...Broadly speaking, there are two approaches to demand forecasting. Survey method and Statistical method are further sub-divided into various methods. The former obtains information about the consumers’ intentions by conducting consumers’ interviews, through collecting experts’ opinions. The later using past experience as a guide and by extrapolating past statistical- relationships suggests the level of future demand. Survey methods are found appropriate for short term forecasting or demand estimation, while statistical methods are more suitable for long term demand forecasting or business and economic forecasting. Either of the methods may be used for forecasting demand for existing products, but the demand for new products, in the absence of any historical data, must be forecast through the survey method only. Under survey methods surveys are conducted about the consumers’ intentions, opinions of experts, survey of managerial plans, or of markets. Data obtained through these methods are analyzed, and forecasts on demand are made. These methods are generally used to make short-run forecast of demand. Survey methods are further sub-divided in to: Consumers’ Survey and Experts’ Opinion and Survey of Managerial Plans. A. Consumers’ Survey: Consumers’ survey involves direct interview of the potential consumers who are contacted by the interviewer and asked how much they would be willing to buy a given product at different prices. Consumers’ survey may take any form...
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...Assignment on Demand Forecasting: Hamza Imam Ansari Erp#10040 Q#1) S t = 12.70 + 1.415t 2007 to 2012 Year= 6 Qtr= 24 2013 will start from 25 Qtr# 1) 12.70 + 1.415*(25) = 48.075 Qtr# 2) 12.70 + 1.415*(26) = 49.49 Qtr# 3) 12.70 + 1.415*(27) = 50.905 Qtr# 4) 12.70 + 1.415*(28) = 52.32 Q#2) Ln Sn = 3.51 + 0.037t Sn = e 3.51 + e 0.037 t Sn = 33.45 + 1.038t Qtr#1) 33.45 + 1.03825 = 35.991 Qtr#2) 33.45 + 1.03826 = 36.087 Qtr#3) 33.45 + 1.03827 = 36.187 Qtr#4) 33.45 + 1.03828 = 36.291 Q# 3) Y = 130.96 + 1.06 D2 – 1.57 D3 + 2.71 D4 + 43.88t Qtr# 1) Y = 130.96 +43.88 (25) = 1227.96 Qtr #2) Y = 130.96 + 1.06 + 43.88(26) = 1272.90 Qtr #3) Y = 130.96 – 1.57 + 43.88(27) = 1314.15 Qtr #4) Y = 130.96 + 2.71 + 43.88(28) = 1362.31 Q#4a) RMSE= SQRT of ( sum of (A-F)2 / n.o of years) 3 MOV AVG = SQRT(2797.667/9) = 17.63 5 MOV AVG = SQRT(3785.4/7) = 23.25 SO 3 Moving Average has given the better answer. b) 3 Moving Average has given the better answer. c) RMSE= SQRT of ( sum of (A-F)2 / n.o of years) 0.4 Weightage= SQRT(3102.247/12) = 16.078 0.5 Weightage= SQRT(2728.635/12) = 15.079 So we will choose 0.5 weightage result. Case Study # 2 Pg # 259 1) Model T was the first affordable car produced by the Henry Ford’s Ford Motor Company since its commencement. It was the first car launched by the Henry Ford to target middle class people and it was the first car which was produced in a large quantity...
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...An Initial Study on the Comparison of Forecast Model for Electricity Consumption in Malaysia. Abstract The purpose of this article is to compare and determine the most suitable technique for forecasting the Electricity Consumption Malaysia. The data was obtained from Statistical Department from January 2008 until December 2012. Five univariate modeling techniques were used include Naïve with Trend Model, Average Percent Change Model, Single Exponential Smoothing, Holt’s Method Model and Holt-winter’s. The data are divided into two parts which are model estimation (fitted) and model evaluation. The selection of the most suitable model was indicated by the smallest value of mean square error (MSE) and Mean Absolute Percentage Error (MAPE.) Based on the analysis, Holt’s Method Model is the most suitable model for forecasting electricity consumption since it has the smallest value of MSE and MAPE. Keywords: Univariate Modelling Techniques; Forecast Model; Mean Absolute Percentage Error; Mean Square Error. Introduction Electricity is one of the most important and used form of energy. Nowadays, electricity is essential for economic development especially for industrial sector. Malaysia, as a developing country, the important of electricity cannot be denied especially in industrial sector. Malaysia’s National electricity utility company (TNB) is the largest in the industry, serving over six million customers throughout the country. TNB is responsible for transmission...
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...Forecasting Assignment Name University of Phoenix Operations Management – MGT 554 Instructor Date Forecasting Assignment Forecasting assists managers (companies) to help predict future demand. Demand management is important because companies can increase value or productivity and reduce costs. Chase, Jacobs, & Aquilano (2005) state, “the purpose of demand management is to coordinate and control all sources of demand so the productive system can be used efficiently and the product delivered on time” (p. 512). When a manager is choosing a forecast method, the manager must analyze the cost of doing the forecast and the opportunity cost of using inaccurate data. In addition, Chase, et al. (2005) state “the manager must look at the following factors: (1) Time horizon to forecast, (2) Data availability, (3) Accuracy required, (4) Size of forecasting budget, and (5) Availability of qualified personnel ( p. 518). This paper will compare and contrast three forecasting methods (Delphi Method, Box Jenkins Technique, and Econometric Models) used by managers to help predict future demand as well as explain how the National Basketball Association (NBA) uses forecasting methods to forecast demand under conditions of uncertainty. The Delphi method is a qualitative technique. Chase, et al. (2005) defines qualitative techniques as “subjective or judgmental and are based on estimates and opinions (p. 513). The Delphi method according to Chase et al., is when a group of experts responds...
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...also on the country's economic development, the situation in the market. The Manpower forecasting model. The Estimated Manpower Forecast Model is designed to capture the future labour requirements of the engineering construction industry by key sectors. Manpower is undoubtedly a valuable asset upon which the construction industry depends. However, rapid changes of the economy, working arrangements, and technology in construction advocate reliable estimations of manpower demand to lessen future skills imbalance. The aim of this research is to develop advanced manpower demand forecasting models, at both project and industry levels, to facilitate manpower planning for the construction industry. At the project level, statistical models for forecasting the demand of labour demand for a construction project were developed using multiple regression analysis. Results reveal that project cost and project type play an important role in determining the project labour requirements. The forecasting models could serve as practical tools for contractors and government to predict the labour requirements and number of jobs created at an early outset, thus enabling proper human resources planning and budgeting. At the industry level, co-integration analysis was applied to develop a long-term relationship between aggregate construction manpower demand and relevant variables.It was proved that the demand and the associated economic factors including construction output, wage, material price...
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...Forecasting and Analyzing World Commodity Prices René Lalonde* Principal Researcher International Department Bank of Canada Zhenhua Zhu Economist Research Department Bank of Canada October 18, 2002 Frédérick Demers** Economist Research Department Bank of Canada Abstract This paper develops simple econometric models to analyze and forecast three components of the Bank of Canada commodity price index (BCPI), namely non-energy commodity prices (BCNE), the West Texas Intermediate crude oil price (WTI), and other energy prices. In the paper, we present different methodologies to identify transitory and permanent components of movements in these prices. A structural vector autoregressive (SVAR) model is used for real BCNE prices, a multiple structural-break technique is employed for real crude oil prices, and an errorcorrection model is constructed for real prices of other energy components. Then we use these transitory and permanent components to develop forecasting models. We assess our models’ performance in various aspects, and our main results indicate: (a) for real BCNE prices, most of the short-run variation is attributed to demand shocks, (b) the world economic activity and real U.S. dollar effective exchange rate explain much of the cyclical variation of real BCNE prices, (c) real crude oil prices have two structural breaks over the sample period, and their link with the world economic activity is strongest in the most recent regime, (d) real prices of other energy components...
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...Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes. To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available. In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include: Trends – long term movements in either direction Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions...
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...uncertain. To cope with future risk and uncertainty, the manager needs to predict the future event. The likely future event has to be given form and content in terms of projected course of variables, i.e. forecasting. Thus, business forecasting is an essential ingredient of corporate planning. Such forecasting enables the manager to minimize the element of risk and uncertainty. Demand forecasting is a specific type of business forecasting. Concepts of Forecasting: The manager can conceptualize the future in definite terms. If he is concerned with future event- its order, intensity and duration, he can predict the future. If he is concerned with the course of future variables- like demand, price or profit, he can project the future. Thus prediction and projection-both have reference to future; in fact, one supplements the other. Suppose, it is predicted that there will be inflation (event). To establish the nature of this event, one needs to consider the projected course of general price index (variable). Exactly in the same way, the predicted event of business recession has to be established with reference to the projected course of variables like sales, inventory etc. Projection is of two types – forward and backward. It is a forward projection of data variables, which is named forecasting. By contrast, the backward projection of data may be named ‘back casting’, a tool used by the new economic historians. For practical managers concerned with futurology,...
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...FORECASTING - a method for translating past experience into estimates of the future. Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of the expected value for some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. The process of climate change and increasing energy prices has led to the usage of Egain Forecasting of buildings. The method uses Forecasting to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces...
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...participants with a much clearer view of the applicability and relevance of economics to decision making within business firms. 2.2 To develop students’ knowledge of applied economics 2.3 To develop students’ analytical skills to a higher level. 2.4 To enhance students’ insight into the operation of business and the nature of problems managers face. 3. Course coverage * Introduction of students to Managerial Economics and the use of models and other analytical concepts in decision making process. * Introduction to the concept of risk and uncertainty and adjustment of decisions to reflect decision maker’s attitude towards risk. * Behaviour of consumers and demand side of the firm and demand estimation and forecasting. * Strategic decisions that managers have to make – eg pricing strategies, production decisions, product quality and designs, advertising and promotion...
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...|VI Learning activity questions: Scope of Managerial Economics | | | | | |1. What is managerial economics all about? | | | |Managerial economics applies economic theory and methods to business and administrative decision making. Managerial economics | |prescribes rules for improving managerial decisions also helps managers recognize how economic forces affect organizations and | |describes the economic consequences of managerial behavior. It links traditional economics with the decision sciences to develop | |vital tools for managerial decision making. Managerial economics identifies ways to efficiently achieve goals. For example, suppose| |a small business seeks rapid growth to reach a size that permits efficient use of national media advertising. Managerial economics | |can be used to identify pricing and production strategies to help meet this short-run objective quickly and effectively. | | ...
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...They were relatively new to the market. Their issue was based on failing to fulfill the great demand for the new product, the issue generated another obstacle which was not being able to deliver on time. Customers were expecting to have their product delivered when needed, but it took almost 6 weeks to receive the product. The genesis of this issue was based on the inability to create an effective forecast. There is not a perfect forecast, but it aids to visualize future demand. 2. What is driving these problems, both systemically and organizationally? The issue is generated due to the inconsistency lead time for the component part. It is taking too long (2-16 weeks). Once again, the lack of forecasting is generating a false number which leads to inconsistency in the production. The estimation of the transfer requirement lose the actuality, leading to affect delivery time. The production inconsistency is higher than the demand difference. On the systematically side, the firm is going through a “panic” ordering period, with the lack of forecasting it is providing poor document support creating a false demand number. The decentralization is creating a bump on their production, in addition, there is not a standardized procedure. On the Organization side, there is a fear to reduce the inventory. The key issue is the lack of forecasting, there is none assigned to work on the forecasting procedure and they are drowning in their own chaos. The appropriate approach is the JIT, to decrease...
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...headquarters in Freeport, Maine in the United States. It was founded in 1912 by Leon Leonwood Bean, and is currently worth US$1.5 billion. The major problem faced by the supply chain of the firm is the over reliance of initial forecasting on estimates and speculations. 1. How does L.L. Bean use past demand data and a specific item forecast to decide how many units of that item to stock? The forecasting team at L.L. Bean decides how many units of an item to stock using the following methodology: i. Divide the past demand data by the forecast demand to compute historical forecast errors, expressed in the form of A/F ratios, for each individual item. ii. The frequency distribution of these errors is compiled across items. iii. The frequency distribution of past forecast errors is used as a probability distribution for future forecast errors. iv. The cost of understocking an item is calculated by subtracting the cost price from the selling price. v. The cost of overstocking an item is computed by subtracting the liquidation price from the cost price of the item. vi. The critical ratio CU / (CU + CO) is calculated, which gives the optimal order size as the quantile of the item’s probability distribution of demand. vii. The commitment quantity is calculated as the product of the critical ratio quantile of the distribution of forecast errors and the frozen forecast data. 2. What item costs and revenues are relevant to the decision...
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...Demand Estimation Stacy D. Lucero Strayer University April 28, 2015 Abstract By using data from the month of April from 26 supermarkets around the country that sells our low-calorie, frozen microwavable dinners we are going to discover which independent variables have the greatest effect on the demand of our products. We are also going to graph a demand and supply curve in order to more accurately forecast our demand. Demand Estimation As an analyst for the leading brand of low-calorie, frozen microwavable dinners my boss has asked me to produce a demand estimation using existing data. We are trying to forecast how to price our products to increase our sales by taking independent variables and estimating how elastic they are in relation to our products. We are also going to determine our demand and supply curve to further our knowledge for more accurate forecasting. Elasticity for Independent Variables We wanted to see how elastic the demand for our product is by using data from 26 supermarkets around the country for the month of April. When referring to the elasticity of demand we are trying to determine if prices changes to our product will help us to sell more of our dinners. To do this we used a regression equation. We then computed the elasticity’s of several independent variables that would have an impact on our sales. The independent variables that we used were the price of the competitor’s product, the per capita income of the area nearest the supermarkets...
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...PPT * What is forecasting? Eg * Need for HRf? * Type of forecasting techniques * RTM, Eg * Relation with methods * Popularity * Usage * Steps involved for calculating (Theory) * Eg (practical) What is Forecasting? * Process of estimating the future requirement of human resources of right quality and right quantity on the basis of past information. * The estimation is subject to error. * HR forecasting done over three planning periods: short range, intermediate range and long range. * Main emphasis of HR forecasting: HR demand & HR supply * Example Year | Sales (In Millions) | FY 93 | 0.75 | FY 94 | 1.55 | FY 95 | 2.35 | FY 96 | 2.22 | FY 97 | 2.38 | FY 98 | 2.54 | FY 99 | 2.37 | FY 00 | 2.55 | FY 01 | 2.67 | FY 02 | 2.89 | FY 03 | 3.11 | FY 04 | 2.94 | FY 05 | 2.81 | FY 06 | 2.72 | FY 07 | 3.01 | FY 08 | 3.35 | FY 09 | 3.42 | FY 10 | 3.51 | FY 11 | 3.62 | Types of Forecasting Techniques: Qualitative: Delphi Method, Market Research, Product Life Cycle Analogy, and Expert Judgment Quantitative: * Time Series: Average, Trend, Seasonal Influence, Cyclical Movement, Random Error, Exponential Smoothing Method, Multiplicative Seasonal Method, * Casual Forecasting: Regression Analysis, Econometric Models, Input-Output Models, Simulation Modelling What is ratio Trend Analysis? Quickest forecasting technique Involves studying past ratios and forecasting future ratio Example:...
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