第八章预测供应链需求

where
w
i 1
n
i
1
a (1 a ) 2 At 2 a (1 a ) 3 At 3 ... a (1 a ) n At n which reduces to the basic, level only, exponential smoothing formula MA Ft 1 aAt (1 a )Ft where
200
Sales
150 100 50 0 0 5 10 Time 15 20 25
Actual sales Average sales
随机性或水平发展的需求，无趋势或季节性因素
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典型时间序列模式Typical Time Series Patterns: 随机有趋势 Random with Trend 250
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Typical Time Series Patterns: Lumpy
Sales
Time
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尖峰需求模式
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8.2预测方法
•1.定性方法Qualitative
调查法Surveys
专家系统Expert systems or rule-based
800 700 600 500 400 300 200 100 0 0
Sales
Actual sales Trend in sales Smoothed trend and seasonal sales 10 20 Time 30 40
随机性需求，有趋势，季节性因素
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n
or RMSE (std. error of forecast) 标准差
SF
t 1
(At Ft )2 n 1
n
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Example (Cont’d)
Using SF and assuming n=2
2 (10001080)2 (1400 1000) SF 2 1 408
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Example Exponential Smoothing Forecasting Time series data
1
Last year This year Getting started 1200
Quarter 2 700
3
900
4
1100
1400
1000
?
Assume a = 0.2. Average first 4 quarters of data and use for previous forecast, say Fo
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8.3 对物流管理者有用的方法
8.3.1.移动平均法Moving Average Basic formula
t MA Ai n i t 1n 1
where i = time period t = current time period n = length of moving average in periods Ai = demand in period i
Location Strategy • Location decisions • The network planning process
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PLANNING
Customer service goals • The product • Logistics service • Ord. proc. & info. sys.
?
Total demand during past 3 months . . . . 360/3 380/3 360/3 380/3
3-month moving average . . . . 120 126.67 120 126.67
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MA w 1A1 w 2 A2 ... w n An
Note To compute a reasonable average for SF, n should range over at least one seasonal cycle in most cases.
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MA aAt a (1 a )1 At 1
加权移动平均
If weights (w ) are exponential in form, then
8.3.2.指数平滑公式 Exponential Smoothing Formulas
I. Level only Ft+1 = a At + (1-a)Ft II. Level and trend St Tt = aAt + (1-a)(St-1 + Tt-1) =ß (St - St-1) + (1-ß )Tt-1
预测供应链需求
I hope you'll keep in mind that economic forecasting is far from a perfect science. If recent history's any guide, the experts have some explaining to do about what they told us had to happen but never did. Ronald Reagan, 1984
网络规划流程
PLANNING 计划
产品 物流服务 Logistics service
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CONTROLLING 控制
service goals
Forecasting in Inventory Strategy
CONTROLLING
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Inventory Strategy • Forecasting • Inventory decisions • Purchasing and supply scheduling decisions • Storage fundamentals • Storage decisions Transport Strategy • Transport fundamentals • Transport decisions
2.历史映射法（时间序列分析Historical projection） 移动平均Moving average 指数平滑Exponential smoothing
•3.因果或联想法Causal or associative 回归分析Regression analysis •4.协同Collaborative
8.1需求预测



1.需求的时间和空间特征（Spatial versus Temporal Demand） 2.尖峰需求和规律性的需求（Lumpy versus Regular Demand） 3.派生需求和独立需求（Derived versus Independent Demand）
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典型时间序列模式Typical Time Series Patterns: 250 随机Random
• Transport fundamentals Customer 客户服务目标 The product • The product • • Transport decisions
库存决策 • Purchasing and supply 采购和供应时间决策 scheduling decisions
• 存储基础知识 Storage fundamentals
ORGANIZING
供应链预测什么
•Demand, sales or requirements
需求，销售或请求
•Purchase prices •购买价格 •Replenishment and delivery
times
•补给和交货时间
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Example (Cont’d)
Begin forecasting
F0 (1200 700 9001100) / 4 975
First quarter of 2nd year F1 0.2A0 (1 0.2)F0 0.2(1100) 0.8(975) 1000 Second quarter of 2nd year F2 0.2A1 (1 0.2)F1 0.2(1400) 0.8(1000) 1080
1
Last year This year Forecast 1200 1400
3
900 ?
4
1100
1000
1080
1064
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Example (Cont’d)
Measuring forecast error as MAD绝对差
| At Ft | MAD t 1 n
Sales 200 150
100
百度文库50 0 0
Actual sales Average sales
5
10 Time
15
20
25
随机性需求，上升趋势，无季节性因素
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Typical Time Series Patterns: Random with Trend & Seasonal
or
IV. Forecast error
N |A t t 1
MAD =
Ft |
N Ft ) 2
SF
N (A t t 1
N
Ft+1 = St + Tt III. Level, trend, and seasonality St = a(At/It-L) + (1-a)(St-1 + Tt-1)
Chapter 8
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产品计划三角形 Product in the Planning Triangle
库存战略 Inventory Strategy
• Forecasting 预测 • Inventory decisions
运输战略 Transport Strategy
运输基础知识 运输决策
ORGANIZING 组织
• Storage decisions
存储决策
• Ord . proc. & info. sys. 订单管理和信息系统
选址战略 Location Strategy
• Location decisions 选址决策 • The network planning process
and SF @ 1.25MAD.
It
Tt
= g(At/St) + (1-g)It-L
=ß (St - St-1) + (1-ß )Tt-1
Ft+1 = (St + Tt)It-L+1
where L is the time period of one full seasonal cycle.
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Example 3-Month Moving Average Forecasting
Month, i . . . 20 21 22 23 24 25 26
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Demand for month, i . . . 120 130 110 140 110 130
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Example (Cont’d) Third quarter of 2nd year F3 0.2A2 (1 0.2)F0 0.2(1000) 0.8(1080) 1064
Summarizing Quarter 2 700 1000
a smoothing constant usually 0.01 to 0.30
Ft 1 forecast for next period At actual demand in current period Ft forecast in current period
Weighted Moving Average