Forecasting and Demand Planning

Mean Squared Error

(MSE) magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods. ∑(A-F) ² / n

Quantitative

1. Time series: based on the assumption that the future is an extension of the past. Historical data is used to predict future demand (Most common), forecasts for future demand rely on understanding past demand. - Naive: Sets the demand for the next time period to be exactly the same as the demand in the last time period. (Works well for mature products ) - simple moving avg: take the avg of historical data - weighted moving avg: multiply each data point by the weighted % - exponential smoothing: last period's forecast, last period's actual demand, and a smoothing factor, which is a number greater than 0 and less than 1 (used as a weighting percentage) ( Forecast = (D x S) + (F x (1-S)) - linear trend: imposing a best fit line across the demand data of an entire time series 2. cause and effect: assumes that one or more factors (independent variables) predict future demand (e.g., seasonality in retail - simple regression: model the relationship between a single independent variable and a dependent variable (demand) by fitting a linear equation to the observed data. - multiple regression: model the relationship between two or more independent variables and a dependent variable (demand) by fitting a linear equation to the observed data.

fundamentals of forecasting

1. forecast is most likely wrong 2. Simple forecast methodologies trump complex ones 3. A correct forecast does not prove your forecast method is correct 4. If you don't use the data regularly, trust it less when forecasting 5. All trends will eventually end 6. most forecasts are biased 7. tech is not the best solution for forecast

Mean Absolute Deviation Percentage

Mean Absolute Percent Error (MAPE) measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error. ∑ ((|A - F|)/ A) / n

Forecasting techniques

Qualitative forecasting which is based on opinion and intuition Quantitative forecasting which uses mathematical models and historical data to make forecasts.

collaborative planning, forecasting, and replenishment CPFR

business practice that combines the intelligence of multiple trading partners who share their plans, forecasts, and delivery schedules with one another in an effort to ensure a smooth flow of goods and services across a supply chain reducing the Bullwhip Effect means the reduction of safety stocks (and associated costs) within and across the trading partners in a supply chain The real value of CPFR comes from the sharing of forecasts among firms, rather than firms relying on sophisticated algorithms and forecasting models to estimate demand

Alleviate the bullwhip effect

collaboration synchronizing the supply chain reducing inventory cpfr: collaborative planning, forecasting, and replenishment

Forecast Bias

consistent deviation from the mean in one direction; either high or low. ∑ Forecast Error = ∑ Actual Demand -∑ Forecast Demand if the sum of forecast error is not zero then there is bias

Dependent demand

demand for an item that is directly related to other items or finished products, such as a component or material used in making a finished product. [Calculated] (wheels, steel bars)

Independent demand

demand for an item that is unrelated to the demand for other items, such as a finished product, a spare part, or a service part. [Forecasted] (Bike)

Mean Absolute Deviation

find deviation for everything (differences) then find the avg

Goal

goal of the forecasting and demand planning process is to minimize forecast error The factors that influence demand must also be considered when forecasting, e.g., market changes, seasonality, competitive activity, etc.

Tracking Signal

if the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted. RSFE / MAD

Bullwhip Effect

in the absence of any other information or visibility, individual supply chain participants are second-guessing what is happening with ordering patterns, and potentially over-reacting, creating the bullwhip effect. When a small demand ripple in the market place is felt by the retailer at the end of the supply chain, the retailerwill then start adjusting their orders to the wholesalers, and the wholesaler in turn will adjust its orders to the distributor, the distributor to the factory, and so on back up the supply chain. When the new demand reaches the material or components supplier at the other end of the supply chain, the magnitude of fluctuation becomes unrecognizable. An overreaction due to uncertainty occurs throughout the entire supply chain.

Forecasting

it estimates future demand for products so that they can be purchased or manufactured in appropriate quantities in advance of need.

Running Sum of Forecast Errors

provides a measure of forecast bias. RSFE indicates the tendency of a forecast to be consistently higher or lower than actual demand. positive RSFE indicates that the forecasts were generally too low, underestimating the demand. In this situation, stock-outs are likely to occur as companies are unable to meet customers' actual demand. A negativeRSFE indicates that the forecasts were generally too high, overestimating demand in this situation, excess inventory and higher carrying costs are likely to occur.

Demand planning

s the process of combining statistical forecasting techniques and judgment to construct demand estimates for products or services.

Forecast error

the difference between the actual demand and the forecast demand. Forecast Error % = ((A -F) / A) x 100

Qualitative

used when data are limited, unavailable, or not currently relevant. examples: new product, new market segment 1. personal insight: someone who is familiar with the situation often provides a worse forecast than someone who knows nothing. 2. jury of exec opinion 3. delphi method (reduce bias bc secret ballot) 4. sales force estimation (ask the sales ppl) 5. customer survey

Forecast

will be wrong the forecast is the basis for most downstream supply chain planning decisions