Exam i Spring 2009

ORIE 3120 Student Name:________________________________ Exam I

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Industrial Data and Systems Analysis, ORIE 3120 Exam I

February 26, 2009

Closed Book / Closed Notes Scientific calculator permitted; using any device to access information you are

expected to memorize would be a violation of academic integrity. Point value of questions is proportional to the estimated time requirement. (1 point 1

minute.) Points possible: 85 Time allowed 105 minutes. Write your name on every page. (The exam paper will be unstapled and distributed

for grading.) Answer all questions in the space provided. Use the back of the page if necessary but

do not answer on a different page unless the exam proctor provides it. Make sure each additional page contains your name. Hand in a complete exam.

Academic integrity is expected of all students of Cornell University, whether in the presence or absence of members of the faculty. Understanding this, I declare I shall not give, use, or receive unauthorized aid in this examination.

Signature of Student:_________________________________________________


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1. Functional Modeling (15 minutes) Nurses are trained in a five-step nursing process that can be summarized as follows. The

first step is Assess. The inputs to this step are the patient interview and observation, the

patient family interview, the health records, and observations by healthcare

professionals. Different data collection methods can be useful in this step. The output of

this step is a patient database. This is input to the next step, Diagnose. It is thought that

application of a standardized terminology is useful in this step. The output of the

diagnose step is a patient diagnosis. This is input to the third step, Plan. In this step, the

patient goals are considered. These goals constrain the planning process. The output of

the plan step should be a set of measurable goals and a nursing care plan. The nursing

care plan is a control input to the fourth step, Implement. The patient status is both an

input and output of this step (show a loop from output back to input). The final step is

Evaluate. It takes as input the patient status, output from the implement step. The control

for this step is the output of measurable goals from the Plan step. There are two outputs

from the evaluate step: the measure of progress towards the patients goals and changes

to the nursing care plan. These changes are fed back into the implement step (feedback

control).

Required: a. (10 minutes) Create an IDEF0 Page A0 (a single page) describing the steps of this

process. All the words in italics above should appear somewhere on the diagram. Points are awarded for format, interpretation, and completeness. Do not supply more information than what is contained in the description above. [Use the next page for your answer.]

b. (5 minutes) Create an IDEF0 Page A-0 describing this process (Provide nursing services) at a high level. This view should not display any inputs or outputs that are intermediate products or intermediate controls of the process, but it should show the major inputs, controls, constraints, resources, mechanisms, and outputs of the process.


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2. Simulation Modeling (15 minutes) A common simulation problem is to model queues with blocking: customers or

jobs which are blocked or turned away because there is no place to hold them. For

the cookie example considered in class, suppose there is only one type of cookie

arriving but there is limited space to store the cookies in front of the oven. If a

tray of cookies arrives and there are already, say, 10 trays of cookies in front of

the oven, then that tray must go somewhere else (perhaps to a refrigerator). From

the point of view of the simulation, those trays just disappear.

Here is how it works: a tray arrives at random intervals, uniformly distributed

between 10.5 and 17 minutes (13.75 minutes on average). If there are 10 trays

already waiting to go into the oven, then this tray simply disappears. If there are 9

or fewer trays in the queue, then the tray joins the queue. If the oven is empty,

then a tray entering the queue will trigger the start of an oven cycle. When an

oven cycle starts, up to 2 trays are removed from the queue and placed into the

oven. The oven cycle lasts exactly 25 minutes. When the oven cycle finishes, if

there are trays in the queue then it starts again. We are interested in simulating the

number of trays in the queue.

Describe a discrete event simulation model for this situation using the graphical modeling language introduced in class (ovals for events, diamonds for conditions, and lozenges for durations). Be sure to introduce your state variables and explain what they mean. Observe all the rules for the graph and label the nodes of the graph with Visual Basic pseudo-code. In particular, recall that you cannot branch on a condition node. If you need to branch, you must set up two condition nodes (one for one branch and another for the opposite branch). Also remember that state variables should be modified only in event nodes (not in condition or duration nodes).


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3. Structured Query Language (20 minutes) Consider the following relational database schema.

A book retail company has many stores located around the country. Each store is

uniquely identified by a StoreID number. The table Stores lists the address of each store. In particular, it lists a two character abbreviation for the state (ex. NY for New York) in the field State. Each book title sold by the company is uniquely identified by a BookID number. The table Books lists all the books sold, together with the list price (ListPrice), which is a numeric quantity. Stores place orders for books. Each order is for a single book title and is uniquely identified by the OrderID number. The table Orders lists many years worth of orders. The field OrderDate is a Date/Time field. The field Quantity is a numeric field indicating the number of books ordered.

Each book is assigned to a genre (category) such as Science Fiction or Romance.

We want to discover if science fiction books sell better in one part of the country than in another. To answer the question, we want to compute the fraction of book sales (quantity * list price) in each state in 2008 that were in the Science Fiction category. The final query must consist of two fields: State and FractionSF.

All queries must be written in SQL. Table prefixes may be omitted if field names are unambiguous. Number your queries Q010, Q020, Q030, etc., in the sequence in which you create them. Please use the following steps even if you see shortcuts.

a) (4 points) Write a query that selects orders (all fields) placed in 2008 (i.e. YEAR(OrderDate)=2008).

b) (4 points) Write one or more queries that compute the total sales value (quantity * list price) of each book grouped by Genre, BookID, and State. Name the sales value field as BookSalesValue.


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c) (4 points) Write one or more queries that compute the total sales value of all books sold in 2008, grouped by State. Name the sales value field as TotalSalesValue.

d) (4 points) Write one or more queries that computes the total sales value of all science fiction books (i.e. Genre=Science Fiction) sold in 2008, grouped by State. Name the sales value field as SFSalesValue.

e) (4 points) Write one or more queries that computes the fraction of 2008 book sales that are science fiction, grouped by State. Do not worry about division by zero. This is simply the ratio of the numbers computed in (d) and (c), matched by State. Name the ratio field as FractionSF.


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4. Inventory Models (20 minutes) a) Throughput (6 minutes). The nominal throughput from a stamping

operation is 120 pieces per hour. The scrap rate is 2% and the rework rate is 3%. The press fails approximately every three hours but it can be repaired in about 15 minutes.

i. What is the effective throughput of the press? You may assume all of the effects (scrap, rework, and failure) are independent and you can simply multiply their effects together.

ii. Using your answer from (i), what is the effective throughput if we also must set up the equipment for 20 minutes after every 600 parts produced?

b) Safety Stock (6 minutes). A product has an average daily demand of 50 units with a standard deviation of 12 units. The lead time to acquire this product is 10 days. Whenever the product is ordered, we order 800 units. We would like to achieve a fill rate of at least 99.5%.

Mark up the chart below to show how you would compute the normalized reorder point for this problem and then use the table below to interpolate a more exact answer. Finally, answer the question, What should be the reorder point for this product?


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Std. Normal Backorders, E[max(Z-k,0)]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.5 1 1.5 2 2.5 3 3.5

Normalized Reorder Point, k

k E[max(Z-k,0) k E[max(Z-k,0) k E[max(Z-k,0)0 0.39894228 1 0.083315471 2.1 0.006468313

0.1 0.350935331 1.1 0.06861951 2.2 0.0048870080.2 0.306894636 1.2 0.056102451 2.3 0.0036615850.3 0.266761242 1.3 0.045527962 2.4 0.0027204440.4 0.230438837 1.4 0.036668143 2.5 0.0020041370.5 0.197796557 1.5 0.029306794 2.6 0.001463880.6 0.168672732 1.6 0.023241968 2.7 0.0010601060.7 0.142879377 1.7 0.018287791 2.8 0.0007610870.8 0.120207234 1.8 0.014275584 2.9 0.0005416740.9 0.100431137 1.9 0.011054351 3 0.000382154


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c) Decoupling Stock and Littles Law (8 minutes). Kingmans approximation for the expected waiting time in a single server queue is

eea

q mccW

+=

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(The notation was explained in class.) The coefficient of variation of the departure process from a queue is approximated by: ( ) 22222 1 aed ccc += Consider two workstations in series. Workstation 1 precedes workstation 2. Jobs arrive at station 1 at the rate of 20 per hour. Jobs can wait in queue in front of each workstation. When a job has finished being processed at station 1 it enters the queue in front of station 2. The coefficient of variation in arrival times is 1.5. On average, the processing time is 2.5 minutes per job at station 1 and 2.8 minutes per job, on average, at station 2. The coefficient of variation in processing times is 0.25 at each station. Use Littles Law and the queueing formulas above to estimate the queueing factor for this system. Recall that the queue factor is the ratio of the time spent in the system (queue time plus valued added time) to the value added time. Value added time corresponds to expected process time in this example.


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5. Lean Manufacturing, the THRUPUT Game, and the PULL Game (15 minutes)

a) Identify four major categories of throughput loss in a factory.

b) In the THRUPUT game, during the initial weeks of play, a major category of lost throughput was classified as unexplained. What was the reason for this or what was implemented to correct this?

c) By reducing setup times, we can reduce lot sizes in a factory. What will happen to fill rates as we reduce the lot sizes and how must we change the reorder point to compensate?

d) List any four items of information that can be found on a General Motors pull ticket.

e) In Pull play of the PULL game, what is the significance of the painted floor areas?

f) Describe a cycle of activities that a worker performs in a manufacturing cell [Hint: MO-CO-MOO]?

g) What do the acronyms RETAD and SMED stand for?


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h) What does it mean to convert internal setup to external?

i) Give two practical examples of ways to reduce setup or changeover time.

j) What are the 5 Ss (Japanese, English, or Americanized versions)?

k) What is the first thing that the new Quality Control manager for Matsushita did to improve the quality at a former Quasar TV plant?

l) Explain what is a pokayoke or give an example.

m) What is the difference between leveling and balancing in the Toyota Production System?

n) As a forklift truck driver, if someone hands me a withdrawal kanban, what should I do with it?

o) As a production cell supervisor, if someone hands me a production kanban, what should I do with it?

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Exam i Spring 2009