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Midterm

Final Ops Cheat Sheet Midterm 1.pdf

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Department
Business
Course
BU385
Professor
Don'tknowhisname
Semester
Fall

Description
4 Techniques for Seasonality Time Series Models Other Measure of Productivity CHAPTER 2 Comp Strategic Planning Prod Additive Model DemandTrend1Nave Methods Partial Measures Seasonality 2Averaging Methods CompetitivenessAbility and performance of ie OutputLabour OutputCapital Multiplicative Model DemandTrend x2A Moving Average an organization in the marketplace compared MultiFactor Measure Seasonality2BWeighted Moving Averageto other organizations that offer similar goods Seasonal Relative see below2CExponential Smoothing ie OutputLabourCapitalEnergy 3Trend Models Key Purchasing Criteria Total Measure 3ANonLinear Trend Seasonal Relative SR Any customer will consider 4 criteria ie Goods ProducedAll Required Inputs3BLinear Trend Proportion of average or trend for a when purchasing something Ensure all denominators are in same unit3CTrendAdjusted Exp Smoothing season in the multiplicative model Price Quality Variety Timeliness 4Techniques for Seasonality ie SR of 12 20 above average Keep in Mind4ATechniques for Cycles Deseasonalize DDRemove SR to Complex Purchases Productivity use of overall resources Cannot use any Time Series Model to more clearly see other components Order QualifiersPurchasing criteriaEfficiency use of fixed set of resources forecast trends because the forecasts lag DD that customers perceive as minimumIt is difficult to measure productivity of behind actual salesReseasonalize RDAdjust the forecast standards of acceptability for purchase services intangible intellectual act etc for seasonal component allow product to be considered RD1Nave Methods Order WinnersPurchasing criteria that Factors That Affect Productivity The forecast for the next period should cause the organization to be perceived as Methods Management Tech Labour equal the results of the last period better than the competition allow Steps for Time Series Decomposition Simple low cost Low accuracy product to be purchased 1Compute the seasonal relatives Three key formulas 2Deseasonalize the demand data CHAPTER 3 Demand Forecasting Stable Time Series DataFA 3Fit a model to the DD demand data Businesses Compete on 4 Criteria tt1Seasonal VariationsFA 4Forecast using this model Cost Quality Flexibility Timeliness ttnDemand ForecastingAn estimate of demand Data w TrendFAAA 5Reaseasonalize the DD forecasts tt1t1t2expected over a future period of time Strategic PlanningProcess of determining longterm plans that will set a direction for the 2AMoving Average 4A Techniques for Trends Three Uses of Forecastsorganization and implementing it through Best method is using associative models F 1Design the system annual plans about tallocation of resources product lines capacity equipment Average of last few actual data values 2 Use the system monthly plans about updated each period Strategic Planning hierarchical Associative Models inventory workforce levels prod Smooths bumps but lags behind changes MissionWhere the org is going now 3Schedule the system dailywk plans VisionWhere the organization desires about purchasing mater staff scheduling Associative Models 2BWeighted Moving Average WMA to be in the future Predictor VariablesVariables that can F ValuesShared beliefs of the tbe used to predict values of the variable Elements of a Good Forecast organizations stakeholders of interest essence of associative mod Assigns different weights to different Accurate Reliable Meaningful Easy Use Goals ObjProvide detail and scope periods must sum to 100 of the companys mission 1Simple Linear Regression Steps in the Forecasting Process StrategiesPlans that determine Process of finding a straight line that 2CExponential Smoothing 1Determine purpose of forecast direction for achieving org goals best fits a set of points on a graph Sophisticated WMA 2Establish a time horizon TacticsAction PlansMethods and AKALeast Squares Line FFAF 3Select a forecasting technique tt1t1t1actions taken to accomplish strategies Same equation as 3B Linear Trend FPrevious Forecast Forecast Error 4Obtain clean and analyze data tequationabx F1FA 5Make the forecast tt1t1Operations StrategyThe approach that is If 1 then FA f reflects recent data 6Monitor the forecast ttused to guide the operations function 2Multiple Regression If 0 then FF
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