1. Demand Forecasting Techniques

2. Push-Pull Strategy Generally implies,
…a business pushing products or services or information to customers (Push Strategy)
…Customers pulling products or services or information from a business (pull strategy)
A situation when a company keeps repeating an advertisement
A situation when a company stops advertising for some time to get viewers to ask/look for more about the product
MARKETING/ADVERTISING
Push
Pull
Forecasting inventory to meet demand
Company begins with customer order
INVENTORY CONTROL
Push
Pull

3. Planning for Push-Pull in Supply Chain Management
Using Forecasting tools (Time Series Analysis) on:
Historical or past demand
Lead time of product
Planned advertising or marketing efforts
State of the economy
Planned price discounts
Actions that competitors have taken

4. Understanding Objective of Forecasting
Example: Wal-mart’s decision on discounting prices of a detergent during the month of July must be shared with manufacturer, transporter, and others in the supply chain that are affected by the forecast of demand
Should all supply chain participants get involved in demand forecasting? Why?
What would happen if participants are not informed about Wal-Mart’s decision?

5. Integrate Demand Planning and Forecasting Through Supply Chain
Planning activities include:
Capacity Planning
Production planning
Promotion planning
Purchasing Planning, etc
Accomplishing integration by having a cross-functional team

6. Understand and Identify Customer Segments
Customers may be grouped by
Similarities
Service Requirements
Demand volumes
Order frequency
Demand volatility
Seasonality

7. Understanding Major factors Influencing Demand Forecast
Example: A super market promoted a certain brand in July 2012 leading to high demand in that month. Demand for this brand will be high again in July 2013 only if it is promoted again in this month Example: Sugarcane is continuously grown for 6 -7 months in a year and sugar cane mills can procure sugarcane to transform them into sugar during this season. (Planting in Maharashtra is done between July and August. Crop matures in months)

8. Determine Appropriate Forecasting Technique
Dimensions of data
Geography
Product groups
Customer groups
Four methods
Qualitative
Time Series
Causal
Simulation

9. Establish Performance and Error Measures for Forecast
Measures should be highly correlated with the objectives of the business decisions based on these forecasts
Example: A mail-order company that uses forecast to place orders with its suppliers up the supply chain . Suppliers take two months to send in the orders. The mail-order must ensure that the forecast is created at lease two months before the start of the sales season because of the 2-month lead time for replenishment.

10. Measures Moving Averages Exponential Smoothing Trend Analysis
Other techniques in case of unavailable or limited information (More speculative than above mentioned)
CAGR
Best-case & Worst-case scenarios

11. Moving Averages (MA)
Sales for December, January, February and March were 350, 400, 360 and 410 units respectively. Calculate the forecast for the month of April using 3-period and 4-period moving averages 3-period MA = ( )/3 = 390 units 4-Period MA = ( )/4 = 380

12. Exponential Smoothing
Forecast (Ft-1)for was 10,000 units and actual sales (Dt-1) for was 11,000. Calculate forecast for year (Ft) using exponential smoothing if alpha or the constant (α) = 0.2 Ft = α Dt-1 + (1 – α) Ft-1 Ft = (0.2 X 11000) + (1 – 0.2) X 10,000 = 10,200 units

13. Sales Volume (in Million units)
Another Example - MA
Sr. No.
Year
Sales Volume (in Million units) 1
2003
120 2
2004
124 3
2005
126 4
2006
122 5
2007
128 6
2008
130 7
2009
132 8
2010
138 9
2011
134 10
2012
136
Find demand forecast for 2013 using
3 period moving average
4 period moving average
6 period moving average

14. Another Example – Trend Analysis
Sr. No.
Year
Sales Volume (in Million units) 1
2003
120 2
2004
124 3
2005
126 4
2006
122 5
2007
128 6
2008
130 7
2009
132 8
2010
138 9
2011
134 10
2012
136
Find demand forecast for 2013 using Trend Analysis

15. Forecasting with Linear trend
Yi = a + bXi Σ Σ Yi
XiYi
a =
b = n Σ
Xi2
where Xi equals a particular time period ‘t’ minus mid-level time period (for odd number of observations).
For even number of observations, Xi = 2d, d = t – mid-point year ^ ^
Yi = a + bXi, Yi equals estimated values of Yi used for forecasting future Yi

16. Coefficient of Determination (R2 and adjusted R2)
R2 = 1 – Adj. R2 = 1 –
Sum of squared errors Σ
(Yi – Y)2 ᶺ Σ
(Yi – Y)2
(n – 1)
(1 – R2) *
(n – k – 1)
Degrees of freedom

17. CAGR & Best-Case & Worse-case
(1/No. Years)
- 1
CAGR = (Ending value/Beginning Value) Best Case & Worst Case Mean + Z-critical value X Mean - Z-critical value X
Standard Deviation
BEST N
Standard Deviation
WORST N
Z-critical values for 90%, 95% and 99% confidence intervals are 1.645, 1.96 and 2.58 respectively

18. bhaktij@gmail.com www.headscratchingnotes.net www.pramaanam.com

19. sources