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10/21/2013
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Concepts and
Techniques for Effective
Forecast Management
The Intricacies of
Forecasting—Simplified
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Introductions – Session Leader � David F. Ross PhD, CFPIM, CSCP
� Senior Manager, Professional Development, APICS
� 35 years of industry, consulting, ERP, education, and professional development experience
� Teaching positions at NU Kellogg School of Management and Elmhurst College
� APICS Member since 1985
� Published six books in supply chain management
Meet your session leaders
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Introductions – Session Leader � Bob Collins CFPIM, CIRM, CSCP
� Director, Professional Development, APICS (Staff position)
� 30 years of industry, consulting, ERP, education, and professional development experience
� Former APICS Instructor and volunteer –Chapter, District and APICS Board of Directors, APICS President (2003)
Meet your session leaders
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Agenda
• 23 major principles of forecasting• Forecasting in the supply chain environment• Defining demand management and role of the demand
planner• Defining forecasting and the forecasting process• Review of qualitative forecasting techniques• Review of quantitative forecasting techniques• Performing forecast decomposition: trends and seasonal
items• Understanding associative (correlation) models• Reviewing the tools to chart forecast error• Detailing why forecasts fail
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Forecasting Themes
“Those who have knowledge don’t predict. Those who predict, don’t have knowledge”- Lao Tzu
“Prediction is very difficult, especially if it’s about the future”- Niels Bohr
“All things pass away; nothing remains”- Heraclitus
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
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Forecasting is Everywhere in the Supply Chain
1. Analyzing customer demand: What should we make and when?
2. Materials: Who do we buy from and how much?
3. Production: Are we producing the right amount of the right product?
4. Distribution: Where do we distribute product?
5. Wholesale/retail: What is the proper assortment and allocation of merchandise in stores?
Store
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
2. Demand management is the process of managing all independent demands for a company's product lines and effectively communicating these demands to the master scheduling and top management production functions
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Defining Demand Management
The process of planning, executing, controlling, and monitoring the design,
pricing, promotion, anddistribution of products and services to
bring about transactions that meet organizational and individual needs.
APICS Dictionary, 14th ed.
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Defining Demand Planning
APICS Dictionary, 14th ed.
The process of combining statistical forecasting techniques and judgment to construct demand
estimates for products or services (both high and low volume; lumpy and continuous) across the
supply chain from the suppliers' raw materials to the consumer's needs. Items can be aggregated by product family, geographical location, product life cycle, and so forth, to determine an estimate of consumer demand for finished products, service
parts, and services.
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
2. Demand management is the process of managing all independent demands for a company's product lines and effectively communicating these demands to the master scheduling and top management production functions
3. Demand forecasting is the process of predicting future customer demand for a firm's goods and services
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Demand Management Process Model
PlanningDemand
Prioritizing Demand
Communicating Demand
Influencing Demand
Demand Management
Reviewing Demand
Performance
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
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Roles of Demand Management Functions
Executive Sales Marketing Product/Brand Mgmt
Role• Ensure demand
strategies, tactics, and execution are in place
Role• Make visible sales
plans and volume of demand
Role• Detail marketplace
changes• Detail marketing
strategy and tactics
Role• Detail product plans,
launches, and phase-outs
Responsibilities• Detail demand status
to meet strategic and financial objectives
• Participate in monthly demand consensus review
• Provide leadership and oversight
• Ensure demand plan synchronized with company plans
• Performance accountability
Responsibilities• Detail monthly
customer sales volume and timing
• Detail monthly demand assumptions
• Communicate at least monthly market problems and opportunities
• Communicate any significant changes in demand
Responsibilities• Detail monthly
anticipated changes to marketing strategy and impact on demand
• Detail monthly the assumptions uponwhich marketing strategies are based
• Track and report monthly the impact of the marketplace on anticipated demand
Responsibilities• Detail monthly product
plans, product launches, promotions, and product phase-outs
• Communicate delays in product launches or changes to product plans impacting demand
• Communicate and update life cycle plans and plan assumptions
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Role of the Demand Planner
Economy
Statistical Analysis
Products/ Brands
Marketing Data
Marketing Data Customer
Data
Sales Data
Business Plan
Analyze and
Assimilate
Updated Demand Plan
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S&OP and the Demand Plan
S&OP Meeting
Product Review
Demand Review
Supply Review
Financial Review
Strategies
Resources
Performance Measurements
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
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Forecasting Levels
Planning Horizon Focus
STRATEGICPLANNING
TACTICALPLANNING
OPERATIONSPLANNING
SHORT-TERMPLANNING
ANNUAL –1-10 years
MONTHLY –3-12 Months
WEEKLY –1-52 Weeks
DAILY –1-365 Days
Financial Goals and Objectives
Product Families
Finished Goods
Manufacturing/ Purchased items
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Forecast Required byExpected corporate growth for the next 5 years (long range)
Executive team: investment, profit, and asset/capital planning
Product life cycles (long range) Marketing: product planning
Total production required for next five years (long range)
Manufacturing: plant expansion program
Current year’s sales of individual products in family groupings (medium range)
Sales: quotasFinance: expense budgetsManufacturing: labor/machine capacitiesInventory: purchasing and storage
Sales for next week (short term)
Manufacturing: assembly schedules and dispatching prioritiesMaterials: purchase order release and follow-up
Examples of Forecasting by Levels
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
6. Forecasting is a process that has as its objective the prediction of future events or conditions
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Defining Forecasting
An objective estimate of future demand attained by projecting the pattern found in the events of the past into the future.
It is primarily a calculative rather than an intuitive management process
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
6. Forecasting is a process that has as its objective the prediction of future events or conditions
7. Effective forecasting starts with an comprehensive forecast design system
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Forecast System Design Issues
• Time horizon
• Level of aggregate detail
• Size of the historical database
• Forecast control
• Constancy
• Selection of forecasting models
• Designing the forecasting process
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The Forecasting Process
1. Data gathering and preparation
2. Forecast generation
3. Volume and mix
reconciliation #1
4. Apply judgment
8. Documenting assumptions
5. Volume and mix
reconciliation #2
6. Decision making and
authorization
7. Volume and mix
reconciliation #3
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
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General Forecasting Techniques
Based on intuitive or judgmental evaluation
Based on computational projection of a numeric relationship
Qualitative Techniques
Quantitative Techniques
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Forecasting data sources based on historical demand patterns from the company data
Forecasting data sources based on external patterns from information outside the company
Internal (Intrinsic)
External (Extrinsic)
Forecasting Data Sources
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Quantitative Techniques
• Simple average• Moving average• Exponential
smoothing• Time series
decomposition
• Correlation• Regression• Multiple regression• Historical analogy• Leading indicator• Econometric
Qualitative Techniques
• Expert opinion• Sales force estimate• Pyramid forecasting• Panel consensus• Market research• Delphi technique• Visionary forecast• Product life cycle
analysis
Judgmental
Forecasting Categories
Time Series (Intrinsic)
Associative(Extrinsic)
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
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Qualitative Forecasting Techniques
IndependentJudgment
Executive/Management
Judgment
Market Research
Sales ForceEstimates
HistoricalAnalogy
• Expert opinion• Visionary forecast
• Focus group• Survey
• Sales force composite
• Product life cycle analysis
• Panel consensus• Delphi technique• Pyramid
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
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Quantitative Techniques
Simple average
Year-to-date average
Moving average
Weighted moving average
Exponential smoothing
Time series decomposition
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
11. Forecasts are usually wrong
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Principle of Entropy
Fighting the second law of thermodynamics. “Entropy law" is a law of disorder or that dynamically ordered states are "infinitely improbable"
Ludwig Boltzmann
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
11. Forecasts are usually wrong
12. Forecasts are more accurate for aggregate groups
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Detail and Aggregate Forecasts
Period Demand AverageYear-to-Date
Average3 Period Average
3 Period Weighted Average
Exponential Smoothing
Alpha (α )
1 110 110 0.502 78 110.00 110.003 80 94.00 94.00 94.004 122 79.00 89.33 89.33 86.00 87.005 85 101.00 97.50 93.33 98.22 104.506 131 103.50 95.00 95.67 96.22 94.757 120 108.00 101.00 112.67 113.67 112.888 79 125.50 103.71 112.00 115.89 116.449 75 99.50 100.63 110.00 104.22 97.72
10 120 77.00 97.78 91.33 86.33 86.3611 97.50 100.00 91.33 95.89 103.18
Period Demand AverageYear-to-Date
Average3 Period Average
3 Period Weighted Average
Exponential Smoothing
Alpha (α )
1 110 110 0.502 78 110.00 110.003 80 94.00 94.00 94.004 122 79.00 89.33 89.33 86.00 87.005 85 101.00 97.50 93.33 98.22 104.506 131 103.50 95.00 95.67 96.22 94.757 120 108.00 101.00 112.67 113.67 112.888 79 125.50 103.71 112.00 115.89 116.449 75 99.50 100.63 110.00 104.22 97.72
10 120 77.00 97.78 91.33 86.33 86.3611 97.50 100.00 91.33 95.89 103.18
Average 100 98.44 98.77 100.62 100.08 100.40
Detail View of ForecastsAggregate View of Forecasts
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data
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Time Series Patterns
Sales (M)5
4
3
2
1
0
1 2 3 4 5 6 7 8 9 10 11 120Months
Quarter 1 Quarter 2 Quarter 3 Quarter 4
RandomVariation
TrendHorizontal
SeasonalitySeasonality
Trend
RandomVariation
Horizontal
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time series data is affected by trend, seasonal, or cyclical patterns. Forecasters must then “decompose” the patterns into subpatterns to reveal how they impact the behavior of the series
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time series data is affected by trend, seasonal, or cyclical patterns. Forecasters must then “decompose” the patterns into subpatterns to reveal how they impact the behavior of the series
15. A trend is the basic tendency of a measured variable to grow or decline over a long period. The forecast extrapolation can be calculated as additive or a trend factor (percent)
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Trend Quantity Forecast
Three Step Process:
1. Base forecast calculationUse of statistical technique to determine the base forecast from the time series data
2. Trend quantity calculationTt = β (FBt - FBt -1) + (1 – β) Tt - 1
3. Forecast calculationThe trend quantity is added to the base forecast to determine the trended forecast. The forecast is extrapolated into the future by adding the trend quantity to each future period’s trended forecast
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Trend Quantity Forecast – Example
Additive trend quantity forecast using 3 period average
Beta Factor 0.3
Period Demand Base Forecast Trend Quantity Forecast
January Year 1 100February 109March 119April 131 109.33 32.80 142.13May 140 119.67 26.06 145.73June 148 130.00 21.34 151.34July 160 139.67 17.84 157.51August 175 149.33 15.39 164.72September 161.00 14.27 175.27October 189.54November 203.81December 218.09January Year 2 232.36February 246.63
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
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Seasonal Forecast – Calculation
Five Step Process
1.Determine the size of the historical time series to be used in the calculation
Past Demand 1 2 3
Year 1-1 Qtr 1-2 Qtr 1-3 Qtr 1-4 Qtr 2-1Qtr 2-2 Qtr 2-3Qtr 2-4 Qtr 3-1Qtr 3-2Qtr 3-3 Qtr 3-4 qtr
Demand 160 225 350 425 165 190 335 390 175 245 360 430
2.Summarize the historical data by quarter
Summary Total Avg
Yrs 1,2,3 Ist Qtr 500 167Yrs 1,2,3 2nd Qtr 660 220Yrs 1,2,3 3rd Qtr 1,045 348Yrs 1,2,3 4th Qtr 1,245 415Totals 3,450 288
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Seasonal Forecast – Calculation (cont.)
3. Calculate the seasonal index
4. Calculate a base deseasonalized forecast
Summary Total Avg Season Index
Yrs 1,2,3 Ist Qtr 500 167 0.5797Yrs 1,2,3 2nd Qtr 660 220 0.7652Yrs 1,2,3 3rd Qtr 1,045 348 1.2116Yrs 1,2,3 4th Qtr 1,245 415 1.4435Totals 3,450 288 4.000
Forecast (Yr)
1000Avg Forecast per Quarter
250
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Seasonal Forecast – Calculation (cont.)
5. Calculate the new seasonal forecast
New Forecast 4
Year 1 Qtr 2 Qtr 3 Qtr 4 Qtr
Demand 145 191 303 361
Forecast average x seasonal index = 250 x 0.5795 = 145
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
17. Through associative (correlation) analysis, we measure the effects of mutual dependence in values of an item series
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
17. Through associative (correlation) analysis, we measure the effects of mutual dependence in values of an item series
18. An associative model with a single explanatory variable is called a simple regression model. Multiple regression refers to a model with one dependent and two or more explanatory variables
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Multiple Variable Associative Forecast
Four Step Process
1.Establish the dependent (y) and independent (x) variables
Quarter Interest Rates (x1 )Number of
Housing Starts (0,000 units) (x2 )
Sales (US$000,000) (y)
1 4.50 1 2.02 3.60 3 3.03 4.00 2 2.44 3.40 3 3.15 2.90 4 3.76 2.00 6 4.57 2.60 5 4.0
8 2.80 4 3.5Totals 25.8 28 26.2
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Multiple Variable Associative Forecast (cont.)
2. Use Excel to calculate the sales, interest rate, and number of housing starts coefficients
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.995315585
R Square 0.990653114
Adjusted R Square 0.986914359
Standard Error 0.094280904
Observations 8
ANOVA
df SS
Regression 2 4.710555556
Residual 5 0.044444444
Total 7 4.755
Coefficients Standard Error
Sales 3.144444444 1.714808415
Interest rates -0.333333333 0.344265186
Housing starts 0.344444444 0.173561104
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Multiple Variable Associative Forecast (cont.)
3. Determine forecast options
Forecast Options
Interest RatesOpt1 Opt 2 Opt 3 Opt 42.3 2.6 3.0 3.5
Housing StartsOpt1 Opt 2 Opt 3 Opt 45.0 4.8 4.2 3.5
4. Select associative options and determine forecastForecastOption Sales ForecastOpt 1 4.10Opt 2 3.93Opt 3 3.59Opt 4 3.18
Coefficients
Sales 3.144444444
Interest rates -0.333333333
Housing starts 0.344444444
3.144 + (-0.333 x 2.3) + (0.344 x 5.0) = 4.10
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a method for determining forecast error
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Tools for Forecast Error Detection
• Forecast error
• Absolute percent of error (APE)
• Mean absolute deviation (MAD)
• Standard deviation (SD)
• Bias
• Mean Absolute Percent Error (MAPE)
• Tracking signal
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Tools for Forecast Error – Analysis
1. FE = D – F
2. Bias = ∑(D – F) / n
3. MAD = ∑|D – F| / n
4. APE = |D – F| / D
5. MAPE = ∑|D – F/ D| / n
6. TS = ∑(D – F) / MAD
Period Demand ForecastForecast Error (1)
Absolute Error
Bias (2) MAD (3) APE (4) MAPE (5) TS (6)
1 1,0002 1,1003 1,2004 1,050 1,100 -50 50.00 -50.00 50.00 4.76% 4.76% -1.005 900 1,117 -217 216.67 -133.33 133.33 24.07% 14.42% -2.006 1,200 1,050 150 150.00 33.33 138.89 12.50% 13.78% -0.847 900 1,050 -150 150.00 -50.00 141.67 16.67% 14.50% -1.888 800 1,000 -200 200.00 -50.00 153.33 25.00% 16.60% -3.049 1,250 967 283 283.33 38.89 175.00 22.67% 17.61% -1.05
10 1,100 983 117 116.67 9.52 166.67 10.61% 16.61% -0.40Avg. 1,029 1,038 Avg Bias -28.80 Avg MAPE 14.04%
Total Bias -201.59
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
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Analyzing Forecast Fit
Period Demand AverageYear-to-Date
Average3 Period Average
3 Period Weighted Average
Exponential Smoothing
Alpha (α )
1 110 110 0.502 78 110.00 110.003 80 94.00 94.00 94.004 122 79.00 89.33 89.33 86.00 87.005 85 101.00 97.50 93.33 98.22 104.506 131 103.50 95.00 95.67 96.22 94.757 120 108.00 101.00 112.67 113.67 112.888 79 125.50 103.71 112.00 115.89 116.449 75 99.50 100.63 110.00 104.22 97.72
10 120 77.00 97.78 91.33 86.33 86.3611 97.50 100.00 91.33 95.89 103.18
Absoulute errorPeriod Average MAD Y-to-D avg MAD 3 Per avg MAD 3 Per w/avg MAD Expon MAD
12 32.00 32.00 32.00 32.003 14.00 14.00 14.00 23.00 14.00 23.004 43.00 28.50 32.67 26.22 32.67 32.67 36.00 36.00 35.00 27.005 16.00 24.33 12.50 22.79 8.33 20.50 13.22 24.61 19.50 25.136 27.50 25.13 36.00 25.43 35.33 25.44 34.78 28.00 36.25 27.357 12.00 22.50 19.00 24.36 7.33 20.92 6.33 22.58 7.13 23.988 46.50 26.50 24.71 24.41 33.00 23.33 36.89 25.44 37.44 25.909 24.50 26.21 25.63 24.56 35.00 25.28 29.22 26.07 22.72 25.50
10 43.00 28.31 22.22 24.30 28.67 25.76 33.67 27.16 33.64 26.41Avgerage 106.25 25.93 86.36 24.58 90.17 24.84 95.06 27.12 95.84 25.90Per 4:10
|D – Avg| |D – YtD|
∑Avg / n
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final forecast is highly recommended
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final forecast is highly recommended
22. Create an integrated forecasting process that encourages communication, coordination, and collaboration among marketing sales, product management, production, distribution, finance, and forecasting organizations
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Why Forecasts Fail
Management Involvement
Integrated forecasting is needed at the top management, operations management, and operations execution levels of the business
Over-Sophistication
and Cost
Forecasting systems that are too difficult to understand or cost too much to operate are doomed to failure
CompatibilityThere is a lack of compatibility between the forecasting system and the ability of the using organization to understand it
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Why Forecasts Fail (cont.)
Data Accuracy The data used for the forecast must be accurate, timely, complete, and easy to access
Unnecessary Items
Often forecasts are developed for items that should not be forecasted, for example dependent demand item usage
Lack of Management
Control
Forecasters must be diligent in monitoring the forecast to ascertain the degree of error, when the forecast should be altered, and what parameters should be used to guide forecast adjustment
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Principles of Forecasting Management
23. The philosophy of forecast places primary emphasis on the forecasting process rather than on the numbers. If the forecaster has meticulously followed a proper forecasting process, the end result will be as good a forecast as can be delivered
“As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality”- Einstein
Thank you for
attending and
good forecasting!!
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