CH 2: Supply Chain

______ and ______ are the critical building blocks from which all supply chain planning activities are derived and are crucial components of customer satisfaction.

Forecasting and demand planning

Demand planning

Which combines statistical forecasting techniques and judgment to construct demand estimates for products or services.

Independent Demand

Demand for an item unrelated to the demand for other items, such as a finished product, spare part, or or service part.

Dependent Demand

Demand for an item directly related to other items or finished products, such as a component or material used in making a finished product.

Qualitative forecasting

Based on opinion and intuition

Quantitative forecasting

Uses mathematical models and historical data to make forecasts

Historical Analogy

Qualitative, a judgmental forecasting technique based on identifying a sales history comparable to a present situation, such as the sales history of a similar product.

Random variations

These random changes care generally very short term and can be caused by unexpected or unpredictable weather emergencies, natural disasters, labor strikes, war, etc. Eg. Hurricane

Seasonal variations

Are repeating patterns of demand. This pattern can be repeated yearly, with some periods of considerably higher demand than others. Eg. Holiday shopping

Naive Forecasting

Sets the demand for the next period to be precisely the same as the demand in the last period. Works well for mature products with stable demand.

Simple Moving Average Forecasting

Uses a calculated average of historical demand during a specified number of the most recent periods to generate the forecast. Best for short-term forecasting. Practical when demand is not growing or declining rapidly and no seasonality is present.

Weighted Moving Average Forecasting

If company feels that the most recent months are more representative of the current demand than the demand data from further in the past, the company might assign a greater percentage of the total weight to the more recent months. The sum of the weights need to be equal to one.

Exponential Smoothing

The last periods actual demand, the previous periods forecast, and a smoothing factor(number greater than 0 and less than 1. Used as a weighting percentage)

Regression uses the

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

Multiple Linear Regression

Attempts to model the relationship between two or more independent variables and a dependent variable.

The more granular the forecast…

the less accurate it is.

Simple forecasting methods

mog complex ones

MAD(Mean Absolute Deviation)

Measure of forecasting accuracy. Whether the forecast is under or over the actual demand is irrelevant; only the magnitude of the deviation matters in the calculation. ___ = ∑(|A – F|) / n.

Where:

A = Actual demand

F = Forecast demand

n = Number of time periods

MAPE(Mean Absolute Percentage Error)

Measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error. ______= ∑ ((|A – F|)/ A)) / n

Expressed as a percentage

Where:

A = Actual demand

F = Forecast demand

n = Number of time periods

The Bullwhip Effect

Individual supply chain participants second guess what is happening with ordering patterns and potentially over reacting, creating the ____ ____. In periods of rising demand, downstream participants increase orders.

Collaborative Planning, Forecasting, & Replenishment(CPFR)

Is a business practice that combines the intelligence of multiple trading partners who share their plans, forecasts, and delivery schedules 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. CFPR helps w this.