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