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.
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.
Strategic operations management concepts are introduced including value chains, supply chains, and Porter's generic strategies of cost leadership, differentiation, and focus. Just-in-time manufacturing and lean manufacturing principles aim to reduce waste and improve efficiency. Key benefits include reduced inventory, lead times, and costs while improving quality and customer satisfaction. Implementation requires identifying and eliminating sources of muda, mura, and muri waste through continuous improvement efforts.
Demand Forecasting: Forecasting as planning tool, Forecasting Time Horizon, Sources of Data for Forecasting, Accuracy of Forecast, Capacity Planning. Production Planning: Aggregate production Planning, Alternatives for Managing Demand & Supply, Mater Production Schedule, capacity Planning, Overview of MRP, CRP, DRP & MRP-II Production Control: Scheduling & Loading, Scheduling of Job Shops & Floor
Shops, Gantt Chart.
The document discusses material requirements planning (MRP). It describes the key outputs of MRP as calculating demand for component items, determining requirements for subassemblies and raw materials, determining when they are needed, and generating work orders and purchase orders while considering lead time. The document then provides details on when to use MRP, the major inputs to the MRP process including bills of material and master production schedules, the basic steps of MRP including exploding bills of material and netting inventory, lot sizing rules, and time-phasing requirements. Examples are also provided to illustrate how to use an MRP matrix to determine planned order releases and receipts.
A bill of material (BOM) is a list that contains the components, quantities, and costs needed to produce an item or assembly. It includes the item ID, description, cost per item, and total cost of all items. The key information in a BOM allows users to identify the correct parts, order the necessary quantities, and calculate the total materials cost of a project.
Objective of MRP and MRP II in computer studyHaider Alkaisy
This document discusses Material Requirements Planning (MRP) and Manufacturing Resource Planning (MRP2 / MRPII). It defines MRP as a production planning system used to manage manufacturing processes and ensure availability of materials and products while maintaining low inventory levels. MRP2 coordinates all manufacturing resources including materials, finance, and personnel. The document outlines the objectives, components, benefits, and software applications of MRP and MRP2 systems.
Product Design & Process Selection-Manufacturing Joshua Miranda
This chapter discusses product design and manufacturing process selection. It covers typical phases of product development like concept development and product engineering. It emphasizes concurrent engineering where functions work simultaneously to reduce time and costs. It also discusses designing for customers through techniques like quality function deployment and value analysis. The chapter then covers different types of manufacturing processes and considerations for process flow design and global product design.
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.
Strategic operations management concepts are introduced including value chains, supply chains, and Porter's generic strategies of cost leadership, differentiation, and focus. Just-in-time manufacturing and lean manufacturing principles aim to reduce waste and improve efficiency. Key benefits include reduced inventory, lead times, and costs while improving quality and customer satisfaction. Implementation requires identifying and eliminating sources of muda, mura, and muri waste through continuous improvement efforts.
Demand Forecasting: Forecasting as planning tool, Forecasting Time Horizon, Sources of Data for Forecasting, Accuracy of Forecast, Capacity Planning. Production Planning: Aggregate production Planning, Alternatives for Managing Demand & Supply, Mater Production Schedule, capacity Planning, Overview of MRP, CRP, DRP & MRP-II Production Control: Scheduling & Loading, Scheduling of Job Shops & Floor
Shops, Gantt Chart.
The document discusses material requirements planning (MRP). It describes the key outputs of MRP as calculating demand for component items, determining requirements for subassemblies and raw materials, determining when they are needed, and generating work orders and purchase orders while considering lead time. The document then provides details on when to use MRP, the major inputs to the MRP process including bills of material and master production schedules, the basic steps of MRP including exploding bills of material and netting inventory, lot sizing rules, and time-phasing requirements. Examples are also provided to illustrate how to use an MRP matrix to determine planned order releases and receipts.
A bill of material (BOM) is a list that contains the components, quantities, and costs needed to produce an item or assembly. It includes the item ID, description, cost per item, and total cost of all items. The key information in a BOM allows users to identify the correct parts, order the necessary quantities, and calculate the total materials cost of a project.
Objective of MRP and MRP II in computer studyHaider Alkaisy
This document discusses Material Requirements Planning (MRP) and Manufacturing Resource Planning (MRP2 / MRPII). It defines MRP as a production planning system used to manage manufacturing processes and ensure availability of materials and products while maintaining low inventory levels. MRP2 coordinates all manufacturing resources including materials, finance, and personnel. The document outlines the objectives, components, benefits, and software applications of MRP and MRP2 systems.
Product Design & Process Selection-Manufacturing Joshua Miranda
This chapter discusses product design and manufacturing process selection. It covers typical phases of product development like concept development and product engineering. It emphasizes concurrent engineering where functions work simultaneously to reduce time and costs. It also discusses designing for customers through techniques like quality function deployment and value analysis. The chapter then covers different types of manufacturing processes and considerations for process flow design and global product design.
Performance Rating of workers on Assembly Line or Employees in Industry, Systems of Rating: Pace Rating, Westinghouse Rating, Objective Rating, Synthetic Rating
This document discusses inventory models, including the basic economic order quantity (EOQ) model and quantity discounts. It begins by defining inventory and explaining the importance of inventory control. It then covers the basic EOQ model assumptions and formulas for calculating optimal order quantity, expected number of orders per year, time between orders, total cost, and average inventory value. The document also discusses using a reorder point and provides an example calculation. Finally, it introduces quantity discount models, where purchasing larger quantities results in decreased unit costs.
This presentation explains about the Operations Management concept Reorder point, different cases with examples, fixed order interval model, single period model etc.
Aggregate planning involves developing a preliminary production schedule over the next 6-18 months to satisfy forecasted demand at minimum cost. It considers targeted sales, production levels, inventory levels and backlogs. The objectives are to minimize costs and changes while maximizing profits, customer service and resource utilization. Common strategies are level, which maintains steady output/employment, or chase, which matches demand period to period. Techniques to develop plans include linear programming, linear decision rules and simulation models.
Aggregate planning determines production levels, inventory, capacity, and other factors over a time horizon of 1-18 months. The goal is to maximize profit by effectively using existing resources to meet forecasted demand. Key inputs include demand forecasts and production costs. The process specifies operational parameters for each period and identifies the plan that maximizes profit given constraints like capacity limits. Common strategies include chasing demand by varying capacity, using inventory to level production, or utilizing time flexibility through overtime.
This document discusses production and operations management. It begins with definitions of production management and operations management. It then provides a historical overview of the evolution of the field from Adam Smith's specialization of labor to more modern contributions. The rest of the document defines concepts related to production systems including inputs, transformation processes, outputs, and classifications like job shop, batch, mass, and continuous production.
This document discusses how information availability can help reduce supply chain costs and variability. It provides three key points:
1) Information availability helps coordinate supply chain systems and strategies, enabling reduced lead times, better demand forecasts, and lower inventory levels.
2) The "bullwhip effect", where demand variability increases as information moves up the supply chain, can be reduced through strategies like information sharing and collaborative forecasting.
3) Integrating supply chain planning and decision making across companies using shared information can help optimize the system as a whole rather than each company optimizing locally. This coordination allows trade-offs like inventory versus transportation costs to be managed more effectively.
Unit 1 production and operation managementAbu Bashar
This document provides an overview of production and operations management. It discusses key concepts like the objectives of production management being producing quality products at the right quantity, time, and cost. It also compares manufacturing and services, noting differences in things like customer contact, uniformity of inputs/outputs, and ability to store products. The scope of production management is outlined as including activities like facility location, plant layout, product/process design, production planning/control, and quality control.
This document provides an overview of material requirements planning (MRP) and enterprise resource planning (ERP) systems. It defines key concepts in MRP like the master production schedule, bills of material, lead times, and how the gross requirements and net requirements plans are developed. It also describes how MRP has been expanded to ERP systems to integrate broader business functions like customers, suppliers, and other business processes. The advantages of ERP systems are integration across the supply chain and common databases, while disadvantages include high costs of implementation and customization.
The document discusses various lot sizing techniques used to determine optimal batch sizes for production and purchasing. It describes techniques like economic order quantity (EOQ), fixed order quantity (FOQ), lot-for-lot (LFL), periods of supply (POS), period order quantity (POQ), least unit cost (LUC), least total cost (LTC), and part period balancing (PPB). Examples are provided to illustrate how each technique works and the optimal lot size is calculated. The goal of lot sizing is to minimize total inventory costs by balancing setup/ordering costs and carrying costs.
The document discusses materials planning and materials requirements planning (MRP). It explains that MRP is a production planning process that takes inputs like the master production schedule and bill of materials to determine requirements and timing for items. MRP helps control inventory levels, assign production priorities, and plan capacity. It generates work orders, purchase orders, and other reports to schedule item production and ordering. Accurate information is important for effective MRP.
The document discusses aggregate planning and master production scheduling. It provides an overview of aggregate planning, including forecasting demand and setting output levels to meet demand. It also discusses developing a master production schedule by disaggregating the aggregate plan into specific end items and timing of production. The master schedule covers a shorter horizon than the aggregate plan, typically 6-8 weeks. It aims to balance demand with capacity and production resources.
Just in time (JIT) is a production strategy that strives to improve a business' return on investment by reducing in-process inventory and associated carrying costs. Just in time is a type of operations management approach which originated in Japan in the 1950s. It was adopted by Toyota and other Japanese manufacturing firms, with excellent results: Toyota and other companies that adopted the approach ended up raising productivity (through the elimination of waste) significantly.
This document discusses inventory management concepts. It defines independent and dependent demand and describes different types of inventories. It explains the functions of inventory including meeting demand, smoothing production, and protecting against stockouts. Key inventory terms are defined such as lead time, holding costs, and ordering costs. Different inventory models, counting systems, and classification approaches are outlined. The objective of inventory control is to balance customer service and inventory costs. Effective inventory management requires forecasting, lead time knowledge, and cost estimates.
1. Capacity planning involves estimating current capacity, forecasting future capacity needs, identifying options to meet needs, and selecting sources of additional capacity.
2. Capacity can be measured using output rate, input rate, capacity utilization percentage, or capacity cushion and is important for meeting demands, costs, competitiveness, and planning.
3. The document discusses definitions of capacity, the capacity planning process, measurements of capacity, forecasting demands, and considerations for evaluating capacity alternatives.
The Master Production Schedule (MPS) is a plan for the production of individual final items. The MPS breaks down the production plan to show, in each period, the quantity to produce of each final article.
#masterproduction #mps #mrp #erp #manufacturing #manufacturingsoftware #erpsoftware #mrpeasy
Quantitative and qualitative forecasting techniques omHallmark B-school
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
The document discusses various forecasting techniques including judgmental forecasts, time series forecasts, naive forecasts, moving averages, exponential smoothing, linear trends, and associative forecasts using simple linear regression. It describes the basic approaches and formulas for each technique and discusses factors to consider when choosing a forecasting method such as cost, accuracy, data availability, and forecast horizon.
The document discusses capacity planning, which involves determining the production capacity needed by an organization to meet changing demand. It covers determining current and future capacity needs, identifying options to modify capacity, and addressing imbalances between demand and capacity. Short-term adjustments and long-term responses are discussed. Models like present value analysis, aggregate planning, and decision trees can be useful for capacity planning. Economies of scale and concepts like efficiency and utilization are also summarized.
A Simple Case Study of Material Requirement PlanningIOSR Journals
material requirement planning is a technique that uses the bill of material, inventory data and a master schedule to calculate requirements for material. It also takes into account the combination of the bill of material structure and assembly lead times. The result of an MRP plan is a material plan for each item found in the bill of material structure which indicates the amount of new material required, the date on which it is required. The new schedule dates for material that is currently on order. If routings, with defined labor requirements are available, a capacity plan will be created concurrently with the MRP material plan. The MRP plan can be run for any number entities (which could be physically separated inventories) and can include distributor inventories, if the system has access to this type of information. MRP tries to strike the best balance possible between optimizing the service level and minimizing costs and capital lockup. In this paper it is tried to present a practical M.R.P. problem and is shown how it is helpful in optimizing the service level and minimizing costs.
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
Performance Rating of workers on Assembly Line or Employees in Industry, Systems of Rating: Pace Rating, Westinghouse Rating, Objective Rating, Synthetic Rating
This document discusses inventory models, including the basic economic order quantity (EOQ) model and quantity discounts. It begins by defining inventory and explaining the importance of inventory control. It then covers the basic EOQ model assumptions and formulas for calculating optimal order quantity, expected number of orders per year, time between orders, total cost, and average inventory value. The document also discusses using a reorder point and provides an example calculation. Finally, it introduces quantity discount models, where purchasing larger quantities results in decreased unit costs.
This presentation explains about the Operations Management concept Reorder point, different cases with examples, fixed order interval model, single period model etc.
Aggregate planning involves developing a preliminary production schedule over the next 6-18 months to satisfy forecasted demand at minimum cost. It considers targeted sales, production levels, inventory levels and backlogs. The objectives are to minimize costs and changes while maximizing profits, customer service and resource utilization. Common strategies are level, which maintains steady output/employment, or chase, which matches demand period to period. Techniques to develop plans include linear programming, linear decision rules and simulation models.
Aggregate planning determines production levels, inventory, capacity, and other factors over a time horizon of 1-18 months. The goal is to maximize profit by effectively using existing resources to meet forecasted demand. Key inputs include demand forecasts and production costs. The process specifies operational parameters for each period and identifies the plan that maximizes profit given constraints like capacity limits. Common strategies include chasing demand by varying capacity, using inventory to level production, or utilizing time flexibility through overtime.
This document discusses production and operations management. It begins with definitions of production management and operations management. It then provides a historical overview of the evolution of the field from Adam Smith's specialization of labor to more modern contributions. The rest of the document defines concepts related to production systems including inputs, transformation processes, outputs, and classifications like job shop, batch, mass, and continuous production.
This document discusses how information availability can help reduce supply chain costs and variability. It provides three key points:
1) Information availability helps coordinate supply chain systems and strategies, enabling reduced lead times, better demand forecasts, and lower inventory levels.
2) The "bullwhip effect", where demand variability increases as information moves up the supply chain, can be reduced through strategies like information sharing and collaborative forecasting.
3) Integrating supply chain planning and decision making across companies using shared information can help optimize the system as a whole rather than each company optimizing locally. This coordination allows trade-offs like inventory versus transportation costs to be managed more effectively.
Unit 1 production and operation managementAbu Bashar
This document provides an overview of production and operations management. It discusses key concepts like the objectives of production management being producing quality products at the right quantity, time, and cost. It also compares manufacturing and services, noting differences in things like customer contact, uniformity of inputs/outputs, and ability to store products. The scope of production management is outlined as including activities like facility location, plant layout, product/process design, production planning/control, and quality control.
This document provides an overview of material requirements planning (MRP) and enterprise resource planning (ERP) systems. It defines key concepts in MRP like the master production schedule, bills of material, lead times, and how the gross requirements and net requirements plans are developed. It also describes how MRP has been expanded to ERP systems to integrate broader business functions like customers, suppliers, and other business processes. The advantages of ERP systems are integration across the supply chain and common databases, while disadvantages include high costs of implementation and customization.
The document discusses various lot sizing techniques used to determine optimal batch sizes for production and purchasing. It describes techniques like economic order quantity (EOQ), fixed order quantity (FOQ), lot-for-lot (LFL), periods of supply (POS), period order quantity (POQ), least unit cost (LUC), least total cost (LTC), and part period balancing (PPB). Examples are provided to illustrate how each technique works and the optimal lot size is calculated. The goal of lot sizing is to minimize total inventory costs by balancing setup/ordering costs and carrying costs.
The document discusses materials planning and materials requirements planning (MRP). It explains that MRP is a production planning process that takes inputs like the master production schedule and bill of materials to determine requirements and timing for items. MRP helps control inventory levels, assign production priorities, and plan capacity. It generates work orders, purchase orders, and other reports to schedule item production and ordering. Accurate information is important for effective MRP.
The document discusses aggregate planning and master production scheduling. It provides an overview of aggregate planning, including forecasting demand and setting output levels to meet demand. It also discusses developing a master production schedule by disaggregating the aggregate plan into specific end items and timing of production. The master schedule covers a shorter horizon than the aggregate plan, typically 6-8 weeks. It aims to balance demand with capacity and production resources.
Just in time (JIT) is a production strategy that strives to improve a business' return on investment by reducing in-process inventory and associated carrying costs. Just in time is a type of operations management approach which originated in Japan in the 1950s. It was adopted by Toyota and other Japanese manufacturing firms, with excellent results: Toyota and other companies that adopted the approach ended up raising productivity (through the elimination of waste) significantly.
This document discusses inventory management concepts. It defines independent and dependent demand and describes different types of inventories. It explains the functions of inventory including meeting demand, smoothing production, and protecting against stockouts. Key inventory terms are defined such as lead time, holding costs, and ordering costs. Different inventory models, counting systems, and classification approaches are outlined. The objective of inventory control is to balance customer service and inventory costs. Effective inventory management requires forecasting, lead time knowledge, and cost estimates.
1. Capacity planning involves estimating current capacity, forecasting future capacity needs, identifying options to meet needs, and selecting sources of additional capacity.
2. Capacity can be measured using output rate, input rate, capacity utilization percentage, or capacity cushion and is important for meeting demands, costs, competitiveness, and planning.
3. The document discusses definitions of capacity, the capacity planning process, measurements of capacity, forecasting demands, and considerations for evaluating capacity alternatives.
The Master Production Schedule (MPS) is a plan for the production of individual final items. The MPS breaks down the production plan to show, in each period, the quantity to produce of each final article.
#masterproduction #mps #mrp #erp #manufacturing #manufacturingsoftware #erpsoftware #mrpeasy
Quantitative and qualitative forecasting techniques omHallmark B-school
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
The document discusses various forecasting techniques including judgmental forecasts, time series forecasts, naive forecasts, moving averages, exponential smoothing, linear trends, and associative forecasts using simple linear regression. It describes the basic approaches and formulas for each technique and discusses factors to consider when choosing a forecasting method such as cost, accuracy, data availability, and forecast horizon.
The document discusses capacity planning, which involves determining the production capacity needed by an organization to meet changing demand. It covers determining current and future capacity needs, identifying options to modify capacity, and addressing imbalances between demand and capacity. Short-term adjustments and long-term responses are discussed. Models like present value analysis, aggregate planning, and decision trees can be useful for capacity planning. Economies of scale and concepts like efficiency and utilization are also summarized.
A Simple Case Study of Material Requirement PlanningIOSR Journals
material requirement planning is a technique that uses the bill of material, inventory data and a master schedule to calculate requirements for material. It also takes into account the combination of the bill of material structure and assembly lead times. The result of an MRP plan is a material plan for each item found in the bill of material structure which indicates the amount of new material required, the date on which it is required. The new schedule dates for material that is currently on order. If routings, with defined labor requirements are available, a capacity plan will be created concurrently with the MRP material plan. The MRP plan can be run for any number entities (which could be physically separated inventories) and can include distributor inventories, if the system has access to this type of information. MRP tries to strike the best balance possible between optimizing the service level and minimizing costs and capital lockup. In this paper it is tried to present a practical M.R.P. problem and is shown how it is helpful in optimizing the service level and minimizing costs.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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The document discusses various aspects of forecasting, including:
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- Forecasts can be short-term (hours to months) or long-term (years). Both the expected level and accuracy of forecasts are important.
- Qualitative and quantitative methods can be used. Qualitative methods rely more on subjective judgment while quantitative methods analyze objective historical data.
- Specific qualitative methods discussed include Delphi, surveys, consensus among executives. Quantitative time series models examine trends, seasonality and irregular patterns in past data.
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.
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.
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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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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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)
2
Dept. 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
3
Dept. 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. 4
Dept. 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.
t
A
1
t
F
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
/
A
F t
1
t
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
t
t
1
t A
C
F 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
t
t
1
t F
α
1
αD
F
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
Similar
computations
with
=
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.
= 0.8
Month 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
1
1
1
t
t
t T
F
AF
t
t
t
t T
F
F
T )
1
(
)
( 1
1
OR, FITt = Ft + Tt
)
T
(F
α
1
αD
F t
t
t
1
t
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
46. • Given: F1 = 600 units; T1 = 0; D1 = 650 units
Dept. of ME, JSSATE, Bengaluru 46
Trend Adjusted Exponential Smoothing
1
1
1
t
t
t T
F
AF That is, AF9= F9 + T9
8
8
9
9 )
1
(
)
( T
F
F
T
)
T
(F
α
1
αD
F 8
8
8
9
&
Now, start with 2nd month: AF2= F2 + T2
1
1
2
2 )
1
(
)
( T
F
F
T
)
T
(F
α
1
αD
F 1
1
1
2
47. • Given: F1 = 600 units; T1 = 0; D1 = 650 units; = 0.1;
= 0.2.
Dept. of ME, JSSATE, Bengaluru 47
Trend Adjusted Exponential Smoothing
Now, AF2= F2 + T2
1
1
2
2 )
1
(
)
( T
F
F
T
)
T
(F
α
1
αD
F 1
1
1
2
)
0
(600
1
.
0
1
650
*
0.1
F2
That is, T2 = 1.0
&
0
*
)
2
.
0
1
(
)
600
605
(
2
.
0
2
T
Hence, AF2= 605 + 1 = 606
That is, F2 = 605 units
48. • Given: F2 = 605 units; T2 = 1; D2 = 600 units; = 0.1;
= 0.2.
Dept. of ME, JSSATE, Bengaluru 48
Trend Adjusted Exponential Smoothing
Now, AF3= F3 + T3
2
2
3
3 )
1
(
)
( T
F
F
T
)
T
(F
α
1
αD
F 2
2
2
3
)
1
(605
1
.
0
1
600
*
0.1
F3
That is, T3 = 0.88
&
1
*
)
2
.
0
1
(
)
605
4
.
605
(
2
.
0
3
T
Hence, AF3= 605.4 + 0.88 = 606.28
That is, F3 = 605.4 units
49. • Given: F3 = 605.4 units; T3 = 0.88; D3 = 550 units; =
0.1; = 0.2.
Dept. of ME, JSSATE, Bengaluru 49
Trend Adjusted Exponential Smoothing
Now, AF4= F4 + T4
3
3
4
4 )
1
(
)
( T
F
F
T
)
T
(F
α
1
αD
F 3
3
3
4
)
0.88
(605.4
1
.
0
1
550
*
0.1
F4
That is, T4 = -0.245
&
88
.
0
*
)
2
.
0
1
(
)
4
.
605
65
.
600
(
2
.
0
4
T
Hence, AF4= 600.65 - 0.245 = 600.405
That is, F4 = 600.65 units
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 x
Dept. 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 385
Total=
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 = – 15
Cumulative 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