...slump, government excise taxes and other factors such as decreased numbers in both tourist arrivals to the Caribbean island and beer exports to the U.S. As purchasing manager, Benson’s prime responsibility was maintaining adequate inventory levels for all goods and materials used in the company’s production processes, including the purchase of new bottles and the scheduling of deliveries (Erskine, 2004). State the Assignment Question As purchasing manager, Benson was responsible for all goods and materials used in the company’s production process, inclusive of new bottles purchase. Benson had to be cognizant of the fact that orders for new bottles had to be ordered four months in advance to allow for supplier transportation and lead times....
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...Discussion Question #2 Week 5 Lynnlee Palmer RES/342 July 2012 Biman Ghosh Discussion Question #2 Week 5 Components of a Time Series: (1) Secular trend The term “trend” is commonly used in day-to-day conversation. We often complain about the rising trend of population, prices etc. “Trend”, also called “Secular” or “long-term” trend is the basic tendency of production, sales, income, employment etc. to grow or decline over a period. It includes steady movements over a long time and excludes short-range oscillations. Secular trend is attributable to factors such as population change, technological progress, or large-scale shifts in consumer tastes. More populations call for more food, more clothing, and more housing. Technological changes, discovery, or depletion of resources, improvements in business organization and Government intervention in the economy is other major causes for the growth or decline of many economic time series. Secular trends may be linear or nonlinear. (2) Seasonal variation Seasonal variations are those periodic movements in business activity, which occur regularly every year and have their origin in the nature of the year itself. Since they repeat over a period of 12 months, they can be predicted accurately. Almost any type of business activity is susceptible to a seasonal influence to a greater or lesser degree and as such, these variations are regarded as normal phenomena during every year. Although the word...
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...Forecasting helps decide a firm’s marketing strategy, production needs, sales forecast, and helps predict financial needs such as cash flow, profits, and outside financing. Furthermore, it helps make personal based decisions, as well as assist for the long-term future of the firm (Salvatore, 2012). b) Forcasting types range from expensive to inexpensive, as well as simple to complex. Forecasting techniques can be qualitative, and others can be quantitative. Salvatore focuses on qualitative forecasts. These forecasts include: time-series, smoothing techniques (moving averages), barometric forecasts with leading indicators, econometric forecasts, and input-output forecasts. c) A firm determines the most suitable forecasting method to use by using the following criterion: 1. the cost of preparing the forecast and the benefit that results from its use 2. the lead time in decision making 3. the time period of the forecast (short or long term) 4. the level of accuracy required 5. the quality and availability of the data...
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...BSOP330 QUIZ: QUIZ 1: 1. Question : (TCO 7 & 8) Gradual, long-term movement in time series data is called ____________. Student Answer: seasonal variation cycles trends exponential variation random variation Instructor Explanation: Trend is the gradual movement of data up or down over time. Reference Heizer 10th edition page 108 and 119-120. Points Received: 0 of 4 Comments: 2. Question : (TCO 7 & 8) Time series data may exhibit which of the following behaviors? Student Answer: Trend Random variations Seasonality Cycles They may exhibit all of the above. Instructor Explanation: Because time series data contains past data, predicting the future with 100% accuracy is impossible. Sales trends, random variations, seasonality and purchasing cycles will always be present. Therefore all four selections are correct. Reference Heizer 10th. edition page 108. Points Received: 4 of 4 Comments: 3. Question : (TCO 6 & 8) Given an actual demand of 61, a previous forecast of 58, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing? Student Answer: 45.5 57.1 58.9 61.0 65.5 Instructor Explanation: Exponential Smoothing = Old Forecast + (Actual Demand - Old Forecast) = 58.0 + .3(61 - 58) = 58.0 + .9 = 58.9 Points Received: 0 of 4 Comments: 4. Question :...
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...ISCOM 471 December 24, 2012 Planning and Controlling the Supply Chain Within the many different organizations in todays business world there are some key factors that will establish the profitability, growth, and longevity of a company. Some of the things we will discuss will be that of how my chosen corporation American Express applies some of the forecasting techniques to better develop the company. There will also be the analysis of production plans, master production schedules, and carrying inventory and how it relates to the overall American Express budget. Along with all the above we will also compare and contrast how planning usage differentiates between a service organization such as American Express and a manufacturing organization. Lastly we will also compare and contrast the use of material requirements planning system concepts. When it comes to forecasting it is first important to determine the different types of forecasting and how they are classified. In forecasting there are four basic types which are qualitative, time series analysis, casual relationship, and simulation. The first forecasting type qualitative is "subjective or judgmental and are based on estimates and opinions", (Chase, Jacobs, & Aquilano, 2006). Some of the main characteristics of qualitative forecasting are market research which is encompassed by collecting data by surveys and interviews which help determine market hypothesis. This research is most commonly used for long range and new...
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...For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to their heights. Although this correlation is fairly obvious your data may contain unsuspected correlations. You may also suspect there are correlations, but don't know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of your data. Techniques in Determining Correlation There are several different correlation techniques. The Survey System's optional Statistics Moduleincludes the most common type, called the Pearson or product-moment correlation. The module also includes a variation on this type called partial correlation. The latter is useful when you want to look at the relationship between two variables while removing the effect of one or two other variables. Like all statistical techniques, correlation is only appropriate for certain kinds of data. Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such...
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...CHAPTER 5. EMPIRICAL RESULTS, FINDINGS AND ANALYSIS 1. Over all graphical analysis For any index the best way to gauge its long term movement is to plot its movement over a period of time. So here to start with the analysis part , first the overall movement of the daily “close” data for S&P CNX NIFTY FIFTY is examined for the period starting from 2nd May 2002 till 3rd Feb 2012. There are in total 2347 observations and the econometric package EViews 7 has been used to track the movement. The plot is shown in Fig No 5.1. [pic] Fig No 5.1. Daily movement of Nifty Fifty “close” during 02/05/2002 – 03/02/2012 From the graph it is clear that Nifty has shown an upward trend over the period of time. While the upward trend is pretty evident from 2002 to 2007 however since 2007 Nifty movement has been somewhat unstable due to frequent market fluctuation and thus the market seems to be more volatile during this period. In terms of volatility another aspect is visible from the graph that is an upward trend is being followed by further upward trend while a downward trend is being followed by further downward trend and this feature is known as “volatility clustering” and this volatility clustering seems to be present in the index. More about the volatility and the movement of the index will be explored in the further subsections where the task of comparing Nifty movement at times is being taken. 2. Over all statistics The performance of Nifty over the years is tabulated...
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...Time series analysis We are pleased to submit the following report on the “Time Series Analysis”. By completing the report, we have got acquainted importance and relevance of time series on business application. We also perceived idea on the whole process of Time Series Analysis. We acquired knowledge about the method of measuring trend, growth rate, acceleration rate etc. In spite of limitation of time & opportunity we have tried our level best to complete the report. We are pleased to provide you with this report with necessary analysis, references and we shall be available for any clarification, if required. Thank you for assigning us in this study. On behalf of the group Md. Arif Hasan ID: 12-150 Table of Contents Serial No Topic Page No 1 Letter of Transmittal 1 2 Rationale of the study 2 3 Objectives of the report 3 4 Methodology of the report 3 5 What is Time Series 4 6 Uses of Time Series in Business 5 7 Components of a Time Series 5 8 Classical Time Series Model 9 9 Methods of trend measurement 9 10 Least squares method 10 11 The Growth Rate 14 12 The Acceleration Rate 15 13 Rule of 72 16 14 Bibliography 17 Rationale of the study Having been assigned to prepare a report on Time Series Analysis we are submitting the term paper based on our findings and understandings. Time series analysis has vast application and is of huge importance in the field of Business and Economics as well as in decision making thereof. Calculating secular trend we can...
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...Assignment 1 Production System Design Q1) Planners for a company that makes several models of skateboards are about to prepare the aggregate plan that will cover six periods. They have assembled the following information: They now want to evaluate a plan that calls for a steady rate of regular-time output, mainly using inventory to absorb the uneven demand but allowing some backlog. Overtime and subcontracting are not used because they want steady output. They intend to start with zero inventory on hand in the first period. Prepare an aggregate plan and determine its cost using the preceding information. Assume a level output rate of 300 units (skateboards) per period with regular time (i.e., 1,800÷6 = 300). Note that the planned ending inventory is zero. There are 15 workers, and each can produce 20 skateboards per period. Q2) Manager T. C. Downs of Plum Engines, a producer of lawn mowers and leaf blowers, must develop an aggregate plan given the forecast for engine demand shown in the table. The department has a normal capacity of 130 engines per month. Normal output has a cost of $60 per engine. The beginning inventory is zero engines. Overtime has a cost of $90 per engine. a. Develop a chase plan that matches the forecast and compute the total cost of your plan. b. Compare the costs to a level plan that uses inventory to absorb fluctuations. Inventory carrying cost is $2 per engine per month. Backlog cost is $90 per engine per month. Month | 1 | 2 | 3 | 4 |...
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...to (2) DEP receiving to (3) DEP manufacturing to (4) DEP warehouse to (5) DEP/commercial transportation to (6) GARD 1.b. The stages adding value are: the performance criteria of the suppliers (product quality and timeliness in delivery); DEP’s advanced manufacturing equipment and ability to quickly change the manufacturing process; product packaging and prep for shipment; DEP’s internal transportation for delivery. 1.c The stages not adding value are: all of the time the compounds sit in the warehouse; the seven day inventory; the antiquated way (manual) they match manufacturing ticket with the purchase order and prepare shipping documents; possibly the use of common carriers and 6+ days of shipment (max). 2.Minimum order cycle time: 2 days shortest lead time for suppliers+ 6 days orders produced+ 3 days from manufacturing to shipment + 1 day shipping = 8 days minimum. Maximum order cycle time: 9 days longest lead time for suppliers + 8 days orders produced+6 days from manufacturing to shipment + 6 days shipping = 25 days maximum 3. Yes, the performance cycle can be improved using the 25/15% suppliers. The trade-offs are: the compounds may cost more vs. more reliability in filling orders; more cost vs. better service levels; greater reliability in filling orders vs. requirement to maintain high inventory levels; lower inventory levels = lower total costs. 4.a. I’d make the following changes; (1) I’d change the way we...
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...programming problem, the constraint x1 = x2 is a conditional constraint. Answer Selected Answer: False Correct Answer: False Question 6 5 out of 5 points Correct Fractional relationships between variables are not permitted in the standard form of a linear program. Answer Selected Answer: True Correct Answer: True Question 7 5 out of 5 points Correct A business owner is trying to decide whether to buy, rent, or lease office space and has constructed the following payoff table based on whether business is brisk or slow. The conservative (maximin) strategy is: Answer Selected Answer: Lease Correct Answer: Lease Question 8 5 out of 5 points Correct Events that cannot occur at the same time in any trial of an experiment are: Answer Selected Answer: mutually exclusive Correct Answer: mutually exclusive Question 9 5 out of 5 points Correct The probability of observing x...
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...300,000) = $6,300,000 + 0.95(45,000) = $6,300,000 + (42,750) = $6,342,750 Exercise 9.3 Moving Averages 20X2-20X4 $41,750,000 / 3 = $13,916,667 Weighted Moving Averages 20X2 $14,250,000 1 $14,250,000 20X3 $14,000,000 2 $28,000,000 20X4 $13,500,000 3 $40,500,000 __ ___________ 6 $82,750,000 20X5 $82,750,000/6 = $13,791,667 Exponential Smoothing NF = $13,000,000 + 0.95($13,500,000 - $13,000,000) = $13,000,000 + 0.95(500,000) = $13,000,000 + (475,000) = $13,475,000 Moving averages are the easiest to do without a computer and is fast, but less accurate. Add the three most recent years, and divide by three. Weighted moving averages are simple, but require more time. The most recent year gets the highest weight. The oldest data is one and one multiplies the total. The next year is two, and two multiplies the total. The most current data weight is three, and multiplied by three. Finally, use the total of all years, and dived by the total weight for the weight moving average. The hardest to understand in exponential smoothing was the formulas and placement of the...
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... Repeat demand Question 3.3. (TCO 5) Which of the following forecasting methodologies is considered a causal forecasting technique? (Points : 3) Exponential smoothing Weighted moving average Linear regression Historical analogy Market research Question 4.4. (TCO 5) Which of the following forecasting methods uses executive judgment as its primary component for forecasting? (Points : 3) Historical analogy Time series analysis Panel consensus Market research Linear regression Question 5.5. (TCO 5) In business forecasting, what is usually considered a long-term time period? (Points : 3) Three months or longer Six months or longer One year or longer Two years or longer Ten years or longer Question 6.6. (TCO 5) In general, which forecasting time frame best identifies seasonal effects? (Points : 3) Short-term forecasts Quick-time forecasts...
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...Forecasting Project House Sales in USA Presented by: Michelle Deets Ravin Seju Ankan Sinha March 7, 2016 Professor Dawit Zerom ISDS 526 Executive Summary The data in the report “Monthly total houses sold in the United States for the period January 1978 through July 2007” is time series data representing sales in thousands of units. The data has not been seasonally adjusted. Our project was to produce forecasts of housing sales by creating a model using Forecast Pro’s Expert Selection Method. The model was generated by withholding 2 years of data and creating a forecast based upon the data from January 1978 to July 2005. We provided fit measures based upon MAPE and RMSE and evaluated the model’s accuracy MAPE, RMSE, and GMRAE from Forecast Pro’s out-of-sample statistic evaluation table. The MAPE numbers show that the forecast expands from a 7.95% error at the beginning of the holdout period and quickly grows to 32% error within 24 months. The acceptability of this error depends upon which managers are using it. The housing industry touches many fields, from moving to painting to construction to land purchases. This large of an error might could be unacceptable given the amount of risk and resources involved in construction of new single family homes; a manager might prefer to only use the first 6 months of forecasted data to stay within a 10% error range. Looking at the results of the forecast’s graph, the actual data (Exhibit A, represented by the...
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...Time Series Analysis Summary Tokelo Khalema 2008060978 BSc. Actuarial Science University of the Free State Bloemfontein November 1, 2012 Time Series Analysis A time-series is a stochastic process {Xt : t = 1, . . . , T } with a continous state space and discrete time domain. It arises naturally as an ordered series of values observed over time. Examples include daily closing prices of a stock index recorded over several years, say, the flow rate of the River Nile, road casualties in Great Britain over the years 1969-84, etc. Stationary time-series are particularly easy to analyse. A series is stationary if its mean and variance are constant over time. Special aids are available to help determine whether or not a series is stationary. Particularly notable in this regard are the autocorrelation function (ACF) and the partial autocorrelation function (PACF). These are plots of the sample autocorrelation and partial autocorrelation coefficients at various time lags, respectively. If the ACF decays gradually to zero, then the series is non-stationary. If on the other hand the ACF and PACF decay rapidly to zero, then the series is stationary. A series being non-stationary can be brought about by, among others, a trend, irregular fluctuations, or seasonal variation. Non-constant variance, or as commonly called, heteroscedasticity can be eliminated by using a variance-stabilising transformation. A number of ways exist that eliminate a trend. Two of which are, to subtract a regression line...
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