Demand forecasting techniques
This presentation is the property of its rightful owner.
Sponsored Links
1 / 19

Demand Forecasting Techniques PowerPoint PPT Presentation


  • 91 Views
  • Uploaded on
  • Presentation posted in: General

Demand Forecasting Techniques. 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). MARKETING/ADVERTISING.

Download Presentation

Demand Forecasting Techniques

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Demand forecasting techniques

Demand Forecasting Techniques


Push pull strategy

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)

MARKETING/ADVERTISING

  • 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

Push

Pull

  • Forecasting inventory to meet demand

  • Company begins with customer order

INVENTORY CONTROL

Push

Pull


Planning for push pull in supply chain management

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


Understanding objective of forecasting

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?


Integrate demand planning and forecasting through supply chain

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


Understand and identify customer segments

Understand and Identify Customer Segments

  • Customers may be grouped by

    • Similarities

    • Service Requirements

    • Demand volumes

    • Order frequency

    • Demand volatility

    • Seasonality


Understanding major factors influencing demand forecast

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 13-15 months)


Determine appropriate forecasting technique

Determine Appropriate Forecasting Technique

  • Dimensions of data

    • Geography

    • Product groups

    • Customer groups

  • Four methods

    • Qualitative

    • Time Series

    • Causal

    • Simulation


Establish performance and error measures for forecast

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.


Measures

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


Moving averages ma

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 = (400+360+410)/3 = 390 units

4-Period MA = (350+400+360+410)/4 = 380


Exponential smoothing

Exponential Smoothing

Forecast (Ft-1)for 2010-11 was 10,000 units and actual sales (Dt-1) for 2010-11 was 11,000. Calculate forecast for year 2011-12(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


Another example ma

Another Example - MA

Find demand forecast for 2013 using

3 period moving average

4 period moving average

6 period moving average


Another example trend analysis

Another Example – Trend Analysis

Find demand forecast for 2013 using Trend Analysis


Forecasting with linear trend

Forecasting with Linear trend

Yi = a + bXi

Σ

Σ

Yi

XiYi

a =

b =

Σ

n

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


Coefficient of determination r 2 and adjusted r 2

Coefficient of Determination (R2 and adjusted R2)

R2 = 1 –

Adj. R2 = 1 –

Sum of squared errors

Σ

(Yi – Y)2

Σ

(Yi – Y)2

(n – 1)

(1 – R2) *

(n – k – 1)

Degrees of freedom


Cagr best case worse case

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


Bhaktij@gmail com www headscratchingnotes net www pramaanam com

[email protected]


Sources

sources

  • http://www.gsb.stanford.edu/news/bmag/sbsm1008/feature-lee.html

  • http://kelley.iu.edu/mabert/e730/Chopra-Chap-7.pdf


  • Login