2 session 2a_hp case study_2010_cfvg
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This document discusses aggregate demand forecasting through a case study of HP's supply chain. It explains that aggregate forecasts at higher supply chain levels are more accurate than at lower levels. The case study shows how HP forecasted demand for desktop PCs with 110V and 220V power supplies separately and in total. Forecast accuracy improved when considering total demand rather than specific configurations. Key lessons are that delayed product differentiation through aggregate forecasting and demand management helps supply chain competitiveness and cost reduction.
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Forecast accuracy improves at different levels
110 V 220 V Total
Months Demand MA(4) Error Demand MA(4) Error Demand MA(4) Error
1 10000 8000 18000
2 14000 4000 18000
3 16000 2500 18500
4 12000 6500 18500
5 18000 13000 -5000 2000 5250 3250 20000 18250 -1750
6 15000 15000 0 4000 3750 -250 19000 18750 -250
7 14000 15250 1250 3000 3750 750 17000 19000 2000
8 11000 14750 3750 7000 3875 -3125 18000 18625 625
9 13000 14500 1500 5000 4000 -1000 18000 18500 500
10 11000 13250 2250 6000 4750 -1250 17000 18000 1000
MAD 2291.67 1604.17 1020.83
Forecast
Accuracy 83.23% 64.35% 94.38%
(10000+14000+16000+12000)/4)
13000-18000
(5000+1250+3750+1500+2250) / 6 100-[(5+1.25+3.75+1.5+2.25)/(18+15+14+11+13+11)]100](https://image.slidesharecdn.com/2session2ahpcasestudy2010cfvg-140716030719-phpapp02/85/2-session-2a_hp-case-study_2010_cfvg-19-320.jpg)
2 session 2a_hp case study_2010_cfvg
- 1. SESSION#2a: AGGREGATE DEMAND: A CASE STUDY (CFVG: 2012) COMPETITIVE THROUGH DEMAND MANGEMENT: CASE STUDY OF HP SUPPLY CHAIN Dr. RAVI SHANKAR Professor Department of Management Studies Indian Institute of Technology Delhi Hauz Khas, New Delhi 110 016, India Phone: +91-11-26596421 (O); 2659-1991(H); (0)-+91-9811033937 (m) Fax: (+91)-(11) 26862620 Email: r.s.research@gmail.com http://web.iitd.ac.in/~ravi1
- 2. 2 In this session we plan to cover Concept of Aggregate Forecast in a Supply Chain How good is forecast? Case Study: HP
- 3. 3 Forecasting A statement about the future value of a variable of interest Future Sales Weather Stock Prices Other Short term and Long term estimates Several Methods Quantitative History and Patterns Leading Indicators / Associations (Housing Starts & Furniture) Qualitative Judgment Consensus Used for making informed Decisions and taking Actions based on those decisions
- 4. 4 Forecasting Forecasts make a MAJOR IMPACT (Positive or Negative) on: • Revenue • Market Share • Cost • Inventory • Profit Sales will be $200 Million!
- 5. 5 Three Major Types of Forecasts Judgmental – Uses subjective, qualitative “judgment” (opinions, surveys, experts, managers, others). Most useful when there is limited data and with New Product Introductions Time series – Observes what has occurred over previous time periods and assumes that future patterns will follow historical patterns Associative Models – Establishes cause and effect relationships between independent and dependent variables (rainy days and umbrella sales, pricing and sales volume, attendance at sporting events and food sold, others)
- 6. 6 Qualitative Methods Grass Roots Market Research Panel Consensus Executive Judgment Historical analogy Delphi Method Qualitative Methods
- 7. 7 Quantitative Techniques Basic time series approaches Moving averages, simple & weighted Exponential smoothing, simple & trend adjusted Linear regression (linear trend model) Techniques for seasonality and trend - Decomposition of time series Causal approach Simple Linear Regression Multiple Linear Regression
- 8. Time Series Analysis Time series forecasting models try to predict the future based on past data You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel
- 9. Simple Moving Average Formula F = A + A + A +...+A n t t-1 t-2 t-3 t-n The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods
- 10. Simple Moving Average Problem (1) Week Demand 1 650 2 678 3 720 4 785 5 859 6 920 7 850 8 758 9 892 10 920 11 789 12 844 F = A + A + A +...+A n t t-1 t-2 t-3 t-n
- 11. Week Demand 3-Week 6-Week 1 650 2 678 3 720 4 785 682.67 5 859 727.67 6 920 788.00 7 850 854.67 768.67 8 758 876.33 802.00 9 892 842.67 815.33 10 920 833.33 844.00 11 789 856.67 866.50 12 844 867.00 854.83 F4=(650+678+720)/3 =682.67 Calculating the moving averages gives us: ©The McGraw-Hill Companies, Inc., 2004 11
- 12. 500 600 700 800 900 1000 1 2 3 4 5 6 7 8 9 10 11 12 Week Demand Demand 3-Week 6-Week Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother
- 13. 13 Case 1: Case Study of HP(1) Demand Forecasting: The Supply Chain Context
- 14. 14 How do we Forecast-Time Series Analysis Time series forecasting models try to predict the future based on past data. You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel
- 15. 15 Simple Moving Average Formula F = A + A + A +...+A n t t-1 t-2 t-3 t-n The simple moving average model assumes an average is a good estimator of future behavior. The formula for the simple moving average is: Ft = Forecast for the coming period n = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods
- 16. 16 Aggregate Forecasts at SC Level Aggregate forecasts are more accurate Forecast at the most aggregate/generic level possible Similarly, forecast at the most upstream of the supply chain (if possible) If possible, never use forecast information at the lower levels. At the lower levels, decisions should be based on actual demand
- 17. 17 Is it always possible to use it? Only if the power supply can be assembled in small lead time Power supply assembly should be at the end of the manufacturing process Board assembly Hard disk Assembly Testing Power supply 110 V Board assembly Hard disk assembly Testing Power supply 110 V Power supply 220 V Testing Power supply 220 V Delayed product differentiation Product postponement Case 1: HP desktop (Aggregate Forecast)
- 18. 18 Case 1: HP desktop Board assembly Hard disk assembly Testing Power supply 110 V Power supply 220 V Product Product Month 110 V PC 220 V PC 1 10000 8000 2 14000 4000 3 16000 2500 4 12000 6500 5 18000 2000 6 15000 4000 7 14000 3000 8 11000 7000 9 13000 5000 10 11000 6000
- 19. 19 Forecast accuracy improves at different levels 110 V 220 V Total Months Demand MA(4) Error Demand MA(4) Error Demand MA(4) Error 1 10000 8000 18000 2 14000 4000 18000 3 16000 2500 18500 4 12000 6500 18500 5 18000 13000 -5000 2000 5250 3250 20000 18250 -1750 6 15000 15000 0 4000 3750 -250 19000 18750 -250 7 14000 15250 1250 3000 3750 750 17000 19000 2000 8 11000 14750 3750 7000 3875 -3125 18000 18625 625 9 13000 14500 1500 5000 4000 -1000 18000 18500 500 10 11000 13250 2250 6000 4750 -1250 17000 18000 1000 MAD 2291.67 1604.17 1020.83 Forecast Accuracy 83.23% 64.35% 94.38% (10000+14000+16000+12000)/4) 13000-18000 (5000+1250+3750+1500+2250) / 6 100-[(5+1.25+3.75+1.5+2.25)/(18+15+14+11+13+11)]100
- 20. 20 Learning Lesson of Case 1 What are the Learning Lessons of this case Study?
- 21. 21 Product Redesign Helps Supply Chain Competitiveness Demand management helps competitiveness and cost reduction Delayed product differentiation is the key to this redesign Aggregate forecasts are more accurate Forecast at the most aggregate/generic level possible Similarly, forecast at the most upstream of the supply chain (if possible) If possible, never use forecast information at the lower levels. At the lower levels, decisions should be based on actual demand