Supply Chain Chapter 2
collaboration
- reduces bullwhip effect - sharing info through the use of electronic data interchange, point of sale, and web-based systems
synchronizing the supply chain
- reduces bullwhip effect - coordinate planning and inventory management to minimize the need for reactionary corrections
Collaborative, Planning, Forecasting and Replenishment (CPFR)
- reduces bullwhip effect - share plans, forecasts and delivery schedules with one another in an effort to ensure a smooth flow
reducing inventory
- reduces bullwhip effect - through the use of JIT, vendor managed inventory, and quick response
two cause-and-effect models
- simple linear regression - multiple linear regression
Collaborative Planning, Forecasting, and Replenishment is the process of combining statistical forecasting techniques and judgment to construct demand estimates for products or services.
FALSE
Forecasts are more accurate the farther out into the future that you forecast.
FALSE
Independent Demand is 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.
FALSE
measures the size of the forecast error in units. It is calculated as the average of the unsigned errors over a specified period of time.
Mean Absolute Deviation
A correct forecast does not prove your forecast is correct
TRUE
Cause-and-Effect Models can have multiple independent variables.
TRUE
The Qualitative forecasting method is based on opinion & intuition.
TRUE
forecast bias
a consistent deviation from the mean in one or the other direction if forecast error does not = 0 then there is bias in the forecast
casue-and-effect
assumes that there is a "cause" (independent variable) and an "effect"
qualitative forecasting
based on opinion, intuition and judgement - personal insight - jury of executive opinion - delphi method - sales force estimation - customer survey
time series
based on the assumption that the future is an extension of the past - short-term forecasts - data collected during certain time periods most frequently used
random variations
caused by random occurrences (weather emergencies)
customer survey
customers are directly approached and asked to give their opinions about the particular product
personal insight
forecast based on the insight of the most experience, most knowledgeable, or most senior person available
The key building blocks from which all supply chain planning activities are derived crucial components of customer satisfaction
forecasting and demand planning
quantitative forecasting
forecasting uses mathematical models and historical data to make forecasts
forecast
is an estimate of future demand
linear trend forecasting
is imposing a best fit line across the demand data of an entire time series
forecasting
is the business function that attempts to estimate future demand for products so that they can be purchased or manufactured in appropriate quantities
demand
is the need for a particular product or component
mean squared error (MSE)
magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods
Mean absolute percent error (MAPE)
measure the size of the error in percentage terms
Mean absolute deviation (MAD)
measure the size of the forecast error in units
exponential smoothing
more sophisticated version of the weighted moving average 3 elements 1) last period's forecast 2) last period's actual demand 3) a smoothing factor between 0-1 0.5 means actual and forecasted are equally important gives you ability to weight actual or forecast based on what's more important to you
trend variations
movement of a variable over time demand going up and down
cyclical variations
not easily predicted - can extend over multiple years - follows a wavelike pattern
jury of executive opinion
people who know the most about the product and the marketplace would likely form a jury to discuss and determine the forecast
running sum of forecast errors (RSFE)
provides a measure of forecast bias positive means forecasts were too low - underestimated demand negative - means forecast were too high - overestimated demand
reducing the bullwhip effect
reduction of safety stocks within and across the trading partners in a supply chain
seasonal variations
repeating pattern of demand from year to year, over some time interval (ex: snow shovel sales)
delphi method
same as JOEO - input is collected seperately to reduce groupthink and bias
sales force estimation
same as JOEO but is preformed only with sales people
naive forecasting
sets the demand for the next time period to be exactly the same as the last or current time period. - works well w/ mature products
weighted moving average forecasting
similar to a simple moving average except that not all historical time periods are valued equally disadvantage: -better than simple moving average, but lags behind actual demand
forecast accuracy - tracking signal
simple indicator that forecast bias is present determines if forecast is within acceptable control limits - warning when falls outside
simple linear regression
single independent and dependent variable ex: If you launch a new product, price increase, how will one variable impact your forecast Historically if I add more money to my advertising, my sales go up, you can rely on that data
bullwhip effect
the assumptions when something goes wrong in the supply chain multiply the farther from the source backs up the supply chain
the farther out into the future you forecast
the greater the deviation will be
dependent demand
the parts of the bike
forecast error
the size of the forecast error can be measured in units or percentages difference between actual demand and forecast demand
multiple linear regression
two or more independent variables and a dependent variable
simple moving average
uses a calculated average of historical demand during a specified number of the most recent time periods to generate the forecast disadvantage: - fails to identify trends or seasonal effects.
regression
uses the historical relationship between an independent and a dependent variable to predict the future values of the dependent variable
independent demand
predicted first, then dependent demand is calculated for an item unrelated to the demand for other items (finished product or spare/service parts) ex: the finished bike
demand planning
process of combining statistical techniques and judgement to construct demand estimates for products or services
What does the acronym CPFR represent?
Collaborative Planning, Forecasting, & Replenishment
Which one of the following is NOT a type of qualitative forecasting?
Naïve method Those that are: - Sales force composite - Consumer survey - Jury of executive opinion
When creating a quantitative forecast, data should be evaluated to detect for a repeating pattern of demand from year to year, or over some other time interval, with some periods of considerably higher demand than others. This is known as a?
Seasonal Variation
all trends eventually end
TRUE
simple forecasting is better than complex forecasting
TRUE
technology is not the answer, it's the tool need to use sound logic
TRUE
trust data that is not used regularly less
TRUE
In the absence of any other information or visibility, individual supply chain participants can begin second-guessing what is happening with ordering patterns, and potentially start over-reacting. This is known as?
The Bullwhip Effect