• Why forecast?• Understanding patterns of demand• Types of Forecasting Methods

– Qualitative methods– Quantitative methods

• Roadmapping for future

Forecasting

Why forecast?

• Customer Requirements and patterns of demand needs to be understood & carefully managed

• Future demand is invariably subject to uncertainty• Demand must be managed for efficient utilisation of

resources in terms of:– Plant– Capacity– Materials– Human resources– Organisational system

Challenge for Logistics Executives

• A major problem is how to balance demand against

available capacity

• manufacturing firms often rely on inventories

• service organisations cannot rely on inventories of

finished goods to act as a buffer between a

constrained level of supply and a fluctuating level of

demand

Typical scenario

Productionand Supply

The Operation

Demand

The Customers

Managing/AggregatingCapacity to Demand

Often there is a mismatch between demand andand supply (i.e. conditions are dynamic not static)

Forecasting

Forecasting therefore:

• must be expressed in terms that are useful

• must be accurate

• should give indication of relative uncertainty

• should take account of seasonality

• should take into account of weekly/daily demand

fluctuation

Patterns of demand

The amount of capacity required is made difficult by two factors:

• the demand varies considerably over relatively short periods of time (hours, minutes) e.g. retails outlets, restaurants, banks etc.

• the time taken to perform the service may itself vary from customer to customer

Therefore important to understand the patterns and determinants of demand

Understanding Demand

• does demand follow a regular predictable cycle? i.e. hourly, daily, weekly etc. and what causes these variations?

• are changes random in nature? if so, what are the underlying causes?

• can demand be disaggregated by market segment to reflect such components as:– use patterns by a particular type of customer or

for a particular purpose– variations in the net profitability of each

completed transactions?

Strategies to cope with fluctuations in demandAlternative options available:

• Level capacity plans– Ignore fluctuations & keep activity level constant

• Capacity-leading (lead demand plans)– Produce in advance of demand

• Capacity lagging (chase strategy) plans i.e. capacity changed to follow demand

– Adjust capacity to reflect the fluctuations in demand

– Methods of adjusting include:• changing the number of service people

• overtime

• changing the hours worked

• part-time/short term contract staff

• using subcontractors

Managing Demand

• Influence demand to minimise changes in capacity• Methods for influencing demand include:

– price changes - January sales, airlines Saturday night

stay to qualify for a lower fare

– advertising and promotions

– developing non-peak demand

– Alternative products/services

Managing Demand continued

• developing complementary services (cash dispensers, banking services by Sainsbury, insurance by travel agents

• using reservation or appointment systems - theatre/hotel bookings, travel agencies

• making the customer wait or queue• Operations Manager may choose to implement mixture

of these strategies• Yield Management – is the application of information to

improve revenue– American airlines adjust fares to fill empty seats (but still keep a few in

reserve for full paying passengers). This real-time pricing strategy maximises revenue.

– Marriott hotels have installed a yield management system

Long Term Planning

It is therefore important to plan (long, medium and short term planning)

1) Long term typically >2years• To decide whether demand is sufficient to enter a

market• To determine long term capacity needs for facility

design

2) Medium to short term planning

• To adapt capacity & resources in the medium term typically 6 months to 2 years– Recruit or shed labour– Balance production across multiple sites– Ensure supply chain can ramp up or down to ensure

consistent supply

• To enable efficient responsiveness in the short term typically up to 6 months– ‘Real’ production & personnel scheduling – Material & inventory planning– Maintenance planning

It is crucial to balance production efficiency & customer response

Demand Management

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:Raw Materials, Component parts,Sub-assemblies, etc.

Independent Demand:Finished Goods

Independent Demand

What a firm can do to manage it?

• Can take an active role to influence demand

• Can take a passive role and simply respond to demand

Types of Forecasts• Forecasts should be produced as an integrated

part of decision making framework• 2 categories of forecasting approaches

1) Qualitative (Judgmental)

2) Quantitative

– Time Series Analysis– Causal Relationships– Simulation

Components of Demand

• Average demand for a period of time

• Trend

• Seasonal element

• Cyclical elements

• Random variation

Finding Components of Demand

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

xx

xx

xx

x

x

Year

Sal

es

Seasonal variationSeasonal variation

Linear

Trend

Linear

Trend

Types of forecasting methods

Forecasting Methods

Qualitative Quantitative

Economicindicators

Sales forcecomposite

Delphi methods

Scenariowriting

Marketresearch

Historical analogy

PanelConsensus

CausalRelationships

Time seriesanalysis

Trendprojection

Decomposition

Smoothing/Extrapolation

Qualitative Methods

Scenario analysis

Market Research Panel Consensus

Economic Indicators

Historical Analogy Delphi Method

QualitativeMethods

Sales Force Composite

Economic indicators• Indicators based on:

– Prices, employment, production

• Provide the basis for interpretative judgemental forecasting

• Types of indicator– Leading provide advanced warning– Coincident reflect current economic performance– Lagging confirm changes previously signalled – Composite of leading indicators signal major changes in

economic activity

• Impact of economic indicators on demand for specific product is very sector specific

Market Research• Sample members of target market• Can provide sophisticated & accurate forecasts on

market potential• Needs expertise in design and interpretation• Usually costly

Historical Analogy• May look at same product in a different market

– e.g. take off of mobile phone telephone phone sales (internet service) in a less developed country

• New product analogous to another in which similar

take up behaviour may occur– e.g. Digital TV based on an historical analogy to provide

video cassette recorders

Scenario Analysis• Origins in strategic military planning• Explores a number of different scenarios• Identifies the principle factors that may affect the

future• Acknowledges that different scenarios may be

plausible from a different starting point• Roadmapping - Visit website (

http://www.bridges-eu.org/) This is currently ongoing project.

Forecasting vs Scenarios

• Forecasting based on past, present experience and determine future projections

• Scenarios: Analyse past, present and determine various options or scenarios i.e what if ….? (what do we do if there is a fire, flood on the roads, virus in the country (SARS).

• Based on these scenarios one prepare different plans to counteract

Panel Consensus Forecasting Methods

• Take the knowledge of more than one expert• Pitfalls

– Consensus methods may be comforting but wrong! – groups take bigger risks!

– Dominant individuals– Individuals with their own agendas

• Use surveys or the Delphi method

Delphi Method

1. Choose the experts to participate representing a variety of knowledgeable people in different areas

2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants

3. Evaluate responses – produce a numerical summary (modal & extreme values)

4. Redistribute summarised results them to the participants along with appropriate new questions or with explanations to any extreme or unusual values

5. Evaluate and summarize responses again, refining forecasts and conditions, and again develop new questions

6. Repeat Step 4 & 5 until clear forecast emerges7. Summarise the final results and distribute to all participants

(Alan McKinon’s Delphi study)

Sales force composite review• Supports short & medium term forecasting• Particularly popular where there is:

– A complex product mix– Few customers– Close contact with customers– Sales force have technical expertise

• Potential sources of errors ….?

Quantitative forecastingBased on mathematical analysis of historical data• Causal modelling• Time series methods

Causal modelling–projects annual sales of washing m/cs

MODEL OFRELATIONSHIPS

Price

Per CapitaIncome

New HouseCompletions

AnnualRetail sales

INPUTS MODEL OUTPUTS

The independent variables The dependent variables

Set the values Obtain forecastTurn handle

Causal models

• Ideally develop a model to predict the dependent variable from one or more independent variables mechanistically

• The model may be:– Univariate with one independent variable– Multivariate with more than one independent variable

• Generally are less interested in the form of the relationship than in its predicative accuracy

Simple Linear Regression Model

Yt = a + bx0 1 2 3 4 5 x (Time)

YThe simple linear regression model seeks to fit a line through various data over time

The simple linear regression model seeks to fit a line through various data over time

Is the linear regression modelIs the linear regression model

a

Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope.

Simple Linear Regression Formulas for Calculating “a” & “b”

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

Simple Linear Regression Problem Data

Week Sales1 1502 1573 1624 1665 177

Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 8853 55 162.4 2499

Average Sum Average Sum

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

Answer: First, using the linear regression formulas, we can compute “a” and “b”

Answer: First, using the linear regression formulas, we can compute “a” and “b”

Yt = 143.5 + 6.3x

180

Period

135140145150155

160165170175

1 2 3 4 5

Sal

es

Sales

Forecast

The resulting regression model is:

Now if we plot the regression generated forecasts against the actual sales we obtain the following chart:

Time Series Analysis

• Time series forecasting models try to predict the future based on past data

• You can pick models based on:

1. Time horizon to forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel

Time series

• A time series is a set of data taken at successive points in time

• Used to predict the future behaviour of the series• Very important for short-term demand forecasting• Most useful in fast moving consumer goods &

commodity markets

Time series – long-term trend

• Is the long-term change in the observations once short-termfluctuations have been removed

• Trend projection is a very basic form of forecasting

Time series – cyclical

• Time series that fluctuate about their long-term trend

• Can be caused by: investment cycles, business confidence

Time

Time series – seasonal• Recurring changes within a one year period• Often exhibited in retail

Time series – irregular variation• The components of the time series not explained by

T, C & S• May be considered as random as it is unpredictable• Always has some cause

Simple Moving Average Formula

F = A + A + A +...+A

ntt-1 t-2 t-3 t-nF =

A + A + A +...+A

ntt-1 t-2 t-3 t-n

• The simple moving average model assumes an average is a good estimator of future behavior

• The formula for the simple moving average is:

Ft = Forecast for the coming periodN = Number of periods to be averagedA t-1 = Actual occurrence in the past period for up to “n” periods

Simple Moving Average Problem (1)

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892

10 92011 78912 844

F = A + A + A +...+A

ntt-1 t-2 t-3 t-nF =

A + A + A +...+A

ntt-1 t-2 t-3 t-n

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33

10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

F4=(650+678+720)/3

=682.67F7=(650+678+720 +785+859+920)/6

=768.67

Calculating the moving averages gives us:

©The McGraw-Hill Companies, Inc., 2004

43

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Dem

and

Demand

3-Week

6-Week

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example

Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

Simple Moving Average Problem (2) Data

Week Demand1 8202 7753 6804 6555 6206 6007 575

Question: What is the 3 week moving average forecast for this data?

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Question: What is the 3 week moving average forecast for this data?

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Simple Moving Average Problem (2) Solution

Week Demand 3-Week 5-Week1 8202 7753 6804 655 758.335 620 703.336 600 651.67 710.007 575 625.00 666.00

F4=(820+775+680)/3

=758.33F6=(820+775+680 +655+620)/5 =710.00

Weighted Moving Average Formula

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-nF = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n

w = 1ii=1

n

While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods

While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods

wt = weight given to time period “t” occurrence (weights must add to one)

wt = weight given to time period “t” occurrence (weights must add to one)

The formula for the moving average is:The formula for the moving average is:

Weighted Moving Average Problem (1) Data

Weights: t-1 .5t-2 .3t-3 .2

Week Demand1 6502 6783 7204

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Note that the weights place more emphasis on the most recent data, that is time period “t-1”

Note that the weights place more emphasis on the most recent data, that is time period “t-1”

Weighted Moving Average Problem (1) Solution

Week Demand Forecast1 6502 6783 7204 693.4

F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

Weighted Moving Average Problem (2) Data

Weights: t-1 .7t-2 .2t-3 .1

Week Demand1 8202 7753 6804 655

Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?

Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?

Weighted Moving Average Problem (2) Solution

Week Demand Forecast1 8202 7753 6804 6555 672

F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672

Exponential Smoothing Model

• Premise: The most recent observations might have the highest predictive value

• Therefore, we should give more weight to the more recent time periods when forecasting

Ft = Ft-1 + (At-1 - Ft-1)Ft = Ft-1 + (At-1 - Ft-1)

constant smoothing Alpha

period past t time in the occurrence ActualA

period past time 1in valueForecast F

period t time coming for the Forecast valueF

:Where

1-t

1-t

t

Time series extrapolation• Elementary time series methods

– Moving average– Exponential smoothing

Web-Based Forecasting: CPFR Defined

• Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.

• Used to integrate the multi-tier or n-Tier supply chain, including manufacturers, distributors and retailers.

• CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain.

• CPFR uses a cyclic and iterative approach to derive consensus forecasts.

Web-Based Forecasting: Steps in CPFR

1. Creation of a front-end partnership agreement

2. Joint business planning

3. Development of demand forecasts

4. Sharing forecasts

5. Inventory replenishment