APPLICATIONS OF FORECASTING IN VARIOUS FIELDS Supply Chain Management The supply chain manager of a manufacturing company is in charge of making sure that the necessary productive resources

APPLICATIONS OF FORECASTING IN VARIOUS FIELDS
Supply Chain Management

The supply chain manager of a manufacturing company is in charge of making sure that the necessary productive resources ( capital, labor, component parts and the like ) are always available for producing the required output demanded of the company by it’s customers. So the supply chain manager must determine a good forecast of the demand for the manufacturing company’s output and from that estimate determine the necessary resources to produce the forecasted amount of output. Demand forecasting forms an essential component of the supply chain process. It’s the driver for almost all supply chain related decisions. While demand forecasting is undeniably important, it’s also one of the most difficult aspects of supply chain planning. Demand is often volatile making demand forecasting both an art and science. “Demand Forecasting defined as the process by which the historical sales data are used to develop an estimate of the expected forecast of customer demand. Demand Forecasting provides an estimate of the goods and services that customers will purchase in the foreseeable future.”
Demand Forecasting facilitates critical business activities like budgeting, financial planning, sales and marketing plans, raw material planning, production planning, risk assessment and formulating mitigation plans. Outlined below are the impacts of Demand Forecasting on Supply Chain Management:
Improved supplier relations and purchasing terms
Better capacity utilization and allocation of resources
Optimization of inventory levels
Improved distribution planning and logistics
Increase in customer service levels
Better product lifecycle management
Facilitates performance management
3 Main Roles of Forecasting in Supply Chain Management
1 . Pivotal in strategic planning of Business
2 . Initiating all push-processes of Supply Chain
3 . Driving all pull-processes of Supply Chain
2 . Economic Forecasting
Economic forecasting is the prediction of any of the elements of economic activity. Such forecasts may be made in great detail or may be very general. In any case, they describe the expected future behavior of all part of the economy and help from the basis of planning. Formal economic forecasting is usually based on a specific theory as to how the economy works. Some theories are complicated and their application requires an elaborating tracing of cause and effect. Others are relatively simple, attributing most developments in the economy to one or two basic factors. Many economists, for example, believe that changes in the supply of money determine the rate of growth of general business activity. Others assign a central role to investment in new facilities-housing, industrial plants, highways, and so forth.

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Many institutions engage in economic forecasting, including International Monetary Fund, World Bank and the OECG, national governments and central banks, and private sector entities, including think-tanks, banks, consultants and companies. Some forecasts are produced annually, but many are updated more frequently. Economists select which variables are important to the subject material under discussion. Economists may use statistical analysis of historical data to determine the apparent relationships between particular independent variables and their relationship to the dependent variable under study. Forecasts are generally based on a sample data rather than a complete population, which introduces uncertainty. The economist conducts statistical tests and develops statistical models (often using regression analysis) to determine which relationships best to describe or predict the behavior of the variables under study. In preparing economic forecasts a variety of information has been used in an attempt to increase the accuracy. Everything from macroeconomic, microeconomic, market data from the future, machine learning, and human behavioral studies have all been used to achieve better forecasts.

3 . Earthquake Forecasting
Earthquake forecasting is a branch of the science of seismology concerned with the probabilistic assessment of general earthquake hazard, including the frequency and magnitude of damaging earthquakes in a given area over years or decades. While forecasting is usually considered to be a type of prediction, earthquake forecasting is often differentiated from earthquake prediction, whose goal is the specification of the time, location, and magnitude of future earthquakes with sufficient precision that a warning can be issued. Both forecasting and prediction of earthquakes are distinguished from earthquake warning systems, which upon detection of an earthquake, provide a real-time warning to regions that might be affected.

Methods for earthquake forecasting generally look for trends or patterns that lead to an earthquake. As these trends may be complex and involve many variables, advanced statistical methods are often needed to understand them, therefore these are sometimes called statistical methods. These approaches tend to have relatively long time periods, making them useful for earthquake forecasting.

4 . Egain Forecasting
Egain forecasting is a method of controlling building heating by calculating demand for heating energy that should be supplied to the building in each time unit. By combining physics of structures with meteorology, properties of the building, weather conditions including outdoor temperature, wind power and direction, as well as solar radiation can be taken into account. In the case of conventional heating control, only current outdoor temperature is considered. Since forecasting method contains information about future demand and is not in conflict with other methods of increasing energy efficiency, it is always a foreground solution.

As far as practical use of forecasting method is concerned, usually remote control forecasting receivers are used to send and receive data by means of GPRS or GSM network. Then, the forecasting receivers manage the operation of control panels installed in buildings which adjust distribution of heat energy in the heating system of a given property. Recently, special remote control weather loggers have started to be used in combination with forecasting receivers. The weather loggers measure air temperature and humidity. With high accuracy and the measurements are sent in real time to forecasting receivers to which they are connected.

5 . Land-use Forecasting
Land-use forecasting undertakes to project the distribution and intensity of trip generating activities in the urban area. In practice, land-use models are demand-driven, using as inputs the aggregate information on growth produced by an aggregate economic forecasting activity. Land-use estimates are inputs to the transportation planning process. Today, the transportation planning activities attached to metropolitan planning organizations are the loci for the care and feeding of regional land-use models.

6 . Forecasting In Sport
The rationalization of modern sport has made it possible for social scientists to predict the results of sports events with greater accuracy. In this study we applied multivariate time series analysis to determine the degree to which soccer results could be predicted with three teams in English Premier League. Success was based on the model’s ability to predict the outcome for each of three dependent binary variables; that is, to win, to lose or to draw in the last 10 games of the season. Multivariate ARIMA correctly predicted the outcome with a success rate of nine out of 10 for Winning, eight out of 10 for Losing and nine out of 10 for Drawing. A mix of both shared and new variables in different sets of interactions help predict Winning, Losing, and Drawing. A theory of team empowerment is proposed to better explain the utility of the input variables in predicting game outcome.

At first glance, the notion of forecasting outcomes in sport seems an impossible task. After all, sporting contests, by their very nature, rely mostly on a combination of physical skill, strategy and chance and uncertainty of outcome is a key element in making them. Yet a number of factors come into play that appears to help in the forecasting of outcomes in sport. These include variables such as prior performance, playing at home or away, the overall rating of a team, win-loss record, crowd involvement and team resolve, among others. The ability to identify predictor variables that help to forecast future events in soccer (or any other sport for that matter) is clearly a useful tool. What may be even more important, we suggest, is the ability to interpret and explain the socio-cultural implications of such variables on team behavior, and how such behavior influences game outcome.

7 . Political Forecasting
Political forecasting provides the contextuality needed for decision making and for forecasting ‘non-political’ trends. To gear political forecasting to these needs, rather than mimicking approaches in other areas, requires recognition of the distinctive nature of political trends, and realism regarding forecast uses, which generally do not benefit from ‘precise’ probabilities, predictions of only major events, or ‘sophisticated’ methodology that sacrifices comprehensiveness for explicitness. Approaches borrowed from other forecasting disciplines have been counterproductive, although contextual approaches, including cross-impact analyses and developmental constructs that integrate political and non-political trends, are promising. Explorations of the consistency of scenario dynamics, taking into account policy responses and non- formalizable complexity, are also useful. Thus the separation of political forecasting from political analysis should be minimized, calling for a redirection of effort away from developing methodology uniquely geared to forecasting, and towards organizing more comprehensive and systematic analytical efforts. The tools used here are relatively simple: public opinion (both on who people will vote for and what policies they want to see implemented) is tracked through opinion polls. For forecasts made far out in the future, the opinion polls results are combined with some models about how changing economic or political conditions are likely to affect people’s voting choices
8 . Transportation Forecasting
Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data is combined with other known data, such as population, employment, trip rates, travel costs, etc to develop a traffic demand model for the current situation. Feeding it with predicted data for population, employment, etc. results in estimates of future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station. The current technologies facilitate the access to dynamic data, big data, etc., providing the opportunity to develop new algorithms to improve greatly the predictability and accuracy of the current estimations. Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering: to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have; to estimate the financial and social viability of projects, e.g., using cost-benefit analysis and social impact assessment; and to calculate environmental impacts, e.g., air pollution and noise.

9 . Telecommunications Forecasting
All telecommunications service providers perform forecasting calculations to assist them in planning their networks. Accurate forecasting helps operators to make key investment decisions relating to product development and introduction, advertising, pricing etc., well in advance of product launch, which helps to ensure that the company will make a profit on a new venture and that capital is invested wisely. Forecasting can be conducted for many purposes, so it is important that the reason for performing the calculation is clearly defined and understood. Some common reasons for forecasting include:
Planning and Budgeting – using forecast data can help network planners decide how much equipment to purchase and where to place it to ensure optimum management of traffic loads.

Evaluation – forecasting can help management decide if decisions that have been made will be to the advantage or detriment of the company.

Verification – as new forecast data becomes available it is necessary to check whether new forecasts confirm the outcomes predicted by the old forecasts.

10 . Sales Forecasting
Sales forecasting is the process of estimating future sales. Accurate sales forecasts enable companies to make informed business decisions and predict short term and long term performance. Companies can base their forecasts on past sales data, industry-wide comparisons, and economic trends. it is easier for established companies to predict future sales based on years of past business data. Newly founded companies have to base their forecasts on less verified information, such as market research and competitive intelligence to forecast their future business. Sales forecasting gives insight into how a company allocate its internal resources. In addition to helping a company allocate its internal resources effectively, predictive sales data is important for business when looking to acquire investment capital.

Sales forecasting allows companies to:
Predict achievable sales revenue;
Efficiently allocate resources;
Plan for future growth;
11 . Product Forecasting
Product forecasting is the science of predicting the degree of success a new product will enjoy in the marketplace. To do this, the forecasting model must take into account such things as product awareness, distribution, price, fulfilling unmet needs and competitive alternatives. Forecasting demand and revenue for new variants of existing products is difficult enough. But forecasting for radically innovative products in emerging new categories is an entirely different ball game. There are no past trends to reassuringly extrapolate into the future, just a ton of uncertainty real or not. And after so much investment, the board is sure that this is the product that is going to become the next cash cow. Sure, you could manage their expectations by reminding them that something like 80% of new products fail and name drop a few of the spectacular flops of Fortune 500 companies. But that would be career limiting. A better alternative is to take control of the situation and adopt some of the forecasting best practices approaches that others have found to work.

12 . Technology Forecasting
Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy of a measuring instrument, the number of transistors in a chip in the year 2014, etc. The forecast does not have to state how these characteristics will be achieved. Secondly, technological forecasting usually deals with only useful machines, procedures or techniques. This is to exclude from the domain of technological forecasting those commodities, services or techniques intended for luxury or amusement. Almost all modern manufacturing firms utilize then services of a technological forecaster. Technology forecasting is not mere astrology or palmistry, but a scientific and well defined procedure adopted by a technological forecaster or a consultancy for the forecasting of a particular technology.

13 . Weather Forecasting
Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change. Once calculated by hand based mainly upon changes in barometric pressure, current weather conditions, and sky condition or cloud cover, weather forecasting now relies on computer based models that make many atmospheric factors into account. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills, telecommunications, knowledge of model biases. The inaccuracy of forecasting is due to the chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, the error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes. Hence, forecasts become less accurate as the difference between current time and the time for which the forecast is being made increases. The use of ensembles and model consensus help narrow the error and pick the most likely outcome.

There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Forecasts based on temperature and precipitation are important to agriculture, and therefore to traders within commodity markets. Temperature forecasts are used by utility companies to estimate demand over coming days. On an everyday basis, people use weather forecasts to determine what to wear on a given day. Since outdoor activities are severely curtailed by heavy rain, snow and wind chill, forecasts can be used to plan activities around these events, and to plan ahead and survive them.

14 . Flood Forecasting
Flood forecasting is the use of forecasted precipitation and stream-flow data in rainfall- runoff and stream-flow routing models to forecast flow rates and water levels for periods ranging from a few hours to days ahead, depending on the size of the watershed or river basin. Flood forecasting can also make use of forecasts of precipitation in an attempt to extend the lead-time available. Flood forecasting is an important component of flood warning, where the distinction between the two is that the outcome of flood forecasting is a set of forecast timer-profiles of channel flows or river levels at various locations, while ” flood warning” is the task of making use of these forecasts to tell decisions on warning of floods. Real-time flood forecasting at regional area can be done within seconds by using the technology of artificial neural network. Effective real-time flood forecasting models could be useful for early warning and disaster prevention.