Operations Management MGNT 3430
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This 7 page Class Notes was uploaded by Dr. Kevin Veum on Monday October 12, 2015. The Class Notes belongs to MGNT 3430 at Georgia Southern University taught by Gerard Burke in Fall. Since its upload, it has received 16 views. For similar materials see /class/222037/mgnt-3430-georgia-southern-university in Business, management at Georgia Southern University.
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Date Created: 10/12/15
Operations and Productivity Overview 0 Course questions 0 The O and the M in OM Bads and Services 0 Productivity MGNT 3430 Eff words 5172006 Overview Where is OM in the rm Course queStionS Finance amp Accounting 0 The O and the M in OM Bads and Services Operations Demand Product1v1ty Management Eff words What is Operations Management Involves production 7 Creation of goods and services Directs activities 7 that create value by transforming inputs into outputs The planning organizing and control of the activities that transform an organization s resources people material and equipment into the goods and services needed by its customers Why study OM OM is one of three major functions marketing nance and operations of any organization We want and need to know how goods and services are produced We want to understand What operations managers do OM is typically a costly part of an organization COGS Overview Course questions The O and the M in OM Bads and Services Productivity Eff words The O in OM OM aka operations Operations are valuecreating processes SOP Processes employed in manufacturing Television or Laptop 0 part procurement assembly testing shipping Processes employed in services Banking or Travel Booking 0 Service point data entry service rendered The M in OM Careers in OM Plant manager process improvement consultant purchasing director quality manager Operations Management involves Overview 0 Course questions 0 The O and the M in OM Bads and Services Planning Productivity Organizing Eff words Leading Controlling Many DECISIONS to MAKE CC 77 More gOOder Good and Bad Goods Services 0 Can be resold Reselling unusual Percent of Product that is a Good Percent of Product that is a Senice Can be inventoried 0 Some aspects of 0 quality measurable Selling is distinct 0 from production Difficult to inventory Quality dif cult to measure Selling is part of serv1ce US Service Economy Sector of all Jobs Service 754 Manufacturing 246 Average service pay 96 of all private industry pay Overview 0 Course questions 0 The O and the M in OM Bads and Services 0 Productivity Eff words Productivity 0 How many units of output do we create for each unit we input 0 Output Good or service 0 Input CApital MAnagement Labor Productivity Output Input Units produced Input used SingleFactor amp Multifactor Singlefactor productivity Units output units of a single input 0 Roofer amp Hammer gtgt Output is sq ft of roof covering Input is labor hours Multifactor total factor Units output units of multiple all inputs 0 Roofer amp New Nail Gun gtgt Output is sq ft of roof covering gtgt Inpum labor hours amp spent on new nail gun Basic Forecasting MGNT 3430 Chapter 4 Outline Role of forecasts Types of forecasts History Lesson Basic time series approaches Nai39ve Moving Average Exponential smoothing Qualitative Approaches What is Forecasting Process of predicting a future event Sales will Seldom perfect Best educated guess Typical Forecasts in OPs Supply availability and pricing Should we buy now or later Where should we locate Demand expected How busy are we going to be Inventory decisions Production decisions Staffing decisions Forecasting Approaches Quantitative Methods Qualitative Methods Used when situation Used when situation is stable amp historical is vague amp little data data exist exist Existing products New products Traditional keyboards nfrared keyboards Involves Involves intuition mathematical experience techniques What is a Time Series Set of evenly spaced numerical data Observed at regulartime periods Forecast based only on past values Assumes that factors influencing past and present will continue influence in future Example Year 1998 1999 2000 2001 2002 Sales 787 635 897 932 921 Naive Approach Assumes demand in next period is the same as demand in most recent period If May sales were 48 then June sales will be 48 If GSU football won 9 games last season then they will win 9 games this season Sometimes cost effective amp efficient Moving Average Method MA is a series of arithmetic means Used if little or no trend Equation MA 2 Demand in Previous n Periods n GSU Football Data Year Wins Losses 2005 8 4 2004 9 3 2003 7 4 2002 11 3 2001 12 2 2000 13 2 1999 13 2 1998 14 1 1997 10 3 1996 4 7 Predict this year s of wins Moving average see excel for calculations n1 Weighted Moving Average Used when trend is present Older data usually less important Weights based on intuition Often valued between 0 amp 1 amp sum to 10 Equation WMA ZWeightfor period n X Demand in period n Weighted Wins More weight to most recent season 2005 6 2004 3 2003 1 WMA More weight to 2003 season 2005 1 2004 3 2003 6 WMA Equal weights 2005 333 2004 333 2003 333 WMA