Benihana Simulation Analysis

1 Managing service operations

Page 1 of 14 Benihana simulation analysis


Table of Contents

Introduction

3

Case analysis

4-12

References

13



Introduction The simulation exercise involving the Restaurant “Benihana” provides a classic example of deciding upon the optimal operating strategy for service operations keeping several process variables in view. The simulation consists of five strategic challenges related to Batching, Bar/ dining space design, advertisement & restaurant timing. Like any business, the objective of the simulation was to maximize top and bottom line of the business with simultaneous increase in throughput. The simulation assumes that all other resources of the restaurant geared up to meet the batching strategies adopted by the organization and most importantly the customers shall be malleable to the batching strategies, wait times for batching and reduced dining times. However, the biggest limitation of the simulation is that customer preferences are very dynamic and may change best upon business environment and economics. These types of simulations do not address profit maximization only quantitatively whereas the qualitative issues are ignored or are too complex to map. Discussion on decision parameters: During the simulation, each challenge was analyzed separately for an optimal solution. During this analysis, various combinations of options were run for some of the challenges with a view to maximize profitability. However, in some of these concerning batching ,a strategy of elimination was adopted to arrive at an optimum solution by eliminating a “ no batching approach” since the challenge I had made it obvious that batching was always a much optimal solution to increase profitability due to a substantially higher resource utilization. Batching, as we understand is central to operations management asin this case Maximizing nightly profitability of operations and throughput was taken as a hall mark for an optimal solution design as compared to revenue as revenue alone with lesser profits cannot sustain a business on a long term basis.



Presentation and analysis of simulation results: Challenge no I: Batching or no batching decision The first change was to arrive at a batching or no batching decision. Accordingly two scenarios were chosen separately to run the simulation and arrive at a decision. In all cases it was found that the decision to batch gave the highest nightly profits and a throughput with minimal loss of customers. The results were as follows: Scenario

Revenue($)

Batching No Batching

3155 2909

Nightly Profits ($) 121.8 (201.58)

Asset utilization (%) 57.11 44.57

Lost customers(No.) 95 24

Upon detailed analysis of the financials of operations, the following was observed: 





With batching , the restaurant could serve 71.35 dinners extra and consequently revenue went up as dinners served earned a 400% higher revenue to the restaurant as compared to the drinks sold , which was substantially higher in case of no batching. This has also reflected in the substantially higher average utilization of 57.11% in the dining room for batching as compared to 44.57% capacity utilization in case of nom batching. (Forio,2015) The average wait times in case of batching were 248 % lower in case of batching and the resultant customer loss was also 75% lower. This indicates a significantly better customer wait time’s management in case of batching.(Forio,2015) Though the overall cost of running the restaurant operation with batching ($ 3025.84) and non-batching($ 3035.97) have not varied much, the difference in the revenue / profit earning capabilities in batching has come with much higher capacity utilization of assets / profit margins of the dining room operations.(Forio,2015)

The above observations conclusively establish that batching is a much better approach to management of customers from the perspective of revenue management though it may have other issues such as customer inconvenience in terms of sitting together for dinner with unknown persons. However, this may also be well accepted by customers if the restaurant is able to establish this as a hot trend and a way to network with others. In fact, batching seems to be a successful strategy in Benihana operations that in subsequent challenges, we have focused much more on batching as compared to non-batching operation.



Challenge II: Bar/ Dining room seating design This challenge involves design of the bar/dining room seats leading to higher revenue, profits and throughput. In this case, after observing the significant advantages offered by batching, substantially higher simulation runs were taken with batching as compared to non-batching operation. The details are as follows: Scenario

Nightly Profit ($)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

($55.66) ($495.71) ($3.32) ($428.01) $20.82 ($345.41) $48.55 ($270.16) $80.46 ($226.07) ($226.07) $121.80 ($201.58) $155.61 ($184.70) $213.56 ($184.16) $242.38 ($195.50) $214.79

Total Revenue ($) $2,830.89 $2,146.32 $2,907.98 $2,288.59 $2,946.45 $2,472.92 $2,996.30 $2,652.60 $3,060.09 $2,806.60 $2,806.60 $3,155.34 $2,909.82 $3,268.62 $3,002.26 $3,445.48 $3,065.31 $3,583.55 $3,099.28 $3,627.51

Revenue Bar ($) $125.39 $111.32 $155.48 $218.59 $194.45 $361.43 $244.30 $516.60 $308.09 $713.10 $713.10 $403.34 $871.32 $553.62 $1,031.26 $752.98 $1,183.31 $963.55 $1,315.28 $1,160.51

Revenue Dinner ($) $2,705.50 $2,035.00 $2,752.50 $2,070.00 $2,752.00 $2,111.50 $2,752.00 $2,136.00 $2,752.00 $2,093.50 $2,093.50 $2,752.00 $2,038.50 $2,715.00 $1,971.00 $2,692.50 $1,882.00 $2,620.00 $1,784.00 $2,467.00

Use Batching

Bar Size

Restaurant Size

Yes No Yes No Yes No Yes No Yes No No Yes No Yes No Yes No Yes No Yes

15 15 23 23 31 31 39 39 47 47 47 55 55 63 63 71 71 79 79 87

19 19 18 18 17 17 16 16 15 15 15 14 14 13 13 12 12 11 11 10

It can be seen that the maximum nightly profits of $ 242.38 are made with a bar/ dinner seat size of 79/11, which is also depicted in the graph drawn below(Forio,2015)


6 Managing service operations 300 250 200

Bar seats Nightly Revenue('00,$) % Utilization

150

Customers lost Nightly Profit($)

100 50 0 1

2

3

4

5

6

7

8

The following points are observed from the above: 

Though the profits are higher in a combination of 79/11 bar seats, the revenues ($ 3627) are highest in a combination of 87/10 seats. This indicates that after a limit, increasing bar seats with reduction in dining seats starts eating into profitable dining rooms operations ( 262 versus 246 dinners served) as more customers (52) are lost due to higher wait times. (Forio,2015)



“No Batching” is never a solution as business numbers generated by batching is substantially superior.

Challenge III: Change dining time The 3rd challenge looked into changing the dining duration based upon the time of the day. The idea is to maximize asset utilization and throughput by designing optimal customer durations between three dinner times in slots of 5pm-7pm, 7pm – 8pm (peak time) & 8pm-10.30 pm. The restaurant can adapt various means to reduce service times such as reduction of planning, product /service design, processing, change over & delivery times (Operations Management – Stevenson, William J.,11th edition) Obviously, the restaurant is to design its systems in such a way that customer satisfaction is not compromised resulting is the lowest possible loss of revenue and customers and highest possible profits.Further, in designing the runs, an intelligent guess has been made based upon an observation that dining room profitability needs to maximized to maximize profits and hence the asset utilization of dining room needs to increased. This can be done by optimizing the dining Page 6 of 14 Benihana simulation analysis


time of customers so that more and more numbers of customers can be accommodated. Accordingly, the run was started with a higher times but our aim has been to reduce the time as much as possible. The scenarios which were run on the simulation were as follows: Dining Time ( in minutes) Scenario

1 2 3 4 5 6 7 8 9 10

Nightly Profit ($) $64.34 $124.99 $121.80 $117.36 $117.36 $114.14 $110.91 $220.00 $217.75 $186.01

Total Revenue ($) $3,135.88 $3,165.96 $3,155.34 $3,140.55 $3,140.55 $3,113.48 $3,102.69 $3,221.33 $3,213.83 $3,186.20

Revenue Bar ($) $531.38 $413.96 $403.34 $388.55 $388.55 $347.48 $336.69 $245.33 $237.83 $277.20

Revenue Dinner($)

Open to 7pm

7pm to 8pm to 8pm 10:30pm

$2,604.50 $2,752.00 $2,752.00 $2,752.00 $2,752.00 $2,766.00 $2,766.00 $2,976.00 $2,976.00 $2,909.00

71 60 60 60 60 45 45 45 45 60

71 60 60 60 60 60 60 45 45 45

70 75 60 45 45 60 45 75 45 45

Further, a graphical representation of the data is as follows: 1000

100

Scenario Revenue('00,$) Capacity Utilization(%) Lost customer Nightly Profit($)

10

1 1

2

3

4

5

6



From the above data,it can be observed that both nightly profits and Revenue is maximized when a time of 45/45/75 minutes is chosen for the restaurant. This reinforces our thought that the timings need be as less as possible in order to have maximum throughput and asset utilization. This is especially true for the opening and the peak time butcan be extended during the non-peak time of 8-10.30 hours wherein the customer numbers are dwindling.(Forio,2015) In fact, a further analysis between 45/45/75 and 45/45/45 scenario indicates very little difference in profits and revenue. This signifies that though the asset utilization goes up in the later scenario (53.1% versus 44.3%) due to probably the same customers sitting for a longer period between 810.30 pm, it adds very little to the revenue and profits. A loss of just one customer in both the scenarios indicate that no new customer arrivals after 8 pm. It is not surprising that under these circumstances, 45/45/75 scenario adds to slightly higher profits as the same customer probably orders more items as he is sitting longer.(Forio,2015) Challenge IV: Boost Demand with Advertising and Special Programs The 4th challenge has been to devise a strategy to boost demand through advertising and special programs. In this case, the number of variables are projected in the simulation are three and consist of the advertising budget, advertising campaign and the restaurant opening time. Further, three choices of timings and campaign are also provided. In this case, the following runs were made to arrive at the right strategy: Advt. Budget/Campaign/Ti ming

Scenar io

Revenue( $)

Custome rs Lost

3620

Capacit y Utilizati on (%) 54

20

Nightl y Profit( $) 317

1X/Advertising Budget /5pm 1X/Discount promotion /5pm 1X/Happy Hour /5pm 1X/Happy Hour /6pm 1X/Happy Hour /7pm 2X/Happy Hour /5pm 2.2X/Happy Hour/5pm 2.5X/Happy Hour/5pm 3X/Happy Hour /5pm

1 2

3141

54

20

-110

3 4 5 6 7

3502 2967 2209 4369 4547

54 55 54 74 77

8 6 8 28 34

452 200 -161 515 514

8

4687

79

45

448

9

4821

80

68

286

For a better understanding, a graphical analysis of the above data is also being charted below: Page 8 of 14 Benihana simulation analysis


100 80 60 Scenario Revenue($'00)

40

Capacity Utilisation(%) Customers Lost

20

Nightly Profit($,'0)

0 1

2

3

4

5

6

7

8

9

-20 -40

My observations on the above data sets are as follows: 

Any discount promotion scheme can be ruled out as it leads to an unacceptable negative profitability



Advertisements play a significant role in generating profitability but after a certain threshold, additional expenses on advertising does not generate any additional revenue and profitability but rather depresses them as advertising expenses go up.



It is extremely important to target the right time for the advertisement. It makes common sense to advertise during the opening and peak hours but more so in the opening hours of the restaurant.

Keeping the above aspects in view, it was found that an advertising budget of 2X, advertising campaign targeted at the happy hour and restaurant opening time of 5pm had the maximum impact on revenue ($ 4369) and profitability ($515) with minimum loss of customers (24). This configuration also yielded a very high capacity utilization of 74%.(Forio,2015) Challenge V: Use Different Types of Batching at Different Times



The last challenge in the simulation was to use different types of batching at different times. The simulation challenge consists of 36 variables comprising of three different restaurant timings and four different possible batching combinations under each of these three timings.(Forio,2015) The data runs carried out for this simulation was as follows: Scenario

Open to 7pm

7-8 pm

810.30pm

Revenue ($)

1

Table of 8 Table of 4-8 4 share a table

Table of '8 Table of '8 Table of '8

Table of 4 to 8 Table of 4 to 8 Table of 4 to 8

2 3

3269

Capacity Utilization (%) 57

Customer Lost 28

Nightly Profit ($) 140

3129

56

26

105

3135

56

30

92

My basic premise to carry out this simulation based upon the past challenges was twofold: Profits can be only optimized whenthe asset utilization is maximized at the opening and peak times with minimum loss of customers. This can be only done by batching the largest number of customers (8) in these two slots. At the same time, the restaurant can lenient on batching during 8-10.30 pm slot when the new customers stop arriving. Predictably, the scenario one yielded the best profitability ($ 140) and revenue ($ 3269) in this case with all other scenarios yielding substantially less profits.(Forio,2015) Challenge VI: Design your Best Strategy This simulation challenge involves designing the best strategy for maximizing profitability, revenue and throughput by modifying the batching decision, dining rooms decision, advertising decision and bar decision after applying the learning’s from the past challenges. In order to run the challenge, we made the following strategic presumptions: Batching decision was always maximized at 8 per batch with a view to increase asset utilization and throughput. Based upon learning’s from past challenges, a smaller batching number was run for the 8-10.30 pm slot.(Forio,2015) Again, based upon learning’s from challenge number IV, advertising campaign for happy hour was chosen and slotted for the 5 pm time. As an experiment, we also tried other combinations on this exercise just to test the assumption and to observe that the profits have dipped.(Forio,2015) Again, we started with the presumption that the customer turnaround has to be kept at the minimum for the opening and peak times with a relaxation only at the 8-10.30 slots. This would ensure maximum asset utilization leading to better revenue and profitability.(Forio,2015) Page 10 of 14 Benihana simulation analysis


Lastly we started with a large bar size of 79 and tried various iterations in order to arrive at the best possibly combination as depicted in scenario 3 of the following table(Forio,2015)

It was found that with combination of various variables addressed together as above, a maximum nightly profit of $ 708 and revenue of $ 4330 could be achieved with loss of only 4 customers and a high capacity utilization of 61.93%.(Forio,2015) Scenar io Name

Nightly Profit($,' 0)

Total Revenue($,' 00)

1

$605.47

$4,659

2

$553.42

$4,609

3

$708.17

$4,639

4

$708.17

$4,639

5

$708.17

$4,639

6

$708.17

$4,639

Reven ue Bar($,' 0) $690.9 3 $724.2 6 $309.4 2 $309.4 2 $309.4 2 $309.4

Reven ue Dinner ($'00) $3,969 $3,885 $4,330 $4,330 $4,330 $4,330 Page 11 of 14

Benihana simulation analysis


7

$689.56

$4,900

8

$679.64

$4,675

9

$672.69

$4,423

10

$672.69

$4,423

11

$658.36

$4,462

12

$658.36

$4,462

13

$658.36

$4,462

14

$250.82

$3,294

15

$155.61

$3,269

16

$38.52

$4,490

17

($180.64)

$3,706

18

($197.46)

$3,916

19

($217.19)

$3,985

20

$121.80

$3,155

2 $361.1 1 $398.3 2 $265.1 3 $265.1 3 $332.9 1 $332.9 1 $332.9 1 $329.3 3 $553.6 2 $771.9 4 $811.2 3 $726.0 8 $936.9 5 $403.3 4

$4,539 $4,277 $4,158 $4,158 $4,129 $4,129 $4,129 $2,965 $2,715 $3,718 $2,895 $3,190 $3,049 $2,752

Reflection of lessons learned: My key takeaways from the Benihana simulation analysis were as follows: 

The various critical variables of any restaurant (or a service industry) are interdependent. Therefore even though we may design the most optimum strategies for each department, it is extremely department to test the strategy as a whole for the entire organization and to make necessary adjustments, if required. Further, a right operating strategy can create enormous value in terms of Revenue/ profit generation and throughput using the same set of assets.





Strategies should be aimed at maximizing asset utilization in all departments with minimum loss of customers coupled with necessary advertising campaigns targeted at the right set of customers.



Operations management analytical tools such as linear programming models and simulations can be of significant help in identifying the right strategy for the organization by effectively dealing with many variables at a time, which are present in dynamic business situations.Further, emphasis should be made to identify the right set of variables in order to construct a right and unique operations management model for the organization.



References:



 

Cb.hbsp.harvard.edu, (2015).Login[online]Availableat:https://cb.hbsp.harvard.edu/cbmp/context/coursepacks/3 5435945 [Accessed 06th March, 2015]. Stevenson, W. (2005). Operations management. Boston: McGraw-Hill. Sasser, W. (2004). Benihana of Tokyo. [S.l.]: Harvard Business.


Benihana Simulation Analysis

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