1. Chapter 4: Demand Forecast in Fashion Supply Chains
Fashion Supply Chain Management

2. The Role of Forecasting
Demand forecasts form the basis of all supply chain planning

3. The Role of Forecasting
All push processes in the supply chain are performed in anticipation of customer demand, whereas all pull processes are performed in response to customer demand.
For push process, the merchandising manager must plan a level of available capacity and inventory, and forecast what customer demand will be.
The resulting forecast accuracy enables supply chains to be both more responsive and more efficient in serving their customers

4. Principles of Forecasting
Many types of forecasting models that differ in complexity and amount of data & way they generate forecasts:
Forecasts are rarely perfect
Forecasts are more accurate for grouped data than for individual items
Forecast are more accurate for shorter than longer time periods

5. 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; that is, long-term forecasts have a larger standard deviation of error relative to the mean than short-term forecasts

6. Components of a Forecast and Forecasting Methods
A company must be knowledgeable about numerous factors that are related to the demand forecast.
- Past demand
- Lead time of product replenishment
- Planned advertising or marketing efforts
- State of the economy
- Planned price discounts
- Actions that competitors have taken

7. Basic Approaches to Demand Forecasting
Six steps approach of performing effective forecasting
Understand the objective of forecasting
Integrate demand planning and forecasting throughout the supply chain
Understand and identify customer segments
Identify major factors that influence the demand forecast
Determine the appropriate forecasting technique
Establish performance and error measures for the forecast

8. Forecasting methods are classified into two groups:

9. Qualitative Methods

10. Demand types
Independent demand (ID): is influenced by market conditions outside the firm
Dependent demand (DD): have demand that is related to another item

11. Independent Demand or Dependent Demand?

12. Demand Types
Different demand patterns call for different approaches to inventory management.
For ID, a replenishment philosophy is appropriate; for DD, a requirement philosophy is used.
Philosophy
Content
replenishment philosophy
It is replenished so that products are always on hand for customers. Thus, as inventory begins to run out, an order is triggered for more material and the inventory is replenished.
requirement philosophy
Items are ordered only as required by the scheduled production

13. Class Discussion
How can we improve the accuracy of forecasting in fashion?

14. Improve Forecasting Accuracy
Improve the techniques of forecasting
Use information technology and enhance information sharing
Provide scientific forecasting methods
Use innovative supply chain management
etc…

15. Postpone production
Benetton is planning for the quantity of sweaters under a particular style and size to sell in the coming season. Assume that the sweaters are highly fashionable and will only be sold within a single season. There are two approaches:
- Approach 1: Based on the forecast of individual colors 6 months ago (Stage 0) and produce all ready-to-sell products.
- Approach 2 (postponement): Based on the aggregate forecast of individual colors 6 months ago (Stage 0), produce un-dyed sweaters and then dye the sweaters 1 month before the selling season (Stage 1).

16. Time Series Forecasting
Time-series methods are used to make detailed analyses of past demand patterns over time and project those patterns forward into the future.
One of the basic assumptions of all time-series method is that demand can be decomposed into components such as average level, trend, seasonality, and cycle.

17. Time Series Models Forecaster looks for data patterns as
Data = historic pattern + random variation
Historic pattern to be forecasted:
Level (long-term average) – data fluctuates around a constant mean
Trend – data exhibits an increasing or decreasing pattern
Seasonality – any pattern that regularly repeats itself and is of a constant length
Cycle – patterns created by economic fluctuations
Random Variation cannot be predicted

18. Time Series Forecasting

19. Time Series Forecasting
A fashion boutique sold 1000 units of fashion products. In this year, it sold 200 units in Spring, 350 units in Summer, 300 units in Autumn, and 150 units in Winter. In the coming year, the manager expects to sell 1600 units. Can you predict the demand of the coming four seasons?

20. Demand forecast (A)*(B)
Past sales
Sales in season (1000/4)
Season factor（A）
Spring
200
250
200/250=0.8
Summer
350
350/250=1.4
Autumn
300
300/250=1.2
Winter
150
150/250=0.6
Total
1000
Demand forecast
Expected sales
Sales in every season
（1600/4）（B）
Demand forecast (A)*(B)
400
320
560
480
240
1600

21. Moving Average Forecasting
The simplest method of time-series forecasting is the moving-average method.
For this method, it is assumed that the time series has only a level component plus random components.

22. Moving Average Forecasting
When the moving average is used, a given number of periods (N) is selected for the computations. Then the average demand, At, for the past N periods at time t is computed as follows:
Since we are assuming that the time series is flat (or horizontal), the best forecast for period t+1 is simply a continuation of the average demand observed through period t. Thus, we have

23. Moving Average Forecasts

24. The six-period moving average responds more slowly to demand changes than does the three-period moving average.
As a general rule, the longer the averaging period, the slower the response to demand changes.
A longer period has the advantage of providing stability in the forecast but the disadvantage of responding more slowly to real changes in the demand level.
The forecasting analyst must select the appropriate trade-off between stability and response time by selecting the averaging length N.

25. Class Discussion
Polo Ralph Laurent sells Polo shirts throughout the year. According to the weekly sales data in July, it sold 3,800 units, 3,500, 7,700 units, and 9,000 units, respectively. Can you use moving average method to predict the sales for the first week of August. If the first week of August sold 8,000 units, can you tell the error?

26. Extra Reading After Class
Shen, B., Qian, R., Choi, T.M. Selling Luxury Fashion Online with Social Influences: Demand Changes and Supply Chain Coordination, International Journal of Production Economics. 185, 89-99, 2017.