This presentation is a comparison between by a planned system and classical system, in terms of controlling seasonality and annual forecasting, the final accuracy is measured by S. D.
This document provides an overview of forecasting in the aviation industry. It defines forecasting as predicting future demand based on past data to aid planning, analysis, and control. The document outlines several forecasting methods, including causal, trend analysis, and judgmental. Causal forecasts use statistical relationships between variables, trend analysis extrapolates past trends, and judgmental forecasts rely on expert opinions. The document emphasizes that forecasting is important for strategic planning, budgeting, marketing, production, and comparing actual performance to predictions.
This document discusses forecasting methods. It states that forecasting is both an art and a science, requiring subjective assessment of historical and current data as well as numerical methods. Forecasting is important for planning production, personnel, capacity, and supply chains. Accurate forecasting can improve employee relations, material management, capital usage, customer service, and reduce costs. Forecasts contain best demand estimates and allowances for errors. Qualitative methods include surveys and expert opinions, while quantitative methods use time series analysis and causal models. The appropriate forecasting method depends on the required format, time horizon, data availability, needed accuracy, process behavior, and costs.
Quantitative Math - MATH 132
Credits: Group 4 Reporters S.Y. 2015-2016
The ppt has animations, you'll appreciate the presentation if you'll download it. Thank you
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
Business forecasting uses qualitative and quantitative methods to predict future business conditions and trends. Qualitative methods gather opinions through surveys and focus groups, while quantitative analyzes statistical data to identify trends. Some alternative methods like astrology are unlikely to be more effective than traditional qualitative and quantitative approaches. Forecasting provides benefits like informed decision-making but also costs as data may be unreliable, outdated, or unable to account for unexpected external changes.
This document provides an overview of forecasting and decision making. It defines forecasting as predicting future events based on past and present data to help managers make decisions. Various forecasting techniques are discussed, including qualitative methods like executive opinion and quantitative time series models. Decision making is defined as selecting an action from alternatives to achieve objectives. The document outlines characteristics, types, and advantages of decision making. It also discusses limitations of forecasting like costs and uncertainty.
Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses various forecasting methods used to predict future outcomes when historical data is available or not. It describes subjective qualitative methods like sales force composites, customer surveys, and Delphi techniques that rely on expert opinions. Objective quantitative methods include causal models that examine factors influencing outcomes and time series analysis of historical trends, seasonality, and levels. The document also outlines short, medium, and long-term forecasting horizons and the appropriate techniques for each.
DEMAND FORECASTING-Economic Analysis of Businessabner domingo
Thank you for the encouragement. While pursuing an advanced degree requires hard work, it's important to also make time for rest and leisure. I'll continue studying diligently while maintaining a good life balance.
This document provides an overview of forecasting in the aviation industry. It defines forecasting as predicting future demand based on past data to aid planning, analysis, and control. The document outlines several forecasting methods, including causal, trend analysis, and judgmental. Causal forecasts use statistical relationships between variables, trend analysis extrapolates past trends, and judgmental forecasts rely on expert opinions. The document emphasizes that forecasting is important for strategic planning, budgeting, marketing, production, and comparing actual performance to predictions.
This document discusses forecasting methods. It states that forecasting is both an art and a science, requiring subjective assessment of historical and current data as well as numerical methods. Forecasting is important for planning production, personnel, capacity, and supply chains. Accurate forecasting can improve employee relations, material management, capital usage, customer service, and reduce costs. Forecasts contain best demand estimates and allowances for errors. Qualitative methods include surveys and expert opinions, while quantitative methods use time series analysis and causal models. The appropriate forecasting method depends on the required format, time horizon, data availability, needed accuracy, process behavior, and costs.
Quantitative Math - MATH 132
Credits: Group 4 Reporters S.Y. 2015-2016
The ppt has animations, you'll appreciate the presentation if you'll download it. Thank you
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
Business forecasting uses qualitative and quantitative methods to predict future business conditions and trends. Qualitative methods gather opinions through surveys and focus groups, while quantitative analyzes statistical data to identify trends. Some alternative methods like astrology are unlikely to be more effective than traditional qualitative and quantitative approaches. Forecasting provides benefits like informed decision-making but also costs as data may be unreliable, outdated, or unable to account for unexpected external changes.
This document provides an overview of forecasting and decision making. It defines forecasting as predicting future events based on past and present data to help managers make decisions. Various forecasting techniques are discussed, including qualitative methods like executive opinion and quantitative time series models. Decision making is defined as selecting an action from alternatives to achieve objectives. The document outlines characteristics, types, and advantages of decision making. It also discusses limitations of forecasting like costs and uncertainty.
Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses various forecasting methods used to predict future outcomes when historical data is available or not. It describes subjective qualitative methods like sales force composites, customer surveys, and Delphi techniques that rely on expert opinions. Objective quantitative methods include causal models that examine factors influencing outcomes and time series analysis of historical trends, seasonality, and levels. The document also outlines short, medium, and long-term forecasting horizons and the appropriate techniques for each.
DEMAND FORECASTING-Economic Analysis of Businessabner domingo
Thank you for the encouragement. While pursuing an advanced degree requires hard work, it's important to also make time for rest and leisure. I'll continue studying diligently while maintaining a good life balance.
Demand Forecast & Production Planning Industrial engineering management E-BookLuis Cabrera
This document discusses demand and production planning. It provides an overview of demand planning techniques used to determine production levels and inventory needs. Demand is analyzed using statistical process control methods to set upper and lower limits. Forecasting incorporates factors like trends, seasonality and weighted averages. The Delphi method is used to discuss forecasts among teams. Forecasts are set for multiple periods to plan production and procurement. Demand planning should be led by dedicated analysts to thoroughly analyze data and agree on reliable forecasts used across the organization.
Forecasting :- Introduction & its ApplicationsDeepam Goyal
This document discusses forecasting, including its introduction, characteristics, principles, need, process, areas of application, advantages, and disadvantages. It provides examples of forecasting in supply chain management, economics, earthquakes, buildings, land use, sports, politics, transportation, telecommunications, products, sales, and technology. The document also presents a case study of Henkel, a manufacturing company that improved sales forecasting accuracy from 69.3% to 85.3% by implementing social forecasting with incentives for top forecasters.
1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
1. The document discusses 10 different forecasting models: time series moving average, market research, exponential smoothing, jury of executive opinion, naive method, correlation-regression, sales force composite, Delphi technique, and econometric models.
2. It provides examples and explanations of simple and weighted moving averages as well as exponential smoothing. It also outlines advantages and disadvantages of various qualitative forecasting methods.
3. The document concludes with an application example of forecasting apricot production and distribution using a times series seasonal model.
This document discusses various forecasting methods used in operations management. It begins by defining forecasting as predicting future events by taking historical data and projecting it using mathematical models adjusted by managerial judgment. There are three types of forecasts: economic, technological, and demand forecasts which project needs for a company's products. Accurate forecasting is important for human resources, capacity, and supply chain planning. The document then outlines quantitative time series and associative forecasting models as well as qualitative methods like Delphi, educated guesses, surveys, and analogy. It concludes by asking questions about forecasting definitions, accuracy, importance for operations, and long-range demand components.
This document discusses forecasting of diesel fuel prices by a team of students. It provides background on types of diesel fuel and their uses. The document then discusses the purpose and importance of forecasting for businesses. It outlines different qualitative and quantitative forecasting methods that could be used to forecast diesel prices, including executive opinions, Delphi method, time series analysis, exponential smoothing, and linear trend lines. The key factors to consider for price forecasting are also summarized.
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
This document discusses forecasting techniques in time series analysis and causal models. It describes time series models as analyzing a time-ordered sequence of observations over regular intervals to identify trends. These include simple exponential smoothing, which weights older data less and newer data more, and moving averages, which use an average of past periods to forecast the next period. Causal models are based on relationships between dependent and independent variables, assuming past trends will continue influencing future variables. Linear regression is provided as an example causal model that fits a line to measure the effect of a single independent variable.
This document discusses different methods of forecasting. It begins by defining forecasting as a planning tool used to predict future events that will influence an organization. It then outlines the key advantages of forecasting such as helping managers plan ahead and identify weaknesses.
The document describes the main types of forecasting methods: judgmental/qualitative methods which rely on subjective estimates, extrapolative/time series methods which analyze past trends to predict the future, and causal/explanatory methods which use statistical models and variables to forecast. Specific judgmental, extrapolative, and causal techniques are defined such as the Delphi technique, moving averages, and regression analysis.
Demand forecast process and inventory managementAbhishek Kumar
The document discusses demand forecasting and inventory management processes. It begins by explaining that a demand forecast is central to business operations planning and helps determine needed inventory levels. It then outlines the key steps in a demand forecast process including determining the purpose and time horizon, selecting forecast models and products, collecting data, creating the forecast, and revising it. Various forecasting methods are also described, including qualitative and quantitative approaches. The document concludes by discussing inventory management objectives of minimizing disruptions and costs while maintaining adequate stock levels.
Forecasting involves generating numbers or scenarios to predict future occurrences. It is important for short-term and long-term planning as forecasts are based on past data, unlike predictions. Forecasts for shorter time periods like tomorrow or next month will generally be more accurate than forecasts for longer periods like years in the future. It is wise to provide a forecast range rather than a single number since forecasts are seldom totally accurate. Effective forecasts are accurate, reliable, timely, easy to understand, and cost-effective. Forecasting techniques can be qualitative based on opinions or quantitative using mathematical models to analyze past time-series or explanatory variable data.
Forecasting involves predicting future events and is essential for business decisions regarding production, inventory, personnel, and facilities. There are qualitative and quantitative forecasting methods, with quantitative relying on mathematical models. Key principles of forecasting are that forecasts are rarely perfect, more accurate for groups than individuals, and more accurate over shorter time horizons. Common patterns in time series data include trends, seasonality, and cycles. Quantitative forecasting models analyze these patterns in historical data to generate forecasts.
This document outlines the steps for forecasting, including:
1) Determining the use and items to be forecasted, as well as the time horizon, which can be short, medium, or long-term.
2) Selecting either qualitative or quantitative forecasting approaches depending on the situation.
3) Collecting and reducing relevant and reliable data from primary and secondary sources.
4) Exploring patterns in time series data like trends, cycles, and seasonality.
5) Selecting an appropriate forecasting model like exponential smoothing or regression and making a forecast.
Quantitative and qualitative forecasting techniques omHallmark B-school
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
This document provides an overview of demand forecasting. It discusses the meaning and importance of demand forecasting, the different levels it can be conducted at (micro, industry, macro), criteria for good forecasting like accuracy and simplicity, and various methods like survey and statistical approaches. Demand forecasting is presented as an essential tool for business planning and decision making regarding production, sales, investment, and more. It allows companies to anticipate future demand and minimize risks.
This document discusses forecasting techniques based on time series analysis. It defines key concepts like extrapolation, time series components, and analytical indicators. Extrapolation involves projecting past trends and patterns into the future, and can be used to forecast trends, cycles, and causal relationships. Time series data has components like trends, seasonality, cycles, and random variations. Analytical indicators like absolute and percentage changes are used to analyze time series data and make forecasts. The document provides an example of forecasting future demand using average absolute increase and average growth rate.
Demand management involves understanding customer needs, planning products and services, and fulfilling demands across a business and its partners. Effective demand management relies on demand forecasting to predict future needs and inform resource planning. Forecasting considers factors like historical data, demand variability, and required accuracy. Accurate forecasts allow businesses to optimize inventory, capacity, and costs to best meet customer demands over different time horizons.
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
This document discusses forecasting techniques. It begins by defining forecasting as predicting future events using historical data and mathematical models. It then discusses different forecasting time horizons including short, medium, and long range. Short range forecasts are less than 1 year, medium 1-3 years, and long more than 3 years. The document also covers qualitative and quantitative forecasting approaches, types of forecasts including economic, technological, and demand, and examples of forecasting techniques like moving averages and exponential smoothing.
Forecasting and decision making are important for businesses to plan effectively amid risk and uncertainty. Economic forecasting helps businesses understand changes in the broader environment so they can formulate strategies. Demand forecasting also allows businesses to predict sales and allocate resources appropriately. Qualitative techniques like expert opinions and surveys, and quantitative techniques like time series analysis are commonly used for demand forecasting. The results of forecasting assist both businesses and governments in planning investments and policies.
annual global forecast for the Meetings and Events industry from AMERICAN EXPRESS MEETINGS & EVENTS, a Major actor in MICE
“Back in Business”: After 2014’s broad industry recovery, it appears that meetings are rising in importance and visibility for many companies around the world, as part of an overall return to business fundamentals. Activity in 2015 is predicted to grow slightly, with a strong focus on meeting higher expectations and achieving more measurable results.
Demand Forecast & Production Planning Industrial engineering management E-BookLuis Cabrera
This document discusses demand and production planning. It provides an overview of demand planning techniques used to determine production levels and inventory needs. Demand is analyzed using statistical process control methods to set upper and lower limits. Forecasting incorporates factors like trends, seasonality and weighted averages. The Delphi method is used to discuss forecasts among teams. Forecasts are set for multiple periods to plan production and procurement. Demand planning should be led by dedicated analysts to thoroughly analyze data and agree on reliable forecasts used across the organization.
Forecasting :- Introduction & its ApplicationsDeepam Goyal
This document discusses forecasting, including its introduction, characteristics, principles, need, process, areas of application, advantages, and disadvantages. It provides examples of forecasting in supply chain management, economics, earthquakes, buildings, land use, sports, politics, transportation, telecommunications, products, sales, and technology. The document also presents a case study of Henkel, a manufacturing company that improved sales forecasting accuracy from 69.3% to 85.3% by implementing social forecasting with incentives for top forecasters.
1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
1. The document discusses 10 different forecasting models: time series moving average, market research, exponential smoothing, jury of executive opinion, naive method, correlation-regression, sales force composite, Delphi technique, and econometric models.
2. It provides examples and explanations of simple and weighted moving averages as well as exponential smoothing. It also outlines advantages and disadvantages of various qualitative forecasting methods.
3. The document concludes with an application example of forecasting apricot production and distribution using a times series seasonal model.
This document discusses various forecasting methods used in operations management. It begins by defining forecasting as predicting future events by taking historical data and projecting it using mathematical models adjusted by managerial judgment. There are three types of forecasts: economic, technological, and demand forecasts which project needs for a company's products. Accurate forecasting is important for human resources, capacity, and supply chain planning. The document then outlines quantitative time series and associative forecasting models as well as qualitative methods like Delphi, educated guesses, surveys, and analogy. It concludes by asking questions about forecasting definitions, accuracy, importance for operations, and long-range demand components.
This document discusses forecasting of diesel fuel prices by a team of students. It provides background on types of diesel fuel and their uses. The document then discusses the purpose and importance of forecasting for businesses. It outlines different qualitative and quantitative forecasting methods that could be used to forecast diesel prices, including executive opinions, Delphi method, time series analysis, exponential smoothing, and linear trend lines. The key factors to consider for price forecasting are also summarized.
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
This document discusses forecasting techniques in time series analysis and causal models. It describes time series models as analyzing a time-ordered sequence of observations over regular intervals to identify trends. These include simple exponential smoothing, which weights older data less and newer data more, and moving averages, which use an average of past periods to forecast the next period. Causal models are based on relationships between dependent and independent variables, assuming past trends will continue influencing future variables. Linear regression is provided as an example causal model that fits a line to measure the effect of a single independent variable.
This document discusses different methods of forecasting. It begins by defining forecasting as a planning tool used to predict future events that will influence an organization. It then outlines the key advantages of forecasting such as helping managers plan ahead and identify weaknesses.
The document describes the main types of forecasting methods: judgmental/qualitative methods which rely on subjective estimates, extrapolative/time series methods which analyze past trends to predict the future, and causal/explanatory methods which use statistical models and variables to forecast. Specific judgmental, extrapolative, and causal techniques are defined such as the Delphi technique, moving averages, and regression analysis.
Demand forecast process and inventory managementAbhishek Kumar
The document discusses demand forecasting and inventory management processes. It begins by explaining that a demand forecast is central to business operations planning and helps determine needed inventory levels. It then outlines the key steps in a demand forecast process including determining the purpose and time horizon, selecting forecast models and products, collecting data, creating the forecast, and revising it. Various forecasting methods are also described, including qualitative and quantitative approaches. The document concludes by discussing inventory management objectives of minimizing disruptions and costs while maintaining adequate stock levels.
Forecasting involves generating numbers or scenarios to predict future occurrences. It is important for short-term and long-term planning as forecasts are based on past data, unlike predictions. Forecasts for shorter time periods like tomorrow or next month will generally be more accurate than forecasts for longer periods like years in the future. It is wise to provide a forecast range rather than a single number since forecasts are seldom totally accurate. Effective forecasts are accurate, reliable, timely, easy to understand, and cost-effective. Forecasting techniques can be qualitative based on opinions or quantitative using mathematical models to analyze past time-series or explanatory variable data.
Forecasting involves predicting future events and is essential for business decisions regarding production, inventory, personnel, and facilities. There are qualitative and quantitative forecasting methods, with quantitative relying on mathematical models. Key principles of forecasting are that forecasts are rarely perfect, more accurate for groups than individuals, and more accurate over shorter time horizons. Common patterns in time series data include trends, seasonality, and cycles. Quantitative forecasting models analyze these patterns in historical data to generate forecasts.
This document outlines the steps for forecasting, including:
1) Determining the use and items to be forecasted, as well as the time horizon, which can be short, medium, or long-term.
2) Selecting either qualitative or quantitative forecasting approaches depending on the situation.
3) Collecting and reducing relevant and reliable data from primary and secondary sources.
4) Exploring patterns in time series data like trends, cycles, and seasonality.
5) Selecting an appropriate forecasting model like exponential smoothing or regression and making a forecast.
Quantitative and qualitative forecasting techniques omHallmark B-school
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
This document provides an overview of demand forecasting. It discusses the meaning and importance of demand forecasting, the different levels it can be conducted at (micro, industry, macro), criteria for good forecasting like accuracy and simplicity, and various methods like survey and statistical approaches. Demand forecasting is presented as an essential tool for business planning and decision making regarding production, sales, investment, and more. It allows companies to anticipate future demand and minimize risks.
This document discusses forecasting techniques based on time series analysis. It defines key concepts like extrapolation, time series components, and analytical indicators. Extrapolation involves projecting past trends and patterns into the future, and can be used to forecast trends, cycles, and causal relationships. Time series data has components like trends, seasonality, cycles, and random variations. Analytical indicators like absolute and percentage changes are used to analyze time series data and make forecasts. The document provides an example of forecasting future demand using average absolute increase and average growth rate.
Demand management involves understanding customer needs, planning products and services, and fulfilling demands across a business and its partners. Effective demand management relies on demand forecasting to predict future needs and inform resource planning. Forecasting considers factors like historical data, demand variability, and required accuracy. Accurate forecasts allow businesses to optimize inventory, capacity, and costs to best meet customer demands over different time horizons.
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
This document discusses forecasting techniques. It begins by defining forecasting as predicting future events using historical data and mathematical models. It then discusses different forecasting time horizons including short, medium, and long range. Short range forecasts are less than 1 year, medium 1-3 years, and long more than 3 years. The document also covers qualitative and quantitative forecasting approaches, types of forecasts including economic, technological, and demand, and examples of forecasting techniques like moving averages and exponential smoothing.
Forecasting and decision making are important for businesses to plan effectively amid risk and uncertainty. Economic forecasting helps businesses understand changes in the broader environment so they can formulate strategies. Demand forecasting also allows businesses to predict sales and allocate resources appropriately. Qualitative techniques like expert opinions and surveys, and quantitative techniques like time series analysis are commonly used for demand forecasting. The results of forecasting assist both businesses and governments in planning investments and policies.
annual global forecast for the Meetings and Events industry from AMERICAN EXPRESS MEETINGS & EVENTS, a Major actor in MICE
“Back in Business”: After 2014’s broad industry recovery, it appears that meetings are rising in importance and visibility for many companies around the world, as part of an overall return to business fundamentals. Activity in 2015 is predicted to grow slightly, with a strong focus on meeting higher expectations and achieving more measurable results.
This webinar, produced by Tnooz and Amadeus, examined new traveler behavior, the effects of unbundling services on business models and how suppliers manage the marketing opportunities and challenges.
Panelists for this FREE webinar were:
Brian Beard, executive technology consultant, Amadeus
Scott Gillespie, travel industry consultant, Gillespie's Guide to Travel + Procurement
Gene Quinn, CEO and producer, Tnooz
Kevin May, Editor and moderator, Tnooz
Andreas Fähndrich, Director Business Development of BEONTRA, a Lockheed Martin company, explains how planning and forecasting can help airports more accurately predict passenger numbers and improve their operations management.
Airport Forecasting, is a collection articles which published in CAMA magazine, most of the airports of the world are forecasted, by using a new concept, and approach i.e Max/Min signal tracking approach, while the accuracy of the model is addressing by mapping to main elements – Displacement and Rotational in the accuracy matrix. Hope to enjoy !
Airline and Airport Big Data: Impact and EfficienciesJoshua Marks
Keynote presentation at Routes 2014 in Chicago - how big data changes aviation efficiencies, and what airlines and airports need to know about cloud data warehouses, real-time integration and predictive analytics.
1. The document discusses the fundamentals of marketing, including identifying and satisfying customer needs profitably. It also discusses the importance of good customer service based on Southwest Airlines' success over 33 years.
2. Key aspects of marketing discussed include market segmentation, identifying customer requirements, and targeting customers based on demographics and psychographics. Airlines must segment the market between business and leisure travelers.
3. The document analyzes business and leisure traveler customer requirements and priorities. It also discusses advantages for airlines in serving the leisure market, such as using larger aircraft and achieving higher load factors and annual utilization.
The document discusses the economic impact and trends in the U.S. airline industry. Some key points:
- The airline industry supports over 10 million U.S. jobs and contributes $846 billion to U.S. GDP annually.
- Domestic airfares have declined in real terms over decades as passenger numbers have tripled, making air travel a relatively good value compared to other transportation and consumer goods whose prices have risen faster.
- U.S. airlines have struggled with volatility and thin profit margins historically but have closed the gap with average corporate profitability in recent years through cost reductions and efficiency gains.
Students at the University of Michigan are researching how to use big data to help predict weather patterns and avoid flight delays related to weather. They analyzed 10 years of hourly weather data, which is a huge dataset, to understand similarities in past weather that could help predict future weather. This predictive analysis using big data has the potential to help airlines be cautious of bad weather in advance and prevent delays or cancellations. The goal is to apply big data computing methods to the large weather dataset to solve the social issue of frequent flight delays and cancellations due to unexpected weather.
The document discusses big data analysis and provides an introduction to key concepts. It is divided into three parts: Part 1 introduces big data and Hadoop, the open-source software framework for storing and processing large datasets. Part 2 provides a very quick introduction to understanding data and analyzing data, intended for those new to the topic. Part 3 discusses concepts and references to use cases for big data analysis in the airline industry, intended for more advanced readers. The document aims to familiarize business and management users with big data analysis terms and thinking processes for formulating analytical questions to address business problems.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
The document discusses various quantitative forecasting techniques including time series methods like moving averages and exponential smoothing. It provides examples of how to calculate 3-period moving averages and exponential smoothing forecasts using sample sales data. Exponential smoothing places more weight on recent observations compared to moving averages. The smoothing constant determines how quickly older data is discounted.
The document discusses using trend and seasonal forecasting models to set targets and measure performance at Amsterdam Airport Schiphol. It analyzes passenger data from 1992 to 2010 to create a trend forecasting model and seasonal forecasting model using 2008 to 2010 data. The seasonal model forecast for 2011 was compared to the trend model forecast to set a practical target passenger number for 2011.
Predicting Air Transport Industry - 2018 Mohammed Awad
Predicting Air Transport Industry based two input parameters - RPKs and ASKs, then using their forecasted results to predicted the Air Transport Industry Performance i.e Load Factor which is simply RPKs/ASKs
Keeping the Same Rule
Forecasting is not an easy task; we have to agree in what path we have to move ahead,
There is one trail forecasting approach, but if we try get the same answer for all our business units, that’s will be great, I know it is a tough way, but it can be achieved in these days.
We force all the three seasonality models results to follow the trend one
Yes we keep the same rule but in one step ahead – it is in the FUTURE
The document discusses forecasting passenger numbers for 2020 at Aeroporti di Roma (FCO) airport. It proposes two scenarios: 1) a preset annual target of 45,062,311 passengers with 4.2% annual growth, and 2) an optimal solution of 45,210,976 passengers. While both scenarios appear fair, the first is recommended to avoid the risk of over-forecasting passenger growth.
Air cargo forecasting for major airports in the world for 2014, about eight airports are study and accuracy forecasting matrix is developed, the study explore a fair results, based on the input of data, the forecast is developed, some of them are good and others are not, and depends on the analyses’ decision.
Predicting Aviation Industry Performance (L/F) - 2019Mohammed Awad
Developing targets is one of the major issues in the aviation industry. In the recent time, many aviation sources predict different figures, based on their own analysis, and sight for the global aviation market. The dilemma, that, there is no basic rules for the predictors, especially aircrafts manufacture companies.
Three key performance indicators are used to measure airport performance: passenger numbers, aircraft movements, and air cargo activity. The document analyzes these indicators for Paris Charles de Gaulle Airport using three years of historical data to develop forecasting models. These models accurately predict 2017 passenger numbers of 67,387,303, aircraft movements of 471,599, air cargo of 1,959,530 metric tons, and air mail of 180,388 metric tons, with expected growth rates ranging from -1.9% to 1.67%. However, the air cargo forecasting model has a lower accuracy due to a coefficient of determination of only 71.21%.
Really this presentation address the performance of airline - how to forecast and know the LOAD FACTOR. in trems of ASK and RPK. case study is LH group. hope to enjoy !!!!!
Mohammed Salem Awadh provides forecasts for passenger traffic at Changi International Airport for 2018-2020. For annual forecasting, two trend models are used to minimize data discrepancies and set accurate targets. For monthly forecasting, seasonality patterns are determined without constraints. The document discusses two scenarios for each year, recommending the scenario with the lower predicted growth to avoid overestimating passenger levels. Overall, the forecasts provide a way to set targets and measure performance based on past traffic data and trends at Changi Airport.
Recently Airberlin reduce their capacity, by concentrating on a more profitable routes, Logically if we are squeezing the capacity (ASK)- SUPPLY , the other part – DEMAND will force to pump out i.e (RPK), then how we can get the complete picture, for 2016, definitely the load Factor should be increase in normal cases, … for this issue , there are two scenarios or results, first if the load factor increase significantly the Airberlin absorb the pressure of shifting their markets by increase the frequencies on their specific route which also aircrafts assist in this process by accepting more passengers than the usual figure in previous periods. But if the load factor is stable it means that a part of airberlin market spill to other competitors, and they are losing their markets ,yes the only benefits from this is the revenue or more specifically the YIELD, i.e is Yield is high enough to implement this reduction if so then they are in a safe side. While the cost will comes down due to the reduction in operation, means reduction in the variable cost.
The document discusses forecasting passenger traffic at Amsterdam Airport Schiphol (AMS) using trend and seasonality models. It aims to set a concurrent target figure based on long-term (21 year) and short-term (3 year) data while fulfilling constraints. Forecasts were generated for 2014 using a power trend model on 21 years of data and seasonality models on 3 years of data. Most segments were accurately forecasted, with only transfers labeled as "fair". The study shows it is possible to design targets using different timescales that provide the same forecast figure, though it requires careful analysis.
Five business Units that Icelandair addressing in their reports , Mainly
1- International Flights,
2- Regional And Greenland Flights,
3- Charter Flights,
4- Cargo,
5- Hotels,
- Four are analyzed while Charter Flights is not (as no seasonality patterned ).
- The analysis is concentrated on the main KPIs as PAX, ASKs, L/F,. ATKs, FTKs, and Room Utilizations.
- So most of airlines working on a clear objectives and that’s come with clear targets which lead us to set a clear picture of forecasting process.
- Based on that, our objective is to develop a clear message for top managements for the key performance figures of the airline, not just to compare month by month approach but to develop the right path ( time series ) in the future to set the right targets which consequently develop K.P. I for the airlines
airBaltic forecasting 2013, based on period of 2009-2012.
many scenarios are addressed, as best , trend, worse, and Final, the accuracy of these models are measure by developing Forecasting Accuracy Matrix.
Aviation Article : Getting The Right PictureMohammed Hadi
The document discusses different approaches to starting an airline, including those from Airbus, Boeing, the airline industry, and a proposed "U curve" approach.
It summarizes the key steps in Airbus's "Start Me Up V-Plan" approach and Boeing's "Startup Boeing - Roadmap" approach, both of which begin with an airline idea and business plan development before moving to aircraft sourcing, certification, and launch.
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The document forecasts annual passenger numbers for Paris Airport (ORY) in 2020. It recommends using two trend models - a general straight line model and a most recent data polynomial model - to minimize data discrepancies and set an accurate annual target. The midpoint between the models' forecasts is the most convenient number, unless the gap is large, in which case the halfway point between the extremes should be used. It presents two scenarios: maintaining 0% annual growth at 32,612,581 passengers, or an optimum solution of 32,772,162 passengers. The first scenario of no growth is recommended to avoid overestimating passenger growth and misleading the final results.
The document discusses predicting seasonal performance for market segments in the airline industry for 2019. It outlines a process for annual and monthly forecasting using trend models to define trends and seasonality. For annual forecasting, it uses a general trend model and most recent data trend model to determine if the trend is positive, negative, or has high discrepancy. For monthly forecasting, it defines seasonality patterns without constraints. It then provides predicted load factor percentages, R-squared values, and data discrepancy levels for 2019 for the total airline industry market and its segments in Africa, Asia Pacific, Europe, Latin America, Middle East, and North America.
Similar to Forecasting - MENA 2012 Conference (20)
Seven Performance Factors that Turkish airline are addressing in their reports , Mainly , Number of Landing, Available Seat Km, Revenue Passenger Km, Passengers Load Factor (%) , Passenger Carried, Cargo And Mails, Km Flown. So most of airlines working on a clear objectives and that’s come with clear targets which lead us to set a clear picture of forecasting process. Most of the results are fairs except Load Factor, the outcome is poor
Reading in the future, it is the third forecasting study concerning IAG, it is addressing the best forecasting scenario approach for setting targets and developing the level of key performance indicators for Airlines, as Load Factor, ASK, and RPK, as demonstrated by airline investors monthly /annually reports.
While the message of this study is to highlight the benefits of Setting Goals and Targets, thus airlines can developed an effective KPI System instead of looking backward – using month by month approach - to compare current performance with past performance instead of achieving targets.
Reading In The Future, it is a series of forecasting presentations, concerning airlines, most of the airlines addressing their performance of current month for current year to the same month of the previous year, i.e is month by month comparison by looking backward, no target setting/ level to achieve. Today the story is different and the approach is unique, airlines can define their seasonality patterned and consequently set their targets in future, and it is your decision to look backward and stick on it, or move onward by setting your targets and try to achieve them..
In 2012, a forecasting 2013 report for Malta International Airport is DONE, today, a second report is delivered, and using the same previous model, the result is fair and acceptable, using MAX/MIN signal tracking approach. Also update for the figures is done to forecast 2015.
The document discusses airline forecasting models, including evaluating a model using R2 and tracking signal over a 3-year period. It then compares the forecasts of the model to IATA's economic analysis from 2011 using a head-to-head analysis. Finally, it determines the model is fair based on the R2 being over 80% and tracking signal between -4 and 4.
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1. Driving Aviation Business
To Optimum Level
( Forecasting )
By : Mohammed Salem Awad
Adviser
Yemenia
Date: 20 March 2012
2. Forecasting
"The easiest way to predict
the future is to invent it."
Immanuel Kant
German Philosopher
Immanuel Kant
3. Outline
- Introduction
- Forecasting as planning tool for airlines and airports.
- Models of forecasting and their implementations in
practice.
- Factors, measuring the accuracy of forecasting.
- Defining airline seasonality model. Short term
forecasting
- Impact of the human touch in refinery the forecasting
results.
- Case Study
- Summary
- Contact
4. Forecasting
- Introduction
-Forecasting play a major roles in
Aviation.
- Industry Forecast
- ICAO , IATA, AIRBUS, BOEING,
- FAA
- Fleet Forecast, AIRBUS, BOEING
- Traffic Forecast, Airlines and
Airports
- Financial Forecast
5. Forecasting
- Forecasting as planning tool for airlines and airports.
- Airline Starting up
- Budget preparation
- Opening new route
- Airport Expansion
- Setting Targets
- Maintenance Planning
- Defining Seasonality
- Financial Planning
7. Forecasting
Trend Forecasting
Tell us in which direction (Growth) of
the historical data, and usually is a
long term forecast.
Seasonal Forecasting
Tell us the Seasonal, Cyclic shocks,
we used it to define the forecasting
Pattern
Trend vs Seasonal Forecasting
Forecasted Year of TREND
= Sum of 12 forecasted Seasonal
Months for same year,
7
8. Forecasting
- Measuring the accuracy of forecasting- Model Fairness
- Coefficient of Correlation
- Signal Tracking
Evaluation Forecasting
R2 = Coef. Of Determination T. S. = Tracking Signal
8
9. Forecasting
- Measuring the accuracy of forecasting- Model Fairness
- Coefficient of Correlation R
- Tracking Signal T.S.
Two Main factors: (conditions)
R2 > 80%
AND
-4 < T.S.< 4
R2 = Coef. Of Determination T. S. = Tracking Signal
9
11. Forecasting
- Traffic Forecasting
Selecting the right forecasting technique is the most successful factor,
since the forecasting pattern of airlines are subjected to many
elements, and each route characterized by its growth and seasonality
patterned, in term of seasonality, it is subjected to summer, winter, back
to school, Haj and Umora.
Basic Mathematical Output
Data Model (Results)
(Passengers)
26. Targets
Targets:
Most of the airlines and airports working on
achieving goals, targets, and evaluate their
achievements by comparing the current achieved
results to results of previous week, month, or year
i.e looking backward to analysis current situation.
But for setting targets we have to look forward,
forecast, develop a plan for current situation, to
achieved these targets in future in most efficient
way, so we can compare the current achievement by
the target one, here we can measure our
performance & KPI.
26
27. Classical Vs Planning System
Classical System Planning System
Comparing with Past Values Comparing with Planned Targets
27
39. Summary
Most of the airlines practice the classical methods, they
evaluate their current performance based on the past results, they just
looking to the back only for one Year ( or same period as month).
While this study explore the effect of historical data in terms
of trends forecast, in which direction the airline business moves, and
the second part is addressing the short term impacts of seasonality
(here months) based on three (3) years monthly data base, keeping in
minds the model fairness constrains i.e (R2) and (T.S.) to minimise the
forecasting errors, then compare the forecasted/planned figures by the
actual one.
The new constrain for this model is to match the accumulated forecasted
months by (Seasonal Model – 3 years data base) with the proposed forecasted year of
Trend analysis (Trend Model – 19 years data base).
Results:
By Planning method the accuracy is high in terms of Standard Deviation i.e 0.037
while the Classical method is 0.092.
39