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23 Cards in this Set

  • Front
  • Back

Forecasting

-the art and science of predicting future events


-No universal forecasting method for all situations


-Forecasts will almost always be wrong; it is matter of how wrong



Ways to Deal with Forecast Error

1. Try to Reduce the Error through better forecasting


2. Build more flexibility into operations and the supply chain


3. Reduce the lead time over which forecasts are required



Essential Forecast Numbers

1. The best Estimate of Demand


2. The Forecasting Error

Sales vs. Demand

-Demand and Sales are not usually the same thing


2. Whenever demand is not constrained by capacity, inventory, or other policies; the forecasting of demand should be the same as the forecasting of sales


3. Otherwise, sales may be somewhat below real customer demand

Planning vs. Forecasting

-Forecasting deals with what we think will happen in the future


-Forecasting is only used to predict future events


- Planning deals with what we think should happen in the future


- Through Planning, we consciously attempt to alter future events


-Good Planning uses a forecast as an input

Who Uses Forecasts

-Marketing uses forecasts for planning products & services, promotion and pricing


-Finance uses forecasting as input to financial planning


-Operations uses forecasting in making decisions on process design, capacity planning and inventory

Forecasting Time Spans

1. Short-Range (less than 6 months in the future); procuring materials and work scheduling


2. Medium-Range (6 months to two years); aggregate Planning


3. Long-range Forecasts ( two or more years): good for planning facilities and processes



Two Common Types of Forecasting Methods

1. Qualitative


2. Quantitative


a. Time Series


b. Causal

Quantitative Forecasting Methods

-Rely On managerial Judgement


- Does not use specific quantitative models


-Uses an implicit mathematical model



Benefits of Quantitative Forecasting Methods

1. Useful when there is a lack of data


2. Also useful when past data is not reliable predictors of the future

Quantitative Forecasting Generalities

Assume that past data and data patterns are reliable predictors of the future



Time-Series Forecasting

-Uses to make detailed analyses of past demand patterns over time


-Uses these demand patterns to predict demand in the future

Types of Time-series Components

1. Cycle


2. Seasonality


3. Trend


4. Level


5. Random Error

Cycle

increasing or decreasing demand over long time periods

Seasonality

a regularly repeated pattern of increasing and decreasing be a yearly pattern or even a daily pattern

Trend

an increase or decrease in the average demand over time



Level

the relatively constant demand during a time interval

Moving Average

-the simplest method of time-series forecasting


- assumed that no seasonal pattern, trend ,or cycle components are assumed to be present in the demand data

Weighted Moving Average

- all months are weighted equally


- Some of the months in the calculation are considered more important and are assigned a higher coefficient

Purposes of Forecast Accuracy

1. To monitor erratic demand observations or outliers (which should be carefully evaluated and perhaps excluded from data analysis

2. To Determine when the forecasting method is no longer tracking actual demand and should be reset


3. To determine the parameter values that provide the forecast with the best accuracy


4. To set safety stocks or safety capacity and thereby ensure a desired level of protection against stockout


Casual Forecasting Methods

1. Develops a cause-and-effect model between demand and other variables


2. Great for predicting turning points in demand


3. Casual Models are usually more accurate than time-series models for medium-to long range forecasts



Factors in Selecting a Forecasting Method/Model

1. User system sophistication (symbiosis)


2. Time and Resources Available


3. Data Availability


4. Data Pattern

Collaborative Planning, Forecasting & Replenishment (CPFR)

1. All parties must be willing to share sensitive information about demand data, future sales promotions, potential orders, new products, and lead times


2. Long-term collaborative Relationship that is mutually beneficial is needed


3. Sufficient Time and Resources must be provided for CPFR to succeed