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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Operations Management Forecasting Chapter 4

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J SATIŞ TAHMİNLEMESİ

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J BİR İŞLETLETMENİN BAŞARISI İLERİYİ NE KADAR İYİ GÖREBİLMESİNE VE UYGUN STRATEJİLER GELİŞTİRMESİNE BAĞLIDIR

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J YÖNETİCİLERİN EN TEMEL GÖREVİ: PLAN YAPMAKTIR

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J PLANLAR GELECEĞE YÖNELİKTİR

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J ÖYLEYSE GELEÇEĞİ TAHMİNLEMEK ZORUNDAYIZ

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J YÖNETİÇİLERİN ALDIĞI KARARLARIN BİR ÇOĞUNDA AZ YADA ÇOK BİR TÜR TAHMİN YER ALIR

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J ÖRNEK BİR BÜRO ŞEFİNİN CUMA GÜNÜ BAZI MEMURLARINA İZİN VEREBİLMEK İÇİN CUMA GÜNÜN İŞ YÜKÜNÜ TAHMİN ETMESİ

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J ASLINDA GÜNLÜK HAYATIMIZIN BİR ÇOK KESİTİNDE DE TAHMİN YAPIYORUZ ÖRNEK;

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J ANCAK BU TAHMİNLERİN BİR KISMI OLDUKÇA KOLAYKEN TAHMİNLENMESİ ÇOK ZOR OLAN KONULARDA VARDIR

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J ÖRNEĞİN BİR OTOMOTİV ENDÜSTRİSİNİN FİNANS MÜDÜRÜNÜN GELEÇEK YILKI MEVSİMLİK FİNANS İHTİYACINI TAHMİNLEMESİ

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Forecasting and optimization are complex Come on! It can‘t go wrong every time...

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Select significant attributes for your forecaster Which one Is mine?

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities Sales will be $200 Million!

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J TAHMİNLEME NEDİR? EDMUND BURK’YE GÖRE: geçmişe bakarak geleceği hiçbir zaman planlayamassınız. PATRICK HENRY İSE; geçmiş olmadan geleceği hiçbir şekilde yargılayacak bir yol bilmiyorum der.

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J tanımlar “ Tahmin;geleçekteki muhtemel olayların belirli bir zamanda saptanması,hesaplanması ya da kestirimidir.

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J TAHMİNLEME SİSTEMİ ÇİZİM

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J GİRDİLER *PAZAR DURUMLAR *GEÇMİŞ SATIŞLAR *İŞLETME STRATEJİLERİ *ENDÜSTRİNİN DURUMU *EKONOMİNİN DURUMU *TİÇARİ REKABET DURUMU *ÜRETİM KAPASİTESİ *IHUKUKİ VE POLİTİK FAKTÖRLER *DİĞER FAKTÖRLER TAHMİNLEME TEKNİKLERİ VEYA MODELLERİ ÇIKTILAR HER MAL İÇİN HER ZAMAN DÖNEMİNDE BEKLENEN TALEP *DİGER FAKTÖRLER KARAR VERİCİ SATIŞ TAHMİNİ HER MAL İÇİN HER ZAMAN DÖNEMİNDE TALEP TAHMİNİ BAŞKALARININ ÖNERİLERİ RİSK DURUMU TECRÜBE İNSİYATİF VE HİS KİŞİSEL DEĞERLER VE GÜDÜLER SOSYAL VE KÜLTÜREL DEGERLER ÖTEKİ FAKTÖRLER TAHMİNLEME SİSTEMİ

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 3 years Sales & production planning, budgeting Long-range forecast 3+ years New product planning, facility location Types of Forecasts by Time Horizon

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Short-term vs. Longer-term Forecasting Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes. Short-term forecasting usually employs different methodologies than longer-term forecasting Short-term forecasts tend to be more accurate than longer-term forecasts.

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Influence of Product Life Cycle Stages of introduction and growth require longer forecasts than maturity and decline Forecasts useful in projecting staffing levels, inventory levels, and factory capacity as product passes through life cycle stages

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Strategy and Issues During a Product’s Life IntroductionGrowth Maturity Decline Standardization Less rapid product changes - more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Over capacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focused Enhance distribution Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Best period to increase market share R&D product engineering critical Practical to change price or quality image Strengthen niche Cost control critical Poor time to change image, price, or quality Competitive costs become critical Defend market position OM Strategy/Issues Company Strategy/Issues HDTV CD-ROM Color copiers Drive-thru restaurants Fax machines Station wagons Sales 3 1/2” Floppy disks Internet

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Types of Forecasts Economic forecasts Address business cycle, e.g., inflation rate, money supply etc. Technological forecasts Predict technological change Predict new product sales Demand forecasts Predict existing product sales

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Seven Steps in Forecasting Determine the use of the forecast Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Product Demand Charted over 4 Years with Trend and Seasonality Year 1 Year 2 Year 3 Year 4 Seasonal peaksTrend component Actual demand line Average demand over four years Demand for product or service Random variation

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Involves small group of high-level managers Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’ disadvantage © 1995 Corel Corp. Jury of Executive Opinion

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Sales Force Composite Each salesperson projects their sales Combined at district & national levels Sales rep’s know customers’ wants Tends to be overly optimistic

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Delphi Method Iterative group process 3 types of people Decision makers Staff Respondents Reduces ‘group- think’ Respondents Staff Decision Makers (Sales?) ( What will sales be? survey) (Sales will be 45, 50, 55) (Sales will be 50!)

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Consumer Market Survey Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer How many hours will you use the Internet next week? © 1995 Corel Corp.

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Overview of Quantitative Approaches Naïve approach Moving averages Exponential smoothing Trend projection Linear regression Time-series Models Associative models

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Quantitative Forecasting Methods (Non-Naive) Quantitative Forecasting Linear Regression Associative Models Exponential Smoothing Moving Average Time Series Models Trend Projection

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values Assumes that factors influencing past and present will continue influence in future Example Year: Sales: What is a Time Series?

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Trend Seasonal Cyclical Random Time Series Components

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Persistent, overall upward or downward pattern Due to population, technology etc. Several years duration Mo., Qtr., Yr. Response © T/Maker Co. Trend Component

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Regular pattern of up & down fluctuations Due to weather, customs etc. Occurs within 1 year Mo., Qtr. Response Summer © T/Maker Co. Seasonal Component

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Repeating up & down movements Due to interactions of factors influencing economy Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle Cyclical Component

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen events Union strike Tornado Short duration & nonrepeating © T/Maker Co. Random Component

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Any observed value in a time series is the product (or sum) of time series components Multiplicative model Y i = T i · S i · C i · R i (if quarterly or mo. data) Additive model Y i = T i + S i + C i + R i (if quarterly or mo. data) General Time Series Models

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Naive Approach Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then June sales will be 48 Sometimes cost effective & efficient © 1995 Corel Corp.

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Equation MA n n Demand in Previous Periods Periods Moving Average Method

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J You’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 1998 using a 3-period moving average © 1995 Corel Corp. Moving Average Example

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Moving Average Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Moving Average Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Moving Average Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Year Sales Actual Forecast Moving Average Graph

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Used when trend is present Older data usually less important Weights based on intuition Often lay between 0 & 1, & sum to 1.0 Equation WMA = Σ(Weight for period n) (Demand in period n) ΣWeights Weighted Moving Average Method

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Actual Demand, Moving Average, Weighted Moving Average Actual sales Moving average Weighted moving average

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Increasing n makes forecast less sensitive to changes Do not forecast trend well Require much historical data © T/Maker Co. Disadvantages of Moving Average Methods

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant ( ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data Exponential Smoothing Method

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 ·A t (1- ) 3 A t (1- ) t- 1 ·A 0 F t = Forecast value A t = Actual value = Smoothing constant F t = F t -1 + ( A t -1 - F t -1 ) Use for computing forecast Exponential Smoothing Equations

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J You’re organizing a Kwanza meeting. You want to forecast attendance for 2000 using exponential smoothing ( =.10). The1995 forecast was © 1995 Corel Corp. Exponential Smoothing Example

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) Time Actual Forecast, F t ( α =.10) (Given) NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) Time Actual Forecast, F t ( α =.10) (Given) ( NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast,F t ( α =.10) (Given) ( NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast,F t ( α =.10) (Given) ( ) NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast,F t ( αααα =.10) (Given) ( ) = NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = NA ( )= Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ( ) = ( ) = NA Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = F t -1 + · ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ( ) = ( ) = NA ( ) = Exponential Smoothing Solution

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Year Sales Actual Forecast Exponential Smoothing Graph

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = %

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = % 9%

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = % 9% 8.1%

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = % 9% 8.1% 90%

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = % 9% 8.1% 90%9%

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F t = A t (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 == = 0.10 = % 9% 8.1% 90%9%0.9%

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PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J Choosing Seek to minimize the Mean Absolute Deviation (MAD) If:Forecast error = demand - forecast Then:

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