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Group 8 Benihana
- 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.