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Supply Chain > Chapter 2 > Flashcards

Chapter 2 Flashcards

1
Q

What is a demand-driven supply chain

A

demand drives all the remaining supply chain activities (food/beverage, apparel, shoes, cosmetics, consumer electronics, automobiles)

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2
Q

What is a supply-driven supply chain

A

supply drives all the remaining supply chain activities (crude oil, natural gas, bananas, airline seats, steel, copper)

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3
Q

What is forecasting

A

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

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4
Q

What is demand planning

A

the process of combining statistical forecasting techniques with business judgement to construct demand estimates for products or services

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5
Q

What is demand

A

Need for a particular product or component

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6
Q

What is independent demand

A

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 (demand for these items is forecasted)

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7
Q

What is dependent demand

A

demand for an item that is directly related to other items or finished products, such as component or material used in making a finished product (demand for these items is calculated or derived)

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8
Q

Forecasting Horizons

A

Short term: forecasting less than three months
- used mainly for tactical decisions like purchasing, production schedule
Medium term: three months to two years
- used to develop a strategy over the next 6 to 18 months
Long term: greater than two years
- used to detect general trends and identify major turning points

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9
Q

Two imporant considerations about a forecast

A
  1. Statistically speaking, the forecast will be inaccurate, and although it may be innacurate its still useful
  2. The forecast is the basis for most “downstream” supply chain planning decisions, so it is critical to be as accurate as possible
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10
Q

The goal of the forecasting and demand planning process is to

A

minimize forecast error

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11
Q

Quantitative forecasting is based on

A

mathematical models and historical data

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12
Q

Qualitative forecasting is based on:

A

opinion and intuition

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13
Q

Qualitative forecasting is used when data is…

A

limited, unavailable, or not currently relevant, ex. new product, new market segment

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14
Q

What is personal insight (forecasting)

A

The forecast is based on the insight of the most experienced, most knowledgeable, or most senior person available
Advantages: fastest and cheapest forecasting technique, can provide a good forecast
Disadvantages: relies on one persons judgements and opinions, but also on their prejudices and ignorance, major disadvantage is unreliability

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15
Q

Jury of Executive Opinion (forecasting)

A

People who know the most about the product and the marketplace would form a jury to discuss and derermine the forecast
Advantages- Decisions are enriched by the experience of competent experts, companies dont have to spend time and resouces collecting data by survey
Disadvantages: experts may introduce bias, may become biased by opinionated leader

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16
Q

Delphi Method (forecasting)

A

Basically the same as Jury of Executive Opinion, but input of each of the participants is collected separately so that people are not influenced by one another

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17
Q

Historical Analogy (forecasting)

A

A judgemental forecasting technique based on identifying a sales history that is comparable to a present situation, such as the sales history of a similar product

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18
Q

Customer survey

A

Customers are directly approached and asked to give their opinions about the particular product

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19
Q

What is Quantitative Forecasting

A

Uses mathematical models and historical data to make forecasts

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20
Q

Time series (forecasting)

A

based on the assumption that the future is an extension of the past, historical data is used to predict future demand

21
Q

Cause and effect (forecasting)

A

assumes that one or more factors (independent variables) predict future demand

22
Q

Time Series Forecasting Model Assumptions

A

Items forecasted will stay steady over time, techniques will smooth out short-term irregularities, the forecast is revised only when new data becomes available

23
Q

Naive Forecast

A

Technique in which the last period’s actuals are used as this period’s forecast without adjustment

24
Q

Moving Average

A

A mathematical result that is calculated by averaging a number of past data points

25
Weighted Moving Average
technique that puts more weight on recent data and less on past data through the use of a weighting factor
26
Exponential Smoothing
Exponentional smoothing weights past observations with exponentially decreasing weights to forecast values
27
Linear Trend Forecast
A simplistic forecasting technique that imposes a line of best fit to time series historical data
28
Random Variations
instability in the data caused by random occurrences, ex. spike in demand for wood to repair house damage after a natural disaster
29
Seasonal Variations
repeating patterns of demand within one single year, pattern can be repeated from year to year with some periods of considerably higher demand than others
30
Cyclical Variations
Wavelike pattern that lasts longer than 1 year, and can extend over multiple years
31
How to do exponential smoothing
Previous month actual * smoothing factor + Previous month Forecast * (1-smoothing factor)
32
Linear trend forecasting
imposing a best fit line across the demand data of an entire time series. used as the basis for forecasting future values by extending the line past the existing data and out into the future while maintaining the slope of the line
33
Cause and Effect Forecasting
A statistical measure that determines the strength of the relationship between one dependent variable and a series of independent variables - two basic models: simple linear regression and multiple linear regression
34
Simple Linear Regression
attempts to model the relationship between a single independent variable and a dependent variable (demand) by fitting a linear equation to the observed data
35
Multiple Linear Regression
attempts to model the relationship between two or more indepdent variables and a dependent variable (demand) by fitting a linear equation to the observed data
36
What are the fundamentals of forecasting
1. Your forecast is likely wrong 2. The more granular the forecast, the less accurate it is (annual forecast for a product family is more likely to be accurate than a weekly forecast for an individual item in that product family) 3. It is easier to forecast next month more accurately than it is to forecast next year 4. Simple forecast methodologies trump complex ones 5. A correct forecast does not prove your forecast method is correct 6. If you don't use the data regularly, trust it less when forecasting 7. All trends will eventually end 8. Its hard to eliminate bias, so most forecasts are biased 9. Technology is not the solution to better forecasting 10. Forecasting is really a blend of art and science
37
What is forecast error
the difference between actual demand and forecast demand (can be quantified as an absolute value)
38
Mean absolute deviation (MAD)
measures the size of the forecast error in units (abs value of sum of all actual - forecast/ number of time periods)
39
Mean absolute percent error (MAPE)
The size of the error in percentage terms (sum of actual minus forecast/actual)/number of periods
40
Mean Squared Error
Sum of Actual-Forecast Squared divided by number of time periods
41
Forecast bias
consistent deviation from the mean in one direction, either high or low
42
Running Sum of Forecast Errors (RSFE)
provides a measure of forecast bias (sum of forecast error for period t) - Positive RSFE indicated forecasts too low - Negative RSFE indicates that the forecasts were generally too high, overestimating demand
43
Tracking Signal
RSFE/MAD, determines if the forecast is within acceptable control limits, if tracking signal falls outside the pre-set control limits theres a bias problem
44
The Bullwhip Effect
An overreaction due to uncertainty occurs throughout the whole supply chain, in the absence of any other info or visibility, individual supply chain participants are second guessing what is happening with ordering patterns and overreacting which causes the bullwhip effect
45
Safety stock
inventory buffer in case of changes in demand
46
How can the bullwhip effect be alleviated?
Collaboration (sharing info through the use of electronic data interchange (EDI), POS data, and web based systems), synchronizing the supply chain (participants coordinate planning and inventory management to minimize need for reactionary corrections), Reducing Inventory (use of just in time vendor managed inventory, and quick response)
47
What is collaborative planning, forecasting, and replenishment?
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 the supply chain, CAN SIGNIFICANTLY REDUCE THE BULLWHIP EFFECT
48
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

Supply Chain (11 decks)

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