Posted: June 18th, 2015

Layout Design- Assignment 6

Layout Design- Assignment 6

!READ ALL BEFORE STARTING ASSIGNMENT that is in (red and black) ink!/
PLEASE FOLLOW THE INSTRUCTIONS WORD FOR WORD!!
Complete Objective Questions 7 and 8 at the end of chapter 8 in the textbook.

Use Microsoft Excel for all computations. Ensure that the Excel file includes the associated cell computations. This information is needed in order to receive full credit on this assignment.

Each problem is to be placed into separate worksheets and all problems are to be placed into one file. For example, the single Excel file should contain one worksheet named 7 and another named 8.
Textbook
Read chapters 8 and 10 (“Waiting Line Models” section only) in the textbook.
http://gcumedia.com/digital-resources/mcgraw-hill/2013/operations-and-supply-chain-management_ebook_14e.php
Prepare your responses in Excel with each problem on a separate tab and show work for each problem step by step. Write a written explanation. INCLUDE THE formulas in calculations ( each number calculated within excel should have formula in it on the fx(formula bar)). ALSO WRITE IN WORDS HOW YOU GOT THE ANSWER BY SHOWING YOUR WORK
1.    Place all answers, both numerical and written, in a single excel spreadsheet.
2.    Place each problem into a separate tab or sheet in an Excel file.
3.    Place labels on spreadsheet inputs and outputs, and use the yellow highlighter on the top menu bar to highlight your final answer.
4.    If the question incorporates graphs, you must replicate the graph on your spreadsheet file.
5.    Do not submit Word files or multiple files for a single assignment.

Lecture Notes
THIS IS JUST A LECTURE NOTES TO GUIDE YOU ON WRITING THE ANSWERS
READ ALL of the writing BEFORE STARTING ASSIGNMENT

Introduction
Today’s organizations are often faced with layout design issues. In addition, organizations can become more efficient if they have, for example, greater insight into product movement and/or customer movement within the context of waiting line theory. This module focuses on strategic importance of layout decisions along with selected techniques to help make optimal layout decisions. In addition, this module focuses upon wait line theory.

Strategic Importance of Layout Decisions
The layout of an organization’s work areas can impact a firm’s operational efficiency and ultimately its competitive position. Imagine how difficult it would be if a production facilities receiving dock was at the same entrance as its outbound shipping dock. Also, consider the distraction challenges of having a call center’s phone operations personnel residing within hearing distance of the firm’s production line. While these are exaggerated examples, they essentially illustrate that organizations need to make a deliberate effort in optimizing the layout design of their work areas.

The layout decision is important in operations because it can facilitate or inhibit the flow of materials and people within the operation. The layout decision begins with understanding the types of processes that reside within the proposed layout design. As discussed by Jacobs, Chase, and Aquilano (2009, pp. 221-222), the different layout types include the following:

Techniques for Solving Layout Problems
There are selected approaches for solving layout problems. As discussed by Jacobs et al. (2009), these include the workcenter layout design approach and the assembly line approach.

The workcenter layout design approach involves the following general steps:

1)      Determine the load between the various functions (e.g., the number of trips and distance between functions).
2)      Determine the physical workspace that is available and the distance between each physical location.
3)      Create a proposed layout based on the load-distance products, with a goal to have the lowest total load-distance possible.
4)      Determine the cost of the proposed layout, with a goal to have the lowest total cost possible.
5)      Improve the layout using expert judgment and trial and error.

For a specific example, refer to Solved Problem 1 starting on page 241 in Jacobs et al. (2009).

The assembly line balancing approach involves the following general steps (Jacobs et al., 2009, p. 228):

1)      Develop a precedence diagram for the tasks.
2)      Determine required cycle time per workstation.
3)      Calculate the theoretical minimum number of workstations.
4)      Select a primarily rule for assigning tasks to workstations.
5)      Balance the line by assigning specific tasks to workstations. Note that multiple layouts may yield the same number of work stations needed.
6)      Calculate the efficiency of the balanced line.
7)      If the efficiency is unacceptable, rebalance the line using a different decision rule.

The overall goal of the layout design is to achieve the best possible efficiency. For a specific example, refer to Solved Problem 2 starting on page 242 in Jacobs et al. (2009).

Introduction to Waiting Line Concepts
Waiting line models, also referred to as queuing models, are intuitively easy to appreciate, but do involve a fair amount of computation. Queues need no formal definition, because we encounter them almost every day in obvious as well as subtle ways. The obvious encounters are in the post office, fast food restaurants, tollbooths and traffic backups, clinics and hospitals, etc. The subtle encounters are also numerous. For example, every time you surf the Internet, you are probably queued in the server before your request to download is honored, although you most often do not even realize you are in a queue because the queues are processed at electronic speeds. Now and then, however, when servers are slow or there are many requests ahead of you, the queues get to be annoying and they manifest themselves via slow response times (Meredith, Shafer, & Turban, 2002)
Queuing models can get complicated very fast, and even the simple ones are based on assumptions that only approximate real-life situations. Fortunately, there are a handful of simple models that apply to many business situations, and they tend to be pretty good approximations of these situations. Our focus in this module is the M/M/1 model. However before moving into the M/M/1 model, some background information about arrival behavior and service behavior is needed.
Arrival Behavior
It is understood that a queue arises in situations where there are arrivals that require service. For example, in the case of planes landing at an airport, the arrivals are planes that want to land at the airport and the service is providing a runway for a certain amount of time for the plane to land. Arrival time patterns can be constant (example: at peak time there is a landing at Chicago’s O’Hare International airport every 50 seconds, almost like clockwork!), Poisson (example: number of customers arriving at a bank during a given interval), or may follow other probability distributions. The average arrival rateis represented by the Greek letter ? (pronounced lambda). Since many business situations tend to have arrivals that approximate the Poisson distribution, this module focuses on arrival patterns that follow a Poisson distribution (Meredith et al., 2002).
Service Behavior
Service time patterns or length of service is the time required to provide the service to a single arrival). The average service time is represented by 1/µ (the denominator is pronounced as mew). This means that if the average service time is 0.5 minutes, then µ = 2 because 1/2 = 0.5. This is done because µ represents the average service rate. If it takes 0.5 minutes to service one arrival, then 2 arrivals can be serviced per minute. Service time can follow varied distributions. The most prominent of these is the negative exponential distribution. Since many business situations tend to have service behavior that approximate the negative exponential distribution, this module focuses on service patterns that follow a negative exponential distribution (Meredith et al., 2002).
M/M/1 Model
The M/M/1 Model is a single-channel queuing model with Poisson arrival behavior and negative exponential service time behavior. The following assumptions are required for this model, as documented in Heizer and Render (2011, pp. 746-747):
1)      Arrivals are served on a FIFO (first-in, first-out) basis and every arrival waits to be served regardless of the length of the queue.
2)      Arrivals are independent of preceding arrivals but the average number of arrivals does not change over time.
3)      Arrivals are described by a Poisson probability distribution and come from an infinite population.
4)      Service times vary from one customer to the next and are independent of one another, but their average rate is known.
5)      Service times occur according to the negative exponential distribution.
6)      The service rate is faster than the arrival rate.
A good example of the M/M/1 Model is a drive-through car wash at a service station. There is a single waiting line, i.e., single entry point. There is only service area, i.e., one car wash (see Figure D.1 in Heizer and Render, 2011). Another example is bank service area where there is a single line and only one bank teller.
Table D.3 in Heizer and Render (2011) provides all the necessary formulas for solving M/M/1 problems. All that is needed is the Arrival Rate (?) and the Service Rate (µ). Note that the Arrival Rate (?) and the Service Rate (µ) must be expressed in number of units per unit time. For example, if the Arrival Rate (?) is given as “2 units every 10 seconds”, then it is recommended that the Arrival Rate (?) be expressed as “12 units per minute” (i.e., ((60 seconds per minute /10 seconds)* 2 units) = 12 units per minute). Expressing the Service Rate (µ) is done the same way. Note also that the Arrival Rate (?) and the Service Rate (µ) need to be expressed in the same units of time. For example, if the Arrival Rate (?) is expressed “units per minute”, the Service Rate (µ) also needs to be expressed “units per minute”.

Conclusion
The type of layout that an organization chooses has implications for the role that operations plays in delivering competitive advantage to the organization. Selected techniques are readily available that can help organizations make optimal layout decisions which will ultimately lead to a greater competitive advantage. Organizations can also gain greater insight into organizational efficiency using waiting line theory.

References

Heizer, J. & Render, B. (2011). Operations management (10th ed.). Upper Saddle River, NJ: Prentice Hall.
Jacobs, F. R., Chase, R. B., & Aquilano, N. (2009). Operations and supply management with student DVD-Rom (12th ed.). New York, NY: McGraw-Hill.
Meredith, J., Shafer, S., & Turban, E. (2002). Quantitative business modeling. Mason, OH: South-Western.

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