...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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...Tool 3. Demand Analysis Economic Analysis of Tobacco Demand Nick Wilkins, Ayda Yurekli, and Teh-wei Hu DRAFT USERS : PLEASE PROVIDE FEEDBACK AND COMMENTS TO Joy de Beyer ( jdebeyer@worldbank.org) and Ayda Yurekli (ayurekli@worldbank.org) World Bank, MSN G7-702 1818 H Street NW Washington DC, 20433 USA Fax : (202) 522-3234 Contents I. Introduction 1 Purpose of this Tool 1 Who Should Use this Tool 2 How to Use this Tool 2 II. Define the Objectives of the Analysis 4 The Reason for Analysis of Demand 4 The Economic Case for Demand Intervention 4 Analysis of Demand for the Policy Maker 5 Design an Analysis of Demand Study 6 Components of a Study 6 The Nature of Econometric Analysis 7 Resources Required 7 Summary 8 References and Additional Information 8 III. Conduct Background Research 9 IV. Build the Data Set 11 Choose the Variables 11 Data Availability 11 Data Types 12 Prepare the Data 13 Data Cleaning and Preliminary Examination 14 Preparing the Data Variables 14 References and Additional...
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...Assignment 1: Making Decisions Based on Demand and Forecasting An Assignment Submitted by Name of Student Name of Establishment Class XXXX, Section XXXX, Fall 2013 Assignment 1: Making Decisions Based on Demand and Forecasting Regression analysis is the description about the relationship between two variables where one is dependent and the other is independent. Regression analysis (in statistics), generally, is about any techniques that facilitate modeling and analysis of several variables. It focuses on the relationship between a dependent variable and one or more independent variables (Sykes, 2000). To be specific, regression analysis allows understanding of the typicality of value of the dependent variable changes, while any one of the independent variables is varied. At the same time, the other independent variables must be fixed. Usually, regression analysis estimates the expectation of conditions connected to the dependent variable given the independent variables (Sykes, 2000). Thus, the average value of the dependent variable is calculated using condition that the independent variables are held fixed. Not that often, regression analysis focuses on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. Nevertheless, the regression function is the estimation target, which is a function of the independent variables. In regression analysis, it is also necessary...
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...MARKETING DECISION support system Marketing decision support system (MDSS) A system used to manipulate a collection of data to interpret and explore potential business scenarios in order to make management decisions. Marketing decision support systems (MDSS) are considered by some businesses a key tool in gaining the edge over competitors. MDSS can be used to assist, rather than supersede, employee decision makers in the complicated scenarios which are common in marketing. Also MDSS can be defined as A coordinated collection of data, systems, and techniques with supporting software and hardware by which an organization gathers and interprets relevant information from business and the environment and turns it into a basis for marketing action An emerging trend in the realm of marketing has been the increased application of marketing decision support system (MDSS) technology to aid with decision-making (DM). Developing a sound and robust marketing strategy has never been an easy task. The success or failure of a company’s marketing effort depends on the interaction of numerous internal and external factors, combined with the knowledge and intuition of the decision-makers themselves. Marketing DM requires a comprehensive analysis of environments both inside and outside the firm. It requires a wide range of strategic information, including hard and soft information, and it requires managers to deal with issues that involve a high degree of uncertainty, subjectivity and ambiguity...
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...Almost all managerial decisions are based on forecasts. Every decision becomes operational at some point in the future, so it should be based on forecasts of future conditions. Forecasts are needed throughout an organization -- and they should certainly not be produced by an isolated group of forecasters. Neither is forecasting ever "finished". Forecasts are needed continually, and as time moves on, the impact of the forecasts on actual performance is measured; original forecasts are updated; and decisions are modified, and so on. For example, many inventory systems cater for uncertain demand. The inventory parameters in these systems require estimates of the demand and forecast error distributions. The two stages of these systems, forecasting and inventory control, are often examined independently. Most studies tend to look at demand forecasting as if this were an end in itself, or at stock control models as if there were no preceding stages of computation. Nevertheless, it is important to understand the interaction between demand forecasting and inventory control since this influences the performance of the inventory system. This integrated process is shown in the following figure: The decision-maker uses forecasting models to assist him or her in decision-making process. The decision-making often uses the modeling process to investigate the impact of different courses of action retrospectively; that is, "as if" the decision has already been made under a course of action...
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...Modeling Genius Forecasting Inventory Forecasting Simulations Modeling Decision Tree Conclusion Many companies and businesses use forecasting. Whether its to predict sales growth, consumer demand, profit or plan production, management wants to know how to proceed in making an informed decision about the future. This presentation will examine some of today’s most popular forecasting models by highlighting how leading companies are putting them to use. 3 Mix modeling – Marketing strategy Mix modeling can help with marketing strategies by measuring the potential value of all market input and marketing investments. The goal is a long-term revenue growth. Mix modeling’s multiple-regression technique is conducted based on the number of inputs and how these inputs relate to an outcome. The data that go into creating a marketing mix model includes: • Economic data • Industry data • Category data • Advertising data (including copy testing) Promotional data Competitive data Service data • Product data- Pricing data, Features & performance • Market outcome data- sales, revenue, profits Reference: http://www.decisionanalyst.com/Services/MarketingMixModeling.dai 4 Predictive Modeling for Consumer Demand forecast Predictive modeling is an effective forecasting model for consumer demand. The technique is based on accumulated data regarding consumer behavior. Within this process, there will be a continual monitoring of demand to track changes and...
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...SCM Notes Chapter 5 Demand forecasting Note: Demand-how much you expect to sell in a specified period Slide 1: Data Matters—Video Data driven decision making Focused on how much data is out there All data is not relevant you need to focus on the data that is necessary and you need ot know how to separate that which is important. IBM- “A smarter planet” their quote 5 times as expensive to go get a new customer than to just keep the existing one Slide 2- Learning objectives You should be able to: -Explain the role of demand forecasting -Identify the components of a forecast -Be able to calculate the following forecast: -simple moving average forecast -weighted Moving Average Forecast -Understand the principles behind calculating: -exponential smoothing forecast -linear Trend forecast -Simple and Multiple regressions Note: for data forecasting you will use historical data to calculate future data Slide 3- The Role of Demand Forecasting -Designed to estimate future demand for planning -purchasing Decisions -Inventory decisions -production Decisions -Important to match supply with demand -Results of increased forecast accuracy -lower inventories -reduced stock-outs -smoother production plans -reduced costs -improved customer service Notes: purchasing needs to know how much to buy…or how much will be made in order to know how much to buy… need to know how much inventory to hold.. Production schedule--- how many units will...
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...L.L Bean, Inc. Item Forecasting and Inventory Management 2012/24/11 Q1. How do LL Bean use past demand data and a specific item forecast to decide how many units of that item to stock? Evaluate the LL Bean's forecasting system (i.e., merits vs. shortfall). One of the most important decision making process in business is forecasting. It can help to make your business more profitable. You should be able to guess how many units of that item to stock based on your past data and predicting future demand. Following two processes used by L.L. Bean, to find how many units of that item to stock: First, they used forecasting to predict for that specific item for upcoming season, which is named “frozen forecasting”. It is based on the book forecast and past demand data, which provided by forecasting department. Second, they used historical forecast errors, namely the A/F rations, which mean actual demand multiplied past season’s forecast. L.L. Bean estimates the range of inventory that the product will be in the upcoming season after converting the point forecast into demand distribution. For instance as the article shows that if last year new products had this ration between 0.7 and 1.6 then where frozen forecast is 1000 that means the new product could have an actual demand for the upcoming year of 700 to 1600 units. In order to find out how much profit each unit brought in compared to how much the unit would lose if it was liquidated, they used profit margin calculation....
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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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...Forecasting Methods Assignment University of Phoenix MGT 554: Operations Management Steven Williams August 28, 2006 Introduction Forecasting can be defined as Estimating or predicting future events or conditions. Forecasts may be long-term or short-term. The techniques used may be quantitative (often making sue of computers) or qualitative. Quantitative forecasting models may be classified into (a) causal models in which independent variables are used to forecast dependent variables, and (b) time series models, which produce forecasts by extrapolating the historical values of the variables of interest by, e.g., moving averages. Seasonal Model Seasonality is a pattern that repeats for each period. For example annual seasonal pattern has a cycle that is 12 periods long, if the periods are months, or 4 periods long if the periods are quarters. The seasonal index is required to be found for each month, or other periods, such as quarter, week depending on the data availability (Hossein, 1994-2006). Seasonal Index: Seasonal index represents the extent of seasonal influence for a particular segment of the year. The calculation involves a comparison of the expected values of that period to the grand mean. A seasonal index is how much the average for that particular period tends to be above (or below) the grand average. Therefore, to get an accurate estimate for the seasonal index, compute the average of the first period of the cycle, and the second period...
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...PRODUCTION AND OPERATION MANAGEMENT PART ONE 1. Inputs in to outputs 2. The first operation to the finished product 3. Demand that is controlled by the company 4. Complete Enterprise wide business solution 5. Computer aided design 6.Technological forecast 7. All of the above 8. Production planning and scheduling & control system 9. Functional layout 10. Work measurement PART TWO 1. Define job shop production? Job Shop Production: In this system, products are manufactured to meet the requirements of a specific order. Quality is not given too much importance and the manufacturing of a product takes place as per the specifications given by the customer. This system may be further classified into the following categories: The Job produced only once: Here, a customer visits a firm and places their order. When the product is ready, the customer takes it and leaves. The customer may not visit the firm again to place an order for the same product. Such a firm will have little scope for pre-planning the production of a product. The norm will be that it will plan for the materials, manpower and the process to be followed only after it has received an order from a customer. The job produced at irregular intervals: Here, a customer visits a firm to place orders for the same type of the product at irregular intervals. The firm will not have any idea of the customer's visits. Here as well, the planning for materials, manpower and the process to be followed will start...
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...PROJECT REPORT ON SALES FORECASTING FOR UK SOFTDRINKS MARKET BY GROUP 3 EMBA-MS 2013-2015 | GROUP MEMBERS: | |DHINESH J | |SIRIKONDA KIRAN KUMAR | |RANJANI N | |VISHWESHA | INDEX 1. TOPIC 3 2. INTRODUCTION 3 3. ESTIMATING THE DEMAND FUNCTION 4 4. REGRESSION 5. ANALYSIS 6. CONCLUSION TOPIC The given topic for the assignment is – “to compare the Sales forecasting techniques and analyze sales forecasting using the demand function for any product in the market”. Based on the internal discussion the group has decided to study the trend for the UK soft-drink market, analyze and compare the sales forecasting using the demand function for the following products, a. Bottled Water b. Soft Drinks c. Carbonate Drinks All price information has been adjusted for inflation using UK CPI (Consumer Price Index) REFERENCES The following are the data references/sources used in this assignment, a. 2012 UK Soft-Drink report : Source : British Soft-Drink Association - www.britishsoftdrinks.com (a copy of...
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...and capacity. Timing is a factor that needs built into our decision tree’s framework and in this case, ultimately asking, when and how much capacity? In order to answer the question, we diagnosed the issues and identified limitations of the current capacity, which hampers Harley’s ability to expand and meet demand. It is important to appropriately incorporate risk as it goes well beyond just capacity. For incorporate risk into the analysis and decision making we recognized that a series of tools would be required. The approach involved three analysis tools for considering various factors that are important for decision making: Demand Analysis: We needed to forecast for the demand, supply and margins, while distinguishing demand from output and sales. Scenarios and Capacity Analysis: Next step was to determine the factors for building the scenarios, assign probabilities, estimate the cash flow and then compute the NPV. We modeled capacity adjustments and continuous improvements, then included plans or options for changes and new products. Risk Analysis and addressing management’s success criteria: Our decision needed to account for the risk aversion of the company due to its learning from its history, and also encompass company’s long term strategy and success criteria’s laid out by management. In next few pages we lay out the points incorporating above tools and provide team’s recommendation which is based on Harley-Davidson’s long term strategy and values. We start...
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...operations. Define and describe brief information of the resources defined. Supply chain management (SCM) is the management of the flow of goods. It includes the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption. Interconnected or interlinked networks, channels and node businesses are involved in the provision of products and services required by end customers in a supply chain. Supply chain management has been defined as the "design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand and measuring performance globally. SCM draws heavily from the areas of operations management, logistics, procurement, and information technology, and strives for an integrated approach. Commonly accepted definitions of supply chain management include: • The management of upstream and downstream value-added flows of materials, final goods, and related information among suppliers, company, resellers, and final consumers. • The systematic, strategic coordination of traditional business functions and tactics across all business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain...
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