Insights for Diagram 2 and 3 as well as the data on Dus and Ham:
- Dus has a lower than average #Seats which lowers its relative revenue.
- Dus has a lower than average mealtime per customer which increases its relative revenue compared to the average revenue.
- The revenue of Dus is close to the franchise average.
=> Increasing the number of seats to the franchise average might increase revenue if enough demand exists to fill those seats.
- Ham has a higher than average #seats which increases its relative revenue.
- Ham has a higher than average mealtime per customer which lowers its relative revenue compared to the average revenue.
- The revenue of Ham is above to the franchise average.
=> Decreasing the average mealtime per customer to the franchise average might increase revenue if enough demand exists to fill those seats at a higher rate.
1) The first part of the client objective was to determine how well both restaurants perform. We can conclude that the restaurant in Dus has an annual revenue very close to the franchise average which means its performance is average. Ham has an annual revenue above the franchise average, so it performs above average. We will now need to investigate if the performance of either of those restaurants can be improved.
2) Looking back at the 5 factors that determine revenue: occupancy, #seats, opening hours, mealtime and customer spending which were listed on Diagram 2 we can see that opening hours and customer spending is fixed for all restaurants of the franchise. Opening hours are dictated by the franchisor so this factor cannot be changed to improve revenue performance. The average spending per customer can also not be changed as pricing policy as well as any marketing decisions are made by the franchisor so the only factors a franchisee can influence are #seats of the location they chose for their restaurant and the average mealtime per customer.
This narrows our optimization considerations down significantly:
For Dus, we could potentially improve #seats as it is below the industry average. Improving the average mealtime seems unlikely though as we are significantly below the average already. Dus has, in fact, the lowest mealtime of all restaurants within the franchise.
For Ham, we could potentially improve the average mealtime as it is above the industry average and improving the #seats seems unlikely as we are significantly above the average already.
Now that we have isolated our options we need to investigate which of these options are viable or not:
This case is designed to practice breaking down quantities into its component parts. In this particular case, the revenue of a restaurant will have to be broken down. The case should be tackled in two phases:
Phase 1 (breakdown and benchmarking) should be about isolating the key factors that influence the revenue of a restaurant and then to compare those to the franchise average for benchmarking to identify the factors that can be improved.
Phase 2 (testing improvement options) should be about testing if it is possible to improve any of the factors that have been identified to improve overall revenue.