Operations Management Observations and Case Studies

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Operations Management Strategies

Group Case Assignments:

Case Problem# 1: McDonalds Data Collection

Case Problem# 2: Swift Electronic Supply, Inc.

Case Problem# 3: Yankee Fork and Hoe Company

Introduction

This research provided greater understanding of the application of the analytical tools associated with operations management particularly those related to service/queue times. The analysis is meant to determine the optimal number of servers and waiting time using a waiting-line model. Considering the number of customers in line, the average waiting time, the range of variability in waiting time, and the efficiency of the service facility we decided to choose the priority rule for the order of service. The priority rule we selected is first come, first served (FCFS) which states that customers in line are served on the basis of their chronological arrival and that no other characteristics have any bearing on the selection process. In adopting this rule, an assumption is made that customers are patient; they do not balk, renege or jockey while in the waiting line and that all customers popularly accept this rule as the fairest rule.

The group decided to conduct research at selected restaurants of the McDonald’s Corporation because of its popular patronage and ready access to group participants. McDonald’s Corporation, together with its subsidiaries and franchises operates McDonald’s restaurants in the food service industry worldwide. Its restaurants offer various food items, soft drinks, and coffee and other beverages. As of December 31, 2008, the company operated 31,967 restaurants in 118 countries, of which 25,465 were operated by franchisees; and the company operated 6,502. McDonald’s Corporation was founded in 1948 and is based in Oak Brook, Illinois.

Our group consisted of four people doing observations at various locations and times.

The four observers produced one hundred and eleven (111) 5-minute observations.

Arrival Rate

A customer’s arrival rate was based on when the customer got into line to be served. Counting entrance to the queue eliminated those only using the restroom, applying for jobs, etc. Each observation was five minutes in length and during that defined time frame the number of customers was recorded. To find the average customer arrival rate we tallied the total number of customers recorded (428) and divided it by the number of observations (111). This reported that 3.6 customers per five-minute period was the average for customer arrival rate. We then broke 3.6 down into .77 customers per minute by dividing by 5, then took .77(60) to find the final arrival rate of one customer every 46 seconds. The histogram in figure 1.1 shows the number of observations on the Y-axis and the total customers per minute period on the X-axis, with each of the group member’s data consolidated for corresponding observations by color code.

Here the data shows us that John observed the highest frequency of customers. We do see a slight reduction in customer frequency as the observation number goes up. We can assume that since the later observations were between the 7:00pm and 8:00pm period that this reduction could be caused by an after-dinner peak. Of additional interest, is John's location was outside the only location outside the continental United States.

Service Time
The observing group members were instructed to randomly select customer service times within each five-minute observation period to determine that period’s average service time. Service time commenced when the server took the customer's order and concluded at receipt of the meal. For ease of reporting, each service time was rounded to the nearest minute. All available service times were tallied and divided by the total number of recorded service times, or two-sixty nine (269). This gave us an average service time of 3.5 minutes, or 210 seconds (3.5 x 60). The histogram in...
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