第七章 供应链中的需求预测
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Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-6
Components of an Observation
Observed demand (O) = systematic component (S) + random component (R)
Learning Objectives
1. Understand the role of forecasting for both an enterprise and a supply chain. 2. Identify the components of a demand forecast. 3. Forecast demand in a supply chain given historical demand data using time-series methodologies. 4. Analyze demand forecasts to estimate forecast error.
•
supply chain Used for both push and pull processes
– Production scheduling, inventory, aggregate planning – Sales force allocation, promotions, new production introduction – Plant/equipment investment, budgetary planning – Workforce planning, hiring, layoffs
Di / p for p odd
t –1+( p /2) é ù Dt = ê Dt –( p /2) + Dt +( p /2) + å 2 Di ú / (2 p) ê ú ë û i =t +1–( p /2)
Leabharlann Baidu
= D1 + D5 + å 2 Di / 8
i =2
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-13
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-8
Time-Series Forecasting Methods
• Three ways to calculate the systematic
component
7-5
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Components and Methods
1. Qualitative
– Primarily subjective – Rely on judgment
• Systematic component – expected value of demand
− Level (current deseasonalized demand) − Trend (growth or decline in demand) − Seasonality (predictable seasonal fluctuation) Random component – part of forecast that deviates from systematic component Forecast error – difference between forecast and actual demand
Quarter 2
3 4 1 2 3 4 1 2 3 4 1
Period, t 1
2 3 4 5 6 7 8 9 10 11 12
Demand, Dt 8,000
13,000 23,000 34,000 10,000 18,000 23,000 38,000 12,000 13,000 32,000 41,000
• All of these decisions are interrelated
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-3
Characteristics of Forecasts
1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error 2. Long-term forecasts are usually less accurate than short-term forecasts 3. Aggregate forecasts are usually more accurate than disaggregate forecasts 4. In general, the farther up the supply chain a company is, the greater is the distortion of information it receives
7-10
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Tahoe Salt
Year 1
1 1 2 2 2 2 3 3 3 3 4
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-4
Components and Methods
• Companies must identify the factors that
influence future demand and then ascertain the relationship between these factors and future demand
ì ï ï Dt = í ï ï î
t –1+( p /2) é ù ê Dt –( p /2) + Dt +( p /2) + å 2 Di ú / (2 p) for p even ê ú ë û i =t +1–( p /2) t +[( p –1)/2] i =t –[( p –1)/2]
å
– Multiplicative S = level x trend x seasonal factor – Additive S = level + trend + seasonal factor – Mixed S = (level + trend) x seasonal factor
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
2. Time Series
– Use historical demand only – Best with stable demand
3. Causal
– Relationship between demand and some other factor
4. Simulation
– Imitate consumer choices that give rise to demand
– – – – – – Past demand Lead time of product replenishment Planned advertising or marketing efforts Planned price discounts State of the economy Actions that competitors have taken
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-2
Role of Forecasting in a Supply Chain
• The basis for all planning decisions in a
Dt = L + Tt
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-15
Estimating Seasonal Factors
Di St = Dt
Figure 7-4
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-16
4
Tahoe Salt
Figure 7-2
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-14
Tahoe Salt
Figure 7-3
A linear relationship exists between the deseasonalized demand and time based on the change in demand over time
7-7
•
•
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Basic Approach
1. Understand the objective of forecasting. 2. Integrate demand planning and forecasting throughout the supply chain. 3. Identify the major factors that influence the demand forecast. 4. Forecast at the appropriate level of aggregation. 5. Establish performance and error measures for the forecast.
Table 7-1
7-11
Tahoe Salt
Figure 7-1
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-12
Estimate Level and Trend
Periodicity p = 4, t = 3
7
Demand Forecasting in a Supply Chain
PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 5e
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 1-1 7-1
7-9
Static Methods
Systematic component = (level + trend) ´ seasonal factor
Ft +l = [ L + (t + l )T ]St +l
where L T St Dt Ft = = = = = estimate of level at t = 0 estimate of trend estimate of seasonal factor for Period t actual demand observed in Period t forecast of demand for Period t
Components of an Observation
Observed demand (O) = systematic component (S) + random component (R)
Learning Objectives
1. Understand the role of forecasting for both an enterprise and a supply chain. 2. Identify the components of a demand forecast. 3. Forecast demand in a supply chain given historical demand data using time-series methodologies. 4. Analyze demand forecasts to estimate forecast error.
•
supply chain Used for both push and pull processes
– Production scheduling, inventory, aggregate planning – Sales force allocation, promotions, new production introduction – Plant/equipment investment, budgetary planning – Workforce planning, hiring, layoffs
Di / p for p odd
t –1+( p /2) é ù Dt = ê Dt –( p /2) + Dt +( p /2) + å 2 Di ú / (2 p) ê ú ë û i =t +1–( p /2)
Leabharlann Baidu
= D1 + D5 + å 2 Di / 8
i =2
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-13
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-8
Time-Series Forecasting Methods
• Three ways to calculate the systematic
component
7-5
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Components and Methods
1. Qualitative
– Primarily subjective – Rely on judgment
• Systematic component – expected value of demand
− Level (current deseasonalized demand) − Trend (growth or decline in demand) − Seasonality (predictable seasonal fluctuation) Random component – part of forecast that deviates from systematic component Forecast error – difference between forecast and actual demand
Quarter 2
3 4 1 2 3 4 1 2 3 4 1
Period, t 1
2 3 4 5 6 7 8 9 10 11 12
Demand, Dt 8,000
13,000 23,000 34,000 10,000 18,000 23,000 38,000 12,000 13,000 32,000 41,000
• All of these decisions are interrelated
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-3
Characteristics of Forecasts
1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error 2. Long-term forecasts are usually less accurate than short-term forecasts 3. Aggregate forecasts are usually more accurate than disaggregate forecasts 4. In general, the farther up the supply chain a company is, the greater is the distortion of information it receives
7-10
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Tahoe Salt
Year 1
1 1 2 2 2 2 3 3 3 3 4
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-4
Components and Methods
• Companies must identify the factors that
influence future demand and then ascertain the relationship between these factors and future demand
ì ï ï Dt = í ï ï î
t –1+( p /2) é ù ê Dt –( p /2) + Dt +( p /2) + å 2 Di ú / (2 p) for p even ê ú ë û i =t +1–( p /2) t +[( p –1)/2] i =t –[( p –1)/2]
å
– Multiplicative S = level x trend x seasonal factor – Additive S = level + trend + seasonal factor – Mixed S = (level + trend) x seasonal factor
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
2. Time Series
– Use historical demand only – Best with stable demand
3. Causal
– Relationship between demand and some other factor
4. Simulation
– Imitate consumer choices that give rise to demand
– – – – – – Past demand Lead time of product replenishment Planned advertising or marketing efforts Planned price discounts State of the economy Actions that competitors have taken
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-2
Role of Forecasting in a Supply Chain
• The basis for all planning decisions in a
Dt = L + Tt
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-15
Estimating Seasonal Factors
Di St = Dt
Figure 7-4
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-16
4
Tahoe Salt
Figure 7-2
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 7-14
Tahoe Salt
Figure 7-3
A linear relationship exists between the deseasonalized demand and time based on the change in demand over time
7-7
•
•
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
Basic Approach
1. Understand the objective of forecasting. 2. Integrate demand planning and forecasting throughout the supply chain. 3. Identify the major factors that influence the demand forecast. 4. Forecast at the appropriate level of aggregation. 5. Establish performance and error measures for the forecast.
Table 7-1
7-11
Tahoe Salt
Figure 7-1
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall .
7-12
Estimate Level and Trend
Periodicity p = 4, t = 3
7
Demand Forecasting in a Supply Chain
PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 5e
Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall . 1-1 7-1
7-9
Static Methods
Systematic component = (level + trend) ´ seasonal factor
Ft +l = [ L + (t + l )T ]St +l
where L T St Dt Ft = = = = = estimate of level at t = 0 estimate of trend estimate of seasonal factor for Period t actual demand observed in Period t forecast of demand for Period t