SCM Chapter 2 chapter 1,2,3,4,5,6, Summaries of Supply Management

Download SCM Chapter 2 chapter 1,2,3,4,5,6 and more Summaries Supply Management in PDF only on Docsity!

Planning and forecasting in SCM

N G U Y Ễ N T H Ị B Í C H T R Â M , P H D T R A M. N T B @ O U. E D U. V N

Main contents

DEMAND

FORECASTING

01

AGGREGATE

PLANNING IN A

SUPPLY CHAIN

02

PLANNING

SUPPLY AND

DEMAND IN A

SUPPLY CHAIN

03

Demand pattern

The role of forecasting Forecasting provides an estimate of future demand, the basis for planning and sound business decisions. Accurate demand forecasts Purchasing department to order the right amount of products Operations department to produce the right amount of products Logistics department to deliver the right amount of products

Forecasting techniques

• Qualitative forecasting methods are:
  • Based on intuition or judgmental evaluation
  • Generally used when data are limited,

unavailable, or not currently relevant

• Quantitative forecasting models use

mathematical techniques that are based on

historical data and can include causal

variables to forecast demand.

Qualitative methods Jury of executive opinion Delphi method Sales force composite Consumer survey

Time series forecasting models Naïve forecast Simple moving average Weighted moving average Exponential smoothing Linear trend forecast

Weighted moving average (See Example 5.2)

Linear Trend Forecasting A linear trend forecast can be estimated using simple linear regression to fit a line to a series of data occurring over time. This model is also referred to as the simple trend model. The trend line is determined using the least squares method, which minimizes the sum of the squared deviations to determine the characteristics of the linear equation. The trend line equation is expressed as: Ŷ = b 0 + b 1 x Where Ŷ = forecast or dependent variable; x = time variable; b 0 = intercept of the vertical axis; b 1 = slope of the trend line. (See Example 5.4)

Quantitative methods (cont.) Cause-and-effect forecasting assumes that one or more factors (independent variables) are related to demand and, therefore, can be used to predict future demand. ◦ Simple linear regression forecast ◦ Multiple regression forecast

Multiple regression forecast When several explanatory variables are used to predict the dependent variable, a multiple regression forecast is applicable. Multiple regression analysis works well when the relationships between demand (dependent variable) and several other factors (independent or explanatory variables) impacting demand are strong and stable over time. The multiple regression equation is expressed as: Ŷ = b 0 + b 1 x 1 + b 2 x 2 + … + bkxk Where Ŷ = forecast or dependent variable; xk = k th explanatory or independent variable; b 0 = constant; bk = regression coefficient of the independent variable xk.

2. Aggregate

planning in a

supply chain

Operational parameters in aggregate planning

• Production Rate: the number of units to be completed per unit time (such as per week or per month)
• Workforce: the number of workers/units of capacity needed for production
• Overtime: the amount of overtime production planned
• Machine Capacity Level: the number of units of machine capacity needed for production
• Subcontracting: the subcontracted capacity required over the planning horizon
• Backlog: demand not satisfied in the period in which it arises but carried over to future periods
• Inventory on Hand: the planned inventory carried over the various periods in the planning horizon

Trade-off in aggregate planning CAPACITY (REGULAR TIME, OVERTIME, SUBCONTRACTED) INVENTORY BACKLOG/LOST SALES BECAUSE OF DELAY