Overview
Attached is the syllabus of the course.
Here’s assignment details:
Case 1. PB Chemical Company
Mac Brown knew something had to change. As the new Vice President of Sales &
Marketing for the PB Chemical Company, Mac understood that when you sell a
commodity product, where there is minimal difference between the quality and
price, customer service and proactive selling effort usually are the difference
between success and failure. Unfortunately, PB's sales staff was using a fairly random
method of soliciting sales, where they would work through an alphabetical list of
customers, making phone calls to those who had not placed any orders that month
Often, the difference between whether PB or a competitor got an order simply boiled
down to who called at the time the customer needed materials. If the PB salesperson
called too soon, they didn't get an order. And if they waited too long for a customer to
call, they often lost business to a competitor.
Mac decided it was time for PB to be a bit more proactive and sophisticated in its
sales efforts. He first convinced his counterparts at PB's largest customers that they
could create a more efficient supply chain if they shared their monthly usage data of
various chemicals with PB. That way, PB could better anticipate its customers' needs
for various products. This, in turn, would reduce PB's need to hold inventory as safety
stock, and allow PB to operate more efficiently, and pass some of these cost savings
on to its customers.
PB's five largest customers (that account for 85% of PB's sales) agreed to share their
monthly product use data. Now it was up to Mac to decide what to do with this data,
It has been quite a while since Mac actually did any demand forecasting on his own
and he is far too busy with PB's strategic planning committee to be bothered by such
details anyway. So Mac called one of the firm's top business analysts, Dee Hamrick
and dumped the problem in her lap. Specifically, Mac asked her to come up with a
plan for forecasting demand for PB's products and using these forecasts for maximum advantage.
What issues should Dee consider in coming up with forecasts for PB's
various products? How would you suggest she go about creating forecasts
for each product?
Should Dee try to forecast aggregate monthly product demand for all
customers, or individual monthly product demand for each customer?
Which of these forecasts would be more accurate? Which of these forecasts
would be more useful (and to whom)?
Given the available data, how might Dee and Mac judge or gauge the
accuracy of each product forecast?
Suppose Dee's technical staff could come up with a way of accurately
forecasting monthly demand for PB's products. How should PB use this
information for strategic advantage?
What other information should Dee suggest Mac try to get from PB's
customers?