The Intricacies of Forecasting—Simplified - APICS …apicsr.org/downloads/APICS_2013_Conference_Presentation_Materials... · Techniques for Effective Forecast Management The Intricacies

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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


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




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.


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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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


Demand Management Process Model

PlanningDemand

Prioritizing Demand

Communicating Demand

Influencing Demand

Demand Management

Reviewing Demand

Performance

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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


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


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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5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location


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





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





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


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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8. A forecasting technique is a systematic procedure for producing and analyzing forecasts


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



• 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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9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited


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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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



Simple average

Year-to-date average

Moving average

Weighted moving average

Exponential smoothing

Time series decomposition

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11. Forecasts are usually wrong


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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11. Forecasts are usually wrong

12. Forecasts are more accurate for aggregate groups


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





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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13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data


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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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




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


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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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


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


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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17. Through associative (correlation) analysis, we measure the effects of mutual dependence in values of an item series




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


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



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


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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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


Analyzing Forecast Fit





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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21. The use of multiple methods to arrive at the final forecast is highly recommended





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


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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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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The Intricacies of Forecasting—Simplified - APICS …apicsr.org/downloads/APICS_2013_Conference_Presentation_Materials... · Techniques for Effective Forecast Management The Intricacies