Sales Forecasting
&
Production Planning
Presented by
Dr. Kern Kwong
Decision Form:
Excel Spreadsheet Templates:
 Several templates will be provided:
Historical Data Worksheet
Sales Forecast Worksheet
Shipment Orders Worksheet
Production Schedule Worksheet
 An Excel file containing these templates can be downloaded
online at:
http://www.calstatela.edu/faculty/klai/CL497.htm
 Additional lecture notes, along with some flow charts, can
also be downloaded from there.
 Also from our class Moodle site, with updated historical data
Use your own data from both industry and
company reports:
 After opening the BPG program, you can view all the reports:
Report J (see p.210 of the Player’s Manual for a sample)
Historical Data for Years 1 and 2 – GDP, CPI, product sales, and
product prices.
Report D (see p.215 of the Player’s Manual for a sample)
Company’s Current Operating Information – Output, inventory, and
product sales
Report F (see pp.217-8 of the Player’s Manual for a sample)
Recent Industry Information – Real GDP, exchange rates, product
sales, and product prices.
View reports:
A top-down approach will be used for sales
forecasting:
 Industry Level
The method starts with sales forecasting at the industry level for
each market area:
M1 (Merica 1)
M2 (Merica 2)
M3 (Merica 3)
M4 (Nystok, Pandau, or Sereno)
 Company Level
From industry sales forecasts, company sales forecasts for the
corresponding market areas can then be obtained as:
Company Sales Forecast
= Industry Sales Forecast × Expected Market Share
Need to account for seasonal effects on sales:
 See Section 1.A of the Lecture Notes on Forecasting.
 Seasonal Indices (p.105 of the Player’s Manual)
Q1 (Winter)
Q2 (Spring)
Q3 (Summer)
Q4 (Fall)
0.92
1.01
0.91
1.16
Use a regression model to forecast industry
sales:
 See Section 1.B of the Lecture Notes on Forecasting.
 Dependent variable (Y)
SA Sales:
Seasonally Adjusted Industry Sales
 Independent variables – Predictors (X)
Real GDP: Real Gross Domestic Product
Avg Price: Industry Average Price
Time:
Time Trend Index
Note: Real GDP is an often used indicator for the general demand
and business conditions. The Time variable can capture demand
changes generated by demographic trends.
Try a few different forecasting equations and
identify the best one:
 Model #1:
SA Sales = 0 + 1  Time
 Model #2:
SA Sales = 0 + 1  Real GDP
 Model #3:
SA Sales = 0 + 1  Avg Price
 Model #4:
SA Sales = 0 + 1  Time + 2  Real GDP
All these forecasting equations are to be estimated using Excel on
the Sales Forecast Worksheet.
Try a few different forecasting equations and
identify the best one:
 Model #5:
SA Sales = 0 + 1  Real GDP + 2  Avg Price
 Model #6:
SA Sales = 0 + 1  Avg Price + 2  Time
 Model #7:
SA Sales = 0 + 1  Time + 2  Real GDP + 3  Avg Price
Best Model: highest Adjusted R-square with Correct Sign
(e.g. Coefficient of Avg Price should be negative,
SA Sales decrease when Avg Price increases)
Best Model – M1, Y3Q1
Model
Adj. R-Square Correct Sign
1 Time
.545
Yes
2 Real GDP
.530
Yes
3 Avg Price
.023
Yes
4 Time + Real GDP
.454
No-GDP
5 Real GDP + Avg Price
.437
No-Price
6 Avg Price + Time
.474
No-Price
7 Time + Real GDP + Avg Price
.394
No-Price, GDP
Best Model – M2, Y3Q1
Model
1 Time
2 Real GDP
3 Avg Price
4 Time + Real GDP
5 Real GDP + Avg Price
6 Avg Price + Time
7 Time + Real GDP + Avg Price
Adj. R-Square
Correct
Sign
Best Model – M3, Y3Q1
Model
1 Time
2 Real GDP
3 Avg Price
4 Time + Real GDP
5 Real GDP + Avg Price
6 Avg Price + Time
7 Time + Real GDP + Avg Price
Adj. R-Square
Correct
Sign
Best Model – M4, Y3Q1
Model
1 Time
2 Real GDP
3 Avg Price
4 Time + Real GDP
5 Real GDP + Avg Price
6 Avg Price + Time
7 Time + Real GDP + Avg Price
Adj. R-Square
Correct
Sign
Best Model – M1, Y3Q1
Model
1 Time
2 Real GDP
3 Avg Price
4 Time + Real GDP
5 Real GDP + Avg Price
6 Avg Price + Time
7 Time + Real GDP + Avg Price
Adj. R-Square
Correct
Sign
Step-by-step forecasting exercise:
 When using the Excel template for forecasting, you should
read Sections 2.A to 2.E of the Lecture Notes on Forecasting
for step-by-step instructions.
 We will go through all the steps when looking at the template
later:
1)
2)
3)
4)
5)
To start, prepare initial data on regression variables using available
historical data (see Section 2.A).
After setting up the data, estimate the forecasting regression
equation using Excel (see Section 2.B).
Try different models and select the model that fits the data best (see
Section 2.C).
Enter additional assumptions and your market share projection (see
Section 2.D).
Repeat the forecasting exercise – steps 2 to 4 – after adding new
data every quarter (see Section 2.E).
After obtaining company sales forecasts, we
next determine how much to produce:
 Read Lecture Notes on Production Planning (download it from
http://www.calstatela.edu/faculty/klai/CL497.htm), OR from
Moodle.
 For our production analysis, we will use the following two
Excel templates together:
Shipment Orders Worksheet
Production Schedule Worksheet
To determine a production target, we need to
think about inventory management:
 How much inventory to hold in each market area?
Carrying too little inventory may lead to costly stockouts:
Stockouts can result in not only a loss of present sales but also a loss
of some future sales. Some dissatisfied customers may not come
back.
Carrying too much inventory can be costly too:
 Warehouse storage cost;
 Financing cost for tying up working capital;
 Product obsolescence.
Choose an inventory ratio that balances
between over- and under-stocking costs.
 Choose an inventory-to-sales ratio for each market area (when
using the Shipment Orders Worksheet):
Under normal situations, a ratio from 25% to 45% should be
sufficient for the game.
An example: Suppose the ratio is chosen to be 25%. If the sales
demand is forecasted to be 100,000 units, then
Desired Inventory = 100,000 × 25% = 25,000 units.
The choice of inventory-to-sales ratios will affect how many
product units to be shipped to different market areas.
How should production be scheduled?
Should production capacity be expanded?
 See Chapters 7 & 8 of the BPG Player’s Manual (read also
Section 3 of the Lecture Notes on Production Planning):
Normal operations: 40 hours per line each week
Schedule overtime: Up to 8 hours per line
Add second work shifts (Take 1 quarter to complete)
Create new production lines (Take 1 quarter)
Reactivate some idle lines (Take 1 quarter)
Add more space to a plant (Take 2 quarters)
Build a new plant (Take 3 quarters)
Overall: Keys to Successful Production
Management
 A number of factors are crucial for a company’s success in
production management:
Reasonably accurate sales forecasts;
Excellent inventory control to cope with demand and
production uncertainties;
Proper allocation of product shipments to regional sales offices
and thereby to customers;
Efficient production scheduling to meet current production
targets;
Timely production capacity adjustment (including plant
expansion or construction) to meet future product demand.
Company Sales Forecasts by Market Area
Desired Inventory Ratio
Estimated Shipment Orders to Sales Offices by Market Area
Planned Production Target
Production Scheduling:
Lines, Overtime, and Second Shifts
Production Capacity Expansion:
New Lines or Plants?
Production Cost Analysis
Capital Budgeting Analysis
mgmt49701@gmail.com
every quarter
Screen print of your
decision screen and
research report order (if your ordered)
Hope you will enjoy the
Business Policy Game!