ), FoodSetorNews.om, Available: http: // US-Fast-Food-Market-Outlook-200-Reports/IM04.htm [3] W. A. Wilson, B. L. Dahl and D. D. Johnson, Prourement strategies: impats of quality risks in hard wheat, Western Agriultural Eonomis Assoiation Annual Meeting, Vanouver, British Columbia, June 29-July, [32] W.L. Winston, and S.C. Albright. Pratial Management Siene. Revised 3e. South-Western Publishing, [33] Y. Carson, and A. Maria, Simulation optimization: Methods and appliations, Proeedings of the 997 Winter Simulation Conferene, 997, pp Seyhan Sipahi is an Assistant Professor of Quantitative Methods at Shool of Business, Istanbul University. She earned her B.S. in Business Administration from the Istanbul University and her M.A. and Ph.D. in Quantitative Methods from the Soial Siene Institute of the Istanbul University. Her PhD thesis was on Ranking Cities of Turkey by Livability Using Analyti Hierarhy Proess. In 2007, she was visiting researh sholar at Operations and Deision Tehnologies Department at Kelley Shool of Business, Indiana University. Her primary fields of researh are Spreadsheet Modeling, Multi-Attribute Deision Making in Business, Simulation Optimization, and Management Siene Appliations in Tourism and Sport. She has several national and international artiles published in reputed aademi journals. She is a member of Multi-riteria Deision Making (MCDM) Soiety, and senior member of International Assoiation of Computer Siene and Information Tehnology, (IACSIT). She is assoiate editor of the Journal of the Shool of Business Administration, Istanbul University. She works for Shool of Business Quantitative Methods Department sine 997.