Week_2_Uop_Pizza_Parlor_Bottlenecks

Bottlenecks 1 Bottlenecks In A Process University of Phoenix Jesus Brogel Operations Management 571 Applying the Learning Curve Theory A learning curve is a line displaying the relationship between unit production time and the cumulative number of units produced (Chase, Jacobs, & Aquilano, 2006). Three assumptions set the foundation for the learning curve theory. First, the amount of time required to complete a task or produce a unit will become less each time the process occurs. Second, the unit time will decrease at a decreasing rate and third the reduction in time will follow a predictable pattern. The simulation contained numerous points of measurement for process performance. This paper will identify and use process performance data points to test and validate how an alternative process trends relative to the original process and give a brief analysis of the initial data. The following is a list of process performance data points identified in the simulation: * Queue lengths * Average customer wait time in a queue * Table utilization rates * Sales * Profits * Lost sales * Staff utilization rates * Number of customers who balked The following lists alternatives to the existing process: * Decreasing the number of tables for groups of four customers from 14 to 10 while increasing the number of tables for two customers from zero to eight * Increasing the number of waiters from two to four * Eliminating manual ovens and adding a Plax oven * Incorporating Menu Point for shortened waiter to kitchen to paying customer lead times * Renting Cream Puffs for additional seating The pizza process is considered a high volume operation with short cycle time over a continuing period. As such, the learning curve concepts can be applied to time per unit produced and units produced over time. Customer wait times and queue lengths can be plotted against the number of seated customers from the existing process through the evolution of the alternative process. The resulting learning curve identifies the change in process efficiency. For example, from weeks one to two the average queue length was 3.05 whereas the average wait time was 12.85. Changing the table structure and staff numbers in weeks three and four reduced the queue length to 2.49 or a 19% reduction and the wait time to 6.07 or a 53% reduction. Adding the Plax Oven and incorporating Menu Point in weeks five and six showed an additional reduction in queue length and wait time. Queue length declined to 2.48 while the wait time dropped to 4.88 or a 20% reduction because pizzas came out the Plax faster than the manual ovens. Finally, renting Cream Puffs in weeks seven and eight showed an additional reduction in wait time to 3.42 or a 30% drop. A learning curve plot of customer wait time and queue length reflects a steep curve over a short period. The curve indicates the new process was easy to learn and resulted in reduced queue lengths and wait times. The change in balking customers, sales, lost sales, pizza preparation time, profits, and utilization rates may also be used to develop a learning curve. The initial process data was sufficient to use learning curve concepts in determining the efficiency of an alternative process. However, more incremental data points would have yielded a more useful curve. For example, data collected daily over the eight week period versus weekly would result in a more realistic curve. Additionally, finite individual and organizational learning may be realized with averages broken down by shifts. The Pizza Simulation illustrated how a learning curve can be used to validate an alternative process. Using performance data points, a curve of the pizza process showed that the unit production time decreased and the cumulative number of units produced increased from the existing to alternative process. As a result the average wait time, queue length, and number of customers who balked decreased while profits and sales increased. Learning curves are valuable tools when applied correctly with effective and meaningful data points. References Chase, R., Jacobs, F., & Aquilano, N. (2006). Operations Management for Competitive Advantage. New York: McGraw Hill/Irwin. Phoenix, U. O. (2010). Pizza Simulation.