Barnacle Cleaners Case Study

Barnacle Cleaners Case Study Page 1

BOSTON UNIVERSITY

METROPOLITAN COLLEGE

DEPARTMENT OF ADMINISTRATIVE SCIENCES

BARNACLE CLEANERS1

Angela “Angie” Buonannata faced a challenge. As an operations management consultant, she is better at solving problems than she is at explaining her results to skeptical clients. Her latest client, Barnacle Cleaners, is an all- purpose laundry, dry cleaning, and alteration service targeted to university students. Barnacle makes use of state-of-the-art cleaning technologies, along with new IT systems (including smartphone aps) that appeal to young adults. Barnacle was founded two years ago by then 24 year-old Ron Ososkie, a recent graduate of UMass- Boston, who majored in history. As he stated to Angie:

“Everybody hates to do laundry. As a University student, I (and many of my friends) lugged my clothes home every other weekend. In fact, many of us took this trip for the sole purpose of getting our laundry done.”

Barnacle’s service handles each article of clothing the “right” way (e.g., separates colors and whites, dry cleans when required, removes stains, and alters where necessary). The tag line, “just like your Mom,” appears prominently in their advertising. In addition, most customers take advantage of a fixed price of $250 per semester. This price includes bi-weekly laundry service (but alteration fees are paid separately based on need and mutual agreement). Ron noted that, in most cases, parents are happy to pay for this service.

Although Barnacle has a large customer base, revenue just barely exceeds costs. A study by Barnacle’s accountant made it clear that the cost of Barnacle’s pickup and delivery service is the main cost factor. This service includes pickup and delivery at residence halls and local apartments. But unexpected problems developed, especially with delivery of completed laundry. The main problem is that often customers are not present to receive their completed laundry. In these cases, customers are required to pick up their laundry at the store. According to data collected last year, 36% of deliveries were not successful, and over 80% of customers had to do at least one store pickup during a semester. With the change under consideration, all pickup and delivery costs would be saved by requiring customers to drop off and pick up their laundry (although they could assign agents to do this, which would encourage a pooling of resources, where one customer would drop off or pick up laundry for a group of friends). The cost of the service would be reduced to $225 per semester (plus the alteration fee).

Angie was hired to support this transition although Ron isn’t convinced that her assistance is needed. However, he is particularly keen to the fact that word of mouth on a University Campus carries a lot of weight. His main fear regarding the change under consideration is getting off to a poor start and losing many customers, along with their friends.

With the service system under consideration, drop off would require interaction with a service agent because the handling of any stains or alterations needs to be discussed and (sometimes) a price set. Two options are being considered for the organization of the drop off service. The first option (Figure 1) would consist of a single “common” waiting line, with multiple all-purpose servers who would handle any type of transaction. Under this option, each server would need expertise in all of the services offered. The second option (Figure 2) would consist of two segmented waiting lines, each dedicated to a specific type of transaction – one waiting line for cleaning “with alteration” and one waiting line for cleaning “without alteration.” Labor costs will be saved because only servers working the alterations line need significant expertise. Due to the physical layout, it would be impossible for servers to switch to serving the other lines in the segmented case. In all cases, pickup will be

1 This case was developed by John Maleyeff based on his work in applying queueing models in support of a new business operations development project. All references to people and organizations are fictional. © 2018 (Rev) All rights reserved.

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self-service, with an area designated for this purpose. No servers will be involved with the pickup and therefore Angie’s analysis would only consider the drop off service.

Angie started by overseeing the collection of arrival & service time data corresponding to the drop off service. To estimate service times, data were collected at a full service laundry that was considered similar in nature to the full service “common” line option under consideration at Barnacle. The analysis of these data (shown in the Appendix) concluded that service times for all purpose servers will be exponentially distributed, averaging 3.5 minutes per customer. A separate time study estimated that, for the segmented waiting line option, expected average service times will be higher by 64% in the “with alterations” line (i.e., 5.74 minutes per customer) and lower by 46% in the “without alterations” line (1.89 minutes per customer). The service time distributions would be consistent across all time periods. Arrival rates would vary according to time of day and day of the week. The average arrival rates for all customers are shown in Table 1. Based on past data, Angie estimates that 32% of customers will use the “with alteration” line and 68% of customers will use the “without alteration” line.

Table 1: Total Customer Demand

Timeframe Hours Customers

Weekday Daytime 8:00 am – 4:00 pm 50/hour

Weekday Evening 4:00 pm – 8:00 pm 80/hour

Saturday 12:00 pm – 4:00 pm 110/hour

Early on, Angie sensed that although Ron hired her, it is clear that he does not appreciate the operations management challenges. For example, he explained this about the common line option:

“On weekday daytimes, we expect 50 customers per hour or 400 customers over the eight-hour time period. Since service time per customer averages 3.5 minutes, this equates to 1400 minutes of service [3.5 minutes per customer times 400 customers], or about 23 hours of service. Hence, we need to assign 3 servers because each server works eight hours during that period.”

As she begun work on analyzing the two options and creating a recommendation on which option to choose, Angie was keenly aware that her ability to explain and justify her work would be just as important (or perhaps more important) than the technical aspects of this otherwise routine project.

Figure 1: First Option Configuration

All- Purpose Servers

All Customers

Without Alteration

Servers

Without Alteration Customers

With Alteration

Servers

With Alteration Customers

Figure 2: Second Option Configuration

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APPENDIX: Analysis of Service Times Table A-1 provides the service time data (in minutes) for 60 customers. The time order of the data is listed down each column (e.g., the first two data points are 2.8 and 4.2).

Table A-1: Service Time Data (Minutes per Customer)

2.8 0.3 14.8 9.0 5.8 4.0

4.2 2.0 0.1 1.3 1.8 11.4

2.1 0.8 1.4 1.6 0.1 2.1

0.7 4.4 0.7 3.2 0.6 0.1

10.8 0.1 2.2 2.7 0.5 13.0

0.4 1.6 1.4 2.8 4.0 4.4

1.8 7.7 6.3 9.5 7.4 0.1

1.1 3.4 2.6 0.2 2.9 4.5

4.6 6.7 1.5 8.0 2.6 3.2

1.0 4.6 0.4 0.5 9.5 0.4

The time series plot (Figure A-1) confirms stability of the process generating the data. That is, the process is not changing over time and therefore can be analyzed as one homogeneous data set.

The histogram (Figure A-2) confirms that the data are consistent with an exponential process distribution. The average service time was 3.50 minutes and the standard deviation was 3.52 minutes.

0 2 4 6 8

10 12 14 16

0 10 20 30 40 50 60

Val ue

Order

Figure A-1: Time Series Plot of Service Times

0

5

10

15

20

25

30

1 3 5 7 9 11 13 15

Frequenc y

Service Time (Min)

Figure A-2: Histogram of Service Times