...Accuracy of Demand Forecasting Between Point of Sale and Order History Supply Chain Management TBS908 Table of Contents 1. Executive Summary 4 2. Company Profile 4 3. Demand 5 3.1 Demand Forecasting 6 3.2 Demand Forecasting Methods 6 3.2.1 Opinion Polling / Qualitative Method (subjective): 6 3.2.2 Statistical Methods/Quantitative Approach (objective): 6 4. Order History Vs. Point-of-sale 8 5. Planning Promotions 8 5.1 Promotion Planning and Supply Chain Contracting in a High-Low Pricing Environment 9 5.1.1 Basic Household Inventory Model: 9 6. Types of demand forecast in GCC and UAE 10 7. Objective 10 8. Methodology 11 Table 3 13 Figure 1 13 9. Result 14 10. Recommendations 14 11. Conclusion: 15 11. References 16 12. Appendixes 17 Appendix I 17 Appendix II 19 1. Executive Summary Demand forecasting is essentially anticipating future prospects by reviewing historical data in the most calculated way in an uncontrollable environment. Foreseeing what and when buyers will purchase has never been a simple procedure for producers or retailers. Troubled by the overwhelming undertaking of correctly coordinating supply with interest, makers are always enhancing procedures to accomplish the most noteworthy estimate exactness that will guarantee when the customer enters a store, the item they are searching for is on the rack. This is getting significantly tricky as the uncertainty level increase. In the below report the demand forecast...
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...DEMAND FORECASTING: EVIDENCE-BASED METHODS Forthcoming in the Oxford Handbook in Managerial Economics Christopher R. Thomas and William F. Shughart II (Eds.) Subject to further revisions File: Demandforecasting-17-August-2011-clean.docx 17 August 2011 J. Scott Armstrong The Wharton School, University of Pennsylvania 747 Huntsman, Philadelphia, PA 19104, U.S.A. T: +1 610 622 6480 F: +1 215 898 2534 armstrong@wharton.upenn.edu Kesten C. Green International Graduate School of Business, University of South Australia City West Campus, North Terrace, Adelaide, SA 5000, Australia T: +61 8 8302 9097 F: +61 8 8302 0709 kesten.green@unisa.edu.au # words in body 10,053 (requested range was 6,000 to 9,000) ABSTRACT We reviewed the evidence-based literature related to the relative accuracy of alternative methods for forecasting demand. The findings yield conclusions that differ substantially from current practice. For problems where there are insufficient data, where one must rely on judgment. The key with judgment is to impose structure with methods such as surveys of intentions or expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Avoid methods that lack evidence on efficacy such as intuition, unstructured meetings, and focus groups. Given ample data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, econometric methods are useful given good theory, and few key...
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...The Importance of Accurate Demand Forecasting An Analysis of VMU America Inc. Christopher Chun & William McMahan Oakland University Abstract Most strategic business decisions, such as financial planning and production management, rely heavily on forecasts that attempt to paint an accurate picture of the future periods. Ineffective forecasting can lead businesses to improper conclusions about the environment in which they operate. The purpose of this research is to analyze the effectiveness of VMU Power Systems’ forecasting methods on production demand. Once the type/s of forecasting used is identified, a closer look highlights the fact that improvements to their methods could put the company in a better position to handle their current market demand. The underlying issue is that they are not including all of their products & services in their data set, or more simply put, they are basing their decisions off of incomplete data. This is causing their forecasted results to be far below the actual demand resulting in a lack of resources to produce at the levels required. Other research has been cited throughout this paper to further support the claims that it is possible for VMU to forecast their type of intermittent demand. There are two main recommendations made at the end of this analysis that, if implemented, could improve the accuracy of their models. The first suggestion is to use the SBA forecasting method on their after-sales demand. The second recommendation...
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...HR Plan-09s HR Demand: Demand Forecasting Techniques I. Index/Trend Analysis II. The Delphi Technique III. Moving Average Method IV. Regression Analysis Method I. Index/Trend Analysis Examining the relationship over time between an operational business index, such as level of sales, and the demand for labour (as reflected by the number of employees in the workforce) is a relatively straightforward quantitative demand forecasting technique commonly employed by many organizations (see the following Table) Table: Index/Trend Analysis |Year |Sales |Number of Employees |Index | | |(Tk thousands) | |(Sales [Tk thousands] per Employee) | |2003 |2800 |155 |18.06 | |2004 |3050 |171 |17.83 | |2005 |3195 |166 |19.25 | |2006 |3300 |177 |18.64 | |2007 |3500 |188 |18.64 ...
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...DEMAND FORECASTING - REPORT Forecasted Method Used: In present forecasting method, the “Executive opinion” is used to determine the forecast of sales and results are not validated by any mathematical/statistical method. * Pressure Valves: 1. The total sales forecasted were optimistic at 53560 against the actual sales of 48159. 2. The Mean Absolute Percentage Error(MAPE) value of total sales of the entire family of pressure valves is around 11.2%, which is reasonably acceptable but the MAPE valves for individual members are very high (reaching up to 1500% for PVB34-420). * Fire Valves: 1. The total sales forecasted were pessimistic at 559 against the actual sales of 580. 2. The Mean Absolute Percentage Error (MAPE) value of total sales of the entire family of fire valves is around -3.5% (negative sign represents pessimistic forecast), which is reasonably acceptable but, MAPE for the individual family member Z3000il is -52.88, which is very high. * Effects: It may be severely affect the company’s profit :- 1. Over forecasting (optimistic forecasting) may result in more units of product lying in warehouse which blocks the capital as well as there will be space constraints to keep the stocks. 2. Under forecasting (pessimistic forecasting) may result in loss of market share and the customers and thus, leading the bad goodwill to the company. Our Forecast Method: We have used Regression analysis to forecast the sales. We analysed...
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...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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...3.5 Inferential Statistics: The Plot Thickens We have been talking about ways to calculate and describe characteristics about data. Descriptive statistics tell us information about the distribution of our data, how varied the data are, and the shape of the data. Now we are also interested in information related to our data parameters. In other words, we want to know if we have relationships, associations, or differences within our data and whether statistical significance exists. Inferential statistics help us make these determinations and allow us to generalize the results to a larger population. We provide background about parametric and nonparametric statistics and then show basic inferential statistics that examine associations among variables and tests of differences between groups. Parametric and Nonparametric Statistics In the world of statistics, distinctions are made in the types of analyses that can be used by the evaluator based on distribution assumptions and the levels of measurement data. For example, parametric statistics are based on the assumption of normal distribution and randomized sampling that results in interval or ratio data. The statistical tests usually determine significance of difference or relationships. These parametric statistical tests commonly include t-tests, Pearson product-moment correlations, and analyses of variance. Nonparametric statistics are known as distribution-free tests because they are not based on the assumptions...
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...Benny Breweries: Bottle Replenishment THE BOTTLE REPLENISHMENT DECISION Early in 2014, Manish Krishnan, purchasing manager for Benny Breweries, Mangalore, was trying to determine how many bottles to purchase in the coming year. During 2013, the market had leveled off, and 2014 sales predictions were difficult. On the one hand, Krishnan wanted to be sure that sufficient bottles were available to supply 2014 sales levels, yet also wanted to minimize year-end inventories. Covered storage space for empty bottles was tight, and a bottle design change seemed possible in 2015 or 2016. COMPANY BACKGROUND Benny Breweries was located in Mangalore. Over the years, the company had established an excellent reputation. Benny Beer had begun to gain popularity of late, and as a result, a modest market expansion started in 2010. In February 2014, sales reached the highest level in the company’s history. However, in 2013, the sales increase had been well below the trend average (see Exhibits 1 and 2). Four sales peaks occurred during the year: Holi, Christmas, Easter and Onam (refer appended note). Holi was the highest sales period but each peak caused the company to operate on tight schedules and Benny hired more labor and scheduled extra shifts. BREWING PROCESS Beer brewing started with extraction of sugar from malt by an enzymic process. This sugar was then boiled with hops, producing a sterilized and concentrated solution. The resins extracted from the hops during boiling acted as a preservative...
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...MGT314.8 DEMAND FORECASTING, PRODUCTION OPTIMIZATION AND CAPACITY MANAGEMENT FOR PRAN FOOD PRODUCTS BANGLADESH LIMITED Prepared By: Abdullah Al Rafi 111 0129 530 SM Nabil Afroj 113 0818 030 Faculty: Dr. Kais Zaman North South University Table of Contents Objectives 3 Brief Company Profile 4 Methodologies 5 Need for the forecasting 5 Forecasting Methods 8 Qualitative Analysis 10 Linear Programming 13 Conclusion 17 Objectives The primary objective is to find the demand and supply management techniques and the tools that the company uses to predict the demand for their products. The project will also determine the appropriate changes the company makes in their production process and their capacity management with regard to their forecasted demands. Many companies were and are still established to derive financial profit. In this regard the main aim of PRAN RFL Group is to maximize (optimize) profit. This report is on using Linear programming technique along with forecasting methods to derive the maximum profit from production of soft drink for PRAN RFL, Bangladesh Ltd. Linear Programming of the operations of the company was formulated and optimum results derived using Microsoft Excel. The result shows that two particular items should be produced even when the company should satisfy demands of the other - not - so profitable items in the surrounding of the plants...
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...Managerial Economics Sat. 11:00 – 14:00 Demand Estimation and Forecasting Facilitators : Mr. John Michael G. Favila Mr. Jose Miguel G. Catan Learning Objectives * Identify a wide range of Demand Estimation and Forecast Methods. * Understand the nature of Demand Function * Understand that the Demand Estimation and Forecasting is all about minimizing risk. Demand Estimation and Demand Forecasting; distinguished. * Demand Estimation attempts to quantify the link between the level of for a product and the variables which determines it whereas the Demand Forecasting simply attempts to predict the level of sales at some particular future date. 7 stages of Demand Estimation 1. Statement of a Theory or Hypothesis : This usually comes from a mixture of economic Theory and previous empherical studies. 2. Model Specification : This means determining what variables should be included in the demand model and what mathematical form or forms such a relationship should take. 3. Data Collection : Gathering necessary information. a. Cross-sectional data : Provide information on a group opf entities at a given time. b. Time-serie data: Provide information on the entity over time. i. Quantitative: Data that are expressed in nominal in either ordinal or cardinal. ii. Qualitative: Expressed in categories. 4. Estimation of Parameters : This means computing the value of the coefficient...
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... * We have not include while calculating the dates on which the rooms sold exceeded the capacity of 198 rooms – as the solver could not be run and the hotel had overbooked based on the demand | | Final | Shadow | Constraint | Allowable | Allowable | | Cell | Name | Value | Price | R.H. Side | Increase | Decrease | Dates | $BF$9 | Rooms sold | 198 | 270 | 198 | 2 | 4 | 8/31/2008 | $BF$10 | Rooms sold | 181 | 0 | 198 | 1E+30 | 17 | 9/1/2008 | $BF$11 | Rooms sold | 198 | 205 | 198 | 4 | 17 | 9/2/2008 | $BF$12 | Rooms sold | 198 | 119 | 198 | 25 | 1 | 9/3/2008 | $BF$13 | Rooms sold | 198 | 119 | 198 | 2 | 12 | 9/4/2008 | $BF$14 | Rooms sold | 198 | 119 | 198 | 23 | 22 | 9/5/2008 | $BF$15 | Rooms sold | 198 | 135 | 198 | 15 | 16 | 9/6/2008 | $BF$16 | Rooms sold | 198 | 119 | 198 | 1 | 21 | 9/7/2008 | $BF$17 | Rooms sold | 187 | 0 | 198 | 1E+30 | 11 | 9/8/2008 | $BF$18 | Rooms sold | 198 | 119 | 198 | 24 | 1 | 9/9/2008 | $BF$4 | Rooms sold | 193 | 0 | 198 | 1E+30 | 5 | 8/26/2008 | | | | | | | | | Table – 1 Q2-Which rate classes are closed for these dates? * We have not include while calculating the dates on which the rooms sold exceeded the capacity of 198 rooms – as the solver could not be run and the hotel had overbooked based on the demand * As per the optimization model the rates for which on a particular day the PU value is zero in thee below given matrix, Table -2 are closed for the particular date and corresponding...
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...Abstract Five different forecasting models were used to examine a logo branded coffeemaker that is sold by Starbucks. A simple moving average was used for the first two models using 5 weeks past (figure 1) data and 3 weeks past data (Figure 2. The next method used was a exponential smoothing method with .4 alpha and 5 weeks data (Figure 3) and .2 alpha with 3 weeks data (Figure 4). The mean absolute deviation, mean absolute percent error and tracking signal were calculated based off of the total of all segments. Simple Moving Average Looking at Figure 1 and Figure 2 where a moving average forecast was used, the 3 and 5 week data were very similar in the mean absolute deviation, and percent error. The tracking signal is where they were the most different. Since the cumulative deviation (RSFE) was negative, the tracking number for the three week data was negative which show that forecasts are too high. If you look at the -4 data column, you can see that this number is significantly lower than the rest of the prior week’s numbers. This dip in sales is what caused the forecasts for the 3 week data to be higher while the forecasts for the 5 week data were lower causing a positive RSFE and therefore a positive tracking signal. Exponential Smoothing Figures 3 and 4 used the exponential smoothing forecast method. Comparing with the moving average, the 3 week data with the alpha of .2 most signifies the data that was recorded in Figures 1 and 2. The rule of thumb when switching...
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...Running Head: DEMAND AND FORECASTING Making Decisions Based on Demand and Forecasting [bami] strayer University] Making Decisions Based on Demand and Forecasting The demographics used for the demand analysis are the average yearly income of the house hold in Georgia, the total yearly population, and average kids per house. The rationale behind choosing these demographics is that the demand is highly associated with the average income, and can have a great impact on the demand of the economy, for higher the income, the higher the spending ability of an average house hold. Therefore, it can also be said that the average income is directly proportional to the spending ability of an average house hold, whereas as far as total yearly population is concerned, demand is also associated with the total population, as for demand arises with rise in population. Average kids per house hold also have a strong link with demand. Considering the fact that pizza is highly popular among kids, and is the cause of its major demand. The other independent variables used for conducting a demand analysis are price of the pizza, and price of the soda. The rationale behind choosing these demographics is that the demand is also highly associated with price, as per the demand and supply law, the lower the price the higher the demand, and the higher the price, the lower the demand. Pizza and soda are two main products of a pizza restaurant...
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...Assignment 1: Making Decisions Based on Demand and Forecasting Assignment 1: Making Decisions Based on Demand and Forecasting ECO 550 January 24, 2013 Assignment 1: Making Decisions Based on Demand and Forecasting Page 1 I have been considering opening a Domino’s Pizza in my community within the Virginia Beach area. In this paper I will present to you, a data analysis and a forecast of Domino Pizza’s sales revenue that consist of the demographics within my community. These demographics consist of the population size, the average income per household and the independent variables which include the price in pizza and soda. This demand analysis will be used to give an estimated forecast that will assist in my business making decision technique, which will determine if it will be beneficial for me to open a Domino’s Pizza in my area. To determine if I will enter into the market place in Virginia Beach, I will research the reported demographic and independent variables that are relevant to complete a demand analysis that has been provided to me from different resources within my community. By using Excel to calculate, I will input the data that I have collected to create an estimated regression analysis. Once the calculation has been provided, I will be able to interpret the coefficient of determination, and how it has provided an influence on my decision to open the pizza business in my area. Variables. The significant...
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...Making Decisions Based on Demand and Forecasting Robyn Wilson Strayer University Econ 550 Assignment One January 31, 2013 Report the demographic and independent variables that are relevant to complete a demand analysis providing a rationale for the selection of the variables. Demographics are an important variable when choosing target marketing strategies. The variables are relevant to complete a demand analysis by providing a rationale for the selection of the variables. Whithin my area, Cross, SC, I am looking at local demographics and paying special attention to the following: • Age: Persons under 18 years percent 27.4% • Income levels: Average 39,779 per household • Persons below poverty level: 17.2% • Education: Bachelor degree age 25+ percent 13.1% • Housing: ownership rate 57.9% Making an informed analysis will inform you about the spending and eating habits of the people who live in the servicing area. Demogrphics give you a clear understanding of the areas behavior, values, cultures, interests and lifestyles of the community. Data research was consider because of the amount of time given for the assignment. The success of Domino’s opening a location in Cross, SC will depend on the factors listed above. Having a customer loyalty program that will have frequent customers that will come buy the products will help the company save on selling expenses...
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