practice problem 1_ shah alam palm oil company _ week 2 practice problems _ ctl

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3/15/2016 Practice Problem 1: Shah Alam Palm Oil Company | Week 2 Practice Problems | CTL.SC1x Courseware | edX

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Yes, there appears to be a POSITIVE trend

No, the demand appears to STATIONARY with no trend

Yes, there appears to be a NEGATIVE trend

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Week 2: Forecasting I - Introduction > Week 2 Practice Problems > Practice Problem 1:

Shah Alam Palm Oil Company

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PRACTICE PROBLEM 1: SHAH ALAM PALM OIL COMPANY

Palm oil is harvested from the fruit of oil palm trees and is widely used as

a cooking oil throughout Africa, Southeast Asia, and parts of Brazil. It is

becoming widely used throughout the world as it is a lower cost

alternative to other vegetable oils and has other attractive properties.

You are working for the Shah Alam Palm Oil Company (SAPOC) that

harvests, processes, and sells palm oil throughout the region. You are

asked to review the sales volume (in pounds) of your premium palm oil by

one of your customers, a local grocery store in the region.

Download the spreadsheets with the monthly sales volume of palm oil

here:

In Excel format (link to ShahAlamPalmOil_Data.xlsx)

In LibreOffice format (link ShahAlamPalmOil_Data.ods)

Part 1

Take a look at the data and chart or plot the demand (vertical axis) against

the months (horizontal axis). Do you detect any type of trend over the last

three years?

Course

Overview &Logistics

Entrance

Survey

Week 1:

Overview of

Supply Chain

Management &

Logistics

Week 2:

Forecasting I -

Introduction

Welcome to Week

2

Lesson 1: Demand

Forecasting

Lesson 2: Time

Series Analysis

Week 2 Practice

Problems

Supplemental

Materials for

MicroMasters

Week 2 Graded

Assignment

Homework due Mar

02, 2016 at 15:00 UTC

Week 3:

Forecasting II -

MITx: CTL.SC1x Supply Chain Fundamentals

https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Data.odshttps://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Data.xlsxhttps://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/41862172d4ca493996126432b89395ff/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/b12ad659313d4f769a575fdc15a43442/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/b12ad659313d4f769a575fdc15a43442/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/b4a85bbcabc94dc6bf30fe713fb727a1/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/fc4eda5f7a9b48a4b77428ea851f328f/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/70b1e7abe6cb427fbbea8d16db89ca85/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/d4764459b39c40a6b13557e119d73cc2/https://www.edx.org/https://www.edx.org/https://www.edx.org/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/41862172d4ca493996126432b89395ff/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/b12ad659313d4f769a575fdc15a43442/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/b4a85bbcabc94dc6bf30fe713fb727a1/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/fc4eda5f7a9b48a4b77428ea851f328f/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/70b1e7abe6cb427fbbea8d16db89ca85/https://courses.edx.org/courses/course-v1:MITx+CTL.SC1x_2+1T2016/courseware/aeafb16ac1e64da2a16d98d6cdfa423b/d4764459b39c40a6b13557e119d73cc2/https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Data.odshttps://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Data.xlsx


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You have used 3 of 3 submissions

Part 3

What is the forecast for demand in January 2015 . . .

using a naive model?

Answer: 1512

using a cumulative model?

Answer: 957.94

EXPLANATION

Plotting the data by month so that each year is its own line helps to

identify any seasonality. It appears that there is some sort of

seasonality. For example, there appears to be a "low-demand" period

from January through May and two "high-demand" periods from July

to August and then October through December.


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using a 12 Period Moving Average model?

Answer: 1173.66


Part 4

What is the root mean square error (RMSE) for a next period forecast for

these three years of demand . . .

using a Naive model?

Answer: 383.73

using a Cumulative model?

Answer: 419.89

using a 12 Period Moving Average model?

EXPLANATION

The naive forecast for January 2015 is simply the actual demand for

December 2014, which is 1512.

The cumulative forecast for January 2015 is the average of the

previous 36 time periods, which is 957.94.

The 12 Period Moving Average (M=12) forecast for January 2015 is the

average of the actual demand for all months in 2014, which is

1173.67.


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The Naive Model

The Cumulative Model

The 12 Period Moving Average Model

None of these models are appropriate

Answer: 423.33


Part 5

Which of the three models, if any, do you think is most appropriate for

forecasting the demand in January 2015?

The most appropriate model to use is:

EXPLANATION

To find these, you need to calculate the squared error term for each

observation. Taking the square root of the average of all of these

values gives you the RMSE.

Download the spreadsheets with the monthly sales volume of palm oil

here:

In Excel format (link to ShahAlamPalmOil_Solution.xlsx)

In LibreOffice format (link ShahAlamPalmOil_Solution.ods)

EXPLANATION

https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Solution.odshttps://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+CTL.SC1x_2+1T2016+type@asset+block/ShahAlamPalmOil_Solution.xlsx


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None of these models are appropriate for this demand data. The

main reason is that the palm oil demand displayed both a positive

trend and seasonality. All three of these models assume stationary

demand. The naive model makes no sense since we can clearly see

that demand in January looks nothing like demand in December. And

the cumulative and moving average models assume that the demand

will be close to the average. We will learn about other forecasting

models that handle demand with these other patterns next week.
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