Operations Management

QuickStudy Laminated Reference Guide

Ravi Behara

• Publisher: QuickStudy Reference Guides
• Year: 2009
• Print ISBN: 9781423220183
• eBook ISBN: 9781423237884
• Topic: Business
• Subtopic: Operations
• Index: Business

Quintessential reference to business operations filled with key terms, equations, graphs, processes, models, and more. A perfect tool for any student of business, working professional, or business owner. The business knowledge compressed into six pages can be found nowhere else for this price.

Forecasting

Forecasting involves predicting future events that are then used for planning purposes in business. They typically involve forecasting customer demand for a company’s products. Forecasting methods can be both quantitative and qualitative.

Qualitative Forecasting Methods

• Market research: Systematically determining customer interest in the company’s products and services through surveys
• Sales force estimates: Estimates of future demand made periodically by the company’s sales force
• Executive opinion: Summary forecast based on the opinion, experience, and knowledge of one or more executives or managers
• Expert judgment: Forecast based on judgment of one or more subject matter experts
• Delphi method: Forecasts developed through consensus of a panel of experts who usually do not know each other

Quantitative Forecasting Methods

• Two main models include causal models (linear regression) and time series models, which include the following methods:
• Smoothing methods (simple moving average, weighted moving average, and exponential smoothing)
• Trend projection method

Components of a Time Series

• A pattern formed by repeated observations of demand for a product or service in the order in which they occurred is a time series. Basic patterns of a time series are:
  • Horizontal: Fluctuation of demand around a constant mean
  • Trend: Gradual increase or decrease in the mean of the time series over a longer period of time
  • Seasonal: A repeated pattern of demand increases and decreases that depend on time of day, week, month, or season
  • Cyclical: Periodic increases and decreases (around trend) of the time series over longer periods of time (years or decades)
  • Random: Irregular and unforecastable variations in demand

Measures of Forecast Error

• Accuracy is an important part of any forecast. Forecast error E_t = A_t – F_t, where A_t is actual demand or value and F_t is the forecasted demand or value in time period t.
• Methods to measure forecast error are:
  • Mean squared error (MSE): The average of the squared forecast errors for the historical data is calculated, and the forecasting model that minimizes this mean squared error is then selected for forecasting demand. MSE = [Σ(A_t – F_t)2]/n
  • Mean absolute deviation (MAD): The mean of the absolute values of all forecast errors is calculated, and the forecasting model that minimizes this measure is then selected for forecasting demand. MAD = [Σ|(A_t – F_t)|]/n
  • Mean absolute percentage error (MAPE): The mean of the absolute percentage values of all forecast errors is calculated as a percentage, and the forecasting model that minimizes this measure is then selected for forecasting demand....

Table of contents

• Introduction
• Operations Strategy
• New Product Development
• Project Management
• Strategic Capacity Planning
• Process Management
• Quality Management
• Lean Operations
• Supply Chain Management
• Forecasting
• Inventory Models