OM Activity (sudeep).pptx
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This document discusses various demand forecasting methods. It begins by defining demand forecasting as estimating future demand for products, services, and resources needed for production. It then discusses trends, cycles, and seasonal patterns that influence demand. The document outlines the basic 7-step forecasting process and distinguishes between short-range forecasts of up to one year and long-range forecasts of 3 years or more. Finally, it describes 4 qualitative forecasting methods: executive opinions, the Delphi method, sales force polling, and consumer surveys.
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Forecasting in OPM.pptx
Forecasting is making predictions about future events or trends based on historical and present data. There are qualitative and quantitative forecasting methods. Qualitative methods include executive judgement, sales force opinions, and the Delphi method. Quantitative methods analyze past numerical data to identify trends and patterns using techniques like moving averages, exponential smoothing, and econometric models. Accurate forecasting allows businesses to effectively plan production and operations to meet demand.
Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )
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.
Mkt
Forecasting involves predicting the future by analyzing past and present data. Sales forecasting predicts future sales over a period based on factors like past performance, economic conditions, and market trends. Qualitative forecasting techniques incorporate expert judgment and include consumer surveys, sales force polling, executive opinions, and the Delphi technique. While qualitative techniques allow for rich data, they are also time-intensive and subject to human biases. Quantitative techniques provide structure but lack nuanced insights. An ideal approach combines both.
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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.
A presentation on Demand forecasting
Demand forecasting involves predicting future demand. Key factors in demand forecasting include the period, type of goods, competition level, price, and technology. Demand forecasting is used for short-term purposes like production planning and long-term purposes like capacity planning. Determinants of demand include price, income, related goods prices, tastes, and expectations. Forecasting methods for new products include analyzing substitutes, existing products, consumer opinions, expert opinions, and market tests. Good forecasting methods are accurate, durable, flexible, acceptable, available, and plausible. Macro-level factors like income, investment, population, government spending, and credit policy influence demand forecasts. Recent trends include greater importance of demand forecasting, use
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Introduction to demand forecasting
This is a presentation covering the concepts of demand forecasting. it includes the meaning of demand forecasting, purpose, scope and factors affecting demand forecasting. It also covers the methods of forecasting for both new and existing products.
Demand Forecasting
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.
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Forecasting in OPM.pptx
Forecasting is making predictions about future events or trends based on historical and present data. There are qualitative and quantitative forecasting methods. Qualitative methods include executive judgement, sales force opinions, and the Delphi method. Quantitative methods analyze past numerical data to identify trends and patterns using techniques like moving averages, exponential smoothing, and econometric models. Accurate forecasting allows businesses to effectively plan production and operations to meet demand.
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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.
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Forecasting involves predicting the future by analyzing past and present data. Sales forecasting predicts future sales over a period based on factors like past performance, economic conditions, and market trends. Qualitative forecasting techniques incorporate expert judgment and include consumer surveys, sales force polling, executive opinions, and the Delphi technique. While qualitative techniques allow for rich data, they are also time-intensive and subject to human biases. Quantitative techniques provide structure but lack nuanced insights. An ideal approach combines both.
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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.
A presentation on Demand forecasting
Demand forecasting involves predicting future demand. Key factors in demand forecasting include the period, type of goods, competition level, price, and technology. Demand forecasting is used for short-term purposes like production planning and long-term purposes like capacity planning. Determinants of demand include price, income, related goods prices, tastes, and expectations. Forecasting methods for new products include analyzing substitutes, existing products, consumer opinions, expert opinions, and market tests. Good forecasting methods are accurate, durable, flexible, acceptable, available, and plausible. Macro-level factors like income, investment, population, government spending, and credit policy influence demand forecasts. Recent trends include greater importance of demand forecasting, use
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This document discusses sales forecasting and demand forecasting. It covers the key elements of a good forecast including being timely, reliable, accurate, written, easy to understand and easy to use. It outlines the steps in the forecasting process from determining the purpose to monitoring the forecast. Finally, it describes different forecasting methods including qualitative methods like surveys and test marketing, and quantitative methods like time-series analysis and causal models. The overall goal of sales forecasting is to predict customer demand as accurately as possible.
Introduction to demand forecasting
This is a presentation covering the concepts of demand forecasting. it includes the meaning of demand forecasting, purpose, scope and factors affecting demand forecasting. It also covers the methods of forecasting for both new and existing products.
Demand Forecasting
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.
ForecastingDiscuss the different types of forecasts to include tim.pdf
Forecasting
Discuss the different types of forecasts to include time-series, causal, and qualitative models.
When might a researcher or project manager utilize exponential smoothing?
What benefit does a Delphi technique provide when working with qualitative-based decision
making?
Solution
Forecasting is basically the process of estimating or predicting the future trend, based on the
trend and information of the past and the present.Forecasting is a calculated assumption of how
the trend is going to be in a future date based on what we saw in the past and what we are
observing in the present scenario.
Time series methods:
These methods use historical data to assume future trends.
There are various time series methods such as,
1)Simple Moving Average Method: it is commonly used in technical analysis of financial data
such as stock prices,trading volumes or returns.Among the most popular technical indicators,
moving averages are used to gauge the direction of the current trend.It is calculated by averaging
a number of past data points. Once determined, the resulting average is then plotted onto a chart
in order to allow traders to look at smoothed data rather than focusing on the day-to-day price
fluctuations that are inherent in all financial markets.
As new values become available, the oldest data points must be dropped from the set and new
data points must come in to replace them. Thus, the data set is constantly \"moving\" to account
for new data as it becomes available. This method of calculation ensures that only the current
information is being accounted for.
for example, to calculate a basic 10-day moving average you would add up the closing prices
from the past 10 days and then divide the result by 10. The average thus obtained is plotted on a
chart. As the time progresses, we replace the first variable with the latest variable available ie.
latest closing price of 11th day, therefore getting a new avaerage. We plot this one too in the
chart. The chart thus formed gives a trend which is used for forecasting future movements.
2)Exponentially smoothed moving average:
Over the years, technicians have found two problems with the simple moving average. The first
problem lies in the time frame of the moving average (MA). Most technical analysts believe that
price action, the opening or closing stock price, is not enough on which to depend for properly
predicting buy or sell signals of the MA\'s crossover action. To solve this problem, analysts now
assign more weight to the most recent price data by using the exponentially smoothed moving
average (EMA).It is a type of infinite impulse response filter that applies weighting factors
which decrease exponentially. The weighting for each older datum decreases exponentially,
never reaching zero.
The exponentially smoothed moving average addresses both of the problems associated with the
simple moving average. First, the exponentially smoothed average assigns a greater weight to the
more recent data..
6 Demand forecasting
This document discusses demand forecasting methods used by businesses. It describes short-term and long-term demand forecasting and their purposes. Short-term forecasting relates to existing production capacity and helps with pricing and financial policies, while long-term forecasting informs new plant planning and product demand. The document also outlines survey methods like opinion surveys of salespeople and expert opinions, as well as statistical methods like time series analysis, barometric techniques, and regression/correlation analysis that use past data to project future trends.
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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.
Demand Forecasting in the restaurant management
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This document discusses demand forecasting, including its meaning, purpose, scope, methods, and significance. Demand forecasting predicts future sales trends based on current demand determinants. It is used for both short-term and long-term planning purposes. Determining the appropriate scope involves factors like the forecast period, level, purpose, product type, and relevant demand factors. The forecasting process involves identifying objectives, evaluating the product type, selecting a forecasting method, and interpreting results. Accurate demand forecasts are important for fulfilling objectives, budgeting, production planning, expansion decisions, and performance evaluation.
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Chapter - FIVE - DEMAND FORECASTING.pptx
The document provides an overview of demand forecasting techniques. It discusses the importance of demand forecasting for matching supply and demand. It outlines several qualitative and quantitative forecasting methods, including time series models like simple moving average, weighted moving average, exponential smoothing, and linear trend forecasting. The document also covers topics like forecast accuracy, collaborative planning, and software.
Deman forcasting
This document discusses demand forecasting. It defines forecasting, projection, and prediction and distinguishes between types of forecasts. It describes techniques for demand forecasting and discusses the uses and limitations of each technique. There are several key steps in demand forecasting including determining the nature and determinants of demand for the product, analyzing relevant factors and determinants, and choosing an appropriate forecasting technique given the product characteristics. Demand forecasting is important for business planning functions like production, inventory, profit, and financial planning.
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This document discusses key concepts in supply chain management including demand planning, forecasting, and collaborative planning. It covers:
1. Demand planning involves forecasting independent and dependent demand, and key steps like importing historical data, creating statistical forecasts, and managing forecasts.
2. Forecasting methods include qualitative, time series, causal, and simulation approaches. Multiple forecasting methods are often used to arrive at a combined forecast.
3. Collaborative planning, forecasting, and replenishment (CPFR) involves sharing forecast and business information between supply chain partners to enable automatic replenishment and eliminate excess inventory.
Demand Forecasting Me
1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and use of economic indicators.
Demand+forecasting me
1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and using economic indicators.
demand forecasting
1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and use of economic indicators.
SLE ECONOMICS
Demand forecasting involves estimating future demand for a product or service based on past data and consumer behavior analysis. There are several types of demand forecasting including passive, active, firm-level, industry-level, and national-level forecasting. Common demand forecasting methods include surveys, expert opinions, sales force opinions, and market experimentation. An effective forecasting method should be simple, accurate, economical, flexible, and able to provide quick results. Demand forecasting is important for business planning, budgeting, management decision making, and evaluating performance.
Forecasting
This document provides an overview of forecasting techniques. It defines forecasting as estimating future demand and resources needed to meet that demand. The document outlines the steps in the forecasting process, including determining the purpose and time horizon of forecasts, selecting a technique, gathering and analyzing data, preparing forecasts, and monitoring forecasts. It also discusses different types of forecasts like short, intermediate, and long-term and qualitative and quantitative approaches to forecasting. The overall purpose is to help operations managers understand how to develop accurate forecasts to aid in planning and decision making.
Demand Forecasting
Demand forecasting involves determining what products are needed, where, when, and in what quantities. It is a customer-focused activity that supports logistics planning like capacity, inventory, and business planning. Demand forecasting techniques can be qualitative like surveys or quantitative like time series analysis and regression models. The choice of technique depends on factors like the time period, data availability, and purpose of the forecast. Effective demand forecasting provides benefits like reduced uncertainties and improved operations.
Demand forecasting 12
Demand forecasting is essential for businesses to plan production levels. Common demand forecasting techniques include surveys of consumer intentions, expert opinions, analysis of historical sales data, and use of economic indicators related to demand. The optimal approach considers multiple techniques and applies judgment to account for uncertain factors. Forecasts should be presented to management simply with key assumptions and margin of error highlighted.
Demande forecasating
Demand forecasting can be done using two approaches - obtaining information from experts or consumers, or using past sales data through statistical techniques. [1] Expert surveys include opinion polls and the Delphi technique. [2] Consumer surveys can be a complete enumeration or sample survey. [3] Complex statistical methods include time series analysis, correlation/regression analysis, and simultaneous equation models. Demand forecasting helps with production, financial, and workforce planning as well as decision making.
price discounting strategy. As a result, the hotel decided to focu.docx
price discounting strategy. As a result, the hotel decided to focus on the government market because of the hotel's location.
The government market is price-sensitive (there is an allowable per diem) and not as quality-conscious, and the hotel could selectively discount to this large-volume market. Once again, it is important to point out that marketing planning is a continuous process. Marketing managers must evaluate the situation and adapt to changes that occur. Evaluating the success of the marketing plan is the moment of truth. Managers develop a plan to increase the probability of success, and once the plan is implemented, it is important for management to monitor the results. Any variance from the predicted results should be identified, evaluated, and corrected.
As the environment changes or the results vary, management may need to return to the appropriate step to reformulate marketing strategy or the action plans. The marketing planning process continues as a dynamic procedure, with sufficient flexibility allowing for changes in strategies, action plans, or implementation schedules.
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Sales forecasting The process for determining current sales and estimating future sales for a product or service.
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Expert opinion
Marketers look to a panel of experts with knowledge of the industry and the marketplace to provide a forecast.
Delphi technique The Delphi technique involves collecting forecasts, developing composites, and sending the data to those participating several times until a consensus results.
Sales force forecast
This technique aggregates the sales forecast of each .
Demandforecasting 1207335276942149-9
Demand forecasting involves determining customer demand for products and services in terms of what is needed, where, when, and in what quantities. It is a customer-focused activity that supports other planning functions and is the foundation of a company's logistics process. There are qualitative and quantitative methods for demand forecasting. Qualitative methods include surveys of buyer intentions, expert opinions, the Delphi method, market experimentation, and collective opinions. Quantitative methods include time series models, trend analysis, moving averages, exponential smoothing, and regression models.
3...demand forecasting 1207335276942149-9
Demand forecasting involves determining customer demand for products and services in terms of what, where, when, and how much is needed. It is a foundational process that supports planning activities like capacity, inventory, and business planning. There are qualitative and quantitative forecasting methods. Qualitative methods include surveys, expert opinions, and market experimentation, while quantitative methods analyze past demand trends and patterns using techniques like time series analysis, regression, and exponential smoothing. Accurate demand forecasting provides benefits like higher revenues, improved production planning, and better customer service.
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ForecastingDiscuss the different types of forecasts to include tim.pdf
Forecasting
Discuss the different types of forecasts to include time-series, causal, and qualitative models.
When might a researcher or project manager utilize exponential smoothing?
What benefit does a Delphi technique provide when working with qualitative-based decision
making?
Solution
Forecasting is basically the process of estimating or predicting the future trend, based on the
trend and information of the past and the present.Forecasting is a calculated assumption of how
the trend is going to be in a future date based on what we saw in the past and what we are
observing in the present scenario.
Time series methods:
These methods use historical data to assume future trends.
There are various time series methods such as,
1)Simple Moving Average Method: it is commonly used in technical analysis of financial data
such as stock prices,trading volumes or returns.Among the most popular technical indicators,
moving averages are used to gauge the direction of the current trend.It is calculated by averaging
a number of past data points. Once determined, the resulting average is then plotted onto a chart
in order to allow traders to look at smoothed data rather than focusing on the day-to-day price
fluctuations that are inherent in all financial markets.
As new values become available, the oldest data points must be dropped from the set and new
data points must come in to replace them. Thus, the data set is constantly \"moving\" to account
for new data as it becomes available. This method of calculation ensures that only the current
information is being accounted for.
for example, to calculate a basic 10-day moving average you would add up the closing prices
from the past 10 days and then divide the result by 10. The average thus obtained is plotted on a
chart. As the time progresses, we replace the first variable with the latest variable available ie.
latest closing price of 11th day, therefore getting a new avaerage. We plot this one too in the
chart. The chart thus formed gives a trend which is used for forecasting future movements.
2)Exponentially smoothed moving average:
Over the years, technicians have found two problems with the simple moving average. The first
problem lies in the time frame of the moving average (MA). Most technical analysts believe that
price action, the opening or closing stock price, is not enough on which to depend for properly
predicting buy or sell signals of the MA\'s crossover action. To solve this problem, analysts now
assign more weight to the most recent price data by using the exponentially smoothed moving
average (EMA).It is a type of infinite impulse response filter that applies weighting factors
which decrease exponentially. The weighting for each older datum decreases exponentially,
never reaching zero.
The exponentially smoothed moving average addresses both of the problems associated with the
simple moving average. First, the exponentially smoothed average assigns a greater weight to the
more recent data..
6 Demand forecasting
This document discusses demand forecasting methods used by businesses. It describes short-term and long-term demand forecasting and their purposes. Short-term forecasting relates to existing production capacity and helps with pricing and financial policies, while long-term forecasting informs new plant planning and product demand. The document also outlines survey methods like opinion surveys of salespeople and expert opinions, as well as statistical methods like time series analysis, barometric techniques, and regression/correlation analysis that use past data to project future trends.
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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.
Demand Forecasting in the restaurant management
Demand forecasting predicts future sales trends based on current demand determinants. It is important for business planning purposes. There are several key steps to demand forecasting including determining the objective, nature of goods, appropriate forecasting method, and interpreting results. The period, level, purpose, product type, and other factors must be defined. Demand forecasts help businesses fulfill objectives, prepare budgets, stabilize production and employment, expand, make decisions, and evaluate performance. They are significant for both short and long-term business planning.
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The document provides an overview of demand forecasting techniques. It discusses the importance of demand forecasting for matching supply and demand. It outlines several qualitative and quantitative forecasting methods, including time series models like simple moving average, weighted moving average, exponential smoothing, and linear trend forecasting. The document also covers topics like forecast accuracy, collaborative planning, and software.
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This document discusses demand forecasting. It defines forecasting, projection, and prediction and distinguishes between types of forecasts. It describes techniques for demand forecasting and discusses the uses and limitations of each technique. There are several key steps in demand forecasting including determining the nature and determinants of demand for the product, analyzing relevant factors and determinants, and choosing an appropriate forecasting technique given the product characteristics. Demand forecasting is important for business planning functions like production, inventory, profit, and financial planning.
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This document discusses key concepts in supply chain management including demand planning, forecasting, and collaborative planning. It covers:
1. Demand planning involves forecasting independent and dependent demand, and key steps like importing historical data, creating statistical forecasts, and managing forecasts.
2. Forecasting methods include qualitative, time series, causal, and simulation approaches. Multiple forecasting methods are often used to arrive at a combined forecast.
3. Collaborative planning, forecasting, and replenishment (CPFR) involves sharing forecast and business information between supply chain partners to enable automatic replenishment and eliminate excess inventory.
Demand Forecasting Me
1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and use of economic indicators.
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1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and using economic indicators.
demand forecasting
1. The document discusses various factors, methods, and considerations for accurate demand forecasting.
2. It describes different forecasting time horizons from short-term to long-term and various determinants that influence demand.
3. Several quantitative and qualitative forecasting techniques are outlined, including time series analysis, surveys, expert opinions, and use of economic indicators.
SLE ECONOMICS
Demand forecasting involves estimating future demand for a product or service based on past data and consumer behavior analysis. There are several types of demand forecasting including passive, active, firm-level, industry-level, and national-level forecasting. Common demand forecasting methods include surveys, expert opinions, sales force opinions, and market experimentation. An effective forecasting method should be simple, accurate, economical, flexible, and able to provide quick results. Demand forecasting is important for business planning, budgeting, management decision making, and evaluating performance.
Forecasting
This document provides an overview of forecasting techniques. It defines forecasting as estimating future demand and resources needed to meet that demand. The document outlines the steps in the forecasting process, including determining the purpose and time horizon of forecasts, selecting a technique, gathering and analyzing data, preparing forecasts, and monitoring forecasts. It also discusses different types of forecasts like short, intermediate, and long-term and qualitative and quantitative approaches to forecasting. The overall purpose is to help operations managers understand how to develop accurate forecasts to aid in planning and decision making.
Demand Forecasting
Demand forecasting involves determining what products are needed, where, when, and in what quantities. It is a customer-focused activity that supports logistics planning like capacity, inventory, and business planning. Demand forecasting techniques can be qualitative like surveys or quantitative like time series analysis and regression models. The choice of technique depends on factors like the time period, data availability, and purpose of the forecast. Effective demand forecasting provides benefits like reduced uncertainties and improved operations.
Demand forecasting 12
Demand forecasting is essential for businesses to plan production levels. Common demand forecasting techniques include surveys of consumer intentions, expert opinions, analysis of historical sales data, and use of economic indicators related to demand. The optimal approach considers multiple techniques and applies judgment to account for uncertain factors. Forecasts should be presented to management simply with key assumptions and margin of error highlighted.
Demande forecasating
Demand forecasting can be done using two approaches - obtaining information from experts or consumers, or using past sales data through statistical techniques. [1] Expert surveys include opinion polls and the Delphi technique. [2] Consumer surveys can be a complete enumeration or sample survey. [3] Complex statistical methods include time series analysis, correlation/regression analysis, and simultaneous equation models. Demand forecasting helps with production, financial, and workforce planning as well as decision making.
price discounting strategy. As a result, the hotel decided to focu.docx
price discounting strategy. As a result, the hotel decided to focus on the government market because of the hotel's location.
The government market is price-sensitive (there is an allowable per diem) and not as quality-conscious, and the hotel could selectively discount to this large-volume market. Once again, it is important to point out that marketing planning is a continuous process. Marketing managers must evaluate the situation and adapt to changes that occur. Evaluating the success of the marketing plan is the moment of truth. Managers develop a plan to increase the probability of success, and once the plan is implemented, it is important for management to monitor the results. Any variance from the predicted results should be identified, evaluated, and corrected.
As the environment changes or the results vary, management may need to return to the appropriate step to reformulate marketing strategy or the action plans. The marketing planning process continues as a dynamic procedure, with sufficient flexibility allowing for changes in strategies, action plans, or implementation schedules.
SALES FORECASTING
190 CHAPTER 5 DEVELOPING A MARKETING PLAN
SALES FORECASTING 189
One of the most critical components of a marketing plan is the forecast for sales. Sales forecasting is the process for determining current sales and estimating future sales for a product or service. The success of the firm often results from the accuracy of forecasts. The decisions about the elements of the marketing mix—product-service mix, price, promotion, and distribution— that are made during the situation analysis are based on sales forecasts.
Sales forecasting The process for determining current sales and estimating future sales for a product or service.
Sales Forecasting Techniques
Sales forecasting techniques are separated" into two broad categories: quantitative techniques and qualitative techniques. Quantitative techniques use past data values and employ a set of rules to obtain estimates of future sales. Qualitative techniques rely on judgment or intuition and tend to be used when data are not readily available. Quantitative methods can be further classified as either causal or time series. Both types of quantitative methods use trends in historical data to predict future sales; however, causal analysis techniques establish a cause and effect relationship between variables and the results. Using historical data to establish the relationship between sales and other factors that are believed to influence sales. These techniques model the relationships
Expert opinion
Marketers look to a panel of experts with knowledge of the industry and the marketplace to provide a forecast.
Delphi technique The Delphi technique involves collecting forecasts, developing composites, and sending the data to those participating several times until a consensus results.
Sales force forecast
This technique aggregates the sales forecast of each .
Demandforecasting 1207335276942149-9
Demand forecasting involves determining customer demand for products and services in terms of what is needed, where, when, and in what quantities. It is a customer-focused activity that supports other planning functions and is the foundation of a company's logistics process. There are qualitative and quantitative methods for demand forecasting. Qualitative methods include surveys of buyer intentions, expert opinions, the Delphi method, market experimentation, and collective opinions. Quantitative methods include time series models, trend analysis, moving averages, exponential smoothing, and regression models.
3...demand forecasting 1207335276942149-9
Demand forecasting involves determining customer demand for products and services in terms of what, where, when, and how much is needed. It is a foundational process that supports planning activities like capacity, inventory, and business planning. There are qualitative and quantitative forecasting methods. Qualitative methods include surveys, expert opinions, and market experimentation, while quantitative methods analyze past demand trends and patterns using techniques like time series analysis, regression, and exponential smoothing. Accurate demand forecasting provides benefits like higher revenues, improved production planning, and better customer service.
Similar to OM Activity (sudeep).pptx (20)
ForecastingDiscuss the different types of forecasts to include tim.pdf
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Recently uploaded
Applications of artificial Intelligence in Mechanical Engineering.pdf
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...
AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.Generative AI Use cases applications solutions and implementation.pdf
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
原版一模一样【微信：741003700 】【(爱大毕业证书)爱荷华大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 08 Doors and Windows.pdf
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Software Engineering and Project Management - Software Testing + Agile Method...
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
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Applications of artificial Intelligence in Mechanical Engineering.pdf
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
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Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
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OM Activity (sudeep).pptx
- 2. Demand Forecasting Definition of demand Forecasting: It is defined as estimating the feature demand for product and service and resource. Necessary to produce the output. Estimate the feature demand or service or resource product commonly referred to sales forecast. The sales forecast for demand are the starting point for the entire planing in production for a operation management. Example: Material planning,man planning, capacity planning, production scheduling all depend once forecasting. Forecast are also use to produce profit, revinew, cost, productivity changes price, energy raw material, interstate and price stock and bond.
- 3. Demand Behaviour-trend- cycle- seasonal background. Many people confuse cyclic behaviour with seasonal behaviour but they are really quit different. If the fluctuations are not of a fixed frequency then they are cyclic; if the frequency is unchanging and associated with some aspects ofe the calendar,then the pattern is seasonal. 1. TREND A trend exists when there is a long-term increase or decrease in the data. It does not have to be linear. Sometimes we will refer to a trend as “changing direction”, when it might go from an increasing trend to a decreasing trend. There is a trend in the antidiabetic drug sales data shown in Figure 2.2.
- 4. 2. CYCLE A cycle occurs when the data exhibit rises and falls that are not of a fixed frequency. These fluctuations are usually due to economic conditions, and are often related to the “business cycle”. The duration of these fluctuations is usually at least 2 year. 3.SEASONAL BACKGROUND A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the week. Seasonality is always of a fixed and known frequency. The monthly sales of antidiabetic drugs above shows seasonality which is induced partly by the change in the cost of the drugs at the end of the calendar year.
- 5. STEPS IN FORECASTING PROCESS The 7 basic steps involved in forecasting:- 1. Determining the purpose an objective and the forecast. 2. Select the item for which forecast are needed. 3. Determine the item horizone for the forecast. 4. Select the forecasting model. 5. Gather and analyse the data needed for the forecast. 6. Prepare the forecast. 7. Monitor the forecast.
- 6. 2. SHORT RANGE AND LONG RANGE FORECAST 1. Short range forecast This forecast time spents upto 1 year it can be even For monthly or weekly it is used for planning purchasing Job scheduling, Job assignment and production level. 2. Long range forecast From generally 3years time spent long and forecast are Used in new product planning and development, capital Expendition. A planning for facility location, research and Development.
- 7. 3.QUALITATIVE FORECAST METHODS 1. Executive Opinions The subjective views of executives or experts from sales, production, finance, purchasing, and administration are averaged to generate a forecast about future sales. Usually this method is used in conjunction with some quantitative method, such as trend extrapolation. The management team modifies the resulting forecast, based on their expectations. The advantage of this approach: The forecasting is done quickly and easily, without need of elaborate statistics. Also, the jury of executive opinions may be the only means of forecasting feasible in the absence of adequate data. The disadvantage: This, however, is that of group-think. This is a set of problems inherent to those who meet as a group. Foremost among these are high cohesiveness, strong leadership, and insulation of the group. 2. Delphi Method This is a group technique in which a panel of experts is questioned individually about their perceptions of future events. The experts do not meet as a group, in order to reduce the possibility that consensus is reached because of dominant personality factors. Instead, the forecasts and accompanying arguments are summarized by an outside party and returned to the experts along with further questions. This continues until a consensus is reached. Advantages: This type of method is useful and quite effective for long-range forecasting. The technique is done by questionnaire format and eliminates the disadvantages of group think. There is no committee or debate. Disadvantages: Low reliability is cited as the main disadvantage of the Delphi method, as well as lack of consensus from the returns.
- 8. 3. Sales Force Polling: Some companies use as a forecast source salespeople who have continual contacts with customers. They believe that the salespeople who are closest to the ultimate customers may have significant insights regarding the state of the future market. Forecasts based on sales force polling may be averaged to develop a future forecast. Or they may be used to modify other quantitative and/or qualitative forecasts that have been generated internally in the company The advantages of this forecast are: It is simple to use and understand. It uses the specialized knowledge of those closest to the action. It can place responsibility for attaining the forecast in the hands of those who most affect the actual results. The disadvantages include: salespeople’s being overly optimistic or pessimistic regarding their predictions and inaccuracies due to broader economic events that are largely beyond their control. 4. Consumer Surveys: Some companies conduct their own market surveys regarding specific consumer purchases. Surveys may consist of telephone contacts, personal interviews, or questionnaires as a means of obtaining data. Extensive statistical analysis usually is applied to survey results in order to test hypotheses regarding consumer behav