Forecasting
Forecasting and Operations
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Sales
Production
Inventory
Facilities
Raw Materials
People
Profits
Products
Forecasting Importance
• Boeing, 1997: $2.6 billion write down due to
“raw material shortages, internal and
supplier parts shortages” Wall Street
Journal, Oct 23, 1997
• “IBM sells out new Aetna PC; shortage may
cost millions in potential revenue.” Wall
Street Journal, Oct 7, 1994
• 2008 Daytona 500 – new sponsor – out of
stock
• Empty shelves
Principles of Forecasting
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Impacts of Forecasting
• Forecasting errors can increase the total
cost of ownership for a product
- inventory carrying costs
- obsolete inventory
- lack of sufficient inventory
- quality of products due to accepting
marginal products to prevent stockout
- expediting costs
Forecasting
• Essential for smooth operations of
business organizations
• Estimates of the occurrence, timing, or
magnitude of uncertain future events
• Costs of forecasting: excess labor;
excess materials; expediting costs; lost
revenues
• Cabela’s – fishing rods
Demand Behavior
 Trend
 gradual, long-term up or down
movement
 Cycle
 up & down movement repeating over
long time frame
 Seasonal pattern
 periodic oscillation in demand which
repeats
 Random movements follow no pattern
Forecasting Methods
 Time series
 Regression or causal modeling
 Qualitative methods
 Management judgment, expertise, opinion
 Use management, marketing, purchasing,
engineering
 Delphi method
 Solicit forecasts from experts
Time Series Methods
 Statistical methods using historical
data
 Moving average
 Exponential smoothing
 Linear trend line
 Assume patterns will repeat
 Naive forecasts
 Forecast = data from last period
Moving Average
 Average several
periods of data
Sum of Demand
 Dampen, smooth out
In n Periods
changes
n
 Use when demand is
stable with no trend
or seasonal pattern
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
Daug+Dsep+Doct
MAnov =
3
90 + 110 + 130
=
3
= 110 orders for Nov
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
–
THREE-MONTH
MOVING AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
–
THREE-MONTH
MOVING AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0
FIVE-MONTH
MOVING AVERAGE
–
–
–
–
–
99.0
85.0
82.0
88.0
95.0
91.0
Weighted Moving Average
 Adjusts moving average method
to more closely reflect data
fluctuations
Weighted Moving Average
Example
MONTH
August
September
October
WEIGHT
DATA
17%
33%
50%
130
110
90
Where do the weights come from?
Weighted Moving Average
Example
MONTH
August
September
October
WEIGHT
DATA
17%
33%
50%
130
110
90
3
November forecast WMA3 =
Wi Di

i=1
= (0.50)(90) + (0.33)(110) + (0.17)(130)
= 103.4 orders
3 Month = 110
5 month = 91
Exponential Smoothing
 Averaging method
 Weights most
recent data more
strongly
 Reacts more to
recent changes
 Widely used,
accurate method
Ft +1 = Dt + (1 - )Ft
where
Ft +1 = forecast for next
period
Dt = actual demand for
present period
Ft = previously
determined forecast
for present period
= weighting factor,
smoothing constant
Forecast for Next Period
• Forecast = (weighting factor)x(actual
demand for period)+(1-weighting
factor)x(previously determined forecast
for present period)
Lesser
reaction
to recent demand
0 >  <= 1
Greater
reaction
to recent demand
Exponential Smoothing
PERIOD
MONTH
DEMAND
1
2
3
4
5
6
7
8
9
10
11
12
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
37
40
41
37
45
50
43
47
56
52
55
54
Exponential Smoothing Made
Simple – 4 steps©
• Step 1: First Period Forecasted = Naïve
Forecast
• Step 2: Remaining Periods Forecasted:
(Smoothing Factor (ά) x Previous Actual
Demand)
• Step 3: Remaining Periods Forecasted: (1Smoothing Factor(ά) x Previous Forecast)
• Step 4: Remaining Periods Forecasted: Add
Steps 2 and 3
© Joe Walden, 2011
Forecast Accuracy
 Find a method which minimizes error
 Error = Actual - Forecast
 Mean Absolute Deviation (MAD)
Forecast Control
Why is my forecast out of tolerance?
 Reasons for out-of-control forecasts
 Change in trend
 Appearance of cycle
 Weather changes
 Promotions
 Competition
 Politics
Inventory Management
Inventory Management
• What to order
• When to order
• How much to order
Types of Inventory
 Raw materials
 Purchased parts and supplies
 Labor
 In-process (partially completed) products
 Component parts
 Working capital
 Tools, machinery, and equipment
 Safety stock
 Just-in-case
Inventory Management
• What is Inventory?
• satisfy normal demand patterns
• Inventory management
– Decisions drive other logistics activities
– Different functional areas have different
inventory objectives
– Inventory costs are important to consider
• Inventory turnover
• Inventory carrying costs
Purposes of Inventory
• Enables the firm to achieve economies of
scale
• Balances supply and demand
• Enables specialization in manufacturing
• Provides protection from uncertainties in
demand and order cycle
• Acts as a buffer between critical interfaces
within the supply chain
• Cover up inefficiencies in operations
• Smooth variances in customer demand
• An Addiction
Inventory Management
Philosophies
•Pull
•Push
•Just-in-time
Inventory-Related Costs
• Inventory carrying (holding) costs
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Obsolescence
Inventory shrinkage
Storage costs
Handling costs
Insurance costs
Taxes
Interest charges
Opportunity cost
• Stockouts
Costs Relevant to Inventory
Management
•Carrying costs
-Cost for holding the inventory over time
-The primary cost is the cost of money tied up in
inventory, but also includes obsolescence,
insurance, personal property taxes, and
storage
costs
-Typically, costs range from the cost of short
term
capital to about 40%/year. The
average is about
25%/year of the item
value in inventory.
Goals of inventory management
When to Order
• Reorder point (ROP)
ROP = DD x RC under certainty
ROP = (DD x RC) + SS under uncertainty
Where DD = daily demand
RC = length of replenishment cycle
SS = safety stock
Forms of Reorder Points
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Fixed
Variable
Two Bin
Card
Judgmental
Projected shortfall
Min-Max
Inventory Management
Questions:
• 3 questions:
• EOQ developed by Fred Harris - 1913;
made popular by R.H. Wilson
How Much to Reorder
• Economic order quantity (EOQ) in dollars
EOQ = √2AB/C
Where
EOQ = the most economic order size, in
dollars
A = annual usage, in dollars
B = administrative costs per order of
placing the order
C = carrying costs of the inventory (%)
Basic EOQ Model
Assumptions
1. Constant known demand rate
2. Cost per unit not depend on order
quantity (no quantity discounts)
3. Entire order delivered at one time
4. Ordering and carrying cost known
and independent
5. No stock-out because of certain
demand
So What?
EOQ Example
Annual Usage = 45,000 units
Ordering costs = $30 per order
Carrying costs = 20%
30 =B
0.2 =C
3674.235 =Q
# ORDERS/YEAR = A/Q
= 45,000/3674
= 12.25
Inventory Flows
• Safety stock can prevent against
problem areas
• When fixed order quantity system is
used, time between orders may vary
• When reorder point is reached, fixed
order quantity is ordered
Inventory Turns
• What is it?
• How is it calculated?
• Why is it important?
Aggregate Performance Measure
• Average Inventory Investment: It is the dollar
value of a company’s average level of inventory.
Disadvantage: It makes comparisons of companies
difficult.
• Inventory Turn Over Ratio: It is a measure that
allows for better comparison among companies.
Inventory Turnover = Annual cost of good sold
Average Inventory Investment
Disadvantage: Figures among industry may not be
comparable.
ABC Analysis
• Pareto’s Law – meaningful few and trivial
many
• 80/20 – based on Pareto’s analysis 80
percent of the wealth was in 20 percent of
the people’s hands – in inventory
management usually 20 percent of your
items account for 80 percent of your
inventory dollars
ABC Analysis
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Volume
Dollar value
Annual Dollar Value
Customers