Topic: Operations Management, Degree: MBA, Semester: II Syllabus: Mysore University. Date : Jan 2015.
Please note: This was prepared as a teaching aid. Not for commercial purposes. Sharing to spread the knowledge of operations management. Note : Copyright belongs to respective owners. List of top references used to prepare these slides given.
If you have any questions, comments, improvement suggestions, Email to: niranjanakoodavalli@gmail.com
PRODUCTION AND OPERATIONS
MANAGEMENT
-Management function responsible for producing goods & services
-Objectives of production management
-Functions of production management
-Production system & models
Operations managers must develop an operations strategy that is consistent with the firm's corporate strategy. An operations strategy involves key decisions such as which products to produce internally and which to purchase, how many facilities are needed and where to locate them, what processes and technologies to use, how to distribute products to customers, which suppliers to use and how much to source from them, what human resources and skills are required, and quality measures. The operations strategy provides support for the firm's overall differentiated strategy and competitive approach through efficient and effective execution of operations.
The document summarizes key concepts in management including:
1) Managers are responsible for achieving organizational objectives through efficient use of resources. Their main functions are planning, organizing, leading, and controlling.
2) There are three levels of management - top, middle, and first-line managers - requiring different skills.
3) Entrepreneurs found new business ventures while intrapreneurs create new lines of business within existing companies.
4) An effective business plan outlines the business concept, operations, marketing strategy, finances, and goals.
Operations management involves planning, scheduling, and controlling activities that transform inputs like raw materials, capital, and labor into higher-value outputs like products and services. Key decision areas include quality management, product/process design, location/layout strategies, human resources, supply chain management, inventory management, scheduling, and maintenance. Operations management aims to maximize efficiency and productivity through techniques like lean manufacturing, total quality management, and continuous improvement processes.
This document discusses different approaches to strategic decision making. It describes rational, intuitive, political, administrative, entrepreneurial, adaptive, and planning approaches. The rational approach involves analyzing all alternatives and consequences to maximize gains. The intuitive approach relies on experience, instincts, and gut feelings. The political approach considers pressures from stakeholders. The administrative approach recognizes limitations of information and rationality, aiming to satisfice rather than optimize. The entrepreneurial approach is reactive to opportunities. The adaptive approach solves urgent problems. The planning approach anticipates the future through formal analysis of internal and external factors.
This document discusses green manufacturing. It defines green manufacturing as preventing pollution and waste by developing new processes that reduce or eliminate hazardous substances. The goals of green manufacturing are to identify, quantify, assess, and manage environmental waste flows to minimize environmental impact while maximizing resource efficiency. Benefits include environmental protection, improved company reputation and social responsibility, and new research and technology development opportunities. Challenges include the long-term effort, upfront investment costs, and increased production costs required. The document provides examples of Panasonic's energy-efficient air conditioners and more fuel-efficient asphalt highways.
Topic: Operations Management, Degree: MBA, Semester: II Syllabus: Mysore University. Date : Jan 2015.
Please note: This was prepared as a teaching aid. Not for commercial purposes. Sharing to spread the knowledge of operations management. Note : Copyright belongs to respective owners. List of top references used to prepare these slides given.
If you have any questions, comments, improvement suggestions, Email to: niranjanakoodavalli@gmail.com
PRODUCTION AND OPERATIONS
MANAGEMENT
-Management function responsible for producing goods & services
-Objectives of production management
-Functions of production management
-Production system & models
Operations managers must develop an operations strategy that is consistent with the firm's corporate strategy. An operations strategy involves key decisions such as which products to produce internally and which to purchase, how many facilities are needed and where to locate them, what processes and technologies to use, how to distribute products to customers, which suppliers to use and how much to source from them, what human resources and skills are required, and quality measures. The operations strategy provides support for the firm's overall differentiated strategy and competitive approach through efficient and effective execution of operations.
The document summarizes key concepts in management including:
1) Managers are responsible for achieving organizational objectives through efficient use of resources. Their main functions are planning, organizing, leading, and controlling.
2) There are three levels of management - top, middle, and first-line managers - requiring different skills.
3) Entrepreneurs found new business ventures while intrapreneurs create new lines of business within existing companies.
4) An effective business plan outlines the business concept, operations, marketing strategy, finances, and goals.
Operations management involves planning, scheduling, and controlling activities that transform inputs like raw materials, capital, and labor into higher-value outputs like products and services. Key decision areas include quality management, product/process design, location/layout strategies, human resources, supply chain management, inventory management, scheduling, and maintenance. Operations management aims to maximize efficiency and productivity through techniques like lean manufacturing, total quality management, and continuous improvement processes.
This document discusses different approaches to strategic decision making. It describes rational, intuitive, political, administrative, entrepreneurial, adaptive, and planning approaches. The rational approach involves analyzing all alternatives and consequences to maximize gains. The intuitive approach relies on experience, instincts, and gut feelings. The political approach considers pressures from stakeholders. The administrative approach recognizes limitations of information and rationality, aiming to satisfice rather than optimize. The entrepreneurial approach is reactive to opportunities. The adaptive approach solves urgent problems. The planning approach anticipates the future through formal analysis of internal and external factors.
This document discusses green manufacturing. It defines green manufacturing as preventing pollution and waste by developing new processes that reduce or eliminate hazardous substances. The goals of green manufacturing are to identify, quantify, assess, and manage environmental waste flows to minimize environmental impact while maximizing resource efficiency. Benefits include environmental protection, improved company reputation and social responsibility, and new research and technology development opportunities. Challenges include the long-term effort, upfront investment costs, and increased production costs required. The document provides examples of Panasonic's energy-efficient air conditioners and more fuel-efficient asphalt highways.
Human resources managers must scan the organizational environment and formulate strategies in response to trends. Technological advances require technically skilled employees and impact organizational structure. Factors to consider include technology, organizational structure, employee values and attitudes, management trends, demographics, human resource utilization, and international developments. Successfully addressing a changing environment through strategic planning and adaptation provides organizations with a competitive advantage.
Business process reengineering (BPR) seeks dramatic improvements in critical performance measures like cost, quality, service and speed through fundamentally rethinking and redesigning business processes. It requires taking a clean-sheet approach to processes rather than assuming current processes are optimal. Key steps involve selecting processes for reengineering, appointing cross-functional teams, understanding the current "as-is" process, developing and communicating a vision for an improved "to-be" process, identifying an action plan, and executing the plan through process simplification and standardization while removing non-value adding activities. Common challenges include processes being too broadly or narrowly defined, over-reliance on existing processes, and failure to align BPR with business objectives.
Operations management evolved from the Industrial Revolution in the 18th century, where the steam engine automated production. Scientific management in the early 20th century introduced time and motion studies to optimize workflows. Henry Ford further refined production with assembly lines and interchangeable parts. Quantitative decision models were developed in the 20th century to aid inventory management, forecasting, and project management. Japanese manufacturers influenced operations with quality and productivity improvements. Today, information technology and globalization shape operations management.
The document discusses green manufacturing, defining it as implementing substitutions that reduce energy, resource consumption, waste, and water usage. It outlines the need for green manufacturing due to environmental and business reasons. The goals of green manufacturing are achieving sustainability and conserving resources for future generations. Benefits include improved reputation and reduced costs. Examples of green manufacturing processes and products are provided.
Production Planning and Control (Operations Management)Manu Alias
Production planning and control aims to efficiently utilize resources like materials, people, and facilities to transform raw materials into finished products in an optimal manner. It involves planning, coordinating, and controlling all production activities from procurement to shipping. The key objectives are proper coordination of activities, better control, ensuring uninterrupted production, capacity utilization, and timely delivery. The main stages are planning, action, and control. Important functions include production planning like estimating, routing, and scheduling, as well as production control functions like dispatching, follow up, and inspection. A master production schedule is a production plan that states what will be made, how many units, and when, to coordinate activities and resources.
Capacity planning is the process of determining the production capacity needed by an organization to meet changing demands. It involves assessing existing capacity, forecasting future needs, identifying options to modify capacity, evaluating financial and technological alternatives, and selecting the most suitable option. Capacity planning can be classified as long term or short term based on time horizon and finite or infinite based on resources employed. Long term planning accommodates major changes like new products or facilities while short term addresses intermediate fluctuations through overtime or subcontracting. Factors affecting capacity planning include controllable aspects like labor and facilities as well as less controllable issues like absenteeism or machine breakdowns.
This document discusses loading jobs and scheduling work centers. It defines loading as assigning jobs to work centers to minimize costs and completion times. Infinite loading ignores capacity constraints while finite loading only assigns as much work as can be completed with available capacity. The document also discusses characteristics of high-volume and low-volume operations, input-output processes, Gantt charts, and assignment methods.
Cpgp day01-session 3 - introduction to cpzubeditufail
Cleaner Production is a preventative environmental management approach that focuses on continuously reducing or eliminating waste at the source during production processes. It involves applying strategies like good housekeeping practices, input substitution, process optimization, equipment modifications, and technology changes to increase efficiency and minimize environmental risks. The goal of Cleaner Production is to design and retrofit industrial systems to prevent pollution, maximize conservation of raw materials, energy and water, and reduce health and environmental risks while being cost-effective.
This document discusses innovation management. It defines innovation as a new idea that improves products, processes or services. Innovation management involves guiding new ideas through development, protection, enforcement, and implementation. Key aspects of innovation management include identifying sources of innovation, different types of innovation, models of the innovation process, organizational structures that support innovation, and difficulties in achieving successful innovation management.
The document discusses operation management and production systems. It covers topics like production management, operations management, production system models, decisions made by operations managers, types of production systems, elements of operations strategy, operations competitive priorities, demand forecasting, and forecasting approaches. Specifically, it defines production management as applying management principles to converting raw materials into finished products. It also defines operations management as converting resources into more useful products or services.
The document discusses strategies adopted by world class manufacturers to achieve competitive advantage in production and operations management. It describes how world class manufacturers develop excellent forecasting, product design, capacity planning, facility layout, and inventory management systems. They adopt advanced technologies like CAD, CAM, JIT, and ERP systems to improve quality, flexibility, and customer responsiveness. The goal is to reduce costs while meeting and exceeding customer expectations.
Human resource management systems (HRMS) can provide several benefits to organizations. HRMS automate human resource functions like recruitment, hiring, performance appraisals, compensation, and benefits administration. This reduces paperwork and frees up time for managers to focus on more strategic work. HRMS also improve data storage efficiency, save costs, save time, improve data accuracy, and maintain security of employee data. Current HRMS encompass modules for payroll, work time, benefits administration, HR information management, recruiting, training, and employee self-service.
production and operations management(POM) Complete note kabul university
The Introduction to POM, Scope, Role, and Objectives of POM, Operations Mgt. – Concept; Functions
Product Design and its characteristics;
Product Development Process, Product Development Techniques.
Methods engineering and operations analysis involve analyzing work methods, systems, tools, equipment, layout, and processes to improve productivity, efficiency, quality, and reduce costs. A systematic approach is used, including defining problems and objectives, analyzing current processes, formulating alternatives, evaluating and selecting the best alternative, implementing it, and auditing results. Various techniques are used for data collection and analysis, including motion study, work measurement, diagrams, and statistical tools. The goal is to continuously improve processes and operations.
This document discusses various methods for measuring productivity in the construction industry. It outlines key factors that influence productivity such as pre-construction activities, resource management, and labor characteristics. It then describes different formulas that can be used to calculate productivity, including comparing the ratio of outputs to inputs or measuring the level of profitability and business efficiency. The document also provides an example of a formula for measuring productivity changes related to material waste.
The document discusses innovation management and related topics including:
1) Innovation management involves tasks like innovation within organizations, strategies, and forecasting technology.
2) Companies should strive to be innovative for competitive advantages like responding to changing consumer and market needs. However, companies may lack innovation due to factors such as high costs, fear of failure, or relying on existing business models.
3) Managing innovation requires considering an organization's structure and culture as well as promoting creativity among employees through techniques like brainstorming.
This document provides an overview of economic forecasting. It defines forecasting and economic forecasting, and outlines the 7 key steps in the economic forecasting process: 1) determining the forecast's use, 2) identifying items to forecast, 3) setting time limits, 4) collecting data, 5) selecting a forecasting model, 6) estimating the forecast, and 7) making the forecast. It also discusses forecast types, including qualitative vs. quantitative and short, medium, and long-term, as well as common forecasting methods.
What is Forecasting?
Forecasting is a technique of predicting the future based on the results of previous data. It involves a
detailed analysis of past and present trends or events to predict future events. It uses statistical tools and
techniques. Therefore, it is also called Statistical analysis. In other words, we can say that forecasting acts
as a planning tool that helps enterprises to get ready for the uncertainty that can occur in the future.
Forecasting begins with management's experience and knowledge sharing. To obtain the most numerous
advantages from forecasts, organizations must know the different forecasting methods' more subtle
details. Also, understand what an appropriate forecasting method type can and cannot do, and realize
what forecast type is best suited to a specific need. Let's list down some significant benefits of forecasting:
• Better utilization of resources
• Formulating business plans
• Enhance the quality of management
• Helps in establishing a new business model
• Helps in making the best managerial decisions
A set of observations taken at a particular period of time. For example, having a set of login details at
regular interval of time of each user can be categorized as a time series. Click to explore about, Anomaly
Detection with Time Series Forecasting
What is Prediction?
Prediction is using the data to compute the Outcome of the unseen data.
How does Prediction work?
Firstly, the daily data is fetched from the market once at a time in a day and update it into the database.
Now, the prediction cycle along with learning developed with the use of newly combined data. Historical
data collected and the learning and prediction cycle developed to generate the results. The prediction
results obtained in the form of the various set of periods such as two days, four days, 14 days and so on.
Difference between Prediction and Forecasting
Prediction is the process of estimating the outcomes of unseen data. Forecasting is a sub-discipline of
prediction in which we use time-series data to make forecasts about the future. As a result, the only
distinction between prediction and forecasting is that we consider the temporal dimension. Confusing?
So do we forecast the weather or predict the weather? Consider this, What are the chances that it will
continue to rain in five minutes if it is already raining? Since it is raining right now, regardless of any other
factors that affect the weather (such as air pressure and temperature), the chances of it raining again in
five minutes are high. Right?vThe temporal dimension is whether it is raining right now or not? Without
that forecasting the next 5 mins wouldn't make much sense.
Time-Series refers to data recording at regular intervals of time. Click to explore about, Time Series
Forecasting Analysis
Why Forecasting is important?
Prediction of labor, material and other resources are highly crucial for operating. If the services are
Predicting better, then balanced
Human resources managers must scan the organizational environment and formulate strategies in response to trends. Technological advances require technically skilled employees and impact organizational structure. Factors to consider include technology, organizational structure, employee values and attitudes, management trends, demographics, human resource utilization, and international developments. Successfully addressing a changing environment through strategic planning and adaptation provides organizations with a competitive advantage.
Business process reengineering (BPR) seeks dramatic improvements in critical performance measures like cost, quality, service and speed through fundamentally rethinking and redesigning business processes. It requires taking a clean-sheet approach to processes rather than assuming current processes are optimal. Key steps involve selecting processes for reengineering, appointing cross-functional teams, understanding the current "as-is" process, developing and communicating a vision for an improved "to-be" process, identifying an action plan, and executing the plan through process simplification and standardization while removing non-value adding activities. Common challenges include processes being too broadly or narrowly defined, over-reliance on existing processes, and failure to align BPR with business objectives.
Operations management evolved from the Industrial Revolution in the 18th century, where the steam engine automated production. Scientific management in the early 20th century introduced time and motion studies to optimize workflows. Henry Ford further refined production with assembly lines and interchangeable parts. Quantitative decision models were developed in the 20th century to aid inventory management, forecasting, and project management. Japanese manufacturers influenced operations with quality and productivity improvements. Today, information technology and globalization shape operations management.
The document discusses green manufacturing, defining it as implementing substitutions that reduce energy, resource consumption, waste, and water usage. It outlines the need for green manufacturing due to environmental and business reasons. The goals of green manufacturing are achieving sustainability and conserving resources for future generations. Benefits include improved reputation and reduced costs. Examples of green manufacturing processes and products are provided.
Production Planning and Control (Operations Management)Manu Alias
Production planning and control aims to efficiently utilize resources like materials, people, and facilities to transform raw materials into finished products in an optimal manner. It involves planning, coordinating, and controlling all production activities from procurement to shipping. The key objectives are proper coordination of activities, better control, ensuring uninterrupted production, capacity utilization, and timely delivery. The main stages are planning, action, and control. Important functions include production planning like estimating, routing, and scheduling, as well as production control functions like dispatching, follow up, and inspection. A master production schedule is a production plan that states what will be made, how many units, and when, to coordinate activities and resources.
Capacity planning is the process of determining the production capacity needed by an organization to meet changing demands. It involves assessing existing capacity, forecasting future needs, identifying options to modify capacity, evaluating financial and technological alternatives, and selecting the most suitable option. Capacity planning can be classified as long term or short term based on time horizon and finite or infinite based on resources employed. Long term planning accommodates major changes like new products or facilities while short term addresses intermediate fluctuations through overtime or subcontracting. Factors affecting capacity planning include controllable aspects like labor and facilities as well as less controllable issues like absenteeism or machine breakdowns.
This document discusses loading jobs and scheduling work centers. It defines loading as assigning jobs to work centers to minimize costs and completion times. Infinite loading ignores capacity constraints while finite loading only assigns as much work as can be completed with available capacity. The document also discusses characteristics of high-volume and low-volume operations, input-output processes, Gantt charts, and assignment methods.
Cpgp day01-session 3 - introduction to cpzubeditufail
Cleaner Production is a preventative environmental management approach that focuses on continuously reducing or eliminating waste at the source during production processes. It involves applying strategies like good housekeeping practices, input substitution, process optimization, equipment modifications, and technology changes to increase efficiency and minimize environmental risks. The goal of Cleaner Production is to design and retrofit industrial systems to prevent pollution, maximize conservation of raw materials, energy and water, and reduce health and environmental risks while being cost-effective.
This document discusses innovation management. It defines innovation as a new idea that improves products, processes or services. Innovation management involves guiding new ideas through development, protection, enforcement, and implementation. Key aspects of innovation management include identifying sources of innovation, different types of innovation, models of the innovation process, organizational structures that support innovation, and difficulties in achieving successful innovation management.
The document discusses operation management and production systems. It covers topics like production management, operations management, production system models, decisions made by operations managers, types of production systems, elements of operations strategy, operations competitive priorities, demand forecasting, and forecasting approaches. Specifically, it defines production management as applying management principles to converting raw materials into finished products. It also defines operations management as converting resources into more useful products or services.
The document discusses strategies adopted by world class manufacturers to achieve competitive advantage in production and operations management. It describes how world class manufacturers develop excellent forecasting, product design, capacity planning, facility layout, and inventory management systems. They adopt advanced technologies like CAD, CAM, JIT, and ERP systems to improve quality, flexibility, and customer responsiveness. The goal is to reduce costs while meeting and exceeding customer expectations.
Human resource management systems (HRMS) can provide several benefits to organizations. HRMS automate human resource functions like recruitment, hiring, performance appraisals, compensation, and benefits administration. This reduces paperwork and frees up time for managers to focus on more strategic work. HRMS also improve data storage efficiency, save costs, save time, improve data accuracy, and maintain security of employee data. Current HRMS encompass modules for payroll, work time, benefits administration, HR information management, recruiting, training, and employee self-service.
production and operations management(POM) Complete note kabul university
The Introduction to POM, Scope, Role, and Objectives of POM, Operations Mgt. – Concept; Functions
Product Design and its characteristics;
Product Development Process, Product Development Techniques.
Methods engineering and operations analysis involve analyzing work methods, systems, tools, equipment, layout, and processes to improve productivity, efficiency, quality, and reduce costs. A systematic approach is used, including defining problems and objectives, analyzing current processes, formulating alternatives, evaluating and selecting the best alternative, implementing it, and auditing results. Various techniques are used for data collection and analysis, including motion study, work measurement, diagrams, and statistical tools. The goal is to continuously improve processes and operations.
This document discusses various methods for measuring productivity in the construction industry. It outlines key factors that influence productivity such as pre-construction activities, resource management, and labor characteristics. It then describes different formulas that can be used to calculate productivity, including comparing the ratio of outputs to inputs or measuring the level of profitability and business efficiency. The document also provides an example of a formula for measuring productivity changes related to material waste.
The document discusses innovation management and related topics including:
1) Innovation management involves tasks like innovation within organizations, strategies, and forecasting technology.
2) Companies should strive to be innovative for competitive advantages like responding to changing consumer and market needs. However, companies may lack innovation due to factors such as high costs, fear of failure, or relying on existing business models.
3) Managing innovation requires considering an organization's structure and culture as well as promoting creativity among employees through techniques like brainstorming.
This document provides an overview of economic forecasting. It defines forecasting and economic forecasting, and outlines the 7 key steps in the economic forecasting process: 1) determining the forecast's use, 2) identifying items to forecast, 3) setting time limits, 4) collecting data, 5) selecting a forecasting model, 6) estimating the forecast, and 7) making the forecast. It also discusses forecast types, including qualitative vs. quantitative and short, medium, and long-term, as well as common forecasting methods.
What is Forecasting?
Forecasting is a technique of predicting the future based on the results of previous data. It involves a
detailed analysis of past and present trends or events to predict future events. It uses statistical tools and
techniques. Therefore, it is also called Statistical analysis. In other words, we can say that forecasting acts
as a planning tool that helps enterprises to get ready for the uncertainty that can occur in the future.
Forecasting begins with management's experience and knowledge sharing. To obtain the most numerous
advantages from forecasts, organizations must know the different forecasting methods' more subtle
details. Also, understand what an appropriate forecasting method type can and cannot do, and realize
what forecast type is best suited to a specific need. Let's list down some significant benefits of forecasting:
• Better utilization of resources
• Formulating business plans
• Enhance the quality of management
• Helps in establishing a new business model
• Helps in making the best managerial decisions
A set of observations taken at a particular period of time. For example, having a set of login details at
regular interval of time of each user can be categorized as a time series. Click to explore about, Anomaly
Detection with Time Series Forecasting
What is Prediction?
Prediction is using the data to compute the Outcome of the unseen data.
How does Prediction work?
Firstly, the daily data is fetched from the market once at a time in a day and update it into the database.
Now, the prediction cycle along with learning developed with the use of newly combined data. Historical
data collected and the learning and prediction cycle developed to generate the results. The prediction
results obtained in the form of the various set of periods such as two days, four days, 14 days and so on.
Difference between Prediction and Forecasting
Prediction is the process of estimating the outcomes of unseen data. Forecasting is a sub-discipline of
prediction in which we use time-series data to make forecasts about the future. As a result, the only
distinction between prediction and forecasting is that we consider the temporal dimension. Confusing?
So do we forecast the weather or predict the weather? Consider this, What are the chances that it will
continue to rain in five minutes if it is already raining? Since it is raining right now, regardless of any other
factors that affect the weather (such as air pressure and temperature), the chances of it raining again in
five minutes are high. Right?vThe temporal dimension is whether it is raining right now or not? Without
that forecasting the next 5 mins wouldn't make much sense.
Time-Series refers to data recording at regular intervals of time. Click to explore about, Time Series
Forecasting Analysis
Why Forecasting is important?
Prediction of labor, material and other resources are highly crucial for operating. If the services are
Predicting better, then balanced
A good forecast should be consistent with other business areas, based on relevant past knowledge, consider economic and political factors, and be timely. The right forecasting technique depends on the item, data availability, time, and how situations interact with method characteristics. Techniques include expert opinion, surveys, projections, econometric models, qualitative judgments, quantitative data analysis, naïve trends, and time series analysis decomposing trends, cycles, seasonality, and randomness.
1) Profit planning involves developing budgets to achieve a targeted profit level, including sales forecasts and expense estimates.
2) Key elements in predicting profitability are sales forecasts, which can be done through judgment-based or quantitative methods, and expense forecasts, which estimate fixed, variable, and semi-variable costs.
3) Break-even analysis determines the sales volume needed to cover total costs and yield zero profit. It is used to assess the impact of changes in sales, prices, or costs on profit levels.
This document discusses various forecasting methods used in operations management. It begins by defining forecasting as predicting future events by taking historical data and projecting it using mathematical models adjusted by managerial judgment. There are three types of forecasts: economic, technological, and demand forecasts which project needs for a company's products. Accurate forecasting is important for human resources, capacity, and supply chain planning. The document then outlines quantitative time series and associative forecasting models as well as qualitative methods like Delphi, educated guesses, surveys, and analogy. It concludes by asking questions about forecasting definitions, accuracy, importance for operations, and long-range demand components.
This document discusses forecasting techniques. It begins by defining forecasting as attempting to predict the future using qualitative or quantitative methods. The main steps in the forecasting process are outlined. Several qualitative and quantitative forecasting techniques are then described, including naive methods, moving averages, exponential smoothing, trend projections, and regression analysis. The document provides examples of how forecasting is used in various contexts like sales, production, staffing needs, education, rural settings, petroleum, and technology. Forecasting is summarized as being essential for planning and decision-making across many business and organizational functions.
IRJET- Overview of Forecasting TechniquesIRJET Journal
This document provides an overview of different forecasting techniques, including qualitative and quantitative methods. It discusses several qualitative techniques like the Delphi method, consumer market surveys, and jury of executive opinion. It also examines various quantitative techniques such as the moving average method, weighted moving average method, exponential smoothing, and least squares. The document serves to introduce students to common forecasting approaches and provide examples of each type of technique.
Planning is the process by which managers establish goals and define methods to achieve them. It involves selecting objectives and actions through decision making among alternatives. Planning is goal-oriented, looks ahead, involves choice and decision making, and is a continuous and flexible process that aims for efficiency. However, planning can also lead to rigidity and reduce creativity if not implemented properly. Strategic planning defines long-term vision and goals, while management by objectives aligns objectives across an organization. Forecasting uses past data to predict the future, and both qualitative and quantitative methods inform planning assumptions and decision making.
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.
This document provides an overview of operations management forecasting models and their applications. It defines forecasting and lists its common uses. The key components of a forecast and the forecasting process are described. Both qualitative and quantitative forecasting approaches are discussed, along with their advantages and disadvantages. Specific forecasting techniques covered include time series methods, regression methods, moving averages, exponential smoothing, and naive forecasts. Examples are provided to illustrate weighted moving averages and exponential smoothing.
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.
Budgeting and forecasting are important planning tools for organizations. Budgeting involves creating financial and operational plans for a specified future period, usually annually. It is done through identifying resources needed to achieve goals. Forecasting uses past data and qualitative expert opinions to predict future demand, sales, or other factors. Accurate forecasting is important for strategic planning, budgeting, operations, and finance. While forecasts are never perfectly accurate, quantitative time series analysis and qualitative expert panels are common forecasting methods used.
A statistical forecast provides several key benefits over a manual forecast including reducing the time spent creating and maintaining forecasts so planners can focus on value-added activities. It also helps reduce uncertainty, anticipate changes, and increase communication and knowledge across departments. A statistical forecast is independent from departmental biases and can be created at the appropriate level of product hierarchies. It also allows forecasts over long time horizons, encourages exception-based forecast review rather than manual cell-by-cell review, and can help create a center of excellence for demand planning.
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.
PROMISE 2011: What Prediction Model Should Be?CS, NcState
This document discusses establishing a prediction model to predict testing effort and schedule for a software development organization. It suggests identifying controllable factors that impact testing from historical project data, such as defects injected at different stages. Prediction models would be established using these factors to predict testing effort and schedule. The models would help manage iteration schedules and system testing to meet goals like reducing defects and detecting more during testing. Ongoing data collection and refinement of models is important.
The document outlines key concepts and steps related to forecasting techniques. It discusses features common to all forecasts, why forecasts are generally inaccurate, elements of a good forecast, and the forecasting process. It also covers forecast errors, qualitative and quantitative forecasting methods, and specific techniques like naive forecasts, moving averages, weighted averages, exponential smoothing, trend analysis, and seasonal adjustments. The learning objectives are to understand these forecasting fundamentals and how to apply various quantitative techniques.
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.
Forecasting practice in manufacturing businessMRPeasy
The forecast is an effective tool for planning and managing any type of manufacturing business. Regardless of the industry type, it will reduce the uncertainty and the risks of your business.
#manufacturing #forecasting #materialforecast #materialplanning #inventorymanagement #supplychainmanagement #erp #mrp
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
21SFH19-SFH_Module 4 - Avoiding risks and harmful habits.pdfDr. Bhimsen Soragaon
The Visvesvaraya Technological University, Belagavi, Karnataka, India has introduced a couple of courses for the enhancement students' knowledge in different domains. JSS Academy of Technical Education, Bengaluru is pioneer in disseminating the knowledge through strong learning materials.
21SFH19-SFH_Module 1-Good Health & Its Balance for Positive Mindset.pdfDr. Bhimsen Soragaon
The Visvesvaraya Technological University, Belagavi, Karnataka, India has introduced a couple of courses for the enhancement students' knowledge in different domains. JSS Academy of Technical Education, Bengaluru is pioneer in disseminating the knowledge through strong learning materials.
21SFH19-SFH_Module 2 - Building of healthy lifestyles for better future.pdfDr. Bhimsen Soragaon
This document outlines the course outcomes and program outcomes for a course on health and wellness. The 4 course outcomes are to demonstrate knowledge of health and wellness, maintain a balanced and positive mindset, inculcate healthy lifestyle habits, and follow innovative methods to avoid risks. The course outcomes are mapped to 12 program outcomes relating to engineering knowledge, problem analysis, design, investigations, tool usage, professional responsibilities, and more.
This document provides an overview of line balancing methods and computerized line balancing. It discusses traditional line balancing methods like the largest candidate rule, Kilbridge and Wester method, and ranked positional weights method. It also describes computerized line balancing algorithms like COMSOAL that use heuristics and random selection to explore solutions. The COMSOAL method is explained through an example where work elements are assigned to stations while meeting precedence and cycle time constraints.
Uploaded by Dr. Bhimasen Soragaon, Prof. & Head, Dept. of ME., JSSATE, Bengaluru
All the peers and students are requested to give their feedback on the contents
Bending Stresses are important in the design of beams from strength point of view. The present source gives an idea on theory and problems in bending stresses.
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Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
1. Dept. of ME, JSSATE, Bengaluru 1
―I never think of the future — it comes
soon enough.‖
— Albert Einstein
2. Module – 2:
• Forecasting:
• Steps in forecasting process, approaches to
forecasting, forecasts based on judgment
and opinion, analysis of time series data,
accuracy and control of forecasts, choosing
a forecasting technique, elements of a
good forecast.
(8 hours)
2Dept. of ME, JSSATE, Bengaluru
3. Module Outcome:
At the end of this module, you will be able to:
CO# Course Outcome
Bloom’s
Level
2
Examine various approaches for forecasting the sales
demand for an organization.
4
3Dept. of ME, JSSATE, Bengaluru
Forecasting
Learning Objectives:
After completing this module, you should be able to:
• List the elements of a good forecast.
• Outline the steps in the forecasting process.
• Describe qualitative forecasting techniques.
• Prepare forecasts using quantitative techniques.
• Compute forecast errors and comment on them.
4. 4Dept. of ME, JSSATE, Bengaluru
• Forecast is a prediction of what will occur in the future.
• Meteorologists forecast the weather, sportscasters and
gamblers predict the winners of football games, and
companies attempt to predict how much of their product will
be sold in the future.
• A forecast of product demand is the basis for most important
planning decisions.
• Planning decisions regarding scheduling, inventory,
production, facility layout and design, workforce, distribution,
purchasing, and so on, are functions of customer demand.
• Long-range, strategic plans by top management are based on
forecasts of the type of products consumers will demand in
the future and the size and location of product markets.
Forecasting - Introduction
5. 5
Dept. of ME, JSSATE, Bengaluru
• The forecast should be accurate, and the degree of
accuracy should be stated.
• The forecast should be reliable; it should work
consistently.
• The forecast should be expressed in meaningful
units.
• The forecast should be in writing.
• The forecasting technique should be simple to
understand and use.
• The forecast should be cost-effective. The benefits
should outweigh the costs.
• The forecast should be timely.
Forecasting Elements
Source: OM by W J Stevenson, 2018
6. 6
Dept. of ME, JSSATE, Bengaluru
• Accounting: New product/process cost estimates, profit
projections, cash management.
• Finance: Equipment/equipment replacement needs, timing
and amount of funding/borrowing needs.
• Human resources: Hiring activities, including recruitment,
interviewing, and training; layoff planning, including
outplacement counseling.
• Marketing: Pricing and promotion, e-business strategies,
global competition strategies.
• Operations: Schedules, capacity planning, work assignments
and workloads, inventory planning, make-or-buy decisions,
outsourcing, project management.
• Product/service design: Revision of current features, design
of new products or services.
Uses of Forecasts
Source: OM by W J Stevenson, 2018
7. 7
Dept. of ME, JSSATE, Bengaluru
• Determine the purpose / use of the forecast.
• Establish the time horizon of the forecast.
• Obtain, clean, and analyze appropriate data
• Select the forecasting technique / model(s).
• Make the forecast.
• Implement results and Monitor forecasts errors to
adjust when needed.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
8. 8
Dept. of ME, JSSATE, Bengaluru
• Determine the purpose / use of the forecast.
• The level of detail required in the forecast (what is
needed? when will it be needed?; how will it be
used?)
• The amount of resources (personnel, computer time,
money) that can be justified, and the level of
accuracy necessary.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
9. 9
Dept. of ME, JSSATE, Bengaluru
• Establish the time horizon of the forecast.
• It is the length of time in the future for which the
forecast is to be prepared.
• The forecast must indicate a time interval.
• Accuracy of forecast decreases as the time horizon
increases.
• Walmart basically forecasts taking a quarter-time period for
forecasting its revenues and other expenditures such as cost of
goods sold, purchase expenditure, promotional expenditure, etc.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
10. 10
Dept. of ME, JSSATE, Bengaluru
• Forecasting time horizons.
• Short-range (to mid-range) forecasts are typically for daily,
weekly, or monthly sales demand for up to approximately two
years into the future, depending on the company and the type of
industry. They are primarily used to determine production and
delivery schedules and to establish inventory levels.
• E.g., HP’s printers/month upto 12 – 18 months in future
• A long-range forecast: is usually for a period longer than two years
into the future. A long-range forecast is normally used for strategic
planning—to establish long-term goals, plan new products for
changing markets, enter new markets, develop new facilities,
develop technology, design the supply chain, and implement
strategic programs.
• E.g., Fiat (Italian automaker): strategic plans for new and
continuing products go 10 years into the future.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
11. 11
Dept. of ME, JSSATE, Bengaluru
• Obtain, clean, and analyze appropriate data
• Statistical data and Accumulated expertise who
collect the data.
• The data may need to be “cleaned” to get rid of
outliers and obviously incorrect data before analysis.
• Obtaining the data can involve significant effort.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
12. 12
Dept. of ME, JSSATE, Bengaluru
• Select the forecasting technique / model(s).
• Qualitative and Quantitative
• Make the forecast
• Implement results and Monitor forecasts errors to
adjust when needed.
Steps in Forecasting
Source: OM by W J Stevenson, 2018
13. 13
Dept. of ME, JSSATE, Bengaluru
• Based on judgments,
opinions, intuition, emotions,
or personal experiences. They
do not rely on any rigorous
mathematical computations.
• Based on mathematical
(quantitative) models, and are
objective in nature. They rely
heavily on mathematical
computations.
Forecasting Methods (Approaches)
Qualitative
or
Subjective Forecasting
Methods
Quantitative
or
Objective Forecasting
Methods
14. 14
Dept. of ME, JSSATE, Bengaluru
Forecasting Methods
The best-guess estimates
of a company's
executives. Each
executive submits an
estimate of the company's
sales, which are then
averaged to form the
overall sales forecast.
Information related to
the market that cannot
be collected from the
company's internal
records or the
externally published
sources of data.
16. 16
Dept. of ME, JSSATE, Bengaluru
Forecasting Methods
Sequence of observations taken
at regular intervals (e.g., hourly,
daily, weekly, monthly, quarterly,
annually).
Regression (or causal) forecasting methods
attempt to develop a mathematical
relationship (in the form of a regression
model) between demand and factors that
cause it to behave the way it does.
17. 17
Dept. of ME, JSSATE, Bengaluru
• Trend: refers to a long-term upward or downward movement in
the data. Population shifts, changing incomes, and cultural
changes often account for such movements.
• Seasonality: refers to short-term, fairly regular variations
generally related to factors such as the calendar or time of day.
Restaurants, supermarkets, and theaters experience weekly and
even daily “seasonal” variations.
• Cycles are wavelike variations of more than one year’s duration.
These are often related to a variety of economic, political, and
even agricultural conditions.
• Irregular variations are due to unusual circumstances such as
severe weather conditions, strikes, or a major change in a
product or service.
• Random variations are residual variations that remain after all
other behaviors have been accounted for.
Components of Time Series – Demand Behavior
18. 18
Dept. of ME, JSSATE, Bengaluru
Components of Time-series – Demand Behavior
Trend
Seasonal
Cyclical
Trend with seasonal pattern
Source: OM by Russel & Taylor
19. 19
Dept. of ME, JSSATE, Bengaluru
Time Series Methods (Models)
Method (Method) Description
Naïve Uses last period’s actual demand value as a forecast
Simple Average Uses an average of all past data as a forecast
Simple Moving Average Uses an average of a specified number of the most
recent observations, with each observation
receiving the same emphasis (weight)
Weighted Moving
Average
Uses an average of a specified number of the most
recent observations, with each observation
receiving a different emphasis (weight)
Exponential Smoothing A weighted average procedure with weights
declining exponentially as data become older
Trend Projection
(Causal method)
Technique that uses the least squares method to fit
a straight line to the data
Seasonal Indexes A mechanism for adjusting the forecast to
accommodate any seasonal patterns inherent in the
data
20. 20
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft
1 310 --
2 345 310
3 325 345
4 398 325
5 450 398
6 465 450
7 465
Naïve Method
One weakness of the naive method is that the forecast just traces
the actual data, with a lag of one period; it does not smooth at all.
tA1tF
21. 21
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft
1 38 42 (assumpn.)
2 44 38
3 43 41.00
4 39 41.66
5 48 41.00
6 52 42.40
7 44.00
Simple Average n/AF t1t
22. 22
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft
1 310 --
2 365
3 395 337.50
4 415 356.66
5 450 371.25
6 465 387.00
7 400.00
Simple Average
23. 23
Dept. of ME, JSSATE, Bengaluru
Simple Moving Average
• This method uses an average of a specified number of the most
recent observations (actual demand data), with each observation
receiving the same emphasis (weight).
• The moving average forecast can be computed using the following
equation:
24. 24
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft (2 yr MA)
1 42 --
2 40 --
3 43
4 40
5 41
6 38
7
Simple Moving Average
25. 25
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft (2 yr MA)
1 310 300 (assumpn)
2 365 310 (naïve)
3 395 337.500
4 415 380.00
5 450 405.00
6 465 432.50
7 457.50
Simple Moving Average
26. 26
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft
(3 yr MA)
1 310 300 (assumpn)
2 365 310 (naïve)
3 395 365 (naïve)
4 415 356.66
5 450 391.66
6 465 420.00
7 433.33
Simple Moving Average
27. 27
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft (3 yr WMA)
1 310 (0.2) 300 (aasumpn)
2 365 (0.3) 310 (naïve)
3 395 (0.5) 365 (naïve)
4 415 369.00
5 450 399.00
6 465 428.50
7 450.50
Weighted Moving Average
The forecast for next period (period t+1) will be equal to a
weighted average of a specified number of the most recent
observations. Weights to be used: 0.5, 0.3, 0.2
tt1t ACF Ct - Weight for a period; All weights must add to 100% or 1.00
28. 28
Dept. of ME, JSSATE, Bengaluru
Weighted Moving Average
The forecast for next period (period t+1) will be equal to a
weighted average of a specified number of the most recent
observations. Weights to be used: 0.5, 0.3, 0.2
Sample Calculations:
Forecast for 4th year = {(D3*0.5) + (D2*0.3) + (D1*0.2)} /
(0.5+0.3+0.2)
= (395*0.5 + 365*0.3 + 310*0.2)/1 = 369
Forecast for 5th year = {(D4*0.5) + (D3*0.3) + (D2*0.2)} /
(0.5+0.3+0.2)
= (415*0.5 + 395*0.3 + 365*0.2)/1 = 399
29. 29
Dept. of ME, JSSATE, Bengaluru
Year Actual Demand, Dt Forecast, Ft (4 yr WMA)
1 510 500 (assumpn.)
2 565 510 (naïve)
3 590 565 (naïve)
4 620 590 (naïve)
5 662
6 694
7 707 659.2
8 685.4
Weighted Moving Average
Weights to be used: 0.4, 0.3, 0.2, 0.1
30. Dept. of ME, JSSATE, Bengaluru 30
The forecast for next period (period t+1) will be equal to a
weighted average of a specified number of the most recent
observations. Weights to be used: 0.4, 0.3, 0.2, 0.1
Sample Calculations:
Forecast for 5th year = {(D4*0.4) + (D3*0.3) + (D2*0.2) +
(D1*0.1)} / (0.4+0.3+0.2+0.1)
= (620*0.4 + 590*0.3 + 565*0.2 + 510*0.1)/1 = 589
Forecast for 6th year = {(D5*0.4) + (D4*0.3) + (D3*0.2) +
(D2*0.1)} / (0.4+0.3+0.2+0.1)
= (662*0.4 + 620*0.3 + 590*0.2 + 565*0.1)/1 = 625.3
Weighted Moving Average
31. 31
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
tt1t Fα1αDF
Where, Ft+1 – Forecast for the period t+1 (required forecast)
Ft - Last period’s forecast
Dt - Last periods actual sales or demand value
- smoothing coefficient or constant (between 0 and
1.0), reflects the weight given to the most recent
demand data.
If no last period forecast is available, average the last few periods
or use naive method.
An averaging method that weights the most recent data more
strongly.
As such, the forecast will react more to recent changes in demand.
Virtually, all forecasting computer software packages include
modules for exponential smoothing.
32. 32
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Problem 1: A firm uses simple exponential smoothing with to
forecast demand. The forecast for the week of January 1 was
500 units whereas the actual demand turned out to be 450
units. Calculate the demand forecast for the week of January
8.
Given: Forecast for 1st January, F1 = 500 units
Actual demand for 1st January, D1 = 450 units; = 0.4
Forecast for 8th January, F2 = D1 + (1- ) F1
= 0.4 * 450 + (1-0.4) * 500 = 480 units
33. 33
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Month Actual
Demand, Dt
Forecast Month Actual
Demand, Dt
Forecast
1 37 7 43
2 40 8 47
3 41 9 56
4 37 10 52
5 45 11 55
6 50 12 54
Problem 2: A company has accumulated the demand data for the
past 12 months as shown in the table below. Compute the forecast
from 2nd to 13th month using exponential smoothing method. Use
smoothing constants equal to 0.30 and 0.50. Consider the first
month forecast as 37 units.
Source: OM by Russel & Taylor
34. Dept. of ME, JSSATE, Bengaluru 34
Sample Calculations:
Given: Forecast for 1st year, F1 = 37 units (assumption)
Actual demand for 1st year, D1 = 37 units; = 0.3
Forecast for 2nd month, F2 = D1 + (1- ) F1
= 0.3 * 37 + (1-0.3) * 37 = 37 units
Forecast for 3rd month, F3 = D2 + (1- ) F2
= 0.3*40 + (1-0.3)*37 = 37.9 units
Forecast for 4th month, F4 = D3 + (1- ) F3
= 0.3*41 + (1-0.3)*37.9 = 38.83 units
Exponential Smoothing
Similarcomputationswith=0.5
35. Dept. of ME, JSSATE, Bengaluru 35
Source: OM by Russel & Taylor
36. 36
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Month Actual Demand, Dt Forecast
1 310 300 (given)
2 365
3 395
4 415
5 450
6 465
7
Problem 3: A company has accumulated the demand data for the
past 6 months as shown in the table below. Compute the forecast
from 2nd to 7th month using exponential smoothing method. Use
smoothing constant, equal to 0.10 Consider the first month
forecast as 300 units.
37. Dept. of ME, JSSATE, Bengaluru 37
Sample Calculations:
Given: Forecast for 1st year, F1 = 300 units.
Actual demand for 1st year, D1 = 310 units; = 0.1
Forecast for 2nd month, F2 = D1 + (1- ) F1
= 0.1 * 310 + (1-0.1) * 300 = 301 units
Forecast for 3rd month, F3 = D2 + (1- ) F2
= 0.1*365 + (1-0.1)*301 = 307.4 units
Forecast for 4th month, F4 = D3 + (1- ) F3
= 0.1*395 + (1-0.3)*307.4 = 316.16 units
Exponential Smoothing
38. 38
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Month Actual Demand, Dt Forecast
1 13
2 17
3 19
4 23
5 24
Problem 4: The demand for a product in each of the last five
months is shown below. Apply exponential smoothing with a
smoothing constant of 0.9 to generate a forecast for demand
in month 6.
39. Dept. of ME, JSSATE, Bengaluru 39
Sample Calculations:
Given: Forecast for 1st year, F1 = 13 units (assumed)
Actual demand for 1st year, D1 = 13 units; = 0.9
Forecast for 2nd month, F2 = D1 + (1- ) F1
= 0.9 * 13 + (1-0.9) * 13 = 13 units
Forecast for 3rd month, F3 = D2 + (1- ) F2
= 0.9*17 + (1-0.9)*13 = 13.4 units
Forecast for 4th month, F4 = D3 + (1- ) F3
= 0.9*19 + (1-0.9)*13.4 = 13.96 units
Exponential Smoothing
40. 40
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Problem 5: The demand for a product in each of the last five
months is shown below. Apply exponential smoothing with
smoothing constants of 0.2 & 0.8 to generate a forecast for
demand in month 6.
Month Actual Forecasted
Jan 1,325
1,370
(given)
Feb 1,353 1,361
Mar 1,305 1,359
Apr 1,275 1,349
May 1,210 1,334
Jun -- 1,309
= 0.2
41. 41
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing
Problem 5: The demand for a product in each of the last five
months is shown below. Apply exponential smoothing with
smoothing constants of 0.2 & 0.8 to generate a forecast for
demand in month 6.
= 0.8Month Actual Forecast
Jan 1,325
1,370
(given)
Feb 1,353 1,334
Mar 1,305 1,349
Apr 1,275 1,314
May 1,210 1,283
Jun ? 1,225
42. 42
Dept. of ME, JSSATE, Bengaluru
Exponential Smoothing – Effect of
1200
1220
1240
1260
1280
1300
1320
1340
1360
1380
0 1 2 3 4 5 6 7
Actual
a = 0.2
a = 0.8
43. • What will happen to a moving average or exponential
smoothing model when there is a trend in the data?
• Exponential smoothing forecast with an adjustment
for the trend (added or subtracted).
Dept. of ME, JSSATE, Bengaluru 43
Trend Adjusted Exponential Smoothing
Sales
Month
Actual
Data
Forecast
Regular exponential
smoothing will always
lag behind the trend.
44. • Trend Adjusted Exponential Smoothing Forecast is given by
• Where, T = an exponentially smoothed trend factor. It is a
forecast model for trend.
• Where, Tt = the current period’s trend factor.
• = a smoothing factor for trend, a value
between 0.0 and 1.0.
• reflects the weight given to the most recent trend data. It is
usually determined subjectively based on the judgment of the
forecaster. Dept. of ME, JSSATE, Bengaluru 44
Trend Adjusted Exponential Smoothing
111 ttt TFAF
tttt TFFT )1()( 11
OR, FITt = Ft + Tt
)T(Fα1αDF ttt1t
45. • Compute the trend adjusted exponential forecast for
the 9th month for a firm with the following data.
Assume the forecast for 1st month as 600 units and
corresponding initial trend factor as 0. Take = 0.1 &
= 0.2.
Dept. of ME, JSSATE, Bengaluru 45
Trend Adjusted Exponential Smoothing
Month Actual
Demand, Dt
Forecast Month Actual
Demand, Dt
Forecast
1 650 600
(given)
7 700
2 600 8 710
3 550 9 ?
4 650
5 675
6 625
51. • A seasonal pattern is a repetitive increase and
decrease in demand.
• Many demand items exhibit seasonal behavior.
• Examples: Clothing, Greeting cards, Cold drinks, ACs,
Sweatshirts, Resort services, Restaurants, etc.
• Seasonal patterns can also occur on a monthly, weekly,
or even daily basis (restaurants, shopping malls, movie
theatres, etc.)
• Seasonal factor (index) is a method for reflecting
seasonal patterns in a time series forecast.
• A seasonal factor, Si is a numerical value between 0 &
1.
Dept. of ME, JSSATE, Bengaluru 51
Forecast Using Seasonal Indices
52. • Seasonal factor is the portion of total annual demand
assigned to each season.
• Seasonal factor, Si is computed as:
• Where, Di = sum of seasonal demand; D = Grand
total demand for all the seasons across all the given
years.
• To compute adjusted forecasts for each season, the
seasonal factors are multiplied by the annual
forecasted demand.
Dept. of ME, JSSATE, Bengaluru 52
Seasonal Indices or Factors
53. Dept. of ME, JSSATE, Bengaluru
53
Seasonal Indices
Source: OM by Russel & Taylor
The total demand of all the quarters in three years = 148.7
units.
If this is spread across 12 quarters (3x4),
The average demand per quarter would be
= 148.7/12
= 12.39 units
Ave. demand 14 9.83 7.3 18.43
54. Dept. of ME, JSSATE, Bengaluru
54
Seasonal Indices or Factors
Source: OM by Russel & Taylor
55. • Let, the company has made forecast (by some
projection method) for the next three years as below:
• The seasonally adjusted forecast for the four quarters
of each is computed as:
Dept. of ME, JSSATE, Bengaluru 55
Seasonal Forecasts
Year Forecast, units
2011 58.17
2012 62.47
2013 66.77
58. • A company has accumulated the quarterly demand data
for one of its products for the last six years.
• It has made a forecast of the demand for the next 4 years
as: Year 7: 505 units; Year 8: 535 units; Year 9: 565 units;
Year 10: 595 units.
• Compute the seasonal forecasts for the years 7th to 10th.
Dept. of ME, JSSATE, Bengaluru 58
Seasonal Forecasts
59. • Solution:
• Compute the total quarterly demand and annual
demand.
Dept. of ME, JSSATE, Bengaluru 59
Seasonal Forecasts
Total 480 720 852 348 2400
60. • Compute the seasonal indices or factors every quarter.
• Compute the seasonal forecast
Dept. of ME, JSSATE, Bengaluru 60
Seasonal Forecasts
Total 480 720 852 348 2400
Quarter I 2 3 4
Seasonal
Index
480/2400
= 0.2
720/2400
= 0.3
852/2400
= 0.355
348/2400
= 0.145
61. Dept. of ME, JSSATE, Bengaluru 61
Seasonal Forecasts
62. • Linear regression is a method of forecasting in which a
mathematical relationship is developed between
demand and some other factor that causes demand
behavior.
• A linear trend line relates a dependent variable (…
demand), to one independent variable, time, in the
form of a linear equation:
• Where, a = intercept (at period 0)
• b = slope of the line
• x = the time period
• y = forecast for demand for period xDept. of ME, JSSATE, Bengaluru 62
Forecast by Linear Trend Line (Regression)
Source: OM by Russel & Taylor
63. Dept. of ME, JSSATE, Bengaluru 63
Forecast by Linear Trend Line (Regression)
The parameters of the linear trend line can be calculated
using the least squares formulas for linear regression:
Source: OM by Russel & Taylor
64. • A company has collected 12 months demand data for
one of its products. Compute the forecast for 13th
month by linear trend line (regression).
Dept. of ME, JSSATE, Bengaluru 64
Forecast by Linear Trend Line (Regression)
Month, x 1 2 3 4 5 6 7 8 9 10 11 12
Demand,
y
37 40 41 37 45 50 43 47 56 52 55 54
Source: OM by Russel & Taylor
65. • Solution:
Dept. of ME, JSSATE, Bengaluru 65
Forecast by Linear Trend Line (Regression)
Source: OM by Russel & Taylor
66. • Parameters b and a
Dept. of ME, JSSATE, Bengaluru 66
Forecast by Linear Trend Line (Regression)
Therefore, the linear trend line
equation is
The forecast for 13th month will be
y = 35.2 + 1.72 x 13
= 57.56 units
Source: OM by Russel & Taylor
67. Dept. of ME, JSSATE, Bengaluru 67
Forecast by Linear Trend Line (Regression)
Source: OM by Russel & Taylor
a
Slope of the line, b = 1.72
68. • Cell phone sales for a firm over the last 10 weeks are
shown in the following table. Plot the data, and visually
check to see if a linear trend line would be appropriate.
Then determine the equation of the trend line, and
predict sales for weeks 11 and 12.
Dept. of ME, JSSATE, Bengaluru 68
Forecast by Linear Trend Line (Regression)
Week, x 1 2 3 4 5 6 7 8 9 10
Unit
Sales, y
700 724 720 728 740 742 758 750 770 775
Source: OM by W J Stevenson
69. • Solution: a) Plotting a graph to see the existence of a
linear trend.
Dept. of ME, JSSATE, Bengaluru
69
Forecast by Linear Trend Line (Regression)
Source: OM by W J Stevenson
Existence of linear trend
70. • Solution: b) Computing the parameters of trend line
Dept. of ME, JSSATE, Bengaluru
70
Forecast by Linear Trend Line (Regression)
Source: OM by W J Stevenson
Week, x Unit Sales, y x*y x2
1 700 700 1
2 724 1448 4
3 720 2160 9
4 728 2912 16
5 740 3700 25
6 742 4452 36
7 758 5306 49
8 750 6000 64
9 770 6930 81
10 775 7750 100
55 7407 41358 385Total=
71. • Solution: b) Computing the parameters of trend line
• n= 10;
• x-bar = 55/10 = 5.5
• y-bar = 7407/10 = 740.7; xy = 41358
Dept. of ME, JSSATE, Bengaluru
71
Forecast by Linear Trend Line (Regression)
Source: OM by W J Stevenson
&
Hence, b = 7.51
Hence, a = 699.40
The equation of trend line is, y = 699.40 + 7.51x
72. • Solution: b) Computing the forecasts
• Substituting values of x into the trend line equation,
the forecasts for the next two periods (i.e., x = 11
and x = 12) are:
• F11 = 699.40 + 7.51(11) = 782.01
• F12 = 699.40 + 7.51(12) = 789.52
Dept. of ME, JSSATE, Bengaluru
72
Forecast by Linear Trend Line (Regression)
Source: OM by W J Stevenson
73. • Solution:
• For purposes of illustration, the original data, the trend line, and
the two projections (forecasts) are shown on the following graph:
Dept. of ME, JSSATE, Bengaluru
73
Forecast by Linear Trend Line (Regression)
Source: OM by W J Stevenson
74. • A firm believes that its annual profit (Rs. Lakhs) depends on the
expenditures (Rs. Lakhs) made on R & D activities. The data on
expenditures on R&D activities and the profit of the firm has
been collected for the past six years. Compute the profit of the
firm when the expenditure is Rs. 6 lakhs.
Dept. of ME, JSSATE, Bengaluru 74
Forecast by Linear Trend Line (Regression)
Source: OM by Panneerselvam
75. • Solution:
Dept. of ME, JSSATE, Bengaluru 75
Forecast by Linear Trend Line (Regression)
Source: OM by Panneerselvam
76. • Solution:
Dept. of ME, JSSATE, Bengaluru 76
Forecast by Linear Trend Line (Regression)
The Linear Trend Line Equation (model) is Y = a + bX = 20 + 2X
The profit when the expenditure is Rs. 6 lakhs is
Y = 20 + 2*6 = 32 (Rs. 32 lakhs)
Source: OM by Panneerselvam
77. • The manager of BCCI wants to develop budget for the coming year
using a forecast for spectators’ attendance to matches. The
manager believes that the attendance is directly related to the
number of wins by the team in the past matches. The data on total
annual average attendance for the past eight years is collected.
• The manager believes that the team will win at least seven games
next year. Develop a simple regression equation for this data to
forecast attendance for this level of success.
Dept. of ME, JSSATE, Bengaluru 77
Forecast by Linear Regression
Source: OM by Russel & Taylor
78. • Solution:
Dept. of ME, JSSATE, Bengaluru 78
Forecast by Linear Regression
Source: OM by Russel & Taylor
79. • Solution:
Dept. of ME, JSSATE, Bengaluru 79
Forecast by Linear Regression
Source: OM by Russel & Taylor
= 4.06
The linear trend line developed is
y = 18.46 + 4.06 x
Thus, for x = 7 (wins), the forecast for
attendance is
y = 18.46 + 4.06(7) = 46.88 or 46, 880
spectators
80. • A forecast is never completely accurate; forecasts will
always deviate from the actual demand.
• This difference between the forecast and the actual is the
forecast error.
• Although forecast error is inevitable, the objective of
forecasting is that it be as slight as possible. A large degree
of error may indicate that either the forecasting technique is
the wrong one or it needs to be adjusted by changing its
parameters (for example, in the exponential smoothing
forecast).
• Forecast error is given by Et = Dt – Ft
• Forecast accuracy is measured in terms of the errors.
Dept. of ME, JSSATE, Bengaluru 80
Forecast Accuracy
Source: OM by Russel & Taylor
81. • The model may be inadequate due to (a) the omission
of an important variable, (b) a change or shift in the
variable that the model cannot deal with (e.g., sudden
appearance of a trend or cycle), or (c) the appearance
of a new variable (e.g., new competitor).
• Irregular variations may occur due to severe weather
or other natural phenomena, temporary shortages or
breakdowns, catastrophes, or similar events.
• Random variations. Randomness is the inherent
variation that remains in the data after all causes of
variation have been accounted for.
Dept. of ME, JSSATE, Bengaluru 81
Sources for Forecast Errors
Source: OM by W J Stevenson
82. • Mean Absolute Deviation (MAD)
• The mean absolute deviation, or MAD, is one of the
most popular and simplest to use measures of forecast
error.
• MAD is an average of the absolute difference between
the forecast and actual demand, as computed by the
following formula:
• t = the period number; Dt = demand in period t; Ft = the
forecast for period t; n = the total number of periods
Dept. of ME, JSSATE, Bengaluru 82
Measures of Forecast Accuracy
Source: OM by Russel & Taylor
83. • The actual demand and forecast values for a specific
commodity have been given in the table below. Compute
the MAD and comment on it.
Dept. of ME, JSSATE, Bengaluru 83
Measures of Forecast Accuracy
Source: OM by Russel & Taylor
Month Actual Demand, Dt Forecast, Ft
1 310 315
2 365 375
3 395 390
4 415 405
5 450 435
6 465 480
84. • Solution:
Dept. of ME, JSSATE, Bengaluru 84
Measures of Forecast Accuracy
Month Actual Demand,
Dt
Forecast, Ft Et = Dt – Ft
1 310 315 -5
2 365 375 -10
3 395 390 5
4 415 405 10
5 450 435 15
6 465 480 -15
85. • Solution:
Dept. of ME, JSSATE, Bengaluru 85
Measures of Forecast Accuracy
Month Actual Demand,
Dt
Forecast,
Ft
Et = Dt – Ft Dt – Ft
1 310 315 -5 5
2 365 375 -10 10
3 395 390 5 5
4 415 405 10 10
5 450 435 15 15
6 465 480 -15 15
Total 60
MAD = 60/6 = 10
The smaller the value of MAD, the more accurate is the forecast
86. • Mean Absolute Percent Deviation (MAPD)
• The mean absolute percent deviation (MAPD) measures
the absolute error as a percentage of demand rather
than per period.
• It eliminates the problem of interpreting the measure
of accuracy relative to the magnitude of the demand
and forecast values, as MAD does.
• The mean absolute percent deviation is computed
according to the following formula:
Dept. of ME, JSSATE, Bengaluru 86
Measures of Forecast Accuracy
Source: OM by Russel & Taylor
87. • The actual demand and forecast values for a specific
commodity have been given in the table below. Compute
the MAPD and comment on it.
Dept. of ME, JSSATE, Bengaluru 87
Measures of Forecast Accuracy
Month Actual Demand, Dt Forecast, Ft
1 310 315
2 365 375
3 395 390
4 415 405
5 450 435
6 465 480
88. • Solution:
Dept. of ME, JSSATE, Bengaluru 88
Measures of Forecast Accuracy
Month Actual Demand,
Dt
Forecast, Ft Et = Dt – Ft
1 310 315 -5
2 365 375 -10
3 395 390 5
4 415 405 10
5 450 435 15
6 465 480 -15
= 2400
89. • Solution:
Dept. of ME, JSSATE, Bengaluru 89
Measures of Forecast Accuracy
Month Actual Demand, Dt Forecast, Ft Et = Dt – Ft Dt – Ft
1 310 315 -5 5
2 365 375 -10 10
3 395 390 5 5
4 415 405 10 10
5 450 435 15 15
6 465 480 -15 15
Total = 2400 = 60
MAPD = 60/2400 =
.025 or 2.5%
A lower percent deviation implies a more accurate forecast.
90. • Cumulative Error (CE):
• Cumulative error is computed simply by summing the
forecast errors, as shown in the following formula.
• Large +E indicates forecast is biased low; large -E,
forecast is biased high.
• A preponderance of positive values shows the forecast
is consistently less than the actual value and vice versa.
Dept. of ME, JSSATE, Bengaluru 90
Measures of Forecast Accuracy
Source: OM by Russel & Taylor
E = et
91. • Average Error (AE) or Bias:
• It is computed by averaging the cumulative error over
the number of time periods, using the formula:
• The average error is interpreted similarly to the
cumulative error.
• A positive value indicates low bias, and a negative value
indicates high bias. A value close to zero implies a lack
of bias.
Dept. of ME, JSSATE, Bengaluru 91
Measures of Forecast Accuracy
Source: OM by Russel & Taylor
92. • Mean Squared Error (MSE):
• This measure is obtained by taking the mean of the
square of the error terms.
• Regardless of whether the forecast error has a positive
or negative sign, the squared error will always have a
positive sign.
• Squaring of the error terms serves the important
purpose of amplifying the forecast errors.
• In situations demanding low tolerance for forecast
errors it is desirable to make use of this measure.
• The MSE is computed using: MSE = (Dt – Ft)2 /n
Dept. of ME, JSSATE, Bengaluru 92
Measures of Forecast Accuracy
Source: OM by B Mahadevan
93. • The actual demand and forecast values for a specific
commodity have been given in the table below. Compute
the CE, AE & MSE.
Dept. of ME, JSSATE, Bengaluru 93
Measures of Forecast Accuracy
Month Actual Demand, Dt Forecast, Ft
1 310 315
2 365 375
3 395 390
4 415 405
5 450 435
6 465 480
94. Dept. of ME, JSSATE, Bengaluru 94
Measures of Forecast Accuracy
Month Actual
Demand, Dt
Forecast,
Ft
Et = Dt – Ft (Et)2
1 310 315 -5 25
2 365 375 -10 100
3 395 390 5 25
4 415 405 10 100
5 450 435 15 225
6 465 480 -15 225
= 2400 Et = 0 = 700
Cumulative Error, E = 0; Average Error = 0; MSE = 700/6 = 116.7
95. • The following table shows the actual sales of a product for a
furniture manufacturer and the forecasts made for each of the last
eight months. Calculate CFE, MSE, MAD, and MAPD (MAPE) for
this product.
Dept. of ME, JSSATE, Bengaluru 95
Measures of Forecast Accuracy
Absolute
Error Absolute Percent
Month, Demand, Forecast, Error, Squared, Error, Devn. (Error),
t Dt Ft Et Et
2 |Et| (|Et|/Dt)(100)
1 200 225 -25 625 25 12.5%
2 240 220 20 400 20 8.3
3 300 285 15 225 15 5.0
4 270 290 –20 400 20 7.4
5 230 250 –20 400 20 8.7
6 260 240 20 400 20 7.7
7 210 250 –40 1600 40 19.0
8 275 240 35 1225 35 12.7
Total –15 5275 195 81.3%
96. Dept. of ME, JSSATE, Bengaluru 96
Measures of Forecast Accuracy
CFE = – 15Cumulative forecast error (bias):
E = = – 1.875
– 15
8
Average forecast error (mean bias):
MSE = = 659.4
5275
8
Mean squared error:
MAD = = 24.4
195
8
Mean absolute deviation:
MAPE = = 10.2%
81.3%
8
Mean absolute percent error:
Tracking signal = = = -0.6148
CFE
MAD
-15
24.4
97. • The manager of a large manufacturer of industrial pumps
must choose between two alternative forecasting
techniques. Both techniques have been used to prepare
forecasts for a six month period. Using MAD & MAPD as
criteria, which technique has the better performance
record?
Dept. of ME, JSSATE, Bengaluru 97
Measures of Forecast Accuracy
Source: OM by W J Stevenson
98. • Solution:
Dept. of ME, JSSATE, Bengaluru 98
Measures of Forecast Accuracy
Source: OM by W J Stevenson
Conclusion:
Technique 1 is superior in this
comparison because its MAD is smaller.
99. • Tracking Signal (TS):
• The ratio of cumulative forecast error to the
corresponding value of MAD, used to monitor a
forecast.
• It tells whether the forecasting system is consistently
under or over estimates the demand.
• Forecasts can go “out of control” and start providing
inaccurate forecasts for several reasons
• TS is recomputed each period, with updated, running
values of cumulative error and MAD.
Dept. of ME, JSSATE, Bengaluru 99
Controlling Forecasts
Source: OM by B Mahadevan
100. • Given the following data, compute the tracking signal.
Dept. of ME, JSSATE, Bengaluru 100
Controlling Forecasts
1 8 10
2 11 10
3 12 10
4 14 10
Month
Actual
Demand
Forecast
Demand
102. • Given the forecast demand and actual demand for a
specific product, compute the tracking signal and MAD.
Dept. of ME, JSSATE, Bengaluru 102
Controlling Forecasts
Year
Forecast
Demand, Units
Actual Demand,
Units
1 78 71
2 75 80
3 83 101
4 84 84
5 88 60
6 85 73
105. • Table below has data pertaining to the actual demand
and the forecast for a product using a forecasting system
for 18 time periods in the past. Compute the measures
of forecast accuracy and comment on the usefulness of
the forecasting system.
Dept. of ME, JSSATE, Bengaluru 105
Measures of Forecast Accuracy
Source: OM by B Mahadevan