Read Chapter 16 - Section I only (Pgs. 760-768)

Complete Problems 1 & 2a-b (Chapter 16)

Read Chapter 10

Complete Problems 5 & 9 (Chapter 10)

Craft at least one value-added posting to each of the discussion threads NOT marked as OPTIONAL.




The first several pages of Chapter 16 describe Markov processes. In your own words, please explain what this technique is and how it might be used.



Our Week #6 discussion of the differences between sales and demand was very useful. However, I don't think we really concluded it in a way that would tie back into our primary topic of forecasting. That is, it didn't really bring home why it is so imperative to understand the differences between "sales" and "demand" when we begin to create forecasts. (It also didn't really shed light on the reasons stockouts are so harmful to any/all forecasting efforts!) So I would like students to reflect on the following case taken from a text called "Operations Management" authored by an Ohio State University professor by the name of Lee Krajewski. It reads as follows:

Kay and Michael Passe publish "What's Happening?" -- a biweekly newspaper to publicize local events. What's Happening? has few subscribers; it typically is sold at checkout stands. Much of the revenue comes from advertisers of garage sales and supermarket specials. In an effort to reduce costs associated with printing too many papers or delivering them to the wrong location, Michael implemented a computerized system to collect sales data. Sales-counter scanners accurately record sales data for each location. Since the system was implemented, total sales volume has steadily declined. Selling advertising space and maintaining shelf space at supermarkets are getting more difficult.

Reduced revenue makes controlling costs all the more important. For each issue, Michael carefully makes a forecast based on sales data collected at each location. Then he orders papers to be printed and distributed in quantities matching the forecast. Michael's forecast reflects a downward trend, which is actually present in the sales data. Now only a few papers are left over at only a few locations. Although the sales forecasts accurately predict the actual sales at most locations, What's Happening? is spiraling toward oblivion. Kay suspects that Michael is doing something wrong in preparing the forecast but can find no mathematical errors. Tell her what is happening.

As you craft your advice to Kay, consider the following supplemental questions.

a.) What forecasting technique do you think Michael is using?

b.) What technique would you use?

c.) Why the downward spiral?

d.) What is the inevitable outcome of not doing anything differently with this forecast? Why so?

e.) How to correct the problem--quickly, with limited financial or human resources?

f.) What is the primary lesson of this case?

(Please remember, I do NOT want any one student to craft a post which answers all...or even many...of these questions. Short posts which only attempt to respond to one or two of them are fine.)


3. EOQ 

The central concept of Chapter 10 of our text is EOQ -- Economic Order Quantity. In your own words, please explain what this is and how you think it might be applied in your own organization or one other that is familiar to you.



One of the forecasting techniques we did not get a chance to discuss last week is a widely-used, relatively easy to learn approach called simple linear regression. It is a technique that should have been covered to some extent in BMDS3370 or your first stats class but I would like to explore it just a bit here. Please respond to the following:

"The regression equation to predict demand based on inches of newspaper advertising is y = 25 + 3x. Provide a managerial interpretation of the y-intercept and the slope for this equation."

What I am really seeking here is an explanation of precisely how a manager might use this equation to make better decisions moving forward. Consider using a related example in response. Also, please consider what other information about the model would be critically important for the decision-maker who plans to use it.


Subject Science
Due By (Pacific Time) 10/04/2013 12:00 am
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