1. Forecasting Why forecast? Understanding patterns of demand
Types of Forecasting Methods
Qualitative methods
Quantitative methods
Roadmapping for future 2

2. 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

3. 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 1

4. Managing/Aggregating
Typical scenario
Often there is a mismatch between demand and
and supply (i.e. conditions are dynamic not static)
Demand
Production
and Supply
Managing/Aggregating
Capacity to Demand
The Operation
The Customers

5. 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

6. 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

7. 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?

8. Strategies to cope with fluctuations in demand
Alternative 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

9. 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

10. 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

11. 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

12. 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

13. Demand Management Independent Demand: Finished Goods Dependent Demand:
Raw Materials,
Component parts,
Sub-assemblies, etc. A
B(4)
C(2)
D(2)
E(1)
D(3)
F(2) 3

14. What a firm can do to manage it?
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 4

15. Types of Forecasts
Forecasts should be produced as an integrated part of decision making framework
2 categories of forecasting approaches
Qualitative (Judgmental)
Quantitative
Time Series Analysis
Causal Relationships
Simulation 5

16. Average demand for a period of time Trend Seasonal element
Components of Demand
Average demand for a period of time
Trend
Seasonal element
Cyclical elements
Random variation 7

17. Finding Components of Demand
Seasonal variation 1 2 3 4 x
Year
Linear
Trend
Sales 6

18. Types of forecasting methods
Qualitative
Quantitative
Economic
indicators
Scenario
writing
Causal
Relationships
Time series
analysis
Sales force
composite
Market
research
Delphi
methods
Historical
analogy
Trend
projection
Smoothing/
Extrapolation
Panel
Consensus
Decomposition

19. Qualitative Methods Qualitative Methods Scenario analysis
Economic Indicators
Scenario analysis
Qualitative
Methods
Market Research
Panel Consensus
Historical Analogy
Delphi Method
Sales Force Composite

20. 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

21. Market Research Sample members of target market
Can provide sophisticated & accurate forecasts on market potential
Needs expertise in design and interpretation
Usually costly

22. 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

23. 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 ( This is currently ongoing project.

24. 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

25. 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

26. Delphi Method
Choose the experts to participate representing a variety of knowledgeable people in different areas
Through a questionnaire (or ), obtain forecasts (and any premises or qualifications for the forecasts) from all participants
Evaluate responses – produce a numerical summary (modal & extreme values)
Redistribute summarised results them to the participants along with appropriate new questions or with explanations to any extreme or unusual values
Evaluate and summarize responses again, refining forecasts and conditions, and again develop new questions
Repeat Step 4 & 5 until clear forecast emerges
Summarise the final results and distribute to all participants
(Alan McKinon’s Delphi study) 10

27. 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 ….?

28. Quantitative forecasting
Based on mathematical analysis of historical data
Causal modelling
Time series methods

29. Causal modelling–projects annual sales of washing m/cs
Price
MODEL OF
RELATIONSHIPS
Per Capita
Income
Annual
Retail sales
New House
Completions
INPUTS
OUTPUTS
MODEL
The independent variables
The dependent variables
Set the values
Turn handle
Obtain forecast

30. 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

31. Simple Linear Regression Model
The simple linear regression model seeks to fit a line through various data over time Y a
x (Time)
Yt = a + bx
Is the linear regression model
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. 35

32. Simple Linear Regression Formulas for Calculating “a” & “b”36

33. Simple Linear Regression Problem Data
Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? 37

34. Answer: First, using the linear regression formulas, we can compute “a” and “b”38

35. The resulting regression model is:
Yt = x
Now if we plot the regression generated forecasts against the actual sales we obtain the following chart:
180
Period
135
140
145
150
155
160
165
170
175 1 2 3 4 5
Sales
Forecast 39

36. 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 14

37. 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

38. Time series – long-term trend
Is the long-term change in the observations once short-term
fluctuations have been removed
Trend projection is a very basic form of forecasting

39. Time series – cyclical Time
Time series that fluctuate about their long-term trend
Can be caused by: investment cycles, business confidence

40. Time series – irregular variation
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

41. Simple Moving Average Formula
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 period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n” periods 15

42. Simple Moving Average Problem (1)
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 15

43. Calculating the moving averages gives us:43
F4=( )/3
=682.67
F7=(
)/6
=768.67
The McGraw-Hill Companies, Inc., 2004 16

44. 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 17

45. Simple Moving Average Problem (2) Data
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 18

46. Simple Moving Average Problem (2) Solution
F4=( )/3
=758.33
F6=(
)/5
=710.00 19

47. Weighted Moving Average Formula
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
The formula for the moving average is:
wt = weight given to time period “t” occurrence (weights must add to one) 20

48. Weighted Moving Average Problem (1) Data
Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?
Weights:
t-1 .5
t-2 .3
t-3 .2
Note that the weights place more emphasis on the most recent data, that is time period “t-1” 20

49. Weighted Moving Average Problem (1) Solution
F4 = 0.5(720)+0.3(678)+0.2(650)=693.4 21

50. Weighted Moving Average Problem (2) Data
Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?
Weights:
t-1 .7
t-2 .2
t-3 .1 22

51. Weighted Moving Average Problem (2) Solution
F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672 23

52. Exponential Smoothing Model
Ft = Ft-1 + a(At-1 - Ft-1)
Where : F =
Forecast value
for the
coming
t time
period t F =
Forecast
value in 1
past time
period t - 1 A =
Actual
occurrence
in the
past t time
period t - 1 a =
Alpha
smoothing
constant
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 24

53. Time series extrapolation
Elementary time series methods
Moving average
Exponential smoothing

54. 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. 33

55. 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 33