The document discusses various methods for demand forecasting, including qualitative and quantitative approaches. Qualitative methods involve expert judgment through individual specialists, group consensus, or the Delphi method. Quantitative methods include causal models using regression analysis and time series analysis. Simple time series models discussed are the projection method, simple moving average, weighted moving average, and basic exponential smoothing. Accuracy of forecasts is also addressed through measures like average error, bias, and mean absolute deviation. The document provides an example application of these time series models to a sample demand data set.
The document discusses the challenges in building effective stress testing models that meet regulatory guidelines. It outlines key things regulators look for, such as models being clearly linked to macroeconomic variables and scenario design covering all material risks. It also discusses techniques for incorporating macroeconomic factors into models through macro-to-micro modeling. The document emphasizes balancing model complexity with usability and explains how to build flexible models to address atypical stress scenarios.
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.
Forecasting lessons from FMCG aisles by Thinus Hermann at the 37th Annual SAPICS conference and exhibition, held at Sun City, South Africa on 1 June 2015.
1. Demand forecasting is used to estimate future demand for products over specific time periods and is important for planning operations.
2. Demand can be categorized by the type of goods (consumer vs capital) and time period (short, medium, long term). Quantitative forecasting techniques include trend projection methods like time series analysis and regression.
3. Techniques like ARIMA combine moving averages and autoregressive methods to model trends and differences in time series data. Regression analysis uses statistical methods to model relationships between demand and influencing factors.
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.
This document discusses modeling approaches for operational loss forecasts in stress testing. It describes the seven categories of operational loss events defined by Basel-II, and requirements for operational risk management programs including internal loss data, external loss data, scenario analysis, and business environment factors. It then covers three approaches to calculating operational risk capital and describes a regression-based method used for stress testing that links losses to macroeconomic scenarios. The document discusses defining units of measure, testing unit homogeneity, modeling frequency and severity, and considers Poisson, negative binomial, and time series regressions.
This document discusses enterprise resource planning (ERP) systems and their importance in modern corporate environments. It begins with an introduction to increasing global competition and the need for businesses to gain competitive advantages. The next sections discuss how ERP systems can help integrate key business functions and manage resources and information flow. As an example, the document analyzes financial data from an Indian graphite company to compare metrics like expenses and production over quarters, demonstrating how ERP data analysis can inform business strategies. Finally, it concludes that effective ERP systems are crucial for companies to adapt, grow, and handle challenges like economic recessions in today's business world.
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
The document discusses the challenges in building effective stress testing models that meet regulatory guidelines. It outlines key things regulators look for, such as models being clearly linked to macroeconomic variables and scenario design covering all material risks. It also discusses techniques for incorporating macroeconomic factors into models through macro-to-micro modeling. The document emphasizes balancing model complexity with usability and explains how to build flexible models to address atypical stress scenarios.
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.
Forecasting lessons from FMCG aisles by Thinus Hermann at the 37th Annual SAPICS conference and exhibition, held at Sun City, South Africa on 1 June 2015.
1. Demand forecasting is used to estimate future demand for products over specific time periods and is important for planning operations.
2. Demand can be categorized by the type of goods (consumer vs capital) and time period (short, medium, long term). Quantitative forecasting techniques include trend projection methods like time series analysis and regression.
3. Techniques like ARIMA combine moving averages and autoregressive methods to model trends and differences in time series data. Regression analysis uses statistical methods to model relationships between demand and influencing factors.
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.
This document discusses modeling approaches for operational loss forecasts in stress testing. It describes the seven categories of operational loss events defined by Basel-II, and requirements for operational risk management programs including internal loss data, external loss data, scenario analysis, and business environment factors. It then covers three approaches to calculating operational risk capital and describes a regression-based method used for stress testing that links losses to macroeconomic scenarios. The document discusses defining units of measure, testing unit homogeneity, modeling frequency and severity, and considers Poisson, negative binomial, and time series regressions.
This document discusses enterprise resource planning (ERP) systems and their importance in modern corporate environments. It begins with an introduction to increasing global competition and the need for businesses to gain competitive advantages. The next sections discuss how ERP systems can help integrate key business functions and manage resources and information flow. As an example, the document analyzes financial data from an Indian graphite company to compare metrics like expenses and production over quarters, demonstrating how ERP data analysis can inform business strategies. Finally, it concludes that effective ERP systems are crucial for companies to adapt, grow, and handle challenges like economic recessions in today's business world.
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
Managerial economics is the application of economic theory and methodology to business administration practice and decision making. It helps managers allocate scarce resources efficiently within an organization. Managerial economics draws concepts from microeconomics and uses analytical tools and techniques to improve decision making. It is concerned with both positive economics, which examines what is, and normative economics, which examines what should be to achieve organizational goals. The subject matter of managerial economics includes demand analysis, cost analysis, inventory management, pricing, profit management, and capital budgeting. It is related to and integrates concepts from economics, mathematics, statistics, management theory, and accounting.
Indian Stock Market Using Machine Learning(Volume1, oct 2017)sk joshi
This document summarizes a research paper that uses machine learning and financial ratios to classify stocks traded on the Indian stock market as either "outperformers" or "underperformers" based on their rate of return. The study uses quarterly data from 50 large market capitalization companies over one year. A support vector machine model achieved 80% accuracy in predicting stock performance on a sector-by-sector basis. While promising, the author acknowledges limitations and outlines areas for further improvement, such as incorporating more external factors like macroeconomic data.
IRJET- Predicting Sales in SupermarketsIRJET Journal
This document discusses predicting sales in supermarkets using machine learning techniques. It begins with an abstract that outlines the goal of developing an intelligent forecasting system using different machine learning techniques to build and adjust a sales forecasting model. The document then provides an introduction that discusses the importance of demand forecasting and challenges in predicting sales due to various internal and external factors. It reviews different forecasting techniques including time series models, exponential smoothing, neural networks and discusses using these approaches to more accurately predict future sales values.
Operational research (OR) is a discipline that deals with applying advanced analytical methods to help make better decisions. OR uses scientific methods and especially mathematical modeling to study complex problems. It is considered a subfield of applied mathematics. Some key applications of OR include scheduling, facility planning, planning and forecasting, credit scoring, marketing, and defense planning. OR takes a systems approach, uses interdisciplinary teams, and aims to optimize objectives subject to constraints through quantitative modeling and analysis.
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 forecasting is a technique used to predict consumer demand for goods and services. It is important for suppliers to forecast demand accurately to maintain the optimal level of inventory. Overestimating demand wastes resources while underestimating leads to lost sales. Time series analysis is an objective forecasting method that decomposes historical sales data into trend, seasonality, and irregular components. By analyzing monthly sales data over three years using moving averages and regression, a supplier can forecast sales for the next year with seasonal adjustments to account for periodic fluctuations in demand.
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 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 various quantitative forecasting techniques including time series models. It provides an overview of moving averages, exponential smoothing, trend projections, and decomposition models. Examples are given to illustrate computing forecasts using a three-month simple moving average and a three-month weighted moving average. Exponential smoothing is also introduced as a type of moving average that requires less data to compute forecasts.
Management science uses analytical methods and decision-making techniques to help organizations operate efficiently and manage risk. It draws from fields like applied mathematics, statistics, and computer modeling to solve problems in areas such as production, inventory management, and scheduling. Some common techniques include linear programming, nonlinear programming, integer programming, stochastic programming, queuing theory, and simulation modeling.
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.
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.
Introduction, Meaning and Characteristics of Operations Research Background of Operations Research, Operations Research, Scope of Operations Research, Finance department, Personnel Management, applications of operations research in business, applications of operations research, Hewlett-Packard, CHARACTERISTICS OF OPERATIONS RESEARCH, are addressed.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document discusses the Applied Marketing Research course taken by the author. It explains that the course teaches important marketing analytics skills like conjoint analysis, multidimensional scaling, experimental design, Latin square design, and structural equation modeling. These skills help turn data into actionable business insights. The author chose this course to gain experience applying statistical techniques and presenting results in a clear way to help decision making. Data-driven marketing analytics can help with tasks like product development and positioning by providing a structured understanding of customer preferences.
This powerpoint presentation was done as part of the course STAT 591 titled Mater's Seminar during Third semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh.
This document discusses various methods for estimating and forecasting demand, including direct methods like consumer interviews and market studies. It also discusses empirical demand functions that are derived from actual market data and can be used to model demand. Time-series forecasts use linear trend forecasting to model how a variable changes over time. Seasonal variation is also discussed and can be accounted for using dummy variables. Forecasting accuracy decreases the further into the future forecasts are made and model misspecification can also reduce reliability.
This document discusses demand estimation and forecasting for the Close-Up toothpaste brand. It provides historical sales data from 2007-2016 which shows an upward trend. The trend equation method is used to forecast sales for 2017-2020. Sales are predicted to continue increasing based on the positive slope value in the trend equation. Key details on Close-Up products and marketing positioning are also summarized.
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 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.
This document discusses time series analysis and forecasting methods. It covers several key topics:
1. Time series decomposition which involves separating a time series into seasonal, trend, cyclical, and irregular components. Seasonal and trend components are then modeled and forecasts are made by recomposing these components.
2. Common forecasting techniques including exponential smoothing to reduce random variation, modeling seasonality using seasonal indices, and incorporating trends and cycles.
3. The process of time series forecasting which involves decomposing historical data, modeling each component, and recomposing forecasts by applying the component models to future periods. Accuracy and sources of error in forecasts are also discussed.
Hierarchical Forecasting and Reconciliation in The Context of Temporal HierarchyIRJET Journal
This document discusses hierarchical forecasting and reconciliation for temporally aggregated data that exhibits seasonal patterns. It analyzes 10 years of monthly foreign tourist visitation data to Kerala, India aggregated into monthly, quarterly, half-yearly, and annual levels. Different forecasting strategies are evaluated, including bottom-up, top-down, and optimal combination approaches using exponential smoothing techniques. The mean absolute percentage error is used to compare the accuracy of forecasts from each strategy. Preliminary results suggest the bottom-up approach outperforms other strategies on average and across all levels of the data hierarchy.
Environmental Pollution RecommendationThere is a concern in yo.docxSALU18
Environmental Pollution Recommendation
There is a concern in your community regarding the environment. You've been tasked to research and present the concerns to your local or state government (California)
Perform an internet search to identify an instance of environmental pollution in your state.
Create a 5-to 8-slide PowerPoint® presentation or a 350-to 525-word proposal.
· Identify the effects of this pollution on human health and the environment.
· Explain the causes of this pollution.
· Recommend ways to prevent/clean up this type of environmental pollution.
· Include appropriate images.
Use at least 2 outside references.
Format your presentation and references consistent with APA guidelines.
· For Online and Directed Study students, these are Microsoft® PowerPoint® presentations with notes similar to what you would present orally.
Learning Objectives
After completing this chapter, you should be able to:
• Define a model and describe how models can be used to analyze operating
problems.
• Discuss the nature of forecasting.
• Explain how forecasting can be applied to problems.
• Describe methods of forecasting, including judgment and experience, time-series
analysis, and regression and correlation.
• Construct forecasting models.
• Estimate forecasting errors.
6 .Thinkstock
Models and Forecasting
von70154_06_c06_139-178.indd 139 3/6/13 3:18 PM
CHAPTER 6Section 6.1 Introduction to Models and Decision Making
6.1 Introduction to Models and Decision Making
In order for an organization to design, build, and operate a production facility that is capable of meeting customer demand for services (such as health care) or goods (such as ceiling fans), it is necessary for management to obtain an estimate or forecast of demand
for its products. A forecast is a prediction of the future. It often examines historical data to
determine relationships among key variables in a problem and uses those relationships to
make statements about the future value of one or more of the variables. Once an organiza-
tion has a forecast of demand, it can make decisions regarding the volume of product that
needs to be produced, the number of workers to hire, and other key operating variables.
A model is an abstraction from the real problem of the key variables and relationships in
order to simplify the problem. The purpose of modeling is to provide the user with a bet-
ter understanding of the problem and with a means of manipulating the results for what-
if analyses. Forecasting uses models to help organizations predict important parameters.
Demand is one of those parameters, but cost, revenue, profits, and other variables can also
be forecasted. The purpose of this chapter is to discuss models and describe how they can
be applied to business problems, and to explain forecasting and its role in operations.
Stages in Decision Making
Organizational performance is a result of the decisions that management makes over a
period of time: ...
Managerial economics is the application of economic theory and methodology to business administration practice and decision making. It helps managers allocate scarce resources efficiently within an organization. Managerial economics draws concepts from microeconomics and uses analytical tools and techniques to improve decision making. It is concerned with both positive economics, which examines what is, and normative economics, which examines what should be to achieve organizational goals. The subject matter of managerial economics includes demand analysis, cost analysis, inventory management, pricing, profit management, and capital budgeting. It is related to and integrates concepts from economics, mathematics, statistics, management theory, and accounting.
Indian Stock Market Using Machine Learning(Volume1, oct 2017)sk joshi
This document summarizes a research paper that uses machine learning and financial ratios to classify stocks traded on the Indian stock market as either "outperformers" or "underperformers" based on their rate of return. The study uses quarterly data from 50 large market capitalization companies over one year. A support vector machine model achieved 80% accuracy in predicting stock performance on a sector-by-sector basis. While promising, the author acknowledges limitations and outlines areas for further improvement, such as incorporating more external factors like macroeconomic data.
IRJET- Predicting Sales in SupermarketsIRJET Journal
This document discusses predicting sales in supermarkets using machine learning techniques. It begins with an abstract that outlines the goal of developing an intelligent forecasting system using different machine learning techniques to build and adjust a sales forecasting model. The document then provides an introduction that discusses the importance of demand forecasting and challenges in predicting sales due to various internal and external factors. It reviews different forecasting techniques including time series models, exponential smoothing, neural networks and discusses using these approaches to more accurately predict future sales values.
Operational research (OR) is a discipline that deals with applying advanced analytical methods to help make better decisions. OR uses scientific methods and especially mathematical modeling to study complex problems. It is considered a subfield of applied mathematics. Some key applications of OR include scheduling, facility planning, planning and forecasting, credit scoring, marketing, and defense planning. OR takes a systems approach, uses interdisciplinary teams, and aims to optimize objectives subject to constraints through quantitative modeling and analysis.
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 forecasting is a technique used to predict consumer demand for goods and services. It is important for suppliers to forecast demand accurately to maintain the optimal level of inventory. Overestimating demand wastes resources while underestimating leads to lost sales. Time series analysis is an objective forecasting method that decomposes historical sales data into trend, seasonality, and irregular components. By analyzing monthly sales data over three years using moving averages and regression, a supplier can forecast sales for the next year with seasonal adjustments to account for periodic fluctuations in demand.
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 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 various quantitative forecasting techniques including time series models. It provides an overview of moving averages, exponential smoothing, trend projections, and decomposition models. Examples are given to illustrate computing forecasts using a three-month simple moving average and a three-month weighted moving average. Exponential smoothing is also introduced as a type of moving average that requires less data to compute forecasts.
Management science uses analytical methods and decision-making techniques to help organizations operate efficiently and manage risk. It draws from fields like applied mathematics, statistics, and computer modeling to solve problems in areas such as production, inventory management, and scheduling. Some common techniques include linear programming, nonlinear programming, integer programming, stochastic programming, queuing theory, and simulation modeling.
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.
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.
Introduction, Meaning and Characteristics of Operations Research Background of Operations Research, Operations Research, Scope of Operations Research, Finance department, Personnel Management, applications of operations research in business, applications of operations research, Hewlett-Packard, CHARACTERISTICS OF OPERATIONS RESEARCH, are addressed.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document discusses the Applied Marketing Research course taken by the author. It explains that the course teaches important marketing analytics skills like conjoint analysis, multidimensional scaling, experimental design, Latin square design, and structural equation modeling. These skills help turn data into actionable business insights. The author chose this course to gain experience applying statistical techniques and presenting results in a clear way to help decision making. Data-driven marketing analytics can help with tasks like product development and positioning by providing a structured understanding of customer preferences.
This powerpoint presentation was done as part of the course STAT 591 titled Mater's Seminar during Third semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh.
This document discusses various methods for estimating and forecasting demand, including direct methods like consumer interviews and market studies. It also discusses empirical demand functions that are derived from actual market data and can be used to model demand. Time-series forecasts use linear trend forecasting to model how a variable changes over time. Seasonal variation is also discussed and can be accounted for using dummy variables. Forecasting accuracy decreases the further into the future forecasts are made and model misspecification can also reduce reliability.
This document discusses demand estimation and forecasting for the Close-Up toothpaste brand. It provides historical sales data from 2007-2016 which shows an upward trend. The trend equation method is used to forecast sales for 2017-2020. Sales are predicted to continue increasing based on the positive slope value in the trend equation. Key details on Close-Up products and marketing positioning are also summarized.
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 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.
This document discusses time series analysis and forecasting methods. It covers several key topics:
1. Time series decomposition which involves separating a time series into seasonal, trend, cyclical, and irregular components. Seasonal and trend components are then modeled and forecasts are made by recomposing these components.
2. Common forecasting techniques including exponential smoothing to reduce random variation, modeling seasonality using seasonal indices, and incorporating trends and cycles.
3. The process of time series forecasting which involves decomposing historical data, modeling each component, and recomposing forecasts by applying the component models to future periods. Accuracy and sources of error in forecasts are also discussed.
Hierarchical Forecasting and Reconciliation in The Context of Temporal HierarchyIRJET Journal
This document discusses hierarchical forecasting and reconciliation for temporally aggregated data that exhibits seasonal patterns. It analyzes 10 years of monthly foreign tourist visitation data to Kerala, India aggregated into monthly, quarterly, half-yearly, and annual levels. Different forecasting strategies are evaluated, including bottom-up, top-down, and optimal combination approaches using exponential smoothing techniques. The mean absolute percentage error is used to compare the accuracy of forecasts from each strategy. Preliminary results suggest the bottom-up approach outperforms other strategies on average and across all levels of the data hierarchy.
Environmental Pollution RecommendationThere is a concern in yo.docxSALU18
Environmental Pollution Recommendation
There is a concern in your community regarding the environment. You've been tasked to research and present the concerns to your local or state government (California)
Perform an internet search to identify an instance of environmental pollution in your state.
Create a 5-to 8-slide PowerPoint® presentation or a 350-to 525-word proposal.
· Identify the effects of this pollution on human health and the environment.
· Explain the causes of this pollution.
· Recommend ways to prevent/clean up this type of environmental pollution.
· Include appropriate images.
Use at least 2 outside references.
Format your presentation and references consistent with APA guidelines.
· For Online and Directed Study students, these are Microsoft® PowerPoint® presentations with notes similar to what you would present orally.
Learning Objectives
After completing this chapter, you should be able to:
• Define a model and describe how models can be used to analyze operating
problems.
• Discuss the nature of forecasting.
• Explain how forecasting can be applied to problems.
• Describe methods of forecasting, including judgment and experience, time-series
analysis, and regression and correlation.
• Construct forecasting models.
• Estimate forecasting errors.
6 .Thinkstock
Models and Forecasting
von70154_06_c06_139-178.indd 139 3/6/13 3:18 PM
CHAPTER 6Section 6.1 Introduction to Models and Decision Making
6.1 Introduction to Models and Decision Making
In order for an organization to design, build, and operate a production facility that is capable of meeting customer demand for services (such as health care) or goods (such as ceiling fans), it is necessary for management to obtain an estimate or forecast of demand
for its products. A forecast is a prediction of the future. It often examines historical data to
determine relationships among key variables in a problem and uses those relationships to
make statements about the future value of one or more of the variables. Once an organiza-
tion has a forecast of demand, it can make decisions regarding the volume of product that
needs to be produced, the number of workers to hire, and other key operating variables.
A model is an abstraction from the real problem of the key variables and relationships in
order to simplify the problem. The purpose of modeling is to provide the user with a bet-
ter understanding of the problem and with a means of manipulating the results for what-
if analyses. Forecasting uses models to help organizations predict important parameters.
Demand is one of those parameters, but cost, revenue, profits, and other variables can also
be forecasted. The purpose of this chapter is to discuss models and describe how they can
be applied to business problems, and to explain forecasting and its role in operations.
Stages in Decision Making
Organizational performance is a result of the decisions that management makes over a
period of time: ...
Demand forecasting aims to understand and predict consumer demand for goods. Accurately forecasting demand is important for efficient supply chain management. Errors in demand forecasting can lead to lost sales from underestimating demand or excess inventory from overestimating demand. Various statistical and machine learning techniques can be used to model consumer demand patterns and generate forecasts, including time series analysis, neural networks, and data mining algorithms. Proper data preparation is critical for generating accurate demand forecast models.
Decision support systems and their role in rationalizing the production plansAlexander Decker
This document discusses using decision support systems and linear programming models to optimize production planning at a tire factory in Najaf, Iraq. It presents the research problem, objectives, and data collected from the factory. A mathematical model is applied using linear programming to maximize profits based on constraints of available resources. The model results show the most profitable tire sizes to produce and determines shadow prices and surplus raw materials. Overall, the document examines how quantitative decision support tools can help managers optimize production plans.
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.
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.
This document proposes a methodology for evaluating statistical classification models for churn prediction using a composite indicator. It considers factors beyond just accuracy, like robustness, speed, interpretability and ease of use. The methodology will be tested on classification models applied to real customer data from a Spanish retail company. It also analyzes the impact of different variable selection methods on model performance.
Demand Forecasting of a Perishable Dairy Drink: An ARIMA ApproachIJDKP
Any organization engaged in trading aims to maximize earnings while maintaining costs at
their bare minimum. One of the inexpensive ways to accomplish this objective is through sales forecasting.
Evidence from empirical literature has shown that sales forecasting frequently results in better customer
service, fewer returns of goods, less dead stock, and effective production scheduling. Successful sales forecasting systems are essential for the food sector because of the limited shelf life of food goods and the
significance of product quality. In this paper, we generated sales of forecasts for a perishable dairy drink
using the famous ARIMA approach. We identified the ARIMA (0, 1, 1)(0, 1, 1)12 as the proper model for
modeling the daily sales forecast of the perishable drink. After performing model diagnostics, the model
satisfied all the model assumptions, and a strong positive linear relationship (R
2 > 0.9) was observed when
the actual daily sales were regressed against the forecasted values.
3rd alex marketing club (pharmaceutical forecasting) dr. ahmed sham'aMahmoud Bahgat
#Mahmoud_Bahgat
#Marketing_Club
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*Marketing Club Middle East*
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& now 10 more groups
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Are most welcomed to add Value to us.
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Modelling: What’s next for Financial Services in Europe?GRATeam
This paper outlines a practical roadmap to realising cost savings, delivering a material reduction in the volume and complexity of models by outlining five key principles of model optimisation: develop a comprehensive review of models, harmonise methodologies, re-design model validation/monitoring process, re-think its modelling team’s organisation & governance and build new expertise and recruit talent.
This document discusses demand forecasting techniques. It describes demand forecasting as predicting future business situations to minimize risk and uncertainty. Both qualitative and quantitative techniques are covered. Qualitative techniques include expert opinion methods like panel consensus and Delphi method, as well as consumer survey methods. Quantitative techniques involve statistical analysis like time series analysis, moving averages, exponential smoothing, regression analysis, and input-output analysis. The document outlines the process and limitations of several of these techniques.
This document provides an overview of demand forecasting. It defines demand forecasting as estimating future sales based on marketing plans and external forces. It discusses different categories (passive vs active) and timeframes (short vs long term) of forecasts. The key components and methods of demand forecasting are also outlined, including opinion polling, statistical/analytical techniques like trend projection, regression, and econometric analysis. The importance of demand forecasting is emphasized for production planning, sales forecasting, inventory control, economic policymaking, and long-term growth.
Combining forecast from different models has shown to perform better than single forecast in most time series. To improve the quality of forecast we can go for combining forecast. We study the effect of decomposing a series into multiple components and performing forecasts on each component separately... The original series is decomposed into trend, seasonality and an irregular component for each series. The statistical methods such as ARIMA, Holt-Winter have been used to forecast these components. In this paper we focus on how the best models of one series can be applied to similar frequency pattern series for forecasting using association mining. The proposed method forecasted value has been compared with Holt Winter method and shown that the results are better than Holt Winter method
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
An effective way to optimize key performance factors of supply chainIAEME Publication
This document summarizes an article from the International Journal of Management that discusses optimizing key performance factors in supply chain management. The article begins with an abstract that outlines the goal of using analytical techniques to optimize costs in the outward supply chain. It then reviews relevant literature on supply chain performance measurement and modeling supply chain systems. The methodology section outlines the steps taken, which include identifying key parameters that influence performance, formulating the problem as minimizing total supply chain costs given constraints, validating the model, and implementing the solution. The conclusion emphasizes the importance of supply chain performance measurement for competitiveness.
This document provides an overview of forecasting, including its meaning, definition, process, importance, advantages, limitations, and methods. Forecasting is defined as the systematic estimation of future events or trends based on analysis of past and present data. The key methods of forecasting discussed are regression analysis, business barometers, input-output analysis, survey methods, time series analysis, and the Delphi method. Accurate forecasting is important for effective planning and decision-making but has limitations due to assumptions and uncertain future conditions.
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.
Making Analytics Actionable for Financial Institutions (Part II of III)Cognizant
To identify meaningful use cases for analytics-driven banking and financial services solutions, organizations need a thorough understanding of how customer interactions align with context and anticipate needs, while simplifying the decision-making process.
TOP 10 Forecasting models Meghan WoodsMarketing 188 Dr. .docxturveycharlyn
TOP 10 Forecasting models
Meghan Woods
Marketing 188
Dr. William Rice
4:00- 5:50 pm T-TH Class
Row 2, Seat 1, Group 14
Econometric model
Description: These statistical models identify the relationships between various economic entities within a given study. Econometric models are often arranged under a certain economic theory and the forecast is built around that theory to support it. Economists often use this technique to determine future developments and identify what outcomes they may take in the market.
Advantages:
Only solution to “what if” scenarios
Research accompanied by economists input
Disadvantages:
Merely approximations to reality
Unknown parameter values
1
http://home.iitk.ac.in/~shalab/econometrics/Chapter1-Econometrics-IntroductionToEconometrics.pdf
Real world application: Econometric models are used by marketers and economists alike to forecast when making decisions in policy formation. Fitted models are often a real world representation of economic elements that policy makers must adjust when they see fit.
A set of equations represents the economic behavior occurring in a given market. --->
These results are graphed for forecasters to better interpret results that are then reviewed by economic analysts, and a decision is then reached on what actions to take or not take.
Econometric model
1
Econometric model
1
diffusion index
Description: Used often by economists and traders, this forecasting technique is a summarization of common tendencies that occur within a given data set. A statistical series is analyzed and interpreted by forecasters; if the series shows a greater number of rising data than declining, then the index number is above 50.
Advantages:
More participants likely to respond
Smaller mean-squared errors
Prompt results
Less data crunching
Confidentiality remains intact
Disadvantages:
Small changes cause big change in results
Changes not correlated in results
2
diffusion index
http://www.marketthoughts.com/z20050530.html
2
life cycle analysis
Description: Product life cycle analysis is a quantitative technique of forecasting. It revolves around patterns of past demand in data. This data encompasses these phases that are shown upon a curve model: introduction, growth, maturity, saturation, and decline. Phases of the life cycle help forecasters know when to best execute certain actions based upon similar products.
Pros:
Good for benchmarking performance
Stakeholder engagement tool
Maximize value
Reduce waste
Cons:
Not reliable predictor of true lifespan
False assumptions of life cycle
http://www.environmentalleader.com/2012/03/21/the-benefits-of-life-cycle-analysis/
3
life cycle analysis
Introduction:
small market size
expensive to implement
low sales
high researching & testing costs
Growth:
growth in sales & profit
increase in investment
economies of scale
Maturity:
maintain market share
product modifications and improvement
more effi ...
Similar to MODELING OF THE DEMAND FORECASTING (20)
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
Profiling of Cafe Business in Talavera, Nueva Ecija: A Basis for Development ...IJAEMSJORNAL
This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.
FD FAN.pdf forced draft fan for boiler operation and run its very important f...MDHabiburRhaman1
FD fan or forced draft fan, draws air from the atmosphere and forces it into the furnace through a preheater. These fans are located at the inlet of the boiler to push high pressure fresh air into combustion chamber, where it mixes with the fuel to produce positive pressure. and A forced draft fan (FD fan) is a fan that is used to push air into a boiler or other combustion chamber. It is located at the inlet of the boiler and creates a positive pressure in the combustion chamber, which helps to ensure that the fuel burns properly.
The working principle of a forced draft fan is based on the Bernoulli principle, which states that the pressure of a fluid decreases as its velocity increases. The fan blades rotate and impart momentum to the air, which causes the air to accelerate. This acceleration of the air creates a lower pressure at the outlet of the fan, which draws air in from the inlet.
The amount of air that is pushed into the boiler by the FD fan is determined by the fan’s capacity and the pressure differential between the inlet and outlet of the fan. The fan’s capacity is the amount of air that it can move per unit of time, and the pressure differential is the difference in pressure between the inlet and outlet of the fan.
The FD fan is an essential component of any boiler system. It helps to ensure that the fuel burns properly and that the boiler operates efficiently.
Here are some of the benefits of using a forced draft fan:Improved combustion efficiency: The FD fan helps to ensure that the fuel burns completely, which results in improved combustion efficiency.
Reduced emissions: The FD fan helps to reduce emissions by ensuring that the fuel burns completely.
Increased boiler capacity: The FD fan can increase the capacity of the boiler by providing more air for combustion.
Improved safety: The FD fan helps to improve safety by preventing the buildup of flammable gases in the boiler.
Forced Draft Fan ( Full form of FD Fan) is a type of fan supplying pressurized air to a system. In the case of a Steam Boiler Assembly, this FD fan is of great importance. The Forced Draft Fan (FD Fan) plays a crucial role in supplying the necessary combustion air to the steam boiler assembly, ensuring efficient and optimal combustion processes. Its pressurized airflow promotes the complete and controlled burning of fuel, enhancing the overall performance of the system.What is the FD fan in a boiler?
In a boiler system, the FD fan, or Forced Draft Fan, plays a crucial role in ensuring efficient combustion and proper air circulation within the boiler. Its primary function is to supply the combustion air needed for the combustion process.
The FD fan works by drawing in ambient air and then forcing it into the combustion chamber, creating the necessary air-fuel mixture for the combustion process. This controlled air supply ensures that the fuel burns efficiently, leading to optimal heat transfer and energy production.
In summary, the FD fan i
Principles of Electronic Communication System 4th Edition by Louis Frenzel.pdfAeronKimAbel
A comprehensive textbook that covers the fundamental concepts and principles of electronic communication. It includes detailed explanations of communication theory, practical applications, and modern digital and analog communication systems. The book is designed for students and professionals in the field of electronics and communication engineering, providing a thorough understanding of key topics such as signal transmission, modulation, data communication, and network protocols.
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
Social media management system project report.pdfKamal Acharya
The project "Social Media Platform in Object-Oriented Modeling" aims to design
and model a robust and scalable social media platform using object-oriented
modeling principles. In the age of digital communication, social media platforms
have become indispensable for connecting people, sharing content, and fostering
online communities. However, their complex nature requires meticulous planning
and organization.This project addresses the challenge of creating a feature-rich and
user-friendly social media platform by applying key object-oriented modeling
concepts. It entails the identification and definition of essential objects such as
"User," "Post," "Comment," and "Notification," each encapsulating specific
attributes and behaviors. Relationships between these objects, such as friendships,
content interactions, and notifications, are meticulously established.The project
emphasizes encapsulation to maintain data integrity, inheritance for shared behaviors
among objects, and polymorphism for flexible content handling. Use case diagrams
depict user interactions, while sequence diagrams showcase the flow of interactions
during critical scenarios. Class diagrams provide an overarching view of the system's
architecture, including classes, attributes, and methods .By undertaking this project,
we aim to create a modular, maintainable, and user-centric social media platform that
adheres to best practices in object-oriented modeling. Such a platform will offer users
a seamless and secure online social experience while facilitating future enhancements
and adaptability to changing user needs.
Best Practices for Password Rotation and Tools to Streamline the ProcessBert Blevins
Securing sensitive data is crucial for both individuals and enterprises in the digital era. Password rotation, or regularly changing passwords, has long been a standard security practice. Despite some debate over its effectiveness, password rotation remains an important part of comprehensive security strategies. This guide will explore best practices for password rotation and highlight tools to streamline the process.
The history of rotating passwords dates back to early computer security guidelines, which aimed to reduce the time attackers could exploit stolen credentials by frequently changing passwords. This practice helps mitigate risks associated with credential stuffing, password reuse, and prolonged exposure of compromised passwords. By regularly changing passwords, the time a compromised password can be used is limited, old passwords exposed in breaches are rendered invalid, and regulatory compliance is maintained. Furthermore, frequent changes encourage security awareness among users, reminding them to stay vigilant against phishing and other threats.
To streamline the process of password rotation, various tools and techniques can be employed. Automated password management solutions can schedule and enforce password changes, ensuring compliance with security policies. Additionally, password managers can securely store and generate complex passwords, making it easier for users to adhere to rotation practices without compromising convenience. Implementing multi-factor authentication (MFA) alongside password rotation can further enhance security by adding an extra layer of protection against unauthorized access. By adopting these best practices and utilizing appropriate tools, organizations and individuals can effectively strengthen their cybersecurity posture and safeguard sensitive information.
2. Modeling of the Demand Forecasting
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Capacity planning and decision making for facilities will be mainly based on long-term,
cumulative forecasts [2].
However, forecasts are also required for planning the appropriate levels of inventory,
which generally requires short-term forecasts at a disaggregated level, since specific
components, parts and finite elements must be stocked up for the immediate consumer
demand.
Forecasts influence most functional areas of a firm and are a starting point for making
decisions about the allocation of resources. For instance, production should be daily planned
to meet customer orders, while the procurement department should know how to arrange
deliveries with the production schedule. The finance department needs forecasts to ensure an
adequate level of investment in installation, equipment, and inventory so that budgets can be
planned to better manage the business. The marketing function serves to allocate resources for
various product groups and marketing campaigns [3]. Forecasts also define labor
requirements of a firm so that the HR function can make appropriate hiring and training
decisions when demand is expected to grow.
2. METHODS OF THE DEMAND FORECASTING
Forecasting is based on a combination of qualitative and quantitative indicators [4-6].
Qualitative method. A person’s judgment can be recorded in several ways. Three common
approaches include the individual market specialist, group consensus, and the Delphi method.
They are sometimes referred to as expert methods, since they require people with some
knowledge of products and markets to develop forward-looking estimates for planning needs.
Individual market specialists can be hired to track industry trends, perhaps even
geographically, and work with retailers to assess future demand for products. However,
individuals have preconceptions they may be unaware of, and there is a limit to how much
information one person can get. A group of experts should be used to overcome this, although
it may be much more expensive.
The group consensus unites experts from various fields to reach a common opinion on
future forecasts for a product or group of products. As a rule, the forecasts of the group unite
different factions of the company.
The group is trying to ensure that overly zealous managers do not overestimate the results
of the forecast in order to really meet the firm's expectations of growth. The group can also
make sure that someone is playing the role of a conservative, because this person considers it
less dangerous to underestimate the forecast. However, there are some pitfalls in using a
group consensus. When people from different ranks in a firm get together, this can make staff
at some point agree with senior managers in the group. This wins in reaching a general
agreement and can be a real problem for some.
One way to overcome this problem is to reach an anonymous consensus using the Delphi
method. It requires one person to administer and coordinate the process and interview team
members (respondents) through a series of consecutive questionnaires. At the same time, team
members should be people with experience in a field of interest for the forecast, and an
administrator should only have some knowledge of how to coordinate efforts without unduly
influencing the results.
Questionnaires that are provided to team members include not only demand estimates.
They aim to determine how each participant reaches this estimate. Once everyone has
returned the questionnaires, the administrator should summarize the results and send the final
report to all respondents, but with an indication of who made the forecast hidden from the
team. This process continues until participants reach a certain consensus on the forecast. Of
3. Mikhail Samuilovich Gasparian, Mikhail Vladimirovich Karmanov, Irina Anatolievna Kiseleva,
Vladimir Ivanovich Kuznetsov, Natalia Alekseevna Sadovnikova
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course, this method can be both time consuming and quite expensive to administer, but at the
same time, it can result in good forecasts and, moreover, make an important contribution to
the process over time. Three rounds are a good compromise between the forecast quality, cost,
and efforts.
Quantitative method. Quantitative analysis usually involves two approaches: causal
models and time series methods. Causal models establish a quantitative relationship between
some observable or given variable (for example, advertising costs) and the demand for a
certain product. Time series analysis involves analyzing the historical demand for a product to
forecast future demand.
The most common types of causal models are regression analysis and econometric
models. Although regression models can be involved, a simple linear regression is often used,
where a straight line Y = mX + b is used to describe the relationship between the dependent
variable Y and the independent variable X. A line is placed through a set of points in such a
way that the square distance from the line is minimized – as such, the “smallest square” is
appropriate. Econometric models typically represent some form of a multidimensional
regression model, where independent variables are such factors as disposable income and
industrial output from the economy.
Mathematical details of the regression are not reviewed in this article. There are several
different time series models, most of which work on the basis of the assumption that historical
demand can be "smoothed out" by averaging. A simple time series analysis includes models
such as weighted moving average and basic exponential smoothing. More complex time series
methods include factors of trends, seasonal patterns, and economic cycles. The focus will later
be put on these time series models.
3. SIMPLE TIME SERIES MODELS
Some of the most popular forecasting methods, especially in software, are commonly referred
to as time series models. These models use past data to forecast future demand [7-9]. This
type of forecasting method is especially important for items that are constantly streamlined,
because these methods can be to a great extent “automated” in computer information systems.
The models assume that each observed demand data point consists of some systematic
component and some random component. The time series model is intended to predict a
systematic component, but not a random component. The idea is similar to the logic of quality
control diagrams – that one should not try to respond to the variability of the process if the
latter is within control. Responding (or changing the forecast model) due to random errors is
likely to lead to an increase in errors in future forecasts. It is necessary to try and forecast the
range or variation of this random error. Models can be designed for almost any type of
systematic change in demand, but there is a real danger in predicting the random component.
Projection
The simplest time series method simply forecasts future demand based on demand from the
last period. The forecast for the next period is just a projection of this period t of
demand
Although this method is easy to use, it does not use the data that are easily accessible to
most managers. As such, using more historical data should improve the forecast. Past demand
averages may be more useful [10, 11].
Simple moving average (SMA)
4. Modeling of the Demand Forecasting
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SMA forecast allows for a better use of historical data about demand than the demand for
the past period. The moving average uses the last n demand periods as a forecast for the future
demand periods:
This forecast model is most useful when the level of demand is fairly constant over time.
Then the model makes simple adjustments to this average level instead of assuming that the
level is always constant. Its advantage over the projection model is that the forecast will not
tend to fluctuate when averaged. The average value of the previous n periods can be
considered as an estimate of the average level of demand for period t. As such, level Lt can
be determined as follows:
and, consequently, forecast is only the latest estimate of the level of demand.
Weighted moving average (WMA)
One of the disadvantages of SMA is the equal weight of the data. For example, a 5-period
moving average weighs each of the last 5 demand requests equally – each of them has 20%
impact on the forecast. This contradicts the fact that the most recent data are the most
relevant. As such, WMA allows to pay more attention to the most recent data. This forecast is
as follows:
where is the weight applied to the demand incurred during period t, is the weight
defined for period t-1, etc. It was intuitively expected that the most recent demand data should
be weighed more strongly than older data; therefore, as a rule, one would expect that the
weights would correspond to the following ratio:
Basic exponential smoothing (BES)
The WMA properties will be good in the case where the weights do not only decrease as older
data are used, but where differences between weights are "smooth". Obviously, the weight of
the most recent data should be the largest. Then weights should be gradually reduced, in
accordance with the past periods. The exponentially decreasing weights of the main
exponential smoothing forecast are consistent with this calculation. The prediction equation is
given as follows:
( )
where α is the smoothing parameter between 0 and 1. To show that this forecast is actually
a weighted average forecast, let us consider the algebraic extension of the model.
Since
( )
( )[ ( ) ]
( ) ( ) ( ) ⟦( ) ( )]
This can also be extended
( )
5. Mikhail Samuilovich Gasparian, Mikhail Vladimirovich Karmanov, Irina Anatolievna Kiseleva,
Vladimir Ivanovich Kuznetsov, Natalia Alekseevna Sadovnikova
http://www.iaeme.com/IJCIET/index.asp 167 editor@iaeme.com
( ) ( ) [ ( ) ]
( ) ( ) ( )
Continuing this extension, the model can be written as follows:
( ) ( ) ( )
As such, the exponential smoothing model is actually a WMA model with special
weights. These weights are gradually reduced as they are applied to periods farther from the
current period. Calculations help show that these weights add up to 1:
( ) ( ) ( )
Despite the fact that these weights have important properties, there is no need to track
every weight. Moreover, the system working with the model does not need to store historical
data or calculate something based on old data. All it needs is the smoothing factor α, the
demand of the last period, and the forecast of the last period. The advantage of the model is
that all data of the past demand remain in the forecast of the last period. [12, 13].
Example of a simple time series
The models presented above can be illustrated using a simple data set. Data on the demand for
a product for the period from January to August are given in Table 1. The period designation
in the table is the same as indicated in the models.
Table 1 Seasonal estimates and baseline seasonal factors
Month January February March April May June July August
Period t-7 t-6 t-5 t-4 t-3 t-2 t-1 t
Demand 45 60 42 46 52 47 41 48
A firm wants to forecast demand for September (period t + 1). These calculations are
presented below.
1) Simple projection
SMA (using 4 periods)
2) WMA (using 4 periods )
3)
4) BES (с α = 0.2)
To use BES, the firm uses retrospective demand data to test the model. To do this,
forecasts for each period with available demand data should be calculated. Table 2 shows the
forecast for September and the calculations required to obtain this forecast using an
exponential model.
Smoothing occurs in forecasting based on the past data, thus, the future forecasts are
based on good weights. The use of the past data also allows the forecaster to measure forecast
errors based on the model, assuming that it was actually used in the past to make forecasts.
6. Modeling of the Demand Forecasting
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Table 2 Calculations using the BES method
Month Period Demand 0.2* +0.8* Forecast
January t-7 45 47
February t-6 50 0.2*45+0.8*47 46.6
March t-5 42 0.2*50+0.8*46.6 47.3
April t-4 46 0.2*42+0.8*47.3 46.2
May t-3 52 0.2*46+0.8*46.2 46.2
June t-2 47 0.2*52+0.8*47.3 47.3
July t-1 41 0.2*47+0.8*47.3 47.3
August t 48 0.2*41+0.8*47.3 46
September t-1 0.2*48+0.8*46 46.4
*Assume the January forecast was 47
Forecast accuracy
Since forecasts are erroneous in most cases, the estimation of the forecast inaccuracy can be
just as useful as the forecast of the expected demand. This is why a good forecast should
include an average value and an estimate of how the forecast will vary depending on the
average. This measure helps understand the risk of the forecast and allows to make decisions
that allow for the existing variability. Forecasting involves estimating more than expected
demand – it is also an attempt to estimate uncertainty [14, 15].
Simple average error over n periods is as follows:
∑
In fact, it would be good to know the AEn value, since it indicates how well the forecast
tracks the actual demand. Such a more widespread measure, known as bias, is commonly used
to track this "regular" error and is given as follows:
∑
Managers are interested in forecasts without bias [16, 17]. If there is prejudice, it is likely
that an incorrect functional forecast model is used. Regular bias should, in theory, be
something that can be eliminated by introducing some factor into the model in order to
remove it from the forecast. As such, this simple measure of an error can be one of the most
important ones in determining whether the correct forecasting model is being used.
Mean absolute deviation. The usual average error measurement used in many companies
is known as mean absolute deviation (MAD). It is represented mathematically as follows:
∑| |
where | |is the absolute value of .
Assuming the absolute value of the error conditions, this error measurement reflects
positive and negative deviations between the forecast and actual demand.
Sample forecast error
The data about demand and forecasts presented in Table 2 will be used to illustrate some of
these errors.
7. Mikhail Samuilovich Gasparian, Mikhail Vladimirovich Karmanov, Irina Anatolievna Kiseleva,
Vladimir Ivanovich Kuznetsov, Natalia Alekseevna Sadovnikova
http://www.iaeme.com/IJCIET/index.asp 169 editor@iaeme.com
The demand is compared with the forecast for each period with the resulting errors in
Table 3. This provides the error values Ei for each period t from January to August. The last
two columns of this table indicate the forecast error and the square forecast error for each of
the periods.
Table 3 Value of the error from January to August
Month Demand Forecast Error
January 45 47 2 4
February 50 46.6 -3.4 11.6
March 42 47.3 5.3 27.9
April 46 46.2 0.2 0.1
May 52 46.2 -5.8 33.9
June 47 47.3 0.3 0.1
July 41 47.3 6.3 39.4
August 48 46 -2 3.9
4. EXPONENTIAL SMOOTHING UPDATES – HOLT MODEL
Each period when information becomes available, level, and trend factors can be updated.
This is done through the equations similar to the equations for BES model presented above. In
BES, smoothing parameter α was used to define the extent to which demand information
should be included in the level factor. There are currently two factors: level and trend, and the
second smoothing parameter β is required to define the amount of smoothing that should be
performed according to the trend coefficient. Values for β range between 0 and 1. Below are
updates of the equations for each factor for the case of additive trend:
( )( )
( ) ( )
Managers want to adjust forecasting models for their most popular products. The demand
for one of the products, which has had phenomenal growth since its introduction, is shown in
Table 4. This product has been on the market since January 2017, and the data on demand for
this product are provided for eleven periods through to November 2017. The firm’s analyst
decided to use the Holt model to forecast future demand and, in particular, to obtain a demand
estimate for December 2017. Since the model assumes an additive trend, a straight line may
be suitable for the data to estimate the initial level factor (interception) and the initial trend
coefficient (line slope). Linear regression forms the following Table 4:
Table 4 Monthly demand
Period Month Demand
1 January 1,404
2 February 1,506
3 March 1,521
4 April 1,658
5 May 1,716
6 June 1,805
7 July 1,919
8 August 1,980
9 September 2,077
10 October 2,220
11 November 2,264
12 December
8. Modeling of the Demand Forecasting
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Intersection of monthly demand: 1,297 Slope: 87.9
These two terms then become estimates L and T for period 0 (demand was regressed
against periods from 1 to 11) as follows:
= 1297 = 87.9
With the use of the forecast equation, the forecast for period 1 is as follows:
To continue, the firm now observes the first period of demand, = 1,404. This
information can be used to update the levels and trend factors to make them current as of
period 1. Let us choose a smoothing parameter α = 0.2 for the factor coefficient.
( )( )
( )( ) ( )( )
It is similar with the smoothing parameter β = 0.3 for the trend coefficient:
( ) ( )
( ) ( ) ) ( )
Now a forecast for period 2 can be made.
Other calculations are shown in Table 5, including the forecast for the period of 12
December 2017. The table also contains errors calculated using a model for forecasting past
data. Most importantly, it can be noted that these errors are absent. As such, there is no
regular bias that would indicate the use of the wrong model.
Table 5 Forecasting the demand value using the Holt model
Period Month Demand Level Trend Forecast Error
0 1,297 87.9
1 January 1,404 1,388.7 89 1,384.9 -19.1
2 February 1,506 1,483.4 90.7 1,477.8 -28.2
3 March 1,521 1,563.5 87.6 1,574.2 53.2
4 April 1,658 1,652.5 88 1,651.1 -6.9
5 May 1,716 1,735.5 86.5 1,740.4 24.4
6 June 1,805 1,818.6 85.5 1,822 17
7 July 1,919 1,907.1 86.4 1,904.1 -14.9
8 August 1,980 1,990.8 85.6 1,993.5 13.5
9 September 2,077 2,076.5 85.6 2,076.3 -0.7
10 October 2,220 2,173.7 89.1 2,162.1 -57.9
11 November 2,264 2,263 89.2 2,262.7 -1.3
12 December 2,352.1
5. DESEASONALIZED DEMAND
Data deseasonalization requires the two following steps:
1. Search for average seasonal demand for the full set of seasons for all available data.
2. Ensuring that averages are centered in the appropriate period.
According to the data on demand, seasons can be made from quarters, months, 4-week
periods, weeks, and any other periods where such a picture could be observed. For example, if
quarters make up seasons, one can find average quarterly demand using average values for
every four quarters in a row. Using data from Table 6, the average quarterly demand for the
first year is as follows:
9. Mikhail Samuilovich Gasparian, Mikhail Vladimirovich Karmanov, Irina Anatolievna Kiseleva,
Vladimir Ivanovich Kuznetsov, Natalia Alekseevna Sadovnikova
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Since this is the average value for the first four periods, this average demand value is
focused on period 2.5, which is an average demand index. This can be seen in Table 6, where
the first set of dark diagonal lines shows 111.5 as averaging according to demand data from
98 to 133. The average value of 113.5 is found in a similar way using the average value for
four consecutive periods starting from period 2.
=113.8
Table 6 Deseasonalization of demand
Deseasonalization
Period Quarter Demand Initial Centered
1 Q1 2014 98
2 Q2 2014 106 111.5
3 Q3 2014 109 113.8 112.8
4 Q4 2014 133 116.3 115
5 Q1 2015 107 119.3 117.8
6 Q2 2015 116 122.5 120.8
7 Q3 2015 121 127.5 125
8 Q4 2015 146 131 129.3
9 Q1 2016 127 134.6 132.9
10 Q2 2016 130 138 136.4
11 Q3 2016 136 141 139.6
12 Q4 2016 159 144.3 142.6
13 Q1 2017 139 148.5 146.4
14 Q2 2017 143 153 150.8
15 Q3 2017 153
16 Q4 2017 177
To make the deseasonalized demand focus on each period rather than between them, each
pair of conditionally focused averages above and below each period must be averaged to get
an estimate for a certain period rather than between periods.
This centered average indicator of the average demand is shown in the last column of
Table 6, where the set of bright lines indicates the result of the average value of two numbers:
111.5 and 113.8. It must be noted that this procedure of the demand deseasonalization is
suitable for seasonal situations when the number of seasons is uniform. An even number of
seasons requires to center the deseasonalized data. If the number of seasons is odd, as in the
case when the data were broken into thirteen four-week seasons, then when all seasons are
averaged, the average value will occur in the middle period, and there would have been no
reason to center the average.
6. CONCLUSIONS
Forecasts are erroneous in most cases, but some of them are "more erroneous" than others.
Forecasting the demand for innovative products and fashion products is usually more difficult
than forecasting the demand for more "marketable" products that are sold daily. Aggregate
forecasts for a group of similar products are usually more accurate than individual forecasts
10. Modeling of the Demand Forecasting
http://www.iaeme.com/IJCIET/index.asp 172 editor@iaeme.com
for individual products in a group. Finally, the longer the forecast in the future is, the less
reliable it is.
The commodity-like products that are sold daily, on the other hand, are much more
suitable for quantitative models and require little judgment to forecast demand [18, 19].
However, when knowledge of certain events makes one think that future demand may not
track historical trends, one can view some judgments in order to introduce adjustments to the
models using the past data. In this case, a strong dependence on the past data with the
adjustments based on expert estimates should be used for forecasting.
Studies reveal that simple statistical models work well for some everyday commodity
items. However, some managers believe that the forecasts should be made perfect to solve
most of the problems with the supply chain. There are cases when management contribution is
required, but there comes a moment when it is better to understand inaccuracy in the forecast
and plan, respectively. Once a good forecasting process has been implemented (procedures,
methods, models, and management supervision), the continuous improvement is less
significant and may even harm the forecasting process.
Since forecasts are never accurate, two common solutions are often offered to "correct"
the forecast errors [20, 21].
The first is to reduce the execution time in order to respond to changes more quickly. This
is a good partial solution, but reducing the execution time is not always easy and often
expensive. Besides, reducing the execution time in many cases simply transfers problems
from one part of the supply chain to another.
The second is to "make an order" so that the inventory is not produced before demand.
This solution is also good, but, like reducing the execution time, it tends to shift demand to
the next level of the supply chain.
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