Study Guide for the Second Exam Aggregate Production Planning (APP) 1. What are the major inputs‚ constraints‚ and outputs of the aggregate production plan (APP)? Inputs - Strategic objectives of the corporation‚ policies‚ demand. Constraints - financial constraints (cash) and capacity constraints (machining capacity‚ limited labor in certain skill category‚ a critical component and/or raw material.) Outputs - is to determine the gross levels of inventory‚ overtime‚ subcontracting‚ backordering
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TIME SERIES ANALYSIS Introduction Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting‚ sales forecasting‚ stock market analysis and company internal control. In this paper‚ we will talk about time series and review techniques that are useful for analyzing time series data. Definition of Time Series and Time Series Analysis Time series is an ordered sequence of values of a variable at equally spaced
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000 20X3 $6‚000‚000 20X4 $6‚750‚000 For moving averages and weighted moving averages‚ use only the data for the past three fiscal years. For weighted moving averages‚ assign a value of 1 to the data for 20X2‚ a value of 2 to the data for 20X3‚ and a value of 3 to the data for 20X4. Forecast personnel expenses for fiscal year 20X5 using moving averages‚ weighted moving averages‚ exponential smoothing‚ and time series regression. Moving Averages Fiscal Year Expenses 20X2 $5
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process of find 20X5 will include the use of moving averages‚ weighted moving averages‚ and exponential smoothing. The Palmdale Human Services personal expenses for the past four years are represented in the following data: Fiscal Year | Expense | 20X1 | $5‚250‚000 | 20X2 | $5‚500‚000 | 20X3 | $6‚000‚000 | 20X4 | $6‚750‚000 | For moving averages and weighted moving averages‚ use only the data for the past three fiscal years: Moving Averages Fiscal Year | Expenses | 20X2 | $5‚500‚000
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years: 20 X 1 = $5‚250‚000 20 X 2 = $5‚500‚000 20 X 3 = $6‚000‚000 20 X 4 = $6‚750‚000 Forecast personnel expenses for fiscal year 20X5 using moving averages weighted moving averages‚ exponential smoothing‚ and time series regression. For moving averages and weighted moving averages‚ use only the data for the past three fiscal years. For weighted moving averages‚ assign a value of 1 to the data for 20 X 2‚ a value of 2 to the data for 20 X 3‚ and a value of 3 to the data for 20 X 4. For exponential
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data represent total personnel expenses for the Palmdale Human Service Agency for past four fiscal years: 20X1 $5‚250‚000 20X2 $5‚500‚000 20X3 $6‚000‚000 20X4 $6‚750‚000 Moving Averages 20X2-X4 $18‚250‚000 / 3 = $6‚083‚333 Weighted Moving Averages Fiscal Year Expenses Weight Weighted Score 20X2 $5‚500‚000 1 $5‚500‚000 20X3 $6‚000‚000 2 $12‚000‚000 20X4 $6‚750‚000 3 $20‚250‚000 __ ___________ 6 $37‚750‚000 20X5 $37‚750‚000 /6 =
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Introduction to O&SCM -- Chapter 1 • Definitions • Operations and supply chain management (OSCM) is defined as the design‚ operation‚ and improvement of the systems that create and deliver the firm’s primary products and services • Concerned with the management of the entire system that produces a product or delivers a service • Operations refers to manufacturing and service processes that are used to transform the resources employed by a firm into products desired by customers •
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Lisa Brown Hsm/ 260 Week 5 – Forecasting Checkpoint 3/8/13 Exercise 9.1 20X1 $5‚250‚000 20X2 $5‚500‚000 20X3 $6‚000‚000 20X4 $6‚750‚000 Moving Averages 20X2-X4 $18‚250‚000 / 3 = $6‚083‚333 Weighted Moving Averages Fiscal Year Expenses Weight Weighted Score 20X2 $5‚500‚000 1 $5‚500‚000 20X3 $6‚000‚000 2 $12‚000‚000 20X4 $6‚750‚000 3 $20‚250‚000 __ ___________ 6 $37‚750‚000 20X5 $37‚750‚000 /6 = $6‚291‚667 Exponential Smoothing NF = $6‚300‚000
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best alpha and beta weights in exponential smoothing. Even if a firm has 10‚000 products‚ the constants can be selected very quickly and easily without human intervention. Week 1 2 3 4 5 6 7 Actual Bicycle Sales 8 10 9 11 10 13 — Three-Week Moving Average (8 10
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periods into the forecast and “smoothes” the data. Averaging models are computed by averaging data from several time periods and using the average as the forecast for the next time period. A moving average is an average that is updated or recomputed for every new time period being considered. The most recent information is utilized in each new moving average. This advantage is offset by the disadvantages that (1) it is difficult to choose
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