Group 8 Benihana Memo

1. BENIHANA SIMULATION
PRESENTED BY: GROUP 8

2. 12/20/2015 Benihana Simulation 1
BENIHANA SIMULATION
VARIABLES
There are ten variables in the simulation. Seven are related to demand variation and three are related to process
variation.
Type of Variation Variable Description
Demand X1
batching type for the time
slot open to 7 PM
Demand X2
batching type for the time
slot 7 PM to 8 PM
Demand X3
batching type for the time
slot 8 PM to 1030 PM
Demand X4
bar seat/ restaurant table
ratio
Process X5
average dining time from
open to 7 PM
Process X6
average dining time from 7
PM to 8 PM
Process X7
average dining time from 8
PM to 1030 PM
Demand X8 advertising budget
Demand X9 advertising campaign
Demand X10 opening time
DESIGNED BEST STRATEGY
Through trial and error, we determined the following choices resulted in the greatest profit potential and lowest
risk profile for sub-optimizing our processes to meet demand. We estimated an Average Profit of $633.04,
Maximum Profit of $1,094.2 and a Minimum Profit of $225.25
Variables
Type of Variation Variable
Batching Type - Open to
7PM 8
Batching Type - 7PM - 8PM 8
Batching Type - 8PM - 1030 8
Bar Seats 55
Restaurant Tables 14

3. 12/20/2015 Benihana Simulation 2
Avg Dining Time - Open to
7PM 45
Avg Dining Time - 7PM -
8PM 45
Avg Dining Time - 8PM -
1030 75
Advertising Budget 3x
Advertising Campaign Happy Hour
Opening Time 5 pm
Profit $633.04
Average Profit
Bar Usage Dining Room Usage
Drinks Sold 183.35
Dinners
Served 472.45
Avg. Cust. 6.65
Tables
Served 59.6
Max Cust. 40.15
Avg. Tables
in Use 9.32
Avg. Wait 8.6
Avg. Dining
Time 79.52
Max Wait 28.35 Avg. Cust. 73.6
Lost Cust. 5 Max Cust. 112
Avg. Drinks
Per Cust. 0.72
Avg.
Utilization 65.67%
Revenue $4,999.52
Nightly
Profit $633.04
Minimum Profit
Bar Usage Dining Room Usage
Drinks Sold 99.75
Dinners
Served 411
Avg. Cust. 3.62
Tables
Served 52
Max Cust. 28
Avg. Tables
in Use 8.07

4. 12/20/2015 Benihana Simulation 3
Bar Usage Dining Room Usage
Avg. Wait 2.18
Avg. Dining
Time 74.99
Max Wait 14 Avg. Cust. 63.4
Lost Cust. 0 Max Cust. 112
Avg. Drinks
Per Cust. 0.18
Avg.
Utilization 56.61%
Revenue $4,259.62
Nightly
Profit $225.25
Maximum Profit
Bar Usage Dining Room Usage
Drinks Sold 274.92
Dinners
Served 542
Avg. Cust. 9.97
Tables
Served 68
Max Cust. 53
Avg. Tables
in Use 10.96
Avg. Wait 22.04
Avg. Dining
Time 92.83
Max Wait 36 Avg. Cust. 87.2
Lost Cust. 4 Max Cust. 112
Avg. Drinks
Per Cust. 1.84
Avg.
Utilization 77.90%
Revenue $5,832.38
Nightly
Profit $1,094.27
MODIFICATIONS OF VARAIBLES
We then modified each variable from the position which resulted in the optimal case to see what effect the
change would have on profits.
The greatest impact was probably an obvious one. Changing the opening time from 5PM to 7PM change the
profit to -$744.11 (a reduction in profits of 1,377.15 from the optimal). This leads us to conclude that staying
open longer makes sense. We classified this variable as a demand variable.
The second greatest impact was changing the average dining time from 45 to 75 minutes for pre-peak and peak
dining hours. This reduced profits to -$116.43 (a reduction in profits of $749.47 from the optimal). To serve more
customers (and increase profit), the dining time should be reduced/minimized for pre-peak and peak dining
times. The 45 minute dinners for pre-peak and peak dining hours allows throughput to be maximized, decreases
cycle time, results in fewer customers lost, increases utilization, and serves to increase restaurant capacity. For
post-peak dining, dining time should be longer than pre-peak and post-peak to capitalize on customer purchases.

5. 12/20/2015 Benihana Simulation 4
Post-peak customers are more likely to come to the restaurant to socialize, have a more relaxed experience,
order more, and be entertained by the chef. This increased dining time will enhance the dining experience of the
post-peak customers and may result in repeat customers. We classified dining time as a process variable.
The third greatest impact was changing the advertising campaign from Happy Hour to Discount. This reduced
profits to -$46.42(a reduction in profits of $679.46 from the optimal). It appears that Happy Hour does the best
job of taking advantage of opening at 5PM. Happy hour is an important marketing tool. It increases traffic during
non-peak times, serves as entertainment, and allows word to spread about the restaurant (and perhaps atrracts a
customer/segment that normally wouldn’t visit the restaurant without happy hour prices).We classified this
variable as a demand variable.
Finally, the fourth greatest impact was changing the opening time batch strategy to None. This reduced profits to
-$297.20 (a reduction in profits of $930.24 from the optimal). It appears that batching has a significant impact on
profits. Profit hinges on the number of customers served. In general, by batching and seating customers together
in tables of 8 increases overall profit. Batching is good because it offers higher profitability, increases dining room
utilization, and reduces customers lost. Batching increases restaurant efficiency as well due to decreased labor
costs (batching reduces the number of needed chef and waiters). We also saw that the bar at Benihana is
important. The bar keeps customers engaged while they’re waiting for a table and serves as a staging area for the
restaurant (which will decrease variability in batch size and decrease variability of customer arrivals to the dining
area), generates revenue and increases profit (by selling drinks and food), and serves as a gathering spot for
happy hour. Batching and the bar play a large role in demand management.
SUMMARY
Happy Hour advertising had the biggest impact on profit when processes were optimized – however in all
simulations, the processes need to be at their best in order to best capitalize on the demand. This simulation
opened our eyes to determine how each aspect of Benihana’s restaurant operations contributes to the
performance, revenue, and nightly profit of the restaurant and how demand variability and process variability fit
together. A key of Benihana’s success is due to Benihana’s process flow – typically, Benihana operates at a low cost
and high efficiency (standard product, standard preparations, etc.). In general, when variability was introduced to
the simulation (especially during peak times when demand exceeded supply), we saw a dip in overall profit and
restaurant operations.