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Module 7 ATEM
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What is forecasting?
Attempt to quantify demand in a future time period.
Why is forecasting essential in air transportation management?
Necessary for planning future actions and decisions.
Distinguish between forecasting and planning.
Forecasting predicts future volumes; planning sets objectives and strategies.
How is forecasting integrated into planning in aviation enterprises?
Forecasts inform goals, strategies, and alternative courses of action.
What are the purposes of short-term, medium-term, and long-term forecasts?
Short-term: day-to-day operations, Medium-term: route planning, Long-term: fleet planning.
Give examples of decisions influenced by forecasting in the aviation industry.
Aircraft purchases, route introductions, and commuter aircraft development.
Explain the role of analysis in using forecasting in aviation management.
Helps in making choices among markets, services, and aircraft types.
How does forecasting contribute to planning in aviation firms?
Quantitative estimates of demand inform resource allocation and scheduling.
Why is control important in forecasting for companies in the air transportation sector?
Actual performance is compared to forecasts to assess market conditions.
What is the cautionary note about obtaining statistical data for forecasting?
Different sources may have varied reporting methods; use reputable sources.
What are the advantages of qualitative forecasting methods?
Flexibility, adaptability to changes, and early recognition of anomalies.
Name two types of qualitative forecasting methods.
Focus group and market survey.
What is the Delphi method, and how does it work?
Collects forecasts from an expert panel independently, then creates a consensus forecast.
How does historical analogy differ from other forecasting methods?
Relies on historical events; qualitative and dependent on forecaster's knowledge.
Explain the purpose of barometric forecasting in aviation.
Uses indicators to predict future values of variables, aiding in forecasting demand.
What is the significance of leading economic indicators in aviation forecasting?
Provides early signals of changes in the economy and predicts future demand.
Give an example of a lagging economic indicator relevant to aviation.
Unemployment rate.
How is the Delphi method different from historical analogy in forecasting?
Delphi method involves multiple expert opinions, while historical analogy relies on individual knowledge.
What is a coincident economic indicator in aviation forecasting?
Variables whose changes coincide with changes in other economic variables, e.g., crude oil prices.
What is the focus of judgmental forecasts in aviation?
What is the Delphi method in forecasting?
Involves multiple expert opinions to reach a consensus forecast.
What is the historical analogy method in forecasting?
Relies on individual knowledge of historical patterns to make forecasts.
What is a coincident economic indicator in aviation forecasting?
Variables whose changes coincide with changes in other economic variables, e.g., crude oil prices.
What is the focus of judgmental forecasts in aviation?
Educated guesses based on intuition and subjective evaluations.
How can expert opinion be used in judgmental forecasting?
Drawn from within or outside the company, tapping into managerial experience.
What role does the sales force play in judgmental forecasting?
Provides insights from the marketplace, especially in breaking down sales by territory and customer.
Explain the concept of poll forecasts in aviation.
Based on expressed intentions of the target market, gathered through surveys.
Why might polls be preferable in forecasting certain aviation markets?
Useful when buyers' intentions are not planned carefully or when experts and sales force opinions are unreliable.
What are the advantages of using sales force estimates in forecasting?
Close proximity to the marketplace, knowledge of local trends, and insights into competitor strategies.
How does market experimentation differ from other forecasting methods?
Involves testing new product factors in real-life markets, such as prices or packaging.
What is the primary advantage of market experiments in forecasting?
Provides real-world feedback on the acceptance of changes, like new in-flight meals.
Explain the role of focus groups in qualitative forecasting.
Informal gatherings to discuss a subject, providing insights into research problems, often used in planning and marketing.
What is the potential drawback of relying solely on expert opinion in forecasting?
Opinions are generally less satisfactory than facts, and responsibility for the forecast may be dispersed.
How does judgmental forecasting address situations with sparse or unavailable historical data?
Relies on intuition and subjective evaluation when historical data are lacking.
What is the major limitation of polls or surveys in forecasting aviation markets?
Susceptible to errors such as poor judgment, ignorance, and uncertainty among respondents.
What is the role of judgmental forecasting in decision making in aviation firms?
Often a powerful factor, especially when information is limited or historical data are scarce.
How does market survey differ from other qualitative forecasting methods?
Involves acquiring information by asking people what they think will happen, often using questionnaires.
What is forecasting's role in decision making in aviation firms?
Often a powerful factor, especially when information is limited or historical data are scarce.
How does market survey differ from other qualitative forecasting methods?
Involves acquiring information by asking people what they think will happen, often using questionnaires.
Explain the concept of barometric forecasting in aviation.
Uses current values of indicators to predict future values of other variables, aiding in forecasting.
What is the purpose of using historical analogy in forecasting?
Predicts the future based on historical events, requiring deep knowledge and experience from the forecaster.
What is the primary advantage of using the Delphi method in forecasting?
Collects forecasts from an expert panel independently, reducing steamroller or bandwagon problems.
How does the lagging economic indicator, such as the unemployment rate, relate to aviation forecasting?
Serves as a confirmation of general economic conditions, rather than a predictor.
What is the primary advantage of using market experiments in forecasting?
Provides real-world feedback on the acceptance of changes, such as new product factors.
Why might a company prefer judgmental forecasts in certain situations?
When no information or very little historical data are available, or when adjusting forecasts developed by other methods.
Explain the role of coincident economic indicators in aviation forecasting.
Variables whose changes roughly coincide with changes in other economic variables, e.g., crude oil prices in relation to jet fuel prices.
What role does the availability of electronic data processing play in choosing forecasting methods?
Technology influences the selection of forecasting methods by enabling the analysis of large datasets, implementation of complex models, and real-time processing for more accurate and efficient forecasts.
Explain the concept of a focus group and its application in aviation planning.
A focus group is a structured gathering of individuals aimed at obtaining feedback and insights. In aviation planning, focus groups can be used to gather opinions from travelers, industry experts, or stakeholders to inform decision-making and improve services.
How does market survey differ from market experiments in forecasting?
A market survey involves gathering data through questionnaires, interviews, or observations to understand consumer preferences and trends, while market experiments involve testing hypotheses or strategies in real market conditions to observe actual consumer behavior.
What are the potential risks associated with using historical analogy in forecasting?
Risks of historical analogy in forecasting include overlooking unique current factors, assuming past patterns will repeat, and failing to account for technological or societal changes that alter outcomes.
In what situations might judgmental forecasting be more reliable than quantitative methods?
Judgmental forecasting may be more reliable when dealing with novel or unpredictable situations, limited historical data, or when subjective insights from experts are critical for decision-making.
How does the Delphi method address the issue of potential biases in forecasting?
The Delphi method mitigates biases by obtaining independent and anonymous input from a panel of experts, allowing for diverse perspectives without the influence of group dynamics.
Discuss the significance of coincident economic indicators in the aviation industry.
Coincident economic indicators, such as passenger traffic, fuel prices, and employment levels, provide real-time insights into the current economic conditions, directly impacting aviation demand, costs, and operational decisions.
Explain how qualitative forecasts can adapt to changes in the economy or environment.
Qualitative forecasts are adaptable as experts can update and revise subjective estimates based on changing conditions, emerging trends, or new information, allowing for agile adjustments to forecasted outcomes.
What challenges might be associated with relying on sales force estimates for forecasting?
Challenges include bias or optimism in salesperson estimates, discrepancies between forecasted and actual sales, and limitations in considering broader market dynamics beyond the sales team's interactions.
How can aviation companies effectively use poll forecasts to gather market insights?
Aviation companies can design polls to gather insights on traveler preferences, emerging travel trends, and customer satisfaction, informing decision-making for marketing, service improvements, and route planning based on customer feedback.
Discuss the potential drawbacks of using focus groups in aviation planning.
Drawbacks include potential groupthink, dominant personalities shaping discussions, difficulty in representing diverse traveler perspectives, and challenges in translating group feedback into actionable strategies.
How do qualitative forecasting methods contribute to early detection of anomalies in data?
Qualitative methods, such as focus groups and expert opinions, enable the identification of anomalies by capturing subjective insights, divergent views, and early signals of market shifts that quantitative data alone may not reveal.
In what ways can aviation companies validate or adjust judgmental forecasts?
Companies can validate judgmental forecasts by comparing them to actual outcomes, seeking external expert opinions, conducting post-mortem analyses, and adjusting forecasts based on feedback, market changes, or new information.
What considerations should be taken into account when using market experiments in aviation forecasting?
Considerations include cost, ethical implications of testing on customers, potential impact on brand reputation, legal compliance, and the need for clear objectives, controls, and proper data collection methods.
Considerations for using market experiments in aviation forecasting
Factors such as cost, risk, and acceptance of changes should be considered when conducting market experiments in aviation forecasting.
Role of intuition in judgmental forecasting
Explore the influence of intuitive judgments on the accuracy of forecasts and subsequent decisions in judgmental forecasting.
Barometric forecasting in aviation
Discuss the complementary role of barometric forecasting alongside other quantitative and qualitative methods in aviation forecasting.
Challenges of obtaining accurate data for quantitative forecasting
Discuss issues related to data accuracy, sources, and reliability in quantitative forecasting for the aviation industry.
Relevance of market experiments in forecasting demand for in-flight services
Provide examples of how market experiments can be applied to predict demand for specific airline services in aviation forecasting.
Factors influencing the accuracy of sales force estimates in aviation forecasting
Examine variables that can impact the reliability of sales force opinions in forecasting for aviation companies.
Balancing historical analogy with other forecasting methods
Provide insights into integrating historical analogy with quantitative and qualitative methods for more robust forecasts in aviation forecasting.
What distinguishes quantitative forecasting from qualitative forecasting?
Quantitative forecasting uses statistical data for analysis, while qualitative forecasting relies on non-statistical information.
How is statistical information in forecasting categorized?
Statistical information is categorized into time-series and cross-sectional data.
What is a major advantage of quantitative forecasting?
Tests of reliability can easily determine the accuracy of the forecast.
What are the two broad categories of quantitative forecasting methods covered in the text?
Time-series analysis and regression analysis.
Why might history not always be a correct predictor of the future in quantitative forecasting?
History is not always a correct predictor due to changing circumstances.
How has advanced statistical computer software impacted data collection for quantitative forecasting?
It has simplified data collection and processing, making quantitative forecasting easier.
What cautionary statement applies to the quality of data in quantitative forecasting?
"Garbage in–garbage out" cautionary statement applies.
What is the focus of cross-sectional data in quantitative forecasting?
Cross-sectional data are compiled for different variables at a single point in time.
Define causal forecasts in quantitative forecasting.
Causal forecasts are based on statistical relationships between the forecasted variable and explanatory variables.
What are some limitations of causal models in forecasting?
Difficulty in quantifying all variables, assumption of easier forecasting of explanatory variables, and the assumption of continuity in functional relationships.
What is analysis in time-series data?
It involves studying long-term trends, cyclical variations, seasonal phenomena, and irregular or unique phenomena.
How is a trend defined in time-series analysis?
A trend is a long-term tendency to change with time, influenced by factors such as population, technology, and economic shifts.
What is cyclical variation in time-series analysis?
Cyclical variation is the fluctuation of the forecast variable due to the business cycle.
Explain seasonal variations in time-series analysis.
Seasonal variations are fluctuations associated with time periods, influenced by weather and social customs.
What are irregular variations in time-series analysis?
Irregular variations are erratic, non-recurrent events like strikes, wars, or natural disasters.
How does time-series analysis contribute to forecasting in the aviation industry?
It detects recurring patterns in data, allowing for predictions based on historical sequences.
How is the accuracy of forecasting determined in time-series analysis?
The accuracy depends on predictions of changing factors that may or may not repeat.
What are some examples of variables influencing long-term trends in aviation?
Factors include liberalization, deregulation, changes in technology, population growth, and privatization.
How does cyclical variation in aviation impact forecasting?
It reflects fluctuations in economic activity, affecting variables like new aircraft sales, prices, and profitability in the aviation industry.
What role does statistical correlation play in causal forecasting methods?
It measures the strength and direction of the relationship between variables, essential for identifying causal factors.
How has the introduction of advanced statistical computer software impacted the drawbacks associated with data collection in quantitative forecasting?
Advanced software helps handle larger datasets, identify complex relationships, and improve accuracy, reducing the limitations of data collection.
In time-series analysis, how does the aviation industry use daily oil prices as relevant time-series data?
Oil prices impact operating costs, fuel surcharges, and airline profitability, making them crucial time-series data for forecasting in aviation.
What are some examples of irregular variations in the aviation industry, as mentioned in the text?
Examples include strikes, wars, natural disasters, and pandemics, which have unpredictable and non-recurring effects on aviation operations.
How does the length of the historical period studied impact the projections in time-series analysis?
A longer historical period provides more data for analysis but may include outdated trends, while a shorter period may miss long-term patterns. Both impact the accuracy of projections.
Can you provide an example of a situation where a trend line in time-series analysis might be curved upward?
An upward curve might occur when the trend shows a continuing increase over time, such as the growth in air travel demand due to increased globalization and economic expansion.
What factors are considered when constructing a causal model for forecasting in the aviation industry?
Factors include fuel prices, exchange rates, GDP, consumer confidence, and airline route networks, among others, to understand their impact on aviation operations.
Q26: Can you provide an example of a situation where a trend line in time-series analysis might be curved upward?
An example of a situation where a trend line in time-series analysis might be curved upward is when there is a consistent increase in the variable being studied over time, leading to a positive curvature in the trend line.
Q27: What factors are considered when constructing a causal model for forecasting in the aviation industry?
Factors considered when constructing a causal model for forecasting in the aviation industry may include fuel prices, economic conditions, airline route expansions, and government regulations, among others.
Q28: How do airlines use trend lines in time-series analysis when forecasting variables like departures or enplanements?
Airlines use trend lines in time-series analysis by identifying historical patterns in departures or enplanements to forecast future trends, allowing them to make informed decisions regarding capacity and resource allocation.
Q29: Why is it important to recognize the limitations of causal models in forecasting, as mentioned in the text?
It is important to recognize the limitations of causal models in forecasting to avoid overestimating the accuracy of predictions and to understand that causal relationships may not fully capture the complexity of the aviation industry's dynamics.
Q30: How do seasonal variations in the aviation industry relate to weather and social customs?
Seasonal variations in the aviation industry relate to weather and social customs by influencing travel patterns, such as increased demand for flights during holidays or peak vacation seasons due to favorable weather and social traditions.
Q31: Provide an example of how the aviation industry might be impacted by cyclical variations due to the business cycle.
The aviation industry might be impacted by cyclical variations due to the business cycle, such as experiencing reduced demand during economic recessions and increased demand during economic expansions, reflecting the cyclical nature of economic activity.
Q32: How do causal models differ from time-series models in terms of their approach to forecasting?
Causal models differ from time-series models in their approach to forecasting by incorporating independent variables and causal relationships to predict outcomes, whereas time-series models rely on analyzing historical data patterns to forecast future values.
Q33: Explain how time-series analysis differs from trend extension in forecasting air transportation demand.
Time-series analysis differs from trend extension in forecasting air transportation demand by encompassing more comprehensive data analysis, including seasonality, cyclical variations, and irregular factors, to provide a more detailed forecast of demand dynamics.
Q34: In quantitative forecasting, what challenges may arise when trying to quantify all variables in causal models?
In quantitative forecasting, challenges may arise when trying to quantify all variables in causal models, such as identifying and measuring the strength of causal relationships, accounting for complex interactions among multiple variables, and obtaining accurate historical data for analysis.
Q35: How does the aviation industry deal with the forecasting challenges posed by irregular variations, such as strikes or wars?
The aviation industry deals with forecasting challenges posed by irregular variations, such as strikes or wars, by implementing scenario analysis, contingency planning, and risk management strategies to mitigate the impact of unpredictable events on forecasting accuracy.
Q36: What are the potential implications of assuming that a functional relationship from the past will persist in causal forecasting?
The potential implications of assuming that a functional relationship from the past will persist in causal forecasting include overlooking dynamic shifts in industry dynamics, economic conditions, technological advancements, and regulatory changes that can lead to inaccurate projections and decision-making.
Q37: How does the aviation industry determine the time period specified for a particular trend in time-series analysis?
The aviation industry determines the time period specified for a particular trend in time-series analysis by considering the relevant historical data range, the frequency of observed fluctuations, and the industry's specific characteristics to capture meaningful patterns for forecasting purposes.
Q38: In the context of quantitative forecasting, what is meant by the term "random effect"?
In the context of quantitative forecasting, a "random effect" refers to unaccounted or unpredictable variations that affect the outcome, such as unexpected market shocks, operational disruptions, or other unpredictable events that impact forecasting accuracy.
Q39: How might advancements in technology impact the accuracy of forecasting in the aviation industry?
Advancements in technology may impact the accuracy of forecasting in the aviation industry by enabling more sophisticated data analysis, real-time tracking of customer behavior, predictive analytics, and improved decision support systems, leading to enhanced forecasting precision.
Q40: Can you elaborate on the factors that induce seasonal variations in air transportation demand?
Factors that induce seasonal variations in air transportation demand include holiday seasons, school vacations, climate conditions, cultural events, and leisure travel patterns, all of which contribute to fluctuating demand for air travel at different times of the year.
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