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Integrated Planning – Module 2
1
Agenda
• Forecasting,
• Factors influencing Demand
• Basic Demand Patterns
• Basic Principles of Forecasting
• Principles of Data Collection
• Basic Forecasting Techniques, Seasonality • Sources & Types of Forecasting Errors
3
Forecasting can be conducted at various levels
Strategic
Financial
Operational
Required for Examples
• Product life cycle
• Long-term capacity
planning
• Capital asset/equipment/
human resource
management
• Product line transitions
• Annual volume out
3-5 years
• Buy/build/lease
decisions
• Budgeting
• Financial reporting
• Working capital
management
• Total annual/monthly
volume
• Projected product mix
• Production scheduling
• Purchasing
• Resource planning
• Customer service
management (product
allocations)
• Weekly/monthly SKU-
level demand
• Order size and
frequency
Role of Forecasting in Supply Chain,
• Basis for Strategic & Planning Decisions in SCM • Decisions needing Forecast as Base • Production
- Scheduling
-Inventory Control -- Aggregate Planning - Purchasing
• Marketing-Allocation of Sales-Force -- Promotion Activities -- New Product Launching
• Finance-Plant & Equipment Investment-- Budgetary Planning
• HR
-Workforce Planning-- Recruitment
3- Lay-Offs
5
Sales
history/
orders
Demand planning Sales and operations
planning
Inventory management
Aggregate planning
Materials requirement
planning (MRP)
Master production
scheduling (MPS)
Capacity requirements
planning (CRP)
Shop floor scheduling
and control
Purchasing
BOM
inventory
routing
Production
Distribution/transport
network
Forecasting
Demand management
Distribution management
Production management
Directly impacted by
demand managementForecasting Impact
Higher forecast accuracy improves service levels at lower inventory
Percent
94
95
96
97
98
99
100
2 4 6 8 10 12Required average inventory
Weeks
12
3
Monthly average forecast error
Excellent Far Poor
20% 40% 50%1
2
3
Reducing forecast error
will permit
Reduced inventory to a given
service level
Increased service level for a
given inventory level
Both reduced inventory and
improved service level
0
7
Forecasting error must be measured at different levels
0
10
20
30
40
50
60
-12 -10 -8 -6 -4 -2 -0
SKU/DC (12 oz.
ketchup bottle in
Dallas warehouse)*
SKU level
(12 oz. bottle)
Brand level
(ketchup)
Product family level (all
condiments)
Manufacturing
lead time
* Required level of detail for planning
** A consistently positive or negative bias indicates a tendency to over- or under-forecast which may be easily remedied
• Measure forecasting error as the
mean absolute percent error
• Error of forecasting can be
measured at various levels:
product family, brand, SKU,
SKU/DC and will improve at higher
levels of consolidation
• Frequency of measurement is
usually monthly; however, best
practitioners are doing weekly
forecasts
• Measure bias as the mean
percent error**
(Forecast – actual sales)
Forecast
Mean forecasting error
(Forecast – actual sales)
Forecast
Forecasting tips
Forecasting error
Percent
8
* Demand can be sales, shipments, orders depending on what works best and data available
Range of algorithms can be usedCOMMON FORECAST ALGORITHMS
• Last year plus percent
• Last 3 months
• Experiential smoothing
• Seasonality with trend
• Regression
• Time series
• Real-time regression using
POS data
Model
Simple
Complex
• Last year‟s same period demand* increased by a flat
percentage
• Last 3-month moving average of demand
• Last 12-month moving average with most recent
2-3 months more heavily weighted
• Experiential smoothing with a seasonality factor that
weights periods differently based on relative
historical demand throughout the year
• Incorporates variables other than historical demand
(e.g., price promotional activity) to best fit historical
demand patterns
• Uses Fourier transforms to best fit historical demand
patterns
• Modifies above models with changes in customer
takeaway based on Nielsen; IRI data
Calculation/description
9
Regression-based forecasting on high-promotion items
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
1 10 20 30 40 50 52
Key business drivers
• Off-invoice promotions
–Before a summer holiday
–Without holiday
• Promotion month end week
• Postpromotion period
Fiscal year 1
Fiscal year 2
Fiscal year 3
Higher peaks prior to
Memorial Day and
Labor Day
Fiscal
week
Actual peak shipment week is
last week of fiscal month
Post-promotion
period
Off-invoice
promotions
National cases shipped/week – ketchup example
10
• “Single-point” forecast to manage (i.e., consistency
across functioning)
• Skills to balance art and science of forecasting
Tools and
methodologies
Process • Driven by analytics, supported by market events
• Explicit reconciliation steps
Accountability • Accountability for both forecast error and inventory; need
to balance trade-off
• Rigorous measurement and tracking
Forecasting implementation requires three success factors
Characteristics of demand
Sources of demand
- Customers
- Spare parts
- Promotions
- Intra-company
- Test samples
- Others…
All the sources of demand must beidentified.
Characteristics of demand
Factors influencing demand
- General business and economic conditions
- Competitive factors
- Market trends
- Firm‟s own plans
- Government regulations
- Technology changes
- Others…
Characteristics of demand
Components of demand
- Trend40
35- Seasonality30
- Random variation 25
20- Cyclical variation 15
10
5
02002 2003 2004 2005
Q1 Q2 Q3 Q4
6
Characteristics of Demand
Trend
Seasonal Demand
7Time
Characteristics of Demand
Dynamic
Stable
Average Demand
Time8
Characteristics of demand
Demand Patterns
- Stable versus Dynamic> Stable demand has certain general pattern over time> Dynamic demand tends to be erratic
- Independent versus Dependent> Demand for an item unrelated to demand for other
items. This is independent demand.> Demand that is directly related to derived from the bill
of material structure of other items or end items. This is dependent demand.
Only Independent demand needs to be forecasted.Dependent demand can be calculated.
9
Characteristics of demand
Level of planning and forecast contents
Forecast Time Frame
Business plan Market direction 2 to 10 years
Sales and operations Product lines and 1 to 2 years
families
Master production End item and Months
schedule option
10
Characteristics of demand
Why Forecast?
• Before making plans, an estimate must be made of what conditions will exist over some future period
• Most firms cannot wait until orders are actually received before they start to plan what to produce
• Manufacturers must anticipate future demand and plan to provide the capacity and resources to meet the demand
• Firms that make standard products need to have salable goods immediately available / with shorter delivery time
• Firms that MTO, must have labor and equipment to meet demand
11
Working without Forecast
DemandForecasting Model 12
Principles of Forecasting and DataCollection
Forecasts..
- Are rarely 100% accurate over time
- Should include an estimate of error
- Are more accurate for product lines and families
- Are more accurate for nearer periods of time
While collecting data..
- Record data in terms needed for the forecast
- Record circumstances relating to the data
- Record demand separately for different customer
groups13
Forecasting Techniques
Classification:
- Quantitative Techniques
- Qualitative Techniques
- Intrinsic Techniques
- Extrinsic Techniques
- Short-range Techniques
- Long-range Techniques
14
Qualitative Techniques
• Are based on intuition and informed opinion
• Tend to be subjective
• Are used for business planning andforecasting for new products
• Are used for medium-term to long-termforecasting
15
Quantitative Techniques
• Based on historical data usually available inthe company
• Assume future will repeat past
16
Extrinsic Techniques
• Based on external indicators
• Useful in forecasting total company demandor demand for families of products
17
Forecasting TechniquesMoving Average: (Quantitative, Intrinsic)
3-period moving average
Period Demand Simple Weighted
1 265
2 240
3 295
4 265 267 281
5 310 267 269
6 285 290 300
7 304 287 288
8 312 300 301
9 328 300 308
10 299 315 322
11 313 306
Period Weightage
-3 0.1
-2 0.2
-1 0.7
18
Forecasting Techniques
Moving Average: (Quantitative, Intrinsic)
• Lags the actual sales. More the number of
previous periods included, more is the lag
• Can be used to filter out random variation
• If a trend exists, it is hard to detect
• Calculations become cumbersome when
dealing with many time periods. More data
storage required
19
Problem 1• Over the past three months, the demand for a
product has been 255,219 & 231.Calculate the three month moving average forecast for month 4
• If the actual demand in month 4 is 228,calculatethe forecast for month 5
Answer
Moving Average Demand for 3 months= (255+219+231)/3= 705/3
= 235
Moving Average for fourth month= (219+231+228)/3=678/3
=226
Forecast for month 5 is 226
20
Forecasting TechniquesExponential Smoothing : (Quantitative, Intrinsic)
Period Demand Forecast (FT+1) = FT + alpha (DT - FT)( FT )
alpha
( T ) ( DT ) 0.1 0.5 0.9
alpha= 0.1 alpha= 0.5 alpha= 0.9DT
1 190 180 180 180
2 160 181 185 189
3 220 179 173 163
4 200 183 196 214
5 300 185 198 201
6 240 196 249 290
7 270 201 245 245
8 200 208 257 268
9 290 207 229 207
10 275 215 259 282
11 305 221 267 276
Forecasting Techniques
Exponential Smoothing: (Quantitative,Intrinsic)
• A type of moving average
• Routine method for updating item forecasts
• Satisfactory for short range forecasting
• Can detect trends, but will lag them
• Calculation and data requirements are
manageable
• Easy to „tune‟
22
Problem 3
If the forecast for February was 122 and actual demand was 135,what would be forecast for March if smoothing constant is 0.15, with exponential smoothing techniques.
Answer
In Exponential smoothing, forecast is calculated by formula(FT+1) = FT + alpha (DT - FT)
= 122 + 0.15( 135-122)= 122 + 1.95
= 123.95 say 124
23
Seasonality
Key concepts:
- Seasonality is variation in demand based onthe season.
- Seasonality may be annual, monthly, or evendaily!
- „Seasonal Index‟ is a measure of seasonalvariation. Period average sales
- Seasonal Index =Average sales for all periods
- For forecasting purpose, de-seasonalizeddata is required.
Seasonality
Illustration:Month Year1 Year2 Year3 Monthly Seasonal
Average Index
Jan 10 12 11 11.00 0.327
Feb 13 13 11 12.33 0.367
Mar 33 38 29 33.33 0.992
Apr 45 54 47 48.67 1.448
Period average salesMay 53 56 55 54.67 1.626
Seasonal Index =Jun 57 56 55 56.00 1.666 Average sales for all periods
Jul 33 27 34 31.33 0.932
Aug 20 18 19 19.00 0.565
Sep 19 22 20 20.33 0.605
Oct 18 18 15 17.00 0.506
Nov 46 50 55 50.33 1.498
Dec 48 53 47 49.33 1.468
Total 395 417 398 403.33 12
Average Sales for all months = 33.6
25
Seasonality
Forecasting with Seasonality:
- Historical data is influenced by seasonality;
hence can‟t be used „as-it-is‟ for forecasting
- Following steps are necessary:
# Deseasonalize historical data # Forecast deseasonalized demand
(Baseline Forecast)
# Calculate the seasonal forecast by applying the Seasonal Index to the base forecast.
26
Problem 4Month Average Demand Seasonal Index Forecast
January 30
February 50
March 85
April 110
May 125
June 245
July 255
August 135
September 110
October 90
November 60
27December 30
Month Monthly Seasonal Index New Av. Forecast
Demand Demand
January 30 0.27 166.67 45.28
February 50 0.45 166.67 75.47
March 85 0.77 166.67 128.30
April 110 1.00 166.67 166.04
May 125 1.13 166.67 188.68
June 245 2.22 166.67 369.81
July 255 2.31 166.67 384.90
August 135 1.22 166.67 203.77
September 110 1.00 166.67 166.04
October 90 0.82 166.67 135.85
November 60 0.54 166.67 90.57
December 30 0.27 166.67 45.28
Total 1325 2000.00
Average Sales for Month= 110.42
28
Tracking the Forecast
Limitations of forecasts:
- For several reasons, forecasts tend to go wrong.- We need methods to know how good the
forecasting method is.
- „Tracking‟ is the process of comparing actualdemand with the forecast
- Forecast Error is the difference between actualdemand and forecast demand
- Error can occur in two ways:# Bias# Random Variation
29
Bias
Bias exist when the cumulative Actual Demandvaries from Cumulative Forecast
Month Forecast Actual
Monthly Cumulative Monthly Cumulative
1 100 100 110 110
2 100 200 125 235
3 100 300 120 355
4 100 400 125 480
5 100 500 130 610
6 100 600 110 720
Total 600 720
30
Bias
FORECAST
ACTUAL DEMAND
31MONTHS
Random VariationIn a period actual demand will vary againstaverage demand based on Demand pattern
Month Forecast Actual Variation
(Error)
1 100 105 5
2 100 94 -6
3 100 98 -2
4 100 104 4
5 100 103 3
6 100 96 -4
Total 600 600 032
Random Variation
FORECAST
105 104 103
100
98 9694
ACTUAL
MONTHS 33
Tracking the Forecast
Bias:
- Bias is a systematic error in which the actual demand is consistently above or below the forecast demand
- When bias is noticed, forecasting method should be changed to improve the forecast accuracy
- For a unbiased forecasting method, theCumulative Sum of Errors (CSE) will be zero
34
Tracking the Forecast
Bias: (Illustration)Period Forecast (F) Actual Sales Error
Interpretation:1 1000 1200 200
The bias (Average CSE) indicates2 1000 1000 0 that the there is an underforecast /3 1000 800 -200 positive bias of 20 per period.
4 1000 900 -100
5 1000 1400 400
6 1000 1200 200
7 1000 1100 100
8 1000 700 -300
9 1000 1000 0Cumulative Sum
10 1000 900 -100 of Errors (CSE)Total 10000 10200 200
35
Forecast Error Measurement
• Mean Absolute Deviation
• Normal Distribution
36
Mean Absolute Deviation
• Forecast Error must be measured before it is used for planning or to revise the forecast
• Mean Absolute Deviation ( MAD) commonly used for Error Measurement
• Mean implies Average• Absolute means without reference to plus or
minus
• Deviation refers to the Error • MAD= Sum of Absolute Deviations Number of Observations
in Earlier case,
MAD = 5+6+2+4+3+4 = 24 = 4
6 637
Normal Distribution
1% 15% 30% 30% 15% 4%4% 1%
-3 -2 -1 0 1 2 3
+/- 1 MAD of the Average about 60% of the time
+/- 2 MAD of the Average about 90% of the time
+/- 3 MAD of the Average about 98% of the time
38
Use of MAD• Tracking Signal
- to monitor Quality of Forecast
• Tracking Signal= Algebraic Sum of Forecast ErrorsMAD
• Past Six Month Consumption is - 105,110,103,105,107,and 115 ,where Forecast is 100 per month.
• If MAD is 5
• Tracking Signal =(5+10+3+5+7+15)/5= 45/5
= 9
• Contingency Planning- Manufacturing Department can devise contingency
plan for Capacity Utilization based on information regarding MAD of Forecast
• Safety Stocks
- Demand Variation is to be guarded by Safety Stocks 39with Inventory Investment Decisions
Tracking the Forecast
Mean Absolute Deviation (MAD):
- MAD is a measure of random variation.
- It measures the total error irrespective of the
direction
- For a normally distributed random variation,
Standard Deviation (Sigma) = 1.25*MAD
- MAD can be used to determine:
# Tracking Signal
# Safety Stock
40
Tracking the Forecast
Tracking Signal:
- It is difficult to determine whether the variationis due to bias or random variation.
- If the variation is due to random variation, theerror will correct itself.
- If the variation is due to bias, the forecastingmethod needs to be corrected.
- A tracking signal can be used to monitor thequality of the forecast.
42
Tracking the Forecast
Tracking Signal: (Illustration)Period Forecast Sales Abs. Deviation CSE CSE
Tracking Signal =T W T W T W
MAD1 1000 1200 1200 200 200 200 200
2 1000 1000 1000 0 0 200 200
3 1000 800 1200 200 200 0 400200
4 1000 900 900 100 100 -100 300
5 1000 1400 1400 400 400 300 700 Tracking Signal (T) = 160
6 1000 1200 1200 200 200 500 900 = 1.25
7 1000 1100 1100 100 100 600 1000
12008 1000 700 1300 300 300 300 1300
9 1000 1000 1000 0 0 300 1300 Tracking Signal (W) = 160
10 1000 900 900 100 100 200 1200= 7.5
MAD= 160 160
A tracking signal between +/- 4 means that the forecast is matching the
actual data received.43
Tracking the ForecastMore about forecasts…..
- Forecasts forecast average demand
- Forecasts ignore random variations
- Forecasting methods need to be continuously tracked and improved
- Multiple forecasts should be avoided in a supply chain
- If forecasting does not happen at right place, someone else is forced to do it
- Certain operations are most affected by the forecast errors; postpone them as much as possible
- The main aim of all the forecasting methods is to beat the naïve forecast
44
Thank You
45