The document describes how to create a break-even analysis graph showing fixed costs, variable costs, total costs and total revenue, and how to identify the break-even point where total revenue equals total costs. It also defines margin of safety as the difference between actual sales and break-even sales, representing the strength of the business in knowing profits or losses relative to the break-even point.
Break-even analysis determines the level of sales or production needed for a business to neither profit nor lose money. It categorizes costs as either fixed costs that do not change with production or variable costs that change with production levels. The break-even point is where total revenue from sales equals total costs. The price charged affects total revenue and the sales volume needed to break even. Higher prices may decrease sales volume required but take longer to reach, while lower prices may increase sales but require a higher volume.
Quantitative Trading in Eurodollar Futures Market by Edith Mandel at QuantCon...Quantopian
Although the Fixed-Income market overall still lacks liquidity and overall transparency, the Eurodollar futures are a very liquid and accessible portion of it. Eurodollar market is defined by a set of key features: pro-rata matching, large tick size, overlapping and highly correlated set of contracts, hidden implied liquidity and sticky price quotes. We will describe methodologies suitable for dealing with the market's complexity, making the case that high-frequency market-making, alpha trading & algorithmic execution need to be linked closely to achieve continued success.
Machine Learning Based Cryptocurrency Trading by Arshak Navruzyan at QuantCon...Quantopian
With a daily volume of thirty to fifty million US dollars and a market capitalization over five billion, Bitcoin is becoming interesting as a financial instrument for inclusion in a quantitative trading strategy. We will explore the unique issues of the various exchanges, impact of exogenous events and demonstrate a fully automated machine learning based trading system.
From Backtesting to Live Trading by Vesna Straser at QuantCon 2016Quantopian
Dr. Vesna Straser will discuss the differences in expected slippage between live trading, simulation trading and backtesting. Typically in backtesting signal generation and order fill assumptions are simplified to obtain strategy performance data faster. For example, many commercial back testing software providers will work with sampled data such as minute open or close price points and assume that the signal is triggered at the close of one bar and filled at the close price of the next bar, per the assumed slippage model. Simulation trading, however, will typically run on tick trading data (live or replayed) potentially resulting in quite different dynamics versus back testing. Orders are filled per fill assumptions that may vary significantly between different providers. In live trading, orders are triggered and executed immediately under real market conditions and order type. Depending on the trading strategy, live trading results can differ dramatically from back-testing and/or simulation trading. Vesna will outline the issues, analytics to track, factors to consider and how to account for them to achieve “realistic” back-testing results.
This chapter discusses various methods for describing and exploring quantitative data, including dot plots, stem-and-leaf displays, percentiles, box plots, measures of skewness, scatter diagrams, and contingency tables. It provides examples and explanations of how to construct and interpret each method. Key goals are to develop an understanding of distributions and relationships within data sets.
This presentation summarizes a study that uses an artificial market model to investigate the effect of tick size in competition between stock markets. The model replicates key stylized facts and microstructure statistics. Simulation results show that a market will not lose trading volume share if its tick size is smaller than the 1-tick volatility of returns or if it is smaller than 1/10 of another market's tick size. A market's trading volume share decreases more rapidly when its tick size is larger than the 1-tick volatility. Empirical analysis of two real markets shows similar relationships between tick size and volatility and trading volume share.
This document discusses long-run average cost curves. In the long run, firms can vary both output and plant capacity. The shape of long-run average cost curves results from economies and diseconomies of scale. Sources of economies of scale include specialization, efficient use of equipment, lower per-unit costs, and shared facilities. Sources of diseconomies include limited management and competition for inputs. The activity asks students to analyze diagrams of short-run and long-run average total cost curves and determine optimal output levels and plant sizes under different scenarios.
The document describes how to create a break-even analysis graph showing fixed costs, variable costs, total costs and total revenue, and how to identify the break-even point where total revenue equals total costs. It also defines margin of safety as the difference between actual sales and break-even sales, representing the strength of the business in knowing profits or losses relative to the break-even point.
Break-even analysis determines the level of sales or production needed for a business to neither profit nor lose money. It categorizes costs as either fixed costs that do not change with production or variable costs that change with production levels. The break-even point is where total revenue from sales equals total costs. The price charged affects total revenue and the sales volume needed to break even. Higher prices may decrease sales volume required but take longer to reach, while lower prices may increase sales but require a higher volume.
Quantitative Trading in Eurodollar Futures Market by Edith Mandel at QuantCon...Quantopian
Although the Fixed-Income market overall still lacks liquidity and overall transparency, the Eurodollar futures are a very liquid and accessible portion of it. Eurodollar market is defined by a set of key features: pro-rata matching, large tick size, overlapping and highly correlated set of contracts, hidden implied liquidity and sticky price quotes. We will describe methodologies suitable for dealing with the market's complexity, making the case that high-frequency market-making, alpha trading & algorithmic execution need to be linked closely to achieve continued success.
Machine Learning Based Cryptocurrency Trading by Arshak Navruzyan at QuantCon...Quantopian
With a daily volume of thirty to fifty million US dollars and a market capitalization over five billion, Bitcoin is becoming interesting as a financial instrument for inclusion in a quantitative trading strategy. We will explore the unique issues of the various exchanges, impact of exogenous events and demonstrate a fully automated machine learning based trading system.
From Backtesting to Live Trading by Vesna Straser at QuantCon 2016Quantopian
Dr. Vesna Straser will discuss the differences in expected slippage between live trading, simulation trading and backtesting. Typically in backtesting signal generation and order fill assumptions are simplified to obtain strategy performance data faster. For example, many commercial back testing software providers will work with sampled data such as minute open or close price points and assume that the signal is triggered at the close of one bar and filled at the close price of the next bar, per the assumed slippage model. Simulation trading, however, will typically run on tick trading data (live or replayed) potentially resulting in quite different dynamics versus back testing. Orders are filled per fill assumptions that may vary significantly between different providers. In live trading, orders are triggered and executed immediately under real market conditions and order type. Depending on the trading strategy, live trading results can differ dramatically from back-testing and/or simulation trading. Vesna will outline the issues, analytics to track, factors to consider and how to account for them to achieve “realistic” back-testing results.
This chapter discusses various methods for describing and exploring quantitative data, including dot plots, stem-and-leaf displays, percentiles, box plots, measures of skewness, scatter diagrams, and contingency tables. It provides examples and explanations of how to construct and interpret each method. Key goals are to develop an understanding of distributions and relationships within data sets.
This presentation summarizes a study that uses an artificial market model to investigate the effect of tick size in competition between stock markets. The model replicates key stylized facts and microstructure statistics. Simulation results show that a market will not lose trading volume share if its tick size is smaller than the 1-tick volatility of returns or if it is smaller than 1/10 of another market's tick size. A market's trading volume share decreases more rapidly when its tick size is larger than the 1-tick volatility. Empirical analysis of two real markets shows similar relationships between tick size and volatility and trading volume share.
This document discusses long-run average cost curves. In the long run, firms can vary both output and plant capacity. The shape of long-run average cost curves results from economies and diseconomies of scale. Sources of economies of scale include specialization, efficient use of equipment, lower per-unit costs, and shared facilities. Sources of diseconomies include limited management and competition for inputs. The activity asks students to analyze diagrams of short-run and long-run average total cost curves and determine optimal output levels and plant sizes under different scenarios.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
The role of events in simulation modelingWeibull AS
The need for assessing the impact of events with binaryi outcomes, like loan defaults, occurrence of
recessions, passage of a special legislation, etc., or events that can be treated like binary events like
paradigm shifts in consumer habits, changes in competitor behavior or new innovations, arises often
in economics and other areas of decision making.
By using analogies from intervention analysis a number of interesting and important issues can be
analyzed:
If two events affects one response variable will the combined effect be less or greater than the sum of both?
Will one event affecting more than one response variable increase the effect dramatically?
Is there a risk of calculating the same cost twice?
If an event occurs at the end of a project, will it be prolonged? And what will the costs be?
Questions like this can never be analyzed when using a ‘second layer lump sum’ approach. Even
more important is possibility to incorporate the responses to exogenous events inside the simulation
model, thus having the responses at the correct point on the time line and by that a correct net
present value for costs, revenues and company or project value.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
Public works projects.
In public works and large scale construction or engineering projects – where uncertainty mostly (only) concerns cost, a simplified scenario analysis is often used.
M&A analytics: When two plus two is five or three or .Weibull AS
Mergerrs & Acquisitions (M&A) is a way f or companiies to expannd rapidly a nd much faaster than or ganic growtth – that is coming fromm existing bbusinesses –– would havve allowed. M&A’s have foor decades bbeen a trillioon‐dollar buusiness, butt empirical sstudies repoorts that a significaant proporttion must bee considereed as failurees.
The evils of a single point estimate.
Traditionally, when estimating costs, project value, equity value or budgeting, one number is
generated – a single point estimate. There are many problems with this approach. In budget
work this point is too often given as the best the management can expect, but in some cases
budgets are set artificially low generating bonuses for later performance beyond budget
In this presentation, we will discuss elaborately on strategic operations management, concept of strategy, five tasks of strategic management, strategic management process and importance of strategic management. We will also talk about role of operations in strategic management and elements of operations strategy,.
To know more about Welingkar School’s Distance Learning Program and courses offered, visit: http://www.welingkaronline.org/distance-learning/online-mba.html
Ten strategic Operation Management DecisionSoe Lu Kyaw
This document discusses the 10 strategic operations management decisions that companies can make to achieve differentiation, low costs, and quick response. These 10 decisions, which include decisions around capacity, process design, and vertical integration, support an organization's mission and strategies. While the decisions are the same for goods and services, the importance and implementation may differ depending on the ratio of goods to services provided by the company.
Operation strategy is defined as the total pattern of decisions that shape a company's long-term production capabilities and how they contribute to overall business strategy by balancing customer needs with available resources. It should be linked to and support the business strategy. Key elements of operation strategy include positioning production systems, focusing factories and services, product/service design and development, technology selection, allocating resources to strategic alternatives, and facility planning. Companies now often adopt quality- and time-based strategies instead of just cost minimization or differentiation.
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 outlines an operations strategy framework to improve products, services, and operations processes. It discusses competing priorities, an operations strategy framework including order winners and qualifiers, integration of new products and services processes, and performance objectives and critical success factors. It provides examples of how the framework can be applied to reconcile operations and marketing strategies and set performance goals for a pump manufacturer.
operation management and operation strategyRohit Kumar
Operational management refers to the administration of business practices to create the highest level of efficiency possible within an organization. It involves planning, organizing, and overseeing manufacturing processes, supply chain functions, and other business operations. The key aspects of operational management include:
- Planning - Determining the most effective and efficient ways to use resources to produce goods and services. This includes processes like capacity planning, production planning, etc.
- Organizing - Establishing an organizational structure and assigning responsibilities to ensure smooth workflow and operations.
- Leading - Guiding employees and work teams to achieve operational goals through effective leadership and communication.
- Controlling - Monitoring operations and making corrections to address issues like quality control, inventory management, and
This document outlines key concepts from a session on operations strategy in a global environment. It discusses developing mission and strategies, achieving competitive advantage through operations, strategic operations management decisions, and global operations strategy options including international, multidomestic, global, and transnational strategies. Critical success factors and integrating operations strategy with other functions are also covered.
The document summarizes key concepts in break-even analysis (CVP analysis). It defines break-even point as the level of sales where total revenue equals total costs, meaning no profit or loss. It provides formulas to calculate break-even volume, contribution ratio, break-even revenue, margin of safety, and number of units to achieve a target profit. Example problems demonstrate using these formulas and how to interpret break-even and profit-volume charts. The document also discusses applying CVP analysis to multiple products.
1) A business is introducing a new product and wants to forecast net profit, which depends on uncertain variables like sales volume, price, and costs.
2) Monte Carlo simulation is used to model uncertainty, introducing random market conditions that impact volume and price.
3) The simulation calculates net profit 1,000 times to generate a probability distribution, showing most likely outcomes and risks of loss.
CVP (cost-volume-profit) analysis examines the relationships between costs, volume, and profit. It is a useful short-term planning tool for decision making. Key elements include break-even point, contribution margin, and profit-volume charts. CVP assumes fixed costs are constant at all activity levels and unit variable costs are also constant. It can be applied to single or multiple products if they have a fixed sales mix. The document provides an example CVP analysis for a company with three hair product lines.
Cost-volume-profit (CVP) analysis is a technique used to analyze the relationship between costs, volume, and profits. It uses linear equations to model how total costs and revenues change with production volume. CVP breaks down costs into fixed and variable components and calculates the break-even point, where total revenues equal total costs. It also determines the contribution margin of each unit and how many units must be sold to cover fixed costs. CVP analysis is useful for short-term decision making but assumes costs and prices remain constant, which limits its effectiveness for long-term planning.
This document provides an introduction to cost-volume-profit (CVP) analysis, which is a tool used by managers for planning and decision-making. CVP analysis estimates how changes in costs, sales volumes, prices, and other factors affect a company's profits. It makes assumptions such as costs and revenues changing linearly with volume. CVP analysis can be used to determine break-even points, profit levels at different volumes, and the sales needed to achieve profit targets. It is an important tool for decisions like pricing, production planning, and introducing new products. The document discusses key CVP concepts like fixed and variable costs, contribution margin, profit-volume ratio, break-even point, margin of safety, and multiple product
This document summarizes five major topics related to inventories:
1) Lower of cost or market, which values inventories at the lower of historical cost or net realizable value.
2) Gross profit method, which estimates ending inventory for interim reporting.
3) Retail inventory method, which converts ending retail inventory to ending cost inventory for retailers.
4) Dollar value LIFO retail method, which applies the LIFO method to retail inventories.
5) Changes in inventory methods, which discusses how to account for changes between methods and errors in beginning or ending inventory balances.
This document summarizes five major topics related to inventories:
1) Lower of cost or market, which values inventories at the lower of historical cost or net realizable value.
2) Gross profit method, which estimates ending inventory for interim reporting.
3) Retail inventory method, which converts ending retail inventory to ending cost inventory for retailers.
4) Dollar value LIFO retail method, which applies the LIFO method to retail inventories.
5) Changes in inventory methods, which discusses how to account for changes and errors in inventory methods.
The Intellectual Property Licensing Valuation Model is based on the Monte Carlo simulation and was created using Crystal Ball software. The model provides an Internal Rate of Return (IRR) and Net Present Value (NPV) valuation while taking into account the inherently high uncertainty (risk) of cost and revenue associated with embryonic technology projects. The widget and its associated cost/revenue figures are fictitious. However, the model can be applied to real world projects.
Inventory Optimization as an Essential Part of your SiOP Process- our vision ...Solventure
As Solventure we take pride in being experts in designing and implementing Sales, Inventory and Operations Planning.
Companies that have a good SiOP process can’t imagine how to live without it. It is the key instrument for the CEO to navigate the business along the budget towards its strategic targets.
In this white paper we show how to optimize your inventory and why it is essential.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
The role of events in simulation modelingWeibull AS
The need for assessing the impact of events with binaryi outcomes, like loan defaults, occurrence of
recessions, passage of a special legislation, etc., or events that can be treated like binary events like
paradigm shifts in consumer habits, changes in competitor behavior or new innovations, arises often
in economics and other areas of decision making.
By using analogies from intervention analysis a number of interesting and important issues can be
analyzed:
If two events affects one response variable will the combined effect be less or greater than the sum of both?
Will one event affecting more than one response variable increase the effect dramatically?
Is there a risk of calculating the same cost twice?
If an event occurs at the end of a project, will it be prolonged? And what will the costs be?
Questions like this can never be analyzed when using a ‘second layer lump sum’ approach. Even
more important is possibility to incorporate the responses to exogenous events inside the simulation
model, thus having the responses at the correct point on the time line and by that a correct net
present value for costs, revenues and company or project value.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
Public works projects.
In public works and large scale construction or engineering projects – where uncertainty mostly (only) concerns cost, a simplified scenario analysis is often used.
M&A analytics: When two plus two is five or three or .Weibull AS
Mergerrs & Acquisitions (M&A) is a way f or companiies to expannd rapidly a nd much faaster than or ganic growtth – that is coming fromm existing bbusinesses –– would havve allowed. M&A’s have foor decades bbeen a trillioon‐dollar buusiness, butt empirical sstudies repoorts that a significaant proporttion must bee considereed as failurees.
The evils of a single point estimate.
Traditionally, when estimating costs, project value, equity value or budgeting, one number is
generated – a single point estimate. There are many problems with this approach. In budget
work this point is too often given as the best the management can expect, but in some cases
budgets are set artificially low generating bonuses for later performance beyond budget
In this presentation, we will discuss elaborately on strategic operations management, concept of strategy, five tasks of strategic management, strategic management process and importance of strategic management. We will also talk about role of operations in strategic management and elements of operations strategy,.
To know more about Welingkar School’s Distance Learning Program and courses offered, visit: http://www.welingkaronline.org/distance-learning/online-mba.html
Ten strategic Operation Management DecisionSoe Lu Kyaw
This document discusses the 10 strategic operations management decisions that companies can make to achieve differentiation, low costs, and quick response. These 10 decisions, which include decisions around capacity, process design, and vertical integration, support an organization's mission and strategies. While the decisions are the same for goods and services, the importance and implementation may differ depending on the ratio of goods to services provided by the company.
Operation strategy is defined as the total pattern of decisions that shape a company's long-term production capabilities and how they contribute to overall business strategy by balancing customer needs with available resources. It should be linked to and support the business strategy. Key elements of operation strategy include positioning production systems, focusing factories and services, product/service design and development, technology selection, allocating resources to strategic alternatives, and facility planning. Companies now often adopt quality- and time-based strategies instead of just cost minimization or differentiation.
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 outlines an operations strategy framework to improve products, services, and operations processes. It discusses competing priorities, an operations strategy framework including order winners and qualifiers, integration of new products and services processes, and performance objectives and critical success factors. It provides examples of how the framework can be applied to reconcile operations and marketing strategies and set performance goals for a pump manufacturer.
operation management and operation strategyRohit Kumar
Operational management refers to the administration of business practices to create the highest level of efficiency possible within an organization. It involves planning, organizing, and overseeing manufacturing processes, supply chain functions, and other business operations. The key aspects of operational management include:
- Planning - Determining the most effective and efficient ways to use resources to produce goods and services. This includes processes like capacity planning, production planning, etc.
- Organizing - Establishing an organizational structure and assigning responsibilities to ensure smooth workflow and operations.
- Leading - Guiding employees and work teams to achieve operational goals through effective leadership and communication.
- Controlling - Monitoring operations and making corrections to address issues like quality control, inventory management, and
This document outlines key concepts from a session on operations strategy in a global environment. It discusses developing mission and strategies, achieving competitive advantage through operations, strategic operations management decisions, and global operations strategy options including international, multidomestic, global, and transnational strategies. Critical success factors and integrating operations strategy with other functions are also covered.
The document summarizes key concepts in break-even analysis (CVP analysis). It defines break-even point as the level of sales where total revenue equals total costs, meaning no profit or loss. It provides formulas to calculate break-even volume, contribution ratio, break-even revenue, margin of safety, and number of units to achieve a target profit. Example problems demonstrate using these formulas and how to interpret break-even and profit-volume charts. The document also discusses applying CVP analysis to multiple products.
1) A business is introducing a new product and wants to forecast net profit, which depends on uncertain variables like sales volume, price, and costs.
2) Monte Carlo simulation is used to model uncertainty, introducing random market conditions that impact volume and price.
3) The simulation calculates net profit 1,000 times to generate a probability distribution, showing most likely outcomes and risks of loss.
CVP (cost-volume-profit) analysis examines the relationships between costs, volume, and profit. It is a useful short-term planning tool for decision making. Key elements include break-even point, contribution margin, and profit-volume charts. CVP assumes fixed costs are constant at all activity levels and unit variable costs are also constant. It can be applied to single or multiple products if they have a fixed sales mix. The document provides an example CVP analysis for a company with three hair product lines.
Cost-volume-profit (CVP) analysis is a technique used to analyze the relationship between costs, volume, and profits. It uses linear equations to model how total costs and revenues change with production volume. CVP breaks down costs into fixed and variable components and calculates the break-even point, where total revenues equal total costs. It also determines the contribution margin of each unit and how many units must be sold to cover fixed costs. CVP analysis is useful for short-term decision making but assumes costs and prices remain constant, which limits its effectiveness for long-term planning.
This document provides an introduction to cost-volume-profit (CVP) analysis, which is a tool used by managers for planning and decision-making. CVP analysis estimates how changes in costs, sales volumes, prices, and other factors affect a company's profits. It makes assumptions such as costs and revenues changing linearly with volume. CVP analysis can be used to determine break-even points, profit levels at different volumes, and the sales needed to achieve profit targets. It is an important tool for decisions like pricing, production planning, and introducing new products. The document discusses key CVP concepts like fixed and variable costs, contribution margin, profit-volume ratio, break-even point, margin of safety, and multiple product
This document summarizes five major topics related to inventories:
1) Lower of cost or market, which values inventories at the lower of historical cost or net realizable value.
2) Gross profit method, which estimates ending inventory for interim reporting.
3) Retail inventory method, which converts ending retail inventory to ending cost inventory for retailers.
4) Dollar value LIFO retail method, which applies the LIFO method to retail inventories.
5) Changes in inventory methods, which discusses how to account for changes between methods and errors in beginning or ending inventory balances.
This document summarizes five major topics related to inventories:
1) Lower of cost or market, which values inventories at the lower of historical cost or net realizable value.
2) Gross profit method, which estimates ending inventory for interim reporting.
3) Retail inventory method, which converts ending retail inventory to ending cost inventory for retailers.
4) Dollar value LIFO retail method, which applies the LIFO method to retail inventories.
5) Changes in inventory methods, which discusses how to account for changes and errors in inventory methods.
The Intellectual Property Licensing Valuation Model is based on the Monte Carlo simulation and was created using Crystal Ball software. The model provides an Internal Rate of Return (IRR) and Net Present Value (NPV) valuation while taking into account the inherently high uncertainty (risk) of cost and revenue associated with embryonic technology projects. The widget and its associated cost/revenue figures are fictitious. However, the model can be applied to real world projects.
Inventory Optimization as an Essential Part of your SiOP Process- our vision ...Solventure
As Solventure we take pride in being experts in designing and implementing Sales, Inventory and Operations Planning.
Companies that have a good SiOP process can’t imagine how to live without it. It is the key instrument for the CEO to navigate the business along the budget towards its strategic targets.
In this white paper we show how to optimize your inventory and why it is essential.
This document contains a project report on break even point analysis submitted by students of the Department of Business Management. It includes an index, introduction, definition of break even analysis, calculation of break even point using the equation technique, examples of break even point calculations, importance of break even analysis, assumptions of break even analysis, margin of safety, and advantages and disadvantages of break even analysis.
Outline for Lecture 15Long-Run Production CostsThe Lon.docxgerardkortney
Outline for Lecture 15
Long-Run Production Costs
The Long-Run Cost Curve (five plant sizes)
Suppose that a firm can operate in five alternative plants in the short run, Plants 1 through 5, with respective short-run average total cost curves (ATC1 through ATC5) illustrated by Figure 9.7.
In this illustration, vertical white lines show levels of output at which firm should change its plant to achieve the lowest average total cost.
To see why, suppose that firm produces an output of less than 20 units, say 15 units. In this case, lowest average total cost is achieved in Plant 1 because ATC1 lies below all other ATC curves for 15 units. Provided that plant is a variable resource in the long run, firm chooses Plant 1, indicating that blue section of ATC1 is part of firm’s long-run average total cost curve for output levels below 20 units.
Now, suppose firm raises production to somewhere between 20 and 30 units, say 25 units. In this second case, lowest average total cost is achieved in Plant ____ because ____ lies below all other ATC curves for 25 units. Provided that plant is a variable resource in the long run, firm chooses Plant ____, indicating that blue section of ____ is part of firm’s long-run average total cost curve for output levels between 20 and 30 units.
Similarly, blue section of ____ is part of long-run average total cost curve for output levels between 30 and 50 units, blue section of ____ is part of long-run average total cost curve for output levels between 50 and 60 units, and blue section of ____ is part of long-run average total cost curve for output levels above 60 units.
Given these five cases illustrated by Figure 9.7, how do we obtain long-run average total cost curve? Is it smooth or bumpy? Explain.
The Long-Run Cost Curve (unlimited plant sizes)
The blue long-run average total cost curve in Figure 9.7 is drawn under the assumption that firm can operate in five alternative plants in the short run. However, in modern manufacturing industries (i.e. automobiles, pharmaceuticals, etc.) the number of possible plant sizes is many more than five.
In line with this reasoning, each red average total cost curve in Figure 9.8 represents a possible plant size in the short run.
Given all these red curves illustrated by Figure 9.8, how do we obtain long-run average total cost curve? Is it smooth or bumpy? Explain.
Economies and Diseconomies of Scale
Shape of long-run average total cost curve (Figures 9.8 and 9.9) is explained via economies and diseconomies of scale.
Economies of Scale
In the upper panel of Figure 9.9, economies of scale corresponds to ____ part of the curve; in the output range between zero and q1, average total cost ____ as production rises in the long run.
Explain economies of scale: why is average total cost decreasing with rising output?
Diseconomies of Scale
In the upper panel of Figure 9.9, diseconomies of scale explains ____ part of the curve; in the output range above than q2, avera.
The document discusses break-even analysis, which determines the sales volume needed for a company to cover its total costs. It defines break-even point as the sales level where total revenue equals total costs, resulting in no profit or loss. The document provides examples of calculating break-even point using tables and charts. It also outlines the assumptions and limitations of break-even analysis, and explains its uses for management decision making like determining a target profit level or the effect of a price change.
Cost and Management Accounting II Chapter 1.pdfalemayehu73
CVP (cost-volume-profit) analysis is a tool that examines the relationship between a firm's costs, volume of production/sales, and profits. It can be used to determine the break-even point, which is when total revenue equals total costs. There are three methods for conducting a CVP analysis: contribution margin approach, equation approach, and graphical approach. The document provides examples of how to use the equation and contribution margin approaches to calculate a company's break-even point in units and dollars. Key assumptions of the CVP model include constant costs and sales, no changes in production capacity, and equal sales and production levels.
The document discusses various models for managing inventory and dealing with uncertainty in demand, costs, lead times, and other factors. It describes how uncertainty can be classified as unknown, known, or uncertain and probabilistic models can handle uncertain situations. Different cases of uncertain demand are illustrated, and models for discrete demand, the newsboy problem, shortages, intermittent demand, service level, and target stock levels are explained. Examples are provided to demonstrate how to determine optimal order quantities, safety stocks, reorder levels, and target inventory levels given uncertainty in demand and lead times.
An isoquant shows the various combinations of two inputs that can be used to produce the same quantity of output. Isoquants are typically drawn on graphs showing the tradeoff between capital and labor. An isoquant map combines multiple isoquants, each representing a different output quantity, and can indicate whether a production function exhibits increasing, decreasing, or constant returns to scale based on the distances between isoquants. Economies of scale exist when increasing all inputs results in a proportionately higher output, while diseconomies of scale occur when output increases by less than the proportional increase in inputs.
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Unit 5 (a) marginal costing for BBA,BBA aviation management & B.comparimalas3
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Break-even analysis is a technique that allows businesses to determine the sales volume needed to break even. It involves classifying costs as fixed or variable and calculating the break-even point, which is where total revenue equals total costs. Break-even analysis can be used to understand how changes in output, price, or costs affect profits. While useful for planning, it has limitations as it assumes costs change linearly with volume and ignores factors like multiple products or price changes.
Budgeting with Monte Carlo simulation modelsWeibull AS
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
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This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
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4. Lean Startup Methodology
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18. Toyota’s Six Steps of Kaizen
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20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
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1. Optimal Service Level in Production and Warehousing
Introduction
In the following we will show how sales forecasts can be used to set levels in production or in multi-level
warehousing. Since the problem discussed is the same for both production and warehousing, the two
terms will be used interchangeably.
The calculation will be based on knowledge of the sales distribution, both expected sales and its
variation. In addition will sales usually have a seasonal variance creating a balance act between
production, logistic and warehousing costs.
In the example given below the sales forecasts will therefore have to be viewed as a periodic forecast
(month, quarter, etc.).The production lead time will then determine the production planning (timing).
Purposes of Inventory
1. To maintain independence of operations
2. To meet variation in product demand
3. To allow flexibility in production scheduling
4. To provide a safeguard for variation in raw material delivery time
5. To take advantage of economic purchase-order size
Inventory Costs
1. Holding (or carrying) costs
2. Costs for capital, storage, handling, “shrinkage,” insurance, etc.
3. Setup (or production change) costs
4. Costs for arranging specific equipment setups, etc.
5. Ordering costs
6. Costs of someone placing an order, etc.
7. Shortage costs
8. Costs of canceling an order, etc.
Inventory Systems
1. Single-Period Inventory Model
a. One time purchasing decision (Example: vendor selling t-shirts at a football game)
b. Seeks to balance the costs of inventory overstock and under stock
2. Multi-Period Inventory Models
a. Fixed-Order Quantity Models
b. Event triggered (Example: running out of stock)
3. Fixed-Time Period Models
a. Time triggered (Example: Monthly sales call by sales representative)
The “too much/too little problem”
1. Order too much and inventory is left over at the end of the season
2. Order too little and sales are lost.
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2. To maximize expected profit order Q units so that the expected loss on the Qth unit equals the expected
gain on the Qth unit:
I. Co F(Q) Cu 1 FQ ,
Where Co =The cost of ordering one more unit than what would have been ordered if demandhad been
known – or the increase in profit enjoyed by having ordered one fewer unit,
Cu = The cost of ordering one fewer unit than what would have been ordered if demandhad been
known– or the increase in profit enjoyed by having ordered one more unit, and
F(Q) = Probability Demand for q<= Q
Rearrange terms in the above equation
Cu
II. Prob{Deman d Q} F(Q)
Co Cu
The ratio Cu / (Co + Cu) is called the critical ratio (CR).
The usual way of solving this is to assume that the demand isnormal distributed N(m,s)giving Q as:
III. Q = m + z * s, where: z= (Q-m)/s is normal distributed N(0,1)
Demand however has seldom a normal distribution and to make things worse we usually don’t know
the exact distribution at all. We can only ‘find’ it by Monte Carlo simulation and thus have to
numerically find the Q satisfying equation I.
The optimal service level
The warehouse (or production) level should be set to maximize profit given the sales distribution. This
implies that the probability for stock out (lost sales) should be weighed against warehousing, logistic and
production costs.
If we for the moment assume that all thesecosts can be regarded as a variable cost, will the product
markup (%) determine the optimal warehouse level.
Expected sales
The figure below indicates the sales distribution. Expected sales are 1819 units, but the distribution is
heavily skewed to the right so there is a possibility of sales exceeding expected sales:
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3. By setting the product markup – in the example below it is 300% - we can calculate profit and loss based
on the sales forecast.
Profit and Loss of opportunity
The loss is calculated as the value of lost sales (stock-out) and the cost of having produced and stocked
more than can be expected to be sold.
The profit is calculated as value of sales less production costs of both sold and unsold items.
The figure below indicates what will happen as we produce and stock at different levels of probability of
stock-out. We can see as we successively move to higher production (from left to right on the x-axis)
that expected profit will increase to a point of maximum, the same point where loss is minimized:
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4. At that point we can expect to have some excess stock and in some cases also lost sales. But regardless,
it is at this point that profit is maximized, so this is the optimal stock (production) level.
Product markup
The optimal stock or production level will be a function of the product markup. A high markup will give
room for a higher level of unsold items while a low level will necessitate a focus on cost reduction and
the acceptance of stock- out:
If we put it all together we get the chart below. In this the green curve is the cumulated sales
distribution giving the probability of the level of sales and the red curve give the optimal stock or
production level given the markup.
The Optimal stock and production level
The optimal stock level is then found by drawing a line from the right markup axis (right y-axis) to the
curve (red) for optimal stock level, and down to the x-axis giving the stock level. By continuing the line
from the markup axis to the probability axis (left y-axis) we find the probability level for stock-out (1-the
cumulative probability) and the probability for having a stock level in excess of demand:
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5. By using the sales distribution we can find the optimal stock/production level given the markup and this
would not have been possible with single point sales forecasts – that could have ended up almost
anywhere on the curve for forecasted sales.
Even if a single point forecast managed to find expected sales – as mean, mode or median – it would
have given wrong answers about the optimal stock/production level, since the shape of the sales
distribution would have been unknown.
In this case with the sales distribution having a right tail the level would have been to low – or with low
markup, to high. With a left skewed sales distribution the result would have been the other way around:
The level would have been too high and with low markup probably too low.
In the case of multi-level warehousing, the above analyses have to be performed on all levels and solved
as a simultaneous system.
We can do this!
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6. Risk and Reward
Increased profit comes at a price: increased risk. The graph below describes the situation; the blue curve
shows how profit increases with service level. The spread between the green and red curves indicates a
band where the actual profit will fall, and this shows how the uncertainty in profit increases with service
level. There is no such thing as a free lunch.
On the other hand will the uncertainty band around loss as the service level increases decrease. This of
course lies in the fact that losses due to lost sales diminishes as the service level increases and the fact
that markup is positive (300%) and will easily cover the cost of over-production.
Page 6 of 11
11. Data and analysis
Data
The data needed to perform the analysis sketched above will be found in the internal accounts:
1. Production costs
2. Data on distribution structure (existing and proposed)
3. Logistic costs from production plants to warehouses
4. Warehousing costs
5. Logistic costs from warehouses to shops
6. Product group prices
7. Markup on product groups
8. Sales forecasts for product groups and regions (countries or cities etc.) (will have to be done by
S@R in corporation with Rappala)
Results
1. Optimal warehousing levels in a multi-level structure per product group
2. Optimal production level per product group
3. Probability distribution for profit/loss
4. Optimal warehousing structure (given proposed alternatives)
Further analysis
This study and program can be a basis for an EBITDA/Budgeting model for Rappala, that again can be
used for Balance simulation and further decision making and valuation.
Page 11 of 11