This document outlines a 5-step process for determining the optimal price for a product to maximize profit or revenue. The steps are: 1) find critical points of the revenue and profit functions, 2) analyze historical price and demand data, 3) calculate the estimated optimal prices, 4) determine estimated revenue and profit using these prices, and 5) compare estimated to actual revenue and profit curves. The optimal price to maximize profit is higher than for revenue. The example calculates optimal prices of $8.35 for profit and $6.35 for revenue using a linear demand curve estimated from historical data.
This document outlines a 5-step process for determining the optimal price for a product to maximize profit or revenue. The steps are: 1) find critical points of the revenue and profit functions, 2) analyze historical price and demand data, 3) calculate the estimated optimal prices, 4) determine estimated revenue and profit using those prices, and 5) compare estimated results to actual results. The optimal price to maximize profit is higher than the price for maximum revenue. Applying these steps to sample data, the document estimates an optimal price of $8.35 would maximize profit of $722.
The document summarizes a stock portfolio optimization report that uses Gaussian quadrature to analyze stock market data and optimize a portfolio to minimize risk and maximize returns. It describes collecting stock market data, applying a Gaussian quadrature formula to analyze the data and calculate the probability of gains and expected returns. The results show probabilities of gains around 50% and expected returns between 0.081 to 0.132, indicating the difficulty of predicting stock market behaviors but some ability to modestly optimize returns and reduce risk.
The document describes a process for calculating current and previous period sales measures from monthly sales data for loading into a data warehouse. It includes:
1) Sample input and target tables showing current year, current month, previous month, year-to-date and previous year measures.
2) An SQL query that joins the current monthly sales data to the target table to calculate current measures, and to the previous month's target data to calculate previous month and year-to-date measures.
3) An explanation of how the query calculates current, previous month and previous year measures by joining the sales and target tables on business keys and sales dates.
The document discusses functions in programming. It defines a function as a module that returns a value to the part of the program that called it. It discusses built-in library functions and how to write your own functions. It provides examples of writing functions to calculate sales commissions and explains how to use IPO charts to design functions by describing their input, processing, and output.
This document discusses the application of calculus concepts like derivation and integration in accounting and auditing. It aims to analyze how derivation can be used to optimize processes like maximizing profits and setting prices. Two examples are provided, one on profit maximization where derivation is used to find the production level with maximum weekly profit. The second example applies derivation to determine the price and sales volume that gives maximum utility. The document concludes discussing how these concepts can help accountants and businesses improve processes and make better economic decisions.
This document outlines a 5-step process for determining the optimal price for a product to maximize profit or revenue. The steps are: 1) find critical points of the revenue and profit functions, 2) analyze historical price and demand data, 3) calculate the estimated optimal prices, 4) determine estimated revenue and profit using these prices, and 5) compare estimated to actual revenue and profit curves. The optimal price to maximize profit is higher than for revenue. The example calculates optimal prices of $8.35 for profit and $6.35 for revenue using a linear demand curve estimated from historical data.
This document outlines a 5-step process for determining the optimal price for a product to maximize profit or revenue. The steps are: 1) find critical points of the revenue and profit functions, 2) analyze historical price and demand data, 3) calculate the estimated optimal prices, 4) determine estimated revenue and profit using those prices, and 5) compare estimated results to actual results. The optimal price to maximize profit is higher than the price for maximum revenue. Applying these steps to sample data, the document estimates an optimal price of $8.35 would maximize profit of $722.
The document summarizes a stock portfolio optimization report that uses Gaussian quadrature to analyze stock market data and optimize a portfolio to minimize risk and maximize returns. It describes collecting stock market data, applying a Gaussian quadrature formula to analyze the data and calculate the probability of gains and expected returns. The results show probabilities of gains around 50% and expected returns between 0.081 to 0.132, indicating the difficulty of predicting stock market behaviors but some ability to modestly optimize returns and reduce risk.
The document describes a process for calculating current and previous period sales measures from monthly sales data for loading into a data warehouse. It includes:
1) Sample input and target tables showing current year, current month, previous month, year-to-date and previous year measures.
2) An SQL query that joins the current monthly sales data to the target table to calculate current measures, and to the previous month's target data to calculate previous month and year-to-date measures.
3) An explanation of how the query calculates current, previous month and previous year measures by joining the sales and target tables on business keys and sales dates.
The document discusses functions in programming. It defines a function as a module that returns a value to the part of the program that called it. It discusses built-in library functions and how to write your own functions. It provides examples of writing functions to calculate sales commissions and explains how to use IPO charts to design functions by describing their input, processing, and output.
This document discusses the application of calculus concepts like derivation and integration in accounting and auditing. It aims to analyze how derivation can be used to optimize processes like maximizing profits and setting prices. Two examples are provided, one on profit maximization where derivation is used to find the production level with maximum weekly profit. The second example applies derivation to determine the price and sales volume that gives maximum utility. The document concludes discussing how these concepts can help accountants and businesses improve processes and make better economic decisions.
The document discusses long-run average cost (LAC) curves. It explains that in the long-run, all factors of production are variable and the LAC curve guides entrepreneurs on optimal plant size and output levels. The LAC curve is derived from short-run cost curves and is U-shaped, representing minimum unit costs for different output levels. Economies of scale may exist as costs fall with increasing output up to a point, after which diseconomies of scale can set in as costs begin rising again. The break-even point is where total revenue equals total costs and no economic profit is made.
The document discusses methods for determining how costs behave and estimating cost functions. It explains that total costs can often be explained by changes in a single activity level and that cost behavior is commonly approximated by linear functions within a relevant range. It then outlines different types of linear cost functions and approaches to estimating cost functions, including the industrial engineering method, conference method, account analysis, and quantitative regression analysis. Key steps in estimation include selecting a dependent cost variable and driver, collecting data, plotting relationships, and evaluating estimated functions.
This presentation will help you develop some learning regarding to budgeting its role and importance in planning and control and then will some shed light on Flexible Budgeting, Capacity and Volume of The Flexible Budget, Analysis of the Cost Behavior, Determining the Fixed & Variable Elements of the Semi Variable Expense, High & Low Points Method , Statistical Scatter Graph Method, Method of the Least Square, Preparing a Flexible Budget, Flexible Budget with Multiple Cost Drive and Flexible Budget Input versus Output. This presentation was prepared for my Cost Accounting class project.
This document is a cost sheet analysis of Tata Motors conducted by a group of students. It provides an overview of Tata Motors, including that it is a leading global automobile manufacturer. The analysis then classifies Tata Motors' costs as fixed, variable, or semi-variable/semi-fixed. Key metrics like breakeven point, profit/loss, PV ratio, and margin of safety are calculated. The importance of cost-volume-profit analysis is discussed as providing insights into pricing, costs, and profitability. In conclusion, the analysis provides lessons about focusing on sales volume while managing costs to achieve sustainable growth.
This assignment is done for a small business with the addition of different accounting items the small business can use to maximize their profit.
It has Break-even point for the company. Sales revenue. Target profit maximization.
Cost classification of the company.
The document summarizes several common cost estimation methods used in project and cost analyses, including:
1) Account analysis, which involves reviewing accounts to determine fixed and variable costs.
2) The high-low method, which uses historical cost and activity data from multiple periods to estimate a cost equation.
3) The scatter graph method and regression analysis, which involve plotting historical data on a graph to derive a cost estimation equation.
The document provides examples of how to apply the account analysis and high-low methods to estimate fixed and variable production costs for a company.
This research is to find out whether promotional activities give better results than no promotional activities and how much it effects to purchase probability.
This document provides instructions for planning activity output on a cost center in SAP. It describes how to set a planner profile, enter planned activities for a cost center and activity type, and distribute the planned quantity over multiple periods. Specifically, it shows planning 80,000 hours of plant maintenance on cost center 9101011 for activity type 1PDH01 over fiscal year 2006.
This document provides an overview of cost-volume-profit (CVP) analysis concepts from a managerial accounting textbook. It discusses key assumptions of CVP analysis, how to calculate contribution margin, break-even point, and profit using CVP equations and graphs. It also explains how to use the contribution margin ratio to evaluate the effects of changes in sales volume, variable costs, fixed costs, and selling price on contribution margin and net operating income. Examples are provided to illustrate calculating the impacts of such changes.
This document discusses the application of calculus derivatives in accounting and business administration fields. It provides examples of how derivatives can be used to calculate marginal income and marginal cost. For marginal income, the document gives an example calculating the approximate and exact income from producing and selling one additional unit. For marginal cost, it similarly provides an example of finding the marginal cost function and using it to approximate versus exactly calculating the cost of one additional unit of production. The conclusion discusses how marginal cost calculation is commonly used to determine optimal production levels by finding where marginal benefits from additional production outweigh marginal costs.
Condition technique is a configuration technique in SAP used to configure complex business rules, such as pricing. It consists of several key components, including a field catalog, condition tables, an access sequence, condition types, pricing procedures, and pricing procedure determination. Condition tables contain business rules and are accessed in the order specified by the access sequence. Condition types represent logical components like taxes or discounts. Pricing procedures combine condition types and are assigned to documents like sales orders. Overall, condition technique provides a rules engine for flexibly configuring diverse and changing business rules through its various components.
This document discusses break even analysis for a company. It defines break even point as when total revenue equals total cost. It provides formulas for linear cost, revenue, and profit functions. As an example, it calculates that a company making calculators must sell 20,000 units to break even based on a unit variable cost of Rs.225, fixed cost of Rs.25,00,000, and selling price of Rs.350 per unit. It concludes that selling more than 20,000 units would result in profit while less would result in a loss. It also provides a practice problem to determine the break even point for a product sold at Rs.450 per unit with variable cost of Rs.330 and fixed cost of Rs.
SAP TRM (Treasury and Risk Management) Configuration (1).pdfSAMIR SHAH
This document provides an overview of SAP TRM (Treasury and Risk Management) configuration including:
1. Defining enterprise structures like company codes and portfolios.
2. Configuring business partners, their roles, and number ranges.
3. Configuring transaction manager settings like product types, transaction types, flows, updates, and conditions.
4. Configuring accounting settings such as valuation areas, codes, position management, and account determination.
5. Configuring basic analyzer settings for automatic integration and cash flow indicators.
6. Configuring credit risk analyzer settings including limit checks, types, and product groups.
The document discusses key concepts related to long-run average cost curves including:
- In the long-run, all factors of production are variable and firms can choose different plant sizes. The long-run average cost curve is U-shaped and envelopes short-run average cost curves.
- The long-run marginal cost curve is derived from the intersection of short-run marginal cost curves and the long-run average cost curve.
- Cost-volume-profit analysis uses the long-run average cost curve to determine the output level needed to break even or achieve a target profit level.
- Economies of scale exist when long-run average costs fall as output increases due to factors like financial, technical
CostPerform’s multidimensional costing function leads to improved profitabilityBrian Plowman
CostPerform's new multidimensional costing function allows users to analyze large datasets of sales transactions containing multiple dimensions like product, customer, country, etc. The function automatically imports transaction data, updates cost models for each dimension value, then allocates costs to individual transactions based on drivers. This provides a more accurate view of profitability across dimensions compared to traditional single-dimension costing. The analysis of over 100 million transactions showed the function can efficiently handle "Big Data" volumes and store detailed cost and margin results for each transaction in the database for analysis in tools like QlikView.
Dynamic Business Model Visualizations: Instantly Facilitate LIVELY Business M...Rod King, Ph.D.
The document outlines a framework for creating a 3-act business model plan using storyboarding techniques. It describes storyboarding as a way to pre-visualize sequences of action. The framework involves creating 3 storyboard scenes for the past, present, and future that each focus on a "Job-To-Get-Done". Each job is analyzed in a "Trade-Off Scene" to understand desired vs undesired impacts. Together, the 3 scenes and ultimate goal aim to solve a customer problem across short, medium, and long-term timeframes.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
APLICACION DE LA DERIVADA EN LA CONTABILIDAD andrescaiza6
This document discusses applications of calculus derivatives in accounting and auditing. It aims to analyze how derivatives can be used to optimize costs, expenses, and profit maximization in business operations. Two examples are provided to demonstrate how derivatives can be applied to maximize profits by determining optimal production levels and prices. The document concludes that derivatives provide powerful tools for accountants to simplify and improve accounting processes.
Building an algorithmic price management system using ML: Dynamic talks Chica...Grid Dynamics
Leading retailers and marketplaces like Amazon and Groupon implemented very sophisticated, and highly automated price management solutions over the recent years. These solutions are able to dynamically change prices every several minutes, intelligently personalize discounts, and respond to competitor moves in order to optimize profits and inventory. It creates pressure on other retailers and manufacturers, and challenges traditional price management techniques, making it increasingly more complex to stay competitive and profitable.
In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
The document discusses long-run average cost (LAC) curves. It explains that in the long-run, all factors of production are variable and the LAC curve guides entrepreneurs on optimal plant size and output levels. The LAC curve is derived from short-run cost curves and is U-shaped, representing minimum unit costs for different output levels. Economies of scale may exist as costs fall with increasing output up to a point, after which diseconomies of scale can set in as costs begin rising again. The break-even point is where total revenue equals total costs and no economic profit is made.
The document discusses methods for determining how costs behave and estimating cost functions. It explains that total costs can often be explained by changes in a single activity level and that cost behavior is commonly approximated by linear functions within a relevant range. It then outlines different types of linear cost functions and approaches to estimating cost functions, including the industrial engineering method, conference method, account analysis, and quantitative regression analysis. Key steps in estimation include selecting a dependent cost variable and driver, collecting data, plotting relationships, and evaluating estimated functions.
This presentation will help you develop some learning regarding to budgeting its role and importance in planning and control and then will some shed light on Flexible Budgeting, Capacity and Volume of The Flexible Budget, Analysis of the Cost Behavior, Determining the Fixed & Variable Elements of the Semi Variable Expense, High & Low Points Method , Statistical Scatter Graph Method, Method of the Least Square, Preparing a Flexible Budget, Flexible Budget with Multiple Cost Drive and Flexible Budget Input versus Output. This presentation was prepared for my Cost Accounting class project.
This document is a cost sheet analysis of Tata Motors conducted by a group of students. It provides an overview of Tata Motors, including that it is a leading global automobile manufacturer. The analysis then classifies Tata Motors' costs as fixed, variable, or semi-variable/semi-fixed. Key metrics like breakeven point, profit/loss, PV ratio, and margin of safety are calculated. The importance of cost-volume-profit analysis is discussed as providing insights into pricing, costs, and profitability. In conclusion, the analysis provides lessons about focusing on sales volume while managing costs to achieve sustainable growth.
This assignment is done for a small business with the addition of different accounting items the small business can use to maximize their profit.
It has Break-even point for the company. Sales revenue. Target profit maximization.
Cost classification of the company.
The document summarizes several common cost estimation methods used in project and cost analyses, including:
1) Account analysis, which involves reviewing accounts to determine fixed and variable costs.
2) The high-low method, which uses historical cost and activity data from multiple periods to estimate a cost equation.
3) The scatter graph method and regression analysis, which involve plotting historical data on a graph to derive a cost estimation equation.
The document provides examples of how to apply the account analysis and high-low methods to estimate fixed and variable production costs for a company.
This research is to find out whether promotional activities give better results than no promotional activities and how much it effects to purchase probability.
This document provides instructions for planning activity output on a cost center in SAP. It describes how to set a planner profile, enter planned activities for a cost center and activity type, and distribute the planned quantity over multiple periods. Specifically, it shows planning 80,000 hours of plant maintenance on cost center 9101011 for activity type 1PDH01 over fiscal year 2006.
This document provides an overview of cost-volume-profit (CVP) analysis concepts from a managerial accounting textbook. It discusses key assumptions of CVP analysis, how to calculate contribution margin, break-even point, and profit using CVP equations and graphs. It also explains how to use the contribution margin ratio to evaluate the effects of changes in sales volume, variable costs, fixed costs, and selling price on contribution margin and net operating income. Examples are provided to illustrate calculating the impacts of such changes.
This document discusses the application of calculus derivatives in accounting and business administration fields. It provides examples of how derivatives can be used to calculate marginal income and marginal cost. For marginal income, the document gives an example calculating the approximate and exact income from producing and selling one additional unit. For marginal cost, it similarly provides an example of finding the marginal cost function and using it to approximate versus exactly calculating the cost of one additional unit of production. The conclusion discusses how marginal cost calculation is commonly used to determine optimal production levels by finding where marginal benefits from additional production outweigh marginal costs.
Condition technique is a configuration technique in SAP used to configure complex business rules, such as pricing. It consists of several key components, including a field catalog, condition tables, an access sequence, condition types, pricing procedures, and pricing procedure determination. Condition tables contain business rules and are accessed in the order specified by the access sequence. Condition types represent logical components like taxes or discounts. Pricing procedures combine condition types and are assigned to documents like sales orders. Overall, condition technique provides a rules engine for flexibly configuring diverse and changing business rules through its various components.
This document discusses break even analysis for a company. It defines break even point as when total revenue equals total cost. It provides formulas for linear cost, revenue, and profit functions. As an example, it calculates that a company making calculators must sell 20,000 units to break even based on a unit variable cost of Rs.225, fixed cost of Rs.25,00,000, and selling price of Rs.350 per unit. It concludes that selling more than 20,000 units would result in profit while less would result in a loss. It also provides a practice problem to determine the break even point for a product sold at Rs.450 per unit with variable cost of Rs.330 and fixed cost of Rs.
SAP TRM (Treasury and Risk Management) Configuration (1).pdfSAMIR SHAH
This document provides an overview of SAP TRM (Treasury and Risk Management) configuration including:
1. Defining enterprise structures like company codes and portfolios.
2. Configuring business partners, their roles, and number ranges.
3. Configuring transaction manager settings like product types, transaction types, flows, updates, and conditions.
4. Configuring accounting settings such as valuation areas, codes, position management, and account determination.
5. Configuring basic analyzer settings for automatic integration and cash flow indicators.
6. Configuring credit risk analyzer settings including limit checks, types, and product groups.
The document discusses key concepts related to long-run average cost curves including:
- In the long-run, all factors of production are variable and firms can choose different plant sizes. The long-run average cost curve is U-shaped and envelopes short-run average cost curves.
- The long-run marginal cost curve is derived from the intersection of short-run marginal cost curves and the long-run average cost curve.
- Cost-volume-profit analysis uses the long-run average cost curve to determine the output level needed to break even or achieve a target profit level.
- Economies of scale exist when long-run average costs fall as output increases due to factors like financial, technical
CostPerform’s multidimensional costing function leads to improved profitabilityBrian Plowman
CostPerform's new multidimensional costing function allows users to analyze large datasets of sales transactions containing multiple dimensions like product, customer, country, etc. The function automatically imports transaction data, updates cost models for each dimension value, then allocates costs to individual transactions based on drivers. This provides a more accurate view of profitability across dimensions compared to traditional single-dimension costing. The analysis of over 100 million transactions showed the function can efficiently handle "Big Data" volumes and store detailed cost and margin results for each transaction in the database for analysis in tools like QlikView.
Dynamic Business Model Visualizations: Instantly Facilitate LIVELY Business M...Rod King, Ph.D.
The document outlines a framework for creating a 3-act business model plan using storyboarding techniques. It describes storyboarding as a way to pre-visualize sequences of action. The framework involves creating 3 storyboard scenes for the past, present, and future that each focus on a "Job-To-Get-Done". Each job is analyzed in a "Trade-Off Scene" to understand desired vs undesired impacts. Together, the 3 scenes and ultimate goal aim to solve a customer problem across short, medium, and long-term timeframes.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
APLICACION DE LA DERIVADA EN LA CONTABILIDAD andrescaiza6
This document discusses applications of calculus derivatives in accounting and auditing. It aims to analyze how derivatives can be used to optimize costs, expenses, and profit maximization in business operations. Two examples are provided to demonstrate how derivatives can be applied to maximize profits by determining optimal production levels and prices. The document concludes that derivatives provide powerful tools for accountants to simplify and improve accounting processes.
Building an algorithmic price management system using ML: Dynamic talks Chica...Grid Dynamics
Leading retailers and marketplaces like Amazon and Groupon implemented very sophisticated, and highly automated price management solutions over the recent years. These solutions are able to dynamically change prices every several minutes, intelligently personalize discounts, and respond to competitor moves in order to optimize profits and inventory. It creates pressure on other retailers and manufacturers, and challenges traditional price management techniques, making it increasingly more complex to stay competitive and profitable.
In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
1. Mary Suma|tmarysuma@gmail.com|http://www.linkedin.com/in/sumayyappan| /tmarysuma
PRICE OPTIMIZTION
Determining the optimal selling price using Demand, Revenue
and Profit Function
-Mary Suma
Contents
Objective........................................................................................................................................................................2
Abstract..........................................................................................................................................................................2
Step1 : Derive Optimal Price ..................................................................................................................................3
Revenue Function ..................................................................................................................................................3
Profit Function.........................................................................................................................................................3
Step 2: Analyze price variation in Historical data...........................................................................................3
Step3: Find Optimal price........................................................................................................................................5
Step 4: Find Profit and Revenue using Optimal Price...................................................................................5
Step5 : Pricing for maximum profit or revenue...............................................................................................5
Conclusion.....................................................................................................................................................................7
Reference.......................................................................................................................................................................7
2. Mary Suma|tmarysuma@gmail.com|http://www.linkedin.com/in/sumayyappan| /tmarysuma
Objective
Price optimization is the process of finding the sweet spot and the perfect balance between
profit and value. In this presentation, ‘Pricing to maximize profit’ is explained in 5 simple steps.
• to estimate the right price of a product
• to determine how customers, react to different pricing strategies.
Machine Learning uses historical behaviour of customer’s demand to find the most optimal price
to maximize the profit and revenue.
Abstract
Demand curve represents the relationship between price of the product and the quantity sold at
a certain point in time.
Linear Demand Curve is given by
Demand Curve D(x) = ax+b
Total Revenue is given by
Revenue R(x) = x * D(x) = ax^2+bx
Total Profit is given by
Total Profit P(x) = (x-c) * D(x) = ax^2-axc+bx-bc
Imagine a company that has been selling a product, that follows linear demand Curve
D(x) = ax+b , where alpha(a) = -40 and beta(b) = 500.
Demand function, Quantity vs Price
3. Mary Suma|tmarysuma@gmail.com|http://www.linkedin.com/in/sumayyappan| /tmarysuma
Step1 : Derive Optimal Price
Now, to optimize the profit, find the critical points and check if these critical points of profit
function are maximum points or minimum points. If the second derivative is negative, it is the
maximum point. If one of critical point is maximum, that is the optimal price.
Revenue Function
First Derivative of Revenue function, R’(x) = 2ax+b
• Critical point is when R’(x) = 0
• 2ax+b = 0 ; x = -b/2a, which is the optimal price for Profit
Optimal Price to maximize profit is given by P_MaxProfit = -b/2a
Profit Function
First Derivative of Profit function P’(x) = 2ax – ac+b
• Critical point is when P’(x) = 0
• 2ax – ac+b = 0; x = (-b+ac)/2a
Optimal Price to maximize revenue is given by P_MaxRevenue = (-b+ac)/2a
The price that maximizes profit is always bigger than the one that maximizes total revenue since c is
always positive.
Step 2: Analyze price variation in Historical data
The graph below shows the historical variation in prices and demand for the last 365 days.
Create Demand Curve using historical data.
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Step3: Find Optimal price
Calculate the estimate optimal price for Profit and Revenue function using the formula derived
in step1.
P_MaxProfit = -b/2a
P_MaxRevenue = (-b+ac)/2a
From historical demand function,
Alpha, a = -38.85226
Beta, b = 493.6576 (Intercept)
Hence
P_MaxProfit = 8.35301
P_MaxRevenue = 6.35301
Step 4: Find Profit and Revenue using Optimal Price
Using the estimated price for Revenue and Profit, we now calculate the Revenue and total profit.
Replacing alpha = -40 and beta = 500 in the demand function, we get
• Revenue R(x) = xD(x) = ax^2+bx
Revenue with estimated optimal price, 6.35301 = 1562.076
• Total Profit P(x) = (x-c)D(x) = ax^2-axc+bx-bc
Profit with estimated optimal price, 8.35301 = 722.0756
Step5 : Pricing for maximum profit or revenue
Now lets plot the relationships between estimated profit , estimated revenue , true profit and
true revenue. stat_function can abe used to plot graph of a quadratic function. An alternate
6. Mary Suma|tmarysuma@gmail.com|http://www.linkedin.com/in/sumayyappan| /tmarysuma
approach is to define a new dataframe containing x and y coordinates for the function then
use geom_line to draw the curve.
Estimated Revenue vs Actual Revenue
As represented in the revenue curve, the optimal price is 6.35 and about 248 units will be sold in
order to maximize the revenue to 1,562.
Estimated Profit vs Actual Profit
As represented in the profit curve, the optimal price is 8.35 and about 148 units will be sold in
order to maximize the profit to 722.
(8.35, 722)
(6.35, 1562)
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Inference:
• When the primary objective is maximizing the profit, the price is set as close as
possible to the peak of revenue curve or the profit curve.
• In the example, estimated revenue and estimated profit curves are similar to the
actual revenue and actual profit
Conclusion
Using Price optimization, businesses predict to what degree demand is altered with the change
of price. It is used to measure how sensitive customers can be to price changes. Pricing for
maximum profit, is most effective with established products in stable markets. New products aim
to increase the market share than increasing revenue and hence different pricing strategy
should be used.
Reference
Pricing and revenue optimization - Robert Lewis
Price Optimization - Yuri Fonseca