Lecture 1
0 likes80 views
The document discusses various forecasting techniques in operations management, including methods for long-term, medium-term, and short-term decisions. It covers approaches like moving averages, exponential smoothing, and regression analysis, explaining how they incorporate historical demand data to predict future demand. Additionally, it highlights subjective methods such as Delphi and opinion polls, outlining their advantages and limitations in accurate forecasting.
1 of 8
Download to read offline
![FORECASTING
- Objective
- Scientific
- Free from BIAS
- Reproducible
- Error Analysis Possible
PREDICTION
- Subjective
- Intuitive
- Individual BIAS
- Non - Reproducible
- Error Analysis Limited
OPINION POLLS
Personal interviews e.g. aggregation of opinion of sales representatives to obtain
sales forecast of a region
- Knowledge base (experience) Subjective bias
Questionnaire method
- questionnaire design
- choice of respondents
- obtaining respondents
- analysis and presentation of results (forecasting)
Telephonic conversation
- Fast
DELPHI
METHODS OF FORECASTING
Subjective or intuitive
methods
- Opinion polls, interviews
- DELPHI
Methods based on averaging
of past data
- Moving averages
- Exponential Smoothing
Regression models on
historical data
- Trend extrapolation
Causal or econometric
models
]Time - series analysis using
stochastic models
- Box Jenkins model](https://image.slidesharecdn.com/lecture1-180805131028/85/Lecture-1-3-320.jpg)
Ad
Recommended
Ppt on wpi cpi
Ppt on wpi cpishikha dubey
油
The document discusses the Whole Sale Price Index (WPI) and Consumer Price Index (CPI). The WPI measures price changes in the primary and wholesale markets, while the CPI measures the overall cost of goods and services bought by a typical consumer. The CPI is used to monitor changes in the cost of living over time. It measures price changes of a fixed basket of goods and services of constant quality and quantity to determine inflation rates.Index numbers concepts
Index numbers conceptsjyothi s basavaraju
油
The document discusses various types of index numbers used to measure inflation and changes in prices over time. It describes price indexes as single numbers that indicate price changes across goods compared to a base year. Retail price indexes (RPI) and consumer price indexes (CPI) measure average price changes of goods in a typical consumer basket. Chain indexes use successive years as bases to calculate price relatives and chain them together to measure inflation without a fixed base year.Consumer Price Index and Wholesale Price Index
Consumer Price Index and Wholesale Price IndexLekshmiNathB
油
The document discusses key economic indicators in India, specifically the Wholesale Price Index (WPI), Consumer Price Index (CPI), and Index of Industrial Production (IIP). It details the calculation methods, statistical agencies involved, and provides recent data trends for each index, noting significant changes in inflation rates and industrial production. The document highlights WPI's focus on wholesale goods, CPI's role in tracking retail price changes, and IIP's measurement of industrial activity across various sectors.Consumer price index number
Consumer price index numberHasnain Baber
油
The document discusses consumer price index numbers, which are index numbers that indicate changes in consumer prices for a basket of goods and services. Consumer price indices are needed because general price indices do not accurately show how price changes impact different classes of consumers who have varying consumption patterns. The indices are calculated using methods like the aggregative expenditure method or family budget method and are used by governments and others to formulate economic policies, grant employee allowances, and evaluate purchasing power.Business statistics (Basic concepts of Index Numbers)
Business statistics (Basic concepts of Index Numbers)AhmedToheed3
油
Index numbers are used to measure percentage changes in economic variables like prices, production, and exports over time. They show changes relative to a base time period or location. There are different types of index numbers including price, quantity, and value indexes. A price index measures changes in prices over time by taking the price of commodities in the current year and expressing them as a percentage of prices in the base year.Utility and Demand analysis 2021
Utility and Demand analysis 2021Samita Mahapatra
油
The document discusses various concepts related to consumer demand and utility analysis including the law of diminishing marginal utility, elasticity of demand, and the factors affecting demand. It provides explanations, definitions, and calculations related to marginal utility, demand schedules, and the law of demand, as well as exceptions to this law. Additionally, there are multiple-choice questions for practice and insights on the implications of elasticity for businesses and government policy.Index numbers( simple index number by fixed base method )
Index numbers( simple index number by fixed base method )Nadeem Uddin
油
The document discusses index numbers which measure changes in variables over time. It defines simple and composite index numbers. The three main types of index numbers are price, quantity, and value indexes. Examples are provided to demonstrate calculating price indexes using the fixed base method. Index numbers are calculated for different years using different base periods to compare price changes.Index Number (Composite Index Number)
Index Number (Composite Index Number)IdreesAli14
油
This document discusses different types of index numbers including weighted and unweighted index numbers, composite index numbers, simple aggregative index numbers, and simple average of related indices. It provides definitions and formulas for calculating each type of index number. Limitations of index numbers are also outlined such as only showing relative changes and potential errors in base periods, weights, or purpose versus construction method.Introduction to need of forecasting in business
Introduction to need of forecasting in businessAnuyaK1
油
This document discusses various forecasting methods including qualitative and quantitative approaches. It describes time series forecasting techniques like naive, moving average, and exponential smoothing. Exponential smoothing assigns weights to historical data, with more recent data receiving higher weights. It can be used to forecast things like product demand, sales, or inventory needs by analyzing past trends and patterns. Selecting the appropriate forecasting method and analyzing historical data allows businesses to better plan production levels, staffing needs, and inventory requirements.Mba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting aRai University
油
This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.forecasting.pptx
forecasting.pptxssuser29f010
油
Forecasting involves using past data to estimate future events. It is a vital function that impacts management decisions. There are qualitative and quantitative forecasting methods. Qualitative methods include expert opinions, surveys, and the Delphi technique. Quantitative methods include time series models, decomposition, and regression. Time series models assume the future is related to the past and use trends in historical data. Decomposition breaks down time series into trend, seasonal, cyclical, and random components. Regression establishes relationships between variables to forecast a dependent variable.forecasting.pptx
forecasting.pptxssuser29f010
油
Forecasting methods can be qualitative or quantitative. Qualitative methods include jury of executive opinion, sales force composite, consumer market surveys, Delphi technique, and nominal group discussions. Quantitative methods include time series models using smoothing, decomposition, and regression. Exponential smoothing weights recent periods most heavily to forecast. Decomposition separates time series into trend, seasonal, cyclical, and random components. Regression establishes relationships between variables to forecast.Demand forecasting
Demand forecastingManvi Agarwal
油
The document discusses demand forecasting, which is predicting the future demand for a firm's products and is essential for business planning. It outlines various qualitative and quantitative methods for forecasting, including expert opinion, surveys, trend projection, barometric methods, and econometric analysis. The importance of accurate demand forecasting is emphasized for effective resource allocation, investment decisions, and pricing strategies.Varunapriya
VarunapriyaVarunaPriya3
油
Demand forecasting is used to estimate future demand for a product or service based on an analysis of past demand and current market conditions. There are several statistical methods used for demand forecasting, including trend projection, which fits a trend line to past sales data to project future trends. The barometric technique uses current economic and statistical indicators to predict future changes in demand. Econometric models use a system of independent regression equations to model relationships between economic variables and forecast demand.Demand forecasting
Demand forecastingPhillipChirongwe1
油
Demand forecasting involves anticipating future demand for a company's products and services under uncertain competitive conditions. Accurate forecasting allows companies to efficiently plan production, purchasing, financing, and pricing. There are qualitative techniques like surveys and expert opinions, and quantitative techniques like time series analysis of historical demand data and regression analysis of causal factors. Accurate forecasting is important for production planning, sales forecasting, inventory control, and other business and economic decisions.Forecasting_Quantitative Forecasting.pptx
Forecasting_Quantitative Forecasting.pptxRituparnaDas584083
油
Here are the exponential smoothing forecasts for periods 2-10 using a smoothing constant (留) of 0.1:
2: 815.50
3: 801.95
4: 787.26
5: 783.53
6: 785.38
7: 786.64
8: 784.17
9: 782.29
10: 780.41Forecasting techniques
Forecasting techniquesKeyur Dobaria
油
This document summarizes sales forecasting methods, including qualitative and quantitative approaches. Qualitative methods like opinion surveys, market trials, and the Delphi technique rely on human judgment for long-term or new product forecasting. Quantitative methods use past sales data and include moving average, exponential smoothing, and least squares regression for short to medium-term, old product forecasting. These data-driven techniques estimate future demand based on trends in historical data.Demand forecasting-slide share
Demand forecasting-slide share bodeddula srirama pavan reddy
油
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.Techniques Of Demand Forecasting.pptx
Techniques Of Demand Forecasting.pptxsalonidhawan4
油
This document discusses various techniques for demand forecasting. It defines demand forecasting as estimating the probable demand for a product or service in the future. Some key techniques discussed include:
1. Survey methods like complete enumeration surveys, sample surveys, and end use methods which directly ask consumers about future demand.
2. Opinion poll methods like expert opinion and Delphi methods which collect opinions from sales representatives and executives.
3. Statistical methods like trend projection, barometric, and econometric methods which analyze historical sales data and key economic indicators to forecast future demand.
Trend projection specifically examines time series data using graphical, least squares, or Box-Jenkins methods to extrapolate past trends. Barometric and econDemand-forcasting.pptx introduction meaning
Demand-forcasting.pptx introduction meaningjaya315652
油
Demand forecasting is the process of predicting future demand for a product or service based on past behaviors and various influencing factors. Different methods used for forecasting include buyer intention surveys, expert opinions, controlled experiments, business barometers, and statistical techniques like trend projection and regression analysis. It plays a critical role in business planning and decision-making by helping to understand consumer demand and optimize production.Forecasting
ForecastingSVGANGAD
油
The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.demandforecasting business economics.pptx
demandforecasting business economics.pptxNishthaVerma25
油
Demand forecasting is a systematic process of anticipating future demand for a firm's products and services, crucial for effective production and resource management. Various qualitative and quantitative methods exist for forecasting, including consumer surveys, sales force opinions, and statistical techniques like trend projection and regression analysis. Accurate demand forecasting aids in production planning, sales forecasting, inventory control, and informed economic policy making.Forecasting.ppt
Forecasting.pptDrMahmoudElmarhoumy
油
The document discusses quantitative forecasting methods, including exponential smoothing. Exponential smoothing assigns weights to past observations, with more recent observations receiving greater weight, to generate forecasts. Examples are provided to demonstrate how to calculate forecasts for upcoming time periods using exponential smoothing with different smoothing constants (a). Managers can use exponential smoothing on historical demand data to forecast future demand and support inventory and production planning.Demand forecasting
Demand forecastingKandarp Desai
油
This document discusses demand forecasting techniques. It outlines the objectives of demand forecasting such as understanding the role and reasons for forecasting. It then describes various qualitative and quantitative forecasting methodologies including surveys, sales force composites, exponential smoothing, and regression analysis. Finally, it discusses measuring forecast accuracy using metrics like mean error and developing control limits to monitor forecast performance.Forecasting_Quantitative Forecasting.ppt
Forecasting_Quantitative Forecasting.pptRituparnaDas584083
油
Here are the exponential smoothing forecasts for months June and July using an a constant of 0.3:
Month Demand 0.3 0.5
January 80 84.00 84.00
February 84 82.80 82.00
March 82 83.16 83.00
April 85 82.81 82.50
May 89 83.47 83.75
June 85.13 83.78 83.38
July 86.12 84.01
Ft+1 = Ft + a(At - Ft)Product Design Forecasting Techniquesision.ppt
Product Design Forecasting Techniquesision.pptavidc1000
油
The document discusses quantitative forecasting methods, including naive, moving average, and exponential smoothing approaches. It provides examples of how to calculate forecasts for upcoming time periods using 3-period simple moving averages, weighted moving averages with decreasing weights, and exponential smoothing with different smoothing constants. The examples demonstrate how to iteratively calculate the forecasts for each upcoming period in a time series.demandforecasting-180917122131.pptx
demandforecasting-180917122131.pptxPrabin Pandit
油
Demand forecasting involves anticipating future demand for an organization's products and services under uncertain competitive conditions. Accurate forecasts are essential for production planning, purchasing inputs, and other business decisions. There are qualitative and quantitative forecasting methods. Qualitative methods include consumer surveys, salesforce opinions, and expert panels. Quantitative methods use historical sales data and statistical analysis, such as time series analysis, regression analysis, and econometric modeling of economic factors. Accurate demand forecasting is important for production planning, sales forecasting, inventory control, and long-term strategic planning.Demand forecasting
Demand forecastingShompa Nandi
油
Demand forecasting involves anticipating future demand for a company's products and services under uncertain competitive conditions. It is essential for production planning, purchasing raw materials, and other business decisions. Demand can be forecasted qualitatively using opinion surveys of consumers, salespeople, and experts, or quantitatively using statistical techniques like trend projection, regression analysis, and econometrics that analyze historical demand data and its relationships to economic indicators. Accurate demand forecasting is important for production planning, inventory control, sales forecasting, budgeting, and long-term growth strategies.Structured Programming with C++ :: Kjell Backman
Structured Programming with C++ :: Kjell BackmanShabista Imam
油
Step into the world of high-performance programming with the Complete Guidance Book of C++ Programminga definitive resource for mastering one of the most powerful and versatile languages in computer science.
Whether you're a beginner looking to learn the fundamentals or an intermediate developer aiming to sharpen your skills, this book walks you through C++ from the ground up. You'll start with basics like variables, control structures, and functions, then progress to object-oriented programming (OOP), memory management, file handling, templates, and the Standard Template Library (STL).More Related Content
Similar to Lecture 1 (20)
Introduction to need of forecasting in business
Introduction to need of forecasting in businessAnuyaK1
油
This document discusses various forecasting methods including qualitative and quantitative approaches. It describes time series forecasting techniques like naive, moving average, and exponential smoothing. Exponential smoothing assigns weights to historical data, with more recent data receiving higher weights. It can be used to forecast things like product demand, sales, or inventory needs by analyzing past trends and patterns. Selecting the appropriate forecasting method and analyzing historical data allows businesses to better plan production levels, staffing needs, and inventory requirements.Mba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting aRai University
油
This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.forecasting.pptx
forecasting.pptxssuser29f010
油
Forecasting involves using past data to estimate future events. It is a vital function that impacts management decisions. There are qualitative and quantitative forecasting methods. Qualitative methods include expert opinions, surveys, and the Delphi technique. Quantitative methods include time series models, decomposition, and regression. Time series models assume the future is related to the past and use trends in historical data. Decomposition breaks down time series into trend, seasonal, cyclical, and random components. Regression establishes relationships between variables to forecast a dependent variable.forecasting.pptx
forecasting.pptxssuser29f010
油
Forecasting methods can be qualitative or quantitative. Qualitative methods include jury of executive opinion, sales force composite, consumer market surveys, Delphi technique, and nominal group discussions. Quantitative methods include time series models using smoothing, decomposition, and regression. Exponential smoothing weights recent periods most heavily to forecast. Decomposition separates time series into trend, seasonal, cyclical, and random components. Regression establishes relationships between variables to forecast.Demand forecasting
Demand forecastingManvi Agarwal
油
The document discusses demand forecasting, which is predicting the future demand for a firm's products and is essential for business planning. It outlines various qualitative and quantitative methods for forecasting, including expert opinion, surveys, trend projection, barometric methods, and econometric analysis. The importance of accurate demand forecasting is emphasized for effective resource allocation, investment decisions, and pricing strategies.Varunapriya
VarunapriyaVarunaPriya3
油
Demand forecasting is used to estimate future demand for a product or service based on an analysis of past demand and current market conditions. There are several statistical methods used for demand forecasting, including trend projection, which fits a trend line to past sales data to project future trends. The barometric technique uses current economic and statistical indicators to predict future changes in demand. Econometric models use a system of independent regression equations to model relationships between economic variables and forecast demand.Demand forecasting
Demand forecastingPhillipChirongwe1
油
Demand forecasting involves anticipating future demand for a company's products and services under uncertain competitive conditions. Accurate forecasting allows companies to efficiently plan production, purchasing, financing, and pricing. There are qualitative techniques like surveys and expert opinions, and quantitative techniques like time series analysis of historical demand data and regression analysis of causal factors. Accurate forecasting is important for production planning, sales forecasting, inventory control, and other business and economic decisions.Forecasting_Quantitative Forecasting.pptx
Forecasting_Quantitative Forecasting.pptxRituparnaDas584083
油
Here are the exponential smoothing forecasts for periods 2-10 using a smoothing constant (留) of 0.1:
2: 815.50
3: 801.95
4: 787.26
5: 783.53
6: 785.38
7: 786.64
8: 784.17
9: 782.29
10: 780.41Forecasting techniques
Forecasting techniquesKeyur Dobaria
油
This document summarizes sales forecasting methods, including qualitative and quantitative approaches. Qualitative methods like opinion surveys, market trials, and the Delphi technique rely on human judgment for long-term or new product forecasting. Quantitative methods use past sales data and include moving average, exponential smoothing, and least squares regression for short to medium-term, old product forecasting. These data-driven techniques estimate future demand based on trends in historical data.Demand forecasting-slide share
Demand forecasting-slide share bodeddula srirama pavan reddy
油
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.Techniques Of Demand Forecasting.pptx
Techniques Of Demand Forecasting.pptxsalonidhawan4
油
This document discusses various techniques for demand forecasting. It defines demand forecasting as estimating the probable demand for a product or service in the future. Some key techniques discussed include:
1. Survey methods like complete enumeration surveys, sample surveys, and end use methods which directly ask consumers about future demand.
2. Opinion poll methods like expert opinion and Delphi methods which collect opinions from sales representatives and executives.
3. Statistical methods like trend projection, barometric, and econometric methods which analyze historical sales data and key economic indicators to forecast future demand.
Trend projection specifically examines time series data using graphical, least squares, or Box-Jenkins methods to extrapolate past trends. Barometric and econDemand-forcasting.pptx introduction meaning
Demand-forcasting.pptx introduction meaningjaya315652
油
Demand forecasting is the process of predicting future demand for a product or service based on past behaviors and various influencing factors. Different methods used for forecasting include buyer intention surveys, expert opinions, controlled experiments, business barometers, and statistical techniques like trend projection and regression analysis. It plays a critical role in business planning and decision-making by helping to understand consumer demand and optimize production.Forecasting
ForecastingSVGANGAD
油
The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.demandforecasting business economics.pptx
demandforecasting business economics.pptxNishthaVerma25
油
Demand forecasting is a systematic process of anticipating future demand for a firm's products and services, crucial for effective production and resource management. Various qualitative and quantitative methods exist for forecasting, including consumer surveys, sales force opinions, and statistical techniques like trend projection and regression analysis. Accurate demand forecasting aids in production planning, sales forecasting, inventory control, and informed economic policy making.Forecasting.ppt
Forecasting.pptDrMahmoudElmarhoumy
油
The document discusses quantitative forecasting methods, including exponential smoothing. Exponential smoothing assigns weights to past observations, with more recent observations receiving greater weight, to generate forecasts. Examples are provided to demonstrate how to calculate forecasts for upcoming time periods using exponential smoothing with different smoothing constants (a). Managers can use exponential smoothing on historical demand data to forecast future demand and support inventory and production planning.Demand forecasting
Demand forecastingKandarp Desai
油
This document discusses demand forecasting techniques. It outlines the objectives of demand forecasting such as understanding the role and reasons for forecasting. It then describes various qualitative and quantitative forecasting methodologies including surveys, sales force composites, exponential smoothing, and regression analysis. Finally, it discusses measuring forecast accuracy using metrics like mean error and developing control limits to monitor forecast performance.Forecasting_Quantitative Forecasting.ppt
Forecasting_Quantitative Forecasting.pptRituparnaDas584083
油
Here are the exponential smoothing forecasts for months June and July using an a constant of 0.3:
Month Demand 0.3 0.5
January 80 84.00 84.00
February 84 82.80 82.00
March 82 83.16 83.00
April 85 82.81 82.50
May 89 83.47 83.75
June 85.13 83.78 83.38
July 86.12 84.01
Ft+1 = Ft + a(At - Ft)Product Design Forecasting Techniquesision.ppt
Product Design Forecasting Techniquesision.pptavidc1000
油
The document discusses quantitative forecasting methods, including naive, moving average, and exponential smoothing approaches. It provides examples of how to calculate forecasts for upcoming time periods using 3-period simple moving averages, weighted moving averages with decreasing weights, and exponential smoothing with different smoothing constants. The examples demonstrate how to iteratively calculate the forecasts for each upcoming period in a time series.demandforecasting-180917122131.pptx
demandforecasting-180917122131.pptxPrabin Pandit
油
Demand forecasting involves anticipating future demand for an organization's products and services under uncertain competitive conditions. Accurate forecasts are essential for production planning, purchasing inputs, and other business decisions. There are qualitative and quantitative forecasting methods. Qualitative methods include consumer surveys, salesforce opinions, and expert panels. Quantitative methods use historical sales data and statistical analysis, such as time series analysis, regression analysis, and econometric modeling of economic factors. Accurate demand forecasting is important for production planning, sales forecasting, inventory control, and long-term strategic planning.Demand forecasting
Demand forecastingShompa Nandi
油
Demand forecasting involves anticipating future demand for a company's products and services under uncertain competitive conditions. It is essential for production planning, purchasing raw materials, and other business decisions. Demand can be forecasted qualitatively using opinion surveys of consumers, salespeople, and experts, or quantitatively using statistical techniques like trend projection, regression analysis, and econometrics that analyze historical demand data and its relationships to economic indicators. Accurate demand forecasting is important for production planning, inventory control, sales forecasting, budgeting, and long-term growth strategies.Recently uploaded (20)
Structured Programming with C++ :: Kjell Backman
Structured Programming with C++ :: Kjell BackmanShabista Imam
油
Step into the world of high-performance programming with the Complete Guidance Book of C++ Programminga definitive resource for mastering one of the most powerful and versatile languages in computer science.
Whether you're a beginner looking to learn the fundamentals or an intermediate developer aiming to sharpen your skills, this book walks you through C++ from the ground up. You'll start with basics like variables, control structures, and functions, then progress to object-oriented programming (OOP), memory management, file handling, templates, and the Standard Template Library (STL).MATERIAL SCIENCE LECTURE NOTES FOR DIPLOMA STUDENTS
MATERIAL SCIENCE LECTURE NOTES FOR DIPLOMA STUDENTSSAMEER VISHWAKARMA
油
This PPT contains notes for taking lecture at diploma level.Tally.ERP 9 at a Glance.book - Tally Solutions .pdf
Tally.ERP 9 at a Glance.book - Tally Solutions .pdfShabista Imam
油
Tally.ERP 9 at a Glance.book, a fully completed guidance to learn tally erp 9.0Rapid Prototyping for XR: Lecture 3 - Video and Paper Prototyping
Rapid Prototyping for XR: Lecture 3 - Video and Paper PrototypingMark Billinghurst
油
This is lecture 3 in the course on Rapid Prototyping for XR, taught by Mark Billinghurst on June 10th 2025. This lecture is about Video and Paper prototyping.Proposal for folders structure division in projects.pdf
Proposal for folders structure division in projects.pdfMohamed Ahmed
油
Proposal for folders structure division in projectsComplete University of Calculus :: 2nd edition
Complete University of Calculus :: 2nd editionShabista Imam
油
Master the language of change with the Complete Guidance Book of Calculusyour comprehensive resource for understanding the core concepts and applications of differential and integral calculus. Designed for high school, college, and self-study learners, this book takes a clear, intuitive approach to a subject often considered challenging.Rapid Prototyping for XR: Lecture 5 - Cross Platform Development
Rapid Prototyping for XR: Lecture 5 - Cross Platform DevelopmentMark Billinghurst
油
This is lecture 5 in the course about Rapid Prototyping for XR, taught by Mark Billinghurst. This lecture is about Cross Platform Development.Introduction to Python Programming Language
Introduction to Python Programming Languagemerlinjohnsy
油
This PPT covers features, applications, variable, data types and statements in PythonComplete guidance book of Asp.Net Web API
Complete guidance book of Asp.Net Web APIShabista Imam
油
Unlock the full potential of modern web development with the Complete Guidance Book of ASP.NET Web APIyour all-in-one resource for mastering RESTful services using Microsofts powerful ASP.NET Core framework. This book takes you on a step-by-step journey from beginner to expert, covering everything from routing and controllers to security, performance optimization, and real-world architecture.Introduction to Natural Language Processing - Stages in NLP Pipeline, Challen...
Introduction to Natural Language Processing - Stages in NLP Pipeline, Challen...resming1
油
Lecture delivered in 2021. This gives an introduction to Natural Language Processing. It describes the use cases of NLP in daily life. It discusses the stages in NLP Pipeline. It highlights the challenges involved covering the different levels of ambiguity that could arise. It also gives a brief note on the present scenario with the latest language models, tools and frameworks/libraries for NLP.Rapid Prototyping for XR: Lecture 2 - Low Fidelity Prototyping.
Rapid Prototyping for XR: Lecture 2 - Low Fidelity Prototyping.Mark Billinghurst
油
This is lecture 2 on the Rapid Prototyping for XR course taught by Mark Billingurst on June 10th 2025. This lecture is about Low Fidelity Prototyping.Learning Types of Machine Learning Supervised Learning Unsupervised UNI...
Learning Types of Machine Learning Supervised Learning Unsupervised UNI...23Q95A6706
油
Learning Types of Machine Learning Supervised Learning Unsupervised Learning- semi supervised learning - The Brain and the Neuron Design a Learning System Perspectives and Issues in Machine Learning Concept Learning Task Concept Learning as Search Finding a Maximally Specific Hypothesis Version Spaces and the Candidate Elimination Algorithm
Deep Learning for Natural Language Processing_FDP on 16 June 2025 MITS.pptx
Deep Learning for Natural Language Processing_FDP on 16 June 2025 MITS.pptxresming1
油
This gives an introduction to how NLP has evolved from the time of World War II till this date through the advances in approaches, architectures and word representations. From rule based approaches, it advanced to statistical approaches. from traditional machine learning algorithms it advanced to deep neural network architectures. Deep neural architectures include recurrent neural networks, long short term memory, gated recurrent units, seq2seq models, encoder decoder models, transformer architecture, upto large language models and vision language models which are multimodal in nature.Stay Safe Women Security Android App Project Report.pdf
Stay Safe Women Security Android App Project Report.pdfKamal Acharya
油
Womens security is a critical issue in todays world and its very much needed for every individual
to be acting over such an issue. This document describes a GPS based Women Security System''
that provides the combination of GPS devices as well as provide alerts and messages with an
emergency button trigger whenever somebody is in trouble They might not have so much time, all
that they have to do is generate a distress emergency signal by shaking up their phone. Our system
provides a realizable, cost effective solution to problem detection. Nowdays due to recently
happened cases such as rape by drivers or colleagues, burglary etc., women security, especially
women security has become the foremost priority of the world. System uses the Global Positioning
System (GPS) technology to find out the location of women. The information of women's position
provided by the device can be viewed on Google maps using Internet or specialized software. The
companies are looking for-ward to the security problem and require a system that will efficiently
evaluate the problem of women security working in night shifts, traveling alone. We focus on the
proposed model that can be used to deal with the security issue of women using GPS based tracking
systems.International Journal of Advanced Information Technology (IJAIT)
International Journal of Advanced Information Technology (IJAIT)ijait
油
International journal of advanced Information technology (IJAIT) is a bi monthly open access peer-
reviewed journal, will act as a major forum for the presentation of innovative ideas, approaches,
developments, and research projects in the area advanced information technology applications and
services. It will also serve to facilitate the exchange of information between researchers and industry
professionals to discuss the latest issues and advancement in the area of advanced IT. Core areas of
advanced IT and multi-disciplinary and its applications will be covered during the conferences.Microwatt: Open Tiny Core, Big Possibilities
Microwatt: Open Tiny Core, Big PossibilitiesIBM
油
Microwatt is a lightweight, open-source core based on the OpenPOWER ISA.
Its designed for FPGAs and easy experimentation in chip design.
Ideal for education, prototyping, and custom silicon development.
Fully open, it empowers developers to learn, modify, and innovate.Ad
Lecture 1
- 1. Operation management Forecasting techniques Sudeesh kumar patel m.tech(heat power engg.) vnit Nagpur sudeesh.patel781@gmail.com
- 2. FORECASTING TYPES,OF FORECASTING BASED ON TIME LONG TERM DECISIONS - New Product Introduction - Plant Expansion MEDIUM TERM DECISIONS - Aggregate Production Planning - Manpower Planning - Inventory Policy SHORT TERM DECISIONS - Production planning - Scheduling of job orders A Forecast of Demand is an essential Input for Planning
- 3. FORECASTING - Objective - Scientific - Free from BIAS - Reproducible - Error Analysis Possible PREDICTION - Subjective - Intuitive - Individual BIAS - Non - Reproducible - Error Analysis Limited OPINION POLLS Personal interviews e.g. aggregation of opinion of sales representatives to obtain sales forecast of a region - Knowledge base (experience) Subjective bias Questionnaire method - questionnaire design - choice of respondents - obtaining respondents - analysis and presentation of results (forecasting) Telephonic conversation - Fast DELPHI METHODS OF FORECASTING Subjective or intuitive methods - Opinion polls, interviews - DELPHI Methods based on averaging of past data - Moving averages - Exponential Smoothing Regression models on historical data - Trend extrapolation Causal or econometric models ]Time - series analysis using stochastic models - Box Jenkins model
- 4. DELPHI A structured method of obtaining responses from experts. Utilizes the vast knowledge base of experts Eliminates subjective bias and influencing by members through anonymity Iterative in character with statistical summary at end of each round (Generally 3 rounds) Consensus (or Divergent Viewpoints) usually emerge at the end of the exercise. MOVING AVERAGES Month Demand 3 months MA 6 months MA JAN 199 FEB 202 MAR 199 200.00 APR 208 203.00 MAY 212 206.33 JUN 194 203.66 202.33 JUL 214 205.66 207.83 AUG 220 208.33 210.83 SEP 219 216.66 213.13 OCT 234 223.33 217.46 NOV 219 223.00 218.63 DEC 233 227.66 225.13 MOVING AVERAGES K PERIOD MA = AVERAGE OF K MOST RECENT OBSERVATIONS For instance : 3 PERIOD MA FOR MAY = Demands of Mar, Apr, May / 3 = (199 + 208 + 121) / 3 = 206.33 EXPONENTIAL SMOOTHING
- 5. Simple average method A simple average of demands occurring in all previous time periods is taken as the demand forecast for the next time period in this method. Simple Average : A XYZ television supplier found a demand of 200 sets in July, 225 sets in August & 245 sets in September. Find the demand forecast for the month of october using simple average method. The average demand for the month of October is Simple moving average method: In this method, the average of the demands from several of the most recent periods is taken as the demand forecast for the next time period. The number of past periods to be used in calculations is selected in the beginning and is kept constant (such as 3-period moving average) A XYZ refrigerator supplier has experienced the following demand for refrigerator during past five months. Month Demand February 20 March 30 April 40 May 60 June 45 Find out the demand forecast for the month of July using five-period moving average & three-period moving average using simple moving average method.
- 6. Weighted moving average method: In this method, unequal weights are assigned to the past demand data while calculating simple moving average as the demand forecast for next time period. Usually most recent data is assigned the highest weight factor. The manager of a restaurant wants to make decision on inventory and overall cost. He wants to forecast demand for some of the items based on weighted moving average method. For the past three months he exprienced a demand for pizzas as follows: Month Demand October 400 November 480 December 550 Find the demand for the month of January by assuming suitable weights to demand data. Exponential smoothing method: In this method, weights are assigned in exponential order. The weights decrease exponentially from most recent demand data to older demand data One of the two wheeler manufacturing company exprienced irregular but usually increasing demand for three products. The demand was found to be 420 bikes for June and 440 bikes for July. They use a forecasting
- 7. method which takes average of past year to forecast future demand. Using the simple average method demand forecast for June is found as 320 bikes (Use a smoothing coefficient 0.7 to weight the recent demand most heavily) and find the demand forecast for August. Regression analysis method: In this method, past demand data is used to establish a functional relationship between two variables. One variable is known or assumed to be known; and used to forecast the value of other unknown variable (i.e. demand) Farewell Corporation manufactures Integrated Circuit boards(I.C board) for electronics devices. The planning department knows that the sales of their client goods depends on how much they spend on advertising, on account of which they receive in advance of expenditure. The planning department wish to find out the relationship between their clients advertising and sales, so as to find demand for I.C board. The money spend by the client on advertising and sales (in dollar) is given for different periods in following table : Period(t) Advertising (Xt) $(1,00,000) Sales (Dt) $(1,000.000) Dt 2 Xt 2 XtDt 1 20 6 36 400 120 2 25 8 64 625 200 3 15 7 49 225 105 4 18 7 49 324 126 5 22 8 64 484 176 6 25 9 81 625 225 7 27 10 100 729 270 8 23 7 49 529 161 9 16 6 36 256 96 10 20 8 64 400 120 211 76 592 4597 1599