2_Forecasting and Demand Planning

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.


Factors influencing demand

- General business and economic conditions

- Competitive factors

- Market trends

- Firm‟s own plans

- Government regulations

- Technology changes

- Others…


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


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


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


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


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


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

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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

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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


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


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



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


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 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 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

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Documents

2_Forecasting and Demand Planning