SCM Forecasting & Demand Planning

how can the bullwhip effect be alleviated?

(1) collaboration, (2) synchronizing the supply chain, (3) reducing inventory

qualitative forecasting techniques (5)

(1) personal insight, (2) jury of executive opinion, (3) delphi method, (4) sales force estimation, (5) customer survery

2 basic forecasting techniques

(1) qualitative, (2) quantitative

2 considerations about a forecast

(1) statistically, the forecast will be wrong, (2) the basis of most "downstream" supply chain planning decisions

quantitative forecasting techniques (2)

(1) time series - [1] naive, [2] simple moving average, [3] weighted moving average, [4] exponential smoothing, [5] linear trend, (2) cause & effect - [1] simple regression, [2] multiple regression

fundamentals of forecasting (7)

(1) your forecast is most likely wrong, (2) simple forecasting trumps complex forecasting, (3) a correct forecasting 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) it's hard to eliminate bias, so most forecasts are biased, (7) technology is not the solution to better forecasting

collaborative planning, forecasting, and replenishment (CPFR)

a 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, reduces instability in the supply chain, one solution to the bullship effect

tracking signal

a simple indicator that forecast bias is present, (T.S. = RSFE/MAD), if the T.S. falls outside the pre-set control limits then there is a bias problem with the forecasting method

Forecast

an estimate of future demand

quantitative forecasting

based on mathematical models and historical data to make forecasts

qualitative forecasting

based on opinion & intuition

forecast bias

consistent deviation from the mean in one direction (high or low)

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]

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]

mean squared error (MSE)

magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods (MSE = sum of(A-F)^2/n), A = actual demand, F = forecast demand, n = number of time periods

mean absolute percent error (MAPE)

measures the size of the forecast error in terms of %, (MAPE = sum of((|A-F|)/A)/n), A = actual demand, F = forecast demand, n = number of time periods

mean absolute deviation (MAD)

measures the size of the forecast error in units, (MAD = sum of(|A-F|/n), A = actual demand, F = forecast demand, n = number of time periods

synchronizing the supply chain

participants coordinate planning and inventory management to minimize te need for reactionary corrections, one solution to the bullship effect

the bullwhip effect

potential over-reacting caused by second guessing what is happening with ordering patterns, "what you do in the absence of better info." - Prof. McLaury,

running sum of forecast errors (RSFE)

provides a measure of forecast bias, indicates the tendency of a forecast to be consistently higher or lower than actual demand, a positive RSFE indicates underestimating the demand while a negative RSFE indicates overestimating the demand

collaboration

sharing info. through the use of electronic data interchange (EDI), point of sales (POS) data, and web-based systems, one solution to the bullship effect

forecasting

the business function that estimates future demand for products so that they can be purchased or manufactured in appropriate quantities in advance of need, step 1

forecast error

the difference between the actual demand and the forecast demand, (F.E. Value = Actual Demand - Forecast Demand) or ((F.E. % = (Actual Demand - Forecast Demand)/Actual Demand) x 100)

demand

the need for a particular product or component

demand planning

the process of combining statistical forecasting techniques and judgement to construct demand estimates for products or services, step 2

reducing inventory

through the use of just in time (JIT), vendor managed inventory (VMI), and quick response (QR), one solution to the bullship effect

the goal of forecasting & demand planning

to minimize forecast error

simple linear regression

type of cause & effect forecasting which is a type of quantitative forecasting, attempts to model the relationship between a single independent variable and a dependent variable (demand) by fitting a linear equation to the observed data

multiple linear regression

type of cause & effect forecasting which is a type of quantitative forecasting, attempts to model the relationship between two or more independent variables and a dependent variable (demand) by fitting a linear equation o the observed data

delphi method

type of qualitative forecasting, anonymous jury of executive opinion but their input is done sepearately

personal insight

type of qualitative forecasting, based on the most experienced, knowledgeable, or senior person available

customer survery

type of qualitative forecasting, customers are directly approached and asked to give their opinion about the particular product

sales force estimation

type of qualitative forecasting, jury of executive opinion done w/ non-managerial sales force

jury of executive opinion

type of qualitative forecasting, multiple people who know the most form a group to discuss and determine the forecast

cause & effect forecasting

type of quantitative forecasting, (1) simple linear regression, (2) multiple linear regression

cause & effect

type of quantitative forecasting, assumes that one or more factors (independent variables) predict future demand

time series forecasting

type of quantitative forecasting, based on past data to predict future

naive forecasting

type of time series forecasting which is a type of quantitative forecasting, assumes that demand stays constant

linear trend forecasting

type of time series forecasting which is a type of quantitative forecasting, map out data on a graph and draw a line though the center, "good for long run projections"

exponential smoothing

type of time series forecasting which is a type of quantitative forecasting, more sophisticated version of weighted moving average - requires (1) last period's forecast, (2) last period's actual demand, (3) smoothing factor

weighted moving average

type of time series forecasting which is a type of quantitative forecasting, uses a calculated average of historical demand during a specified number of the most recent time periods to generate the forecast while weighting different time periods differently

simple moving average

type of time series forecasting which is a type of quantitative forecasting, uses a calculated average of historical demand during a specified number of the most recent time periods to generate the forecast, (SMA=total actual demand/# of month)

smoothing factor

used for exponential smoothing forecasting technique of time series of quantitative forecasting, how much weight is given to the forecast vs. the actual demand

regression

uses the historical relationship between an independent and a dependent variable to predict the future values of the dependent variable, i.e., demand