03-09-2012, 03:40 PM
Demand Forecasting
Demand.ppt (Size: 1.43 MB / Downloads: 696)
Introduction
Demand estimates for products and services are the starting point for all the other planning in operations management.
Management teams develop sales forecasts based in part on demand estimates.
The sales forecasts become inputs to both business strategy and production resource forecasts.
Some Reasons WhyForecasting is Essential in OM
New Facility Planning – It can take 5 years to design and build a new factory or design and implement a new production process.
Production Planning – Demand for products vary from month to month and it can take several months to change the capacities of production processes.
Workforce Scheduling – Demand for services (and the necessary staffing) can vary from hour to hour and employees weekly work schedules must be developed in advance.
Qualitative Approaches
Usually based on judgments about causal factors that underlie the demand of particular products or services
Do not require a demand history for the product or service, therefore are useful for new products/services
Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events
The approach/method that is appropriate depends on a product’s life cycle stage
Quantitative Forecasting Approaches
Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself
Analysis of the past demand pattern provides a good basis for forecasting future demand
Majority of quantitative approaches fall in the category of time series analysis
Time Series Analysis
A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand
Analysis of the time series identifies patterns
Once the patterns are identified, they can be used to develop a forecast
Components of a Time Series
Trends are noted by an upward or downward sloping line.
Cycle is a data pattern that may cover several years before it repeats itself.
Seasonality is a data pattern that repeats itself over the period of one year or less.
Random fluctuation (noise) results from random variation or unexplained causes.