Benihana Simulation

Module B: Team 3 Benihana Simulation Report

What are the key decisions to make?

The key decisions that we had to make during the team simulation of the Benihana was to maximize the nightly profit through maximization of throughput time, demand variability, capacity utilization, and service time during three different periods of operations (pre-peak, peak, and post-peak period).  We used different decisions for batching, bar, advertising, and dining during the final stage of the simulation to develop and design the best strategy to maximize overall average nightly profits.

What are the key trade-offs?

Batching Decisions:

After the first period of individual rounds, we discovered during the simulation that batching has a positive effect on nightly profits. With batching, the restaurant is capable of maximizing the usage of the space, using all seats in the bar and dining room.  With a batching process of 8 and ample bar seating, we were able to effectively fill the tables in the dining room while increasing sales from drinks and appetizers in the bar area. Batching also reduced our average customers lost. Our best batching was a table of 8 for all time periods, this fully utilized our restaurant’s capacity.

Bar Decisions:

We have discovered that having a bar serves two different purposes: (1) it allows customer to come in to drink and have a good time while waiting, thus increasing customer satisfaction and engagement (2) it increased nightly revenue due to increased sales in drinks and appetizers prior to being seated for dinner.  We used a bar/table size of 63/13, 71/12, and 79/11 to find out the best utilization rate to accommodate the most customers at a given time. When we simulated with less than 63 bar seats, our nightly revenue realization was dramatically decreased. In our best simulation, we used 63 bar seats and 12 tables.

Dining Time Decisions:

We have learned that shorting the dining time during peak hours (7:00p-8:00p), boosted our throughput rate and thus increased our nightly revenue. The decrease of dining time during the other periods (pre- and post-peak), does not tend to exhibit the same effect as dramatically. We also discovered that increasing dining duration during the post-peak period had a slight positive effect as the throughput rate is slower.

Advertising Decisions:

We determined that advertising balanced out the variability of demand and assisted Benihana to increase demand during the times of typical low demand (pre-peak and post-peak). We tried all three campaigns that were available (1) awareness building, (2) discount promotions, and (3) Happy Hour. Awareness building and discount promotions proved to be less effective than using a Happy Hour campaign.  Awareness building resulted in only a slight increase in profits, while the discount promotion methods proved to effect profitability negatively in most scenarios. Additionally, we starting happy hour at 5:00p to boost pre-peak throughout time in hopes to level out inventory (customers).  This increased inventory of customers earlier rather than most during peak periods. Our optimal budget for advertising was between 2.1 and 2.5x the normal budget amount.