1. Forecasting is essential for supply chain planning and involves forecasting demand using historical data and time-series methods. 2. The components of a demand forecast include the systematic components of level, trend, and seasonality as well as the random error. 3. Common time-series forecasting methods include moving averages, exponential smoothing, and Winter's method which accounts for trend and seasonality.

1. Demand
Forecasting In
A Supply
Chain
CHAPTER #7
PRESENTERS
AMNA JAVED
HASSAN FAREED
ZEESHAN
MOHSIN RAZA
ANNAS KHAN

2. Learning Objectives
Understand the role of forecasting for both an enterprise and a supply chain.
• Identify the components of a demand forecast.
• Forecast demand in a supply chain given historical demand data using time-series methodologies.
• Analyze demand forecasts to estimate forecast error.

3. Role of Forecasting in a Supply Chain
• The basis for all planning decisions in a supply chain
• Used for both push and pull processes Production scheduling, inventory, aggregate planning Sales
force allocation, promotions, new production introduction Plant/equipment investment, budgetary
planning Workforce planning, hiring, layoffs
• All of these decisions are interrelated

4. Characteristics of Forecasts
• Forecasts are always inaccurate and should thus include both the expected value of the forecast
and a measure of forecast error
• Long-term forecasts are usually less accurate than short-term forecasts
• Aggregate forecasts are usually more accurate than disaggregate forecasts
• In general, the farther up the supply chain a company is, the greater is the distortion of
information it receives

5. Components and Methods
• Companies must identify the factors that influence future demand and then ascertain the
relationship between these factors and future demand
1. Past demand
2. Lead time of product replenishment
3. Planned advertising or marketing efforts
4. Planned price discounts
5. State of the economy
6. Actions that competitors have taken

6. Components and Methods
• Qualitative
>Primarily subjective
>Rely on judgment
• Time series
>Use historical demand only
>Best with stable demand
• Causal
>Relationship between demand and some other factor
• Simulation
>Imitate consumer choices that give rise to demand

7. Components of an Observation
Observed demand (O) = systematic component (S) + random component (R)
• Systematic component – expected value of demand
>Level (current deseasonalized demand)
>Trend (growth or decline in demand)
>Seasonality (predictable seasonal fluctuation)
• Random component – part of forecast that deviates from systematic component
• Forecast error – difference between forecast and actual demand

8. Basic Approach
• Understand the objective of forecasting.
• Integrate demand planning and forecasting throughout the supply chain.
• Identify the major factors that influence the demand forecast.
• Forecast at the appropriate level of aggregation.
• Establish performance and error measures for the forecast.

9. 1-Understand the objective of forecasting
Every forecast supports decisions that are based on it, so an important first step is to identify these
decisions clearly. Examples of such decisions include how much of a particular product to make,
how much to inventory, and how much to order. Example: Walmart’s plan to discount detergent

10. 2-Integrate demand planning and forecasting
throughout the supply chain.
A company should link its forecast to all planning activities throughout the supply chain. These
include capacity planning, production planning, promotion planning, and purchasing, among
others.

11. 3-Identify the major factors that influence the
demand forecast
Next, a firm must identify demand, supply, and product-related phenomena that influence the
demand forecast,

12. 4-Forecast at the appropriate level of aggregation
Given that aggregate forecasts are more accurate than disaggregate forecasts, it is important to
forecast at a level of aggregation that is appropriate, given the supply chain decision that is driven
by the forecast.

13. 5-Establish performance and error measures for
the forecast
Companies should establish clear performance measures to evaluate the accuracy and timeliness
of the forecast. These measures should be linked to the objectives of the business decisions based
on these forecasts.

14. Time Series Analysis (For Forecasting Demand)
• Time series analysis comprises methods for analyzing time series date in order to extract
meaningful statistics and other characteristics of date.
• There are two components in any forecasting method:
1. Systematic Components: Systematic Component is expected value of Demand
2. Random Component: Random Component is part of forecast that deviated from systematic
component.

15. Systematic Component
> Systematic component of demand contains level, trend and seasonal factor.
> Equation of calculating systematic component is
Multiplicative: Systematic component = level X trend X seasonal factor
Additive: Systematic component = level + trend + seasonal factor
Mixed: Systematic component = (level + trend) X seasonal factor

16. STATIC METHODS
A static method assumes that the estimates of level, trend, and seasonality within the systematic
component do not vary as new demand is observed.
Systematic component = (level + trend) X seasonal factor
In a static forecasting method, the forecast in Period t for demand in Period t + 1 is given as:
Ft+l = [L + (r + l)T]St+l

17. In order to calculate L,T and S we consider example of Tahoe Salt.

18. Estimating Level and Trend
>Deseasonalized demand represents the demand that would have been observed in the
absence of seasonal fluctuations.
>The periodicity p is the number of periods after which the seasonal cycle repeats.

19. • The following linear relationship exists between the deseasonalized demand, Dt, and time t, based
on the change in demand over time.
Dt = L + Tt
• The initial level, L, is obtained as the intercept coefficient and the trend, T, is obtained as the X
variable coefficient (or the slope) from the sheet containing the regression results
.
• For the Tahoe Salt example, we obtain L = 18,439 and T = 524. For this example, deseasonalized
demand Dt for any Period t is thus given by
Dt= 18,439 + 524t

20. Estimating Seasonal Factors
We can now obtain deseasonalized demand for each period using previous Equation. The seasonal
factor St for Period t is the ratio of actual demand Dt to deseasonalized demand Dt and is given as
St = Dt/Dt
Given r seasonal cycles in the data, for all periods, we obtain the seasonal factor as

21. Adaptive Forecasting
In adaptive forecasting, the estimates of level, trend, and seasonality are updated after each
demand observation.
In adaptive methods, the forecast for Period t + l in Period t uses the estimate of level and trend in
Period t( Lt and Tt respectively) is given as
Ft+l = (Lt+ lTt)St+l

22. The four steps in the adaptive forecasting framework are as follows.
1. Initialize: Compute initial estimates of the level (Lo), trend (To), and seasonal factors (S1, ... , Sp) from the given
data.
2. Forecast: Given the estimates in Period t, forecast demand for Period t + 1 using Equation. Our first forecast is for
Period 1 and is made with the estimates of level. trend, and seasonal factor at Period 0.
3. Estimate error: Record the actual demand Dt+I for Period t + l and compute the error Et+ 1 in the forecast for
Period t + 1 as the difference between the forecast and the actual demand. The error for Period t + 1 is stated as
E t+1 = F t+1 – D t+1
4. Modify estimates: Modify the estimates of level (L t+1), trend (T t+ 1), and seasonal factor (S t+p+1)' given the
error E t+1 in the forecast. It is desirable that the modification be such that if the demand is lower than forecast, the
estimates are revised downward, whereas if the demand is higher than forecast. the estimates are revised upward.

23. Moving Average Method
 The moving average method is used when demand has no observable trend or seasonality.
Systematic component of demand= Level
In this method, the level in period t is estimated as the the average demand over the most recent
N periods.
Lt=(Dt+Dt-1+...+Dt–N+1)/N
Ft+1 = Lt and Ft+n = Lt

24. Example
• A supermarket has experienced weekly demand of milk of D1 = 120,
D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks
Forecast demand for Period 5 using a four-period moving average
What is the forecast error if demand in Period 5 turns out to be
125 gallons?

25. Trend-Corrected Exponential Smoothing (Holt’s Model)
• Appropriate when the demand is assumed to have a level and trend in
the systematic component of demand but no seasonality
Systematic component of demand = level + trend

26. • In Period t, the forecast for future periods is
Ft+1 = Lt + Tt and Ft+n = Lt + nTt

27. Trend- and Seasonality-Corrected Exponential
Smoothing (Winter's Model)
• Appropriate when the systematic component of demand is
assumed to have a level, trend, and seasonal factor
Systematic component = (Level + Trend) x Seasonal factor
Ft+1 = (Lt + Tt)St+1 and Ft+l = (Lt + lTt)St+l

28. • After observing demand for period (t + 1), revise estimates for
level, trend, and seasonal factors
Lt+1 =  (Dt+1/St+1) + (1 – )(Lt + Tt)
Tt+1 = (Lt+1 – Lt) + (1 – )Tt
St+p+1 = (Dt+1/Lt+1) + (1 – )St+1
Where,
 = smoothing constant for level
 = smoothing constant for trend
 = smoothing constant for seasonal factor

29. Measures of Forecast Error
Managers use error analysis to determine whether the current forecasting method is predicting
the systematic component of the demand accurately.
All contingency plans must account for forecast error.
Forecast Error for the period t,
E  F – D
t t t

30. Measures of Forecast Error
 Mean Square Error
MSE tells us the variance in the forecasted demand
 Mean Absolute deviation (MAD) is the average of the absolute deviations over all periods.
Absolute deviation is the absolute value of the forecast error.
n
MSE 
1
n
Et
2
n

t1
At  Et MAD 
1
n
At
n

t1

31. Time Series Models
Forecasting Method Applicability
No trend or seasonality
No trend or seasonality
Moving average
Simple exponential
smoothing
Holt’s model
Winter’s model
Trend but no
seasonality
Trend and seasonality

32. The Role of IT in Forecasting
• Forecasting module is core supply chain software
• Can be used to best determine forecasting methods for the firm and by product categories and
markets
• Real time updates help firms respond quickly to changes in marketplace
• Facilitate demand planning

33. Risk Management
• Errors in forecasting can cause significant misallocation of resources in inventory, facilities,
transportation, sourcing, pricing, and information management
• Common factors are long lead times, seasonality, short product life cycles, few customers and
lumpy demand, and when orders placed by intermediaries in a supply chain
• Mitigation strategies – increasing the responsiveness of the supply chain and utilizing
opportunities for pooling of demand

34. Forecasting in Practice
• Collaborate in building forecasts
• Share only the data that truly provide value
• Be sure to distinguish between demand and sales