1. The document discusses segmenting PPG's clients to boost sales using psychographic tools like surveys, cluster analysis, and factor analysis.
2. The cluster analysis identified three client segments: beauty fans, those who don't care about protection, and those who want beauty and protection. The largest segment is those wanting beauty and protection.
3. The factor analysis showed questions should focus on beauty and protection as one component and characteristics of beauty as another to understand client purchase behavior.
Promotion Analytics - Module 2: Model and EstimationMinha Hwang
This document provides an overview of promotion analytics using scanner data. It discusses estimating baseline and incremental sales from promotions through econometric modeling. Key aspects covered include model specification, interpretation of coefficients, and limitations. The modeling approach involves log-transforming a multiplicative sales model to make it linear and estimating it using ordinary least squares regression. Baseline sales are estimated by turning off promotions, and incremental sales are calculated as the difference between actual and baseline sales.
A small presentation of Demand Forecasting. It covers the Qualitative and Quantitative aspects of Demand Forecasting in Operations Management.
Related Document File: https://www.scribd.com/doc/311049068/Demand-Forecasting
Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.
This document discusses various methods for estimating and forecasting demand, including direct methods like consumer interviews and market studies. It also discusses empirical demand functions that are derived from actual market data and can be used to model demand. Time-series forecasts use linear trend forecasting to model how a variable changes over time. Seasonal variation is also discussed and can be accounted for using dummy variables. Forecasting accuracy decreases the further into the future forecasts are made and model misspecification can also reduce reliability.
This document discusses various demand forecasting methods. Demand forecasting is important for determining business decisions like markets to pursue, products to produce, and staffing levels. Forecasts are rarely perfect and are more accurate for aggregated data over short time periods. Demand forecasts analyze primary and secondary market and demand information to predict future demand for a product or service. Common forecasting methods include opinion polling of consumers and salespeople, time series analysis of past sales data, and statistical techniques like least squares regression and the Delphi method of aggregating expert opinions.
This document discusses various optimization techniques used in economics including how consumers and firms maximize utility and profit. It also covers how to express economic relationships using equations, tables, and graphs to show concepts like total, average, and marginal revenue and costs. Geometric relationships between marginal, average, and total values on graphs are explained. The steps for profit maximization and optimization problems are outlined. Various management tools are also listed.
This document discusses demand estimation and forecasting for the Close-Up toothpaste brand. It provides historical sales data from 2007-2016 which shows an upward trend. The trend equation method is used to forecast sales for 2017-2020. Sales are predicted to continue increasing based on the positive slope value in the trend equation. Key details on Close-Up products and marketing positioning are also summarized.
1. The document discusses segmenting PPG's clients to boost sales using psychographic tools like surveys, cluster analysis, and factor analysis.
2. The cluster analysis identified three client segments: beauty fans, those who don't care about protection, and those who want beauty and protection. The largest segment is those wanting beauty and protection.
3. The factor analysis showed questions should focus on beauty and protection as one component and characteristics of beauty as another to understand client purchase behavior.
Promotion Analytics - Module 2: Model and EstimationMinha Hwang
This document provides an overview of promotion analytics using scanner data. It discusses estimating baseline and incremental sales from promotions through econometric modeling. Key aspects covered include model specification, interpretation of coefficients, and limitations. The modeling approach involves log-transforming a multiplicative sales model to make it linear and estimating it using ordinary least squares regression. Baseline sales are estimated by turning off promotions, and incremental sales are calculated as the difference between actual and baseline sales.
A small presentation of Demand Forecasting. It covers the Qualitative and Quantitative aspects of Demand Forecasting in Operations Management.
Related Document File: https://www.scribd.com/doc/311049068/Demand-Forecasting
Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.
This document discusses various methods for estimating and forecasting demand, including direct methods like consumer interviews and market studies. It also discusses empirical demand functions that are derived from actual market data and can be used to model demand. Time-series forecasts use linear trend forecasting to model how a variable changes over time. Seasonal variation is also discussed and can be accounted for using dummy variables. Forecasting accuracy decreases the further into the future forecasts are made and model misspecification can also reduce reliability.
This document discusses various demand forecasting methods. Demand forecasting is important for determining business decisions like markets to pursue, products to produce, and staffing levels. Forecasts are rarely perfect and are more accurate for aggregated data over short time periods. Demand forecasts analyze primary and secondary market and demand information to predict future demand for a product or service. Common forecasting methods include opinion polling of consumers and salespeople, time series analysis of past sales data, and statistical techniques like least squares regression and the Delphi method of aggregating expert opinions.
This document discusses various optimization techniques used in economics including how consumers and firms maximize utility and profit. It also covers how to express economic relationships using equations, tables, and graphs to show concepts like total, average, and marginal revenue and costs. Geometric relationships between marginal, average, and total values on graphs are explained. The steps for profit maximization and optimization problems are outlined. Various management tools are also listed.
This document discusses demand estimation and forecasting for the Close-Up toothpaste brand. It provides historical sales data from 2007-2016 which shows an upward trend. The trend equation method is used to forecast sales for 2017-2020. Sales are predicted to continue increasing based on the positive slope value in the trend equation. Key details on Close-Up products and marketing positioning are also summarized.
This document summarizes sales forecasting methods, including qualitative and quantitative approaches. Qualitative methods like opinion surveys, market trials, and the Delphi technique rely on human judgment for long-term or new product forecasting. Quantitative methods use past sales data and include moving average, exponential smoothing, and least squares regression for short to medium-term, old product forecasting. These data-driven techniques estimate future demand based on trends in historical data.
The document discusses various econometric methods used to study markets, including conjoint analysis, factor analysis, discriminant analysis, cost and return analysis, price spreads, producer price index, multiple regression analysis, and marketing efficiency measures like Shepherd's formula and marketing margin-cost ratio. Conjoint analysis determines how people value product attributes. Factor analysis groups correlated variables. Discriminant analysis predicts outcomes for categorical dependent variables. Multiple regression analyzes relationships between independent and dependent metrics. Other sections define key marketing terms and formulas.
Unilever uses a state-of-the-art customer demand planning system to forecast demand. It blends historical shipment data, promotional data, and current order data to generate statistical forecasts, which are then adjusted based on planned promotion predictions and point of sale data. This approach has helped Unilever reduce inventory levels and improve customer service. The document also discusses different forecasting techniques like time series analysis, causal methods, and judgmental forecasts, and how to measure forecast accuracy.
This study examines the relationship between customer relationship management (CRM), business strategy, and firm performance. It finds that CRM has an indirect effect on performance through its impact on differentiation and cost leadership strategies. Differentiation and cost leadership were found to fully mediate the relationship between CRM and performance. The impact of CRM on differentiation was greater for firms in highly commoditized industries than those in low commoditization industries, but industry commoditization did not affect the impact of CRM on cost leadership. The research provides recommendations for managers on focusing CRM efforts on business strategies and collaboration between CRM and strategy teams.
Review: trade level productivity measurement: critical challenges and solutionsRandi Ilhamm
This paper discusses the challenges of measuring productivity at the trade level for contractors and subcontractors. It identifies critical challenges such as those related to individual trades, firms, and the construction industry overall. The paper also proposes potential solutions to these challenges. Through interviews and questionnaires with 42 respondents, it analyzes the key challenges and rates potential solutions. The paper concludes that accurately measuring trade-level productivity is difficult due to challenges stemming from the nature of the industry and businesses within it. Better solutions are needed to help contractors more effectively track this important metric.
This document discusses demand estimation through regression analysis. It explains that regression analysis is used to model the relationship between a dependent variable (like quantity demanded) and independent variables (like price, income, etc.). By minimizing the errors between actual data points and the estimated regression line, regression analysis provides the "line of best fit" for estimating demand relationships. The document outlines different marketing research approaches used to collect demand data, including consumer surveys and market experiments. It also discusses the identification problem in directly observing demand from price-quantity data due to shifting supply curves.
Demand forecasting is predicting future demand for a firm's products. It helps with production planning and scheduling, acquiring inputs, financial planning, pricing strategies, and advertising planning. The key steps involve specifying objectives, determining timelines, choosing forecasting methods, collecting and adjusting data, estimating results, and interpreting them. Common techniques include surveys, statistical methods, opinion polls, trend projection methods, barometric methods, and econometric methods. Consumer survey techniques involve complete enumeration, sample surveys, and end-use methods. Expert opinion, Delphi methods, and managerial surveys are also used. Statistical techniques include trend projections, barometric indicators, and econometric regression and simultaneous equation models.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Demand forecasting involves predicting future demand to aid in production planning, resource allocation, and financial planning. The objectives of demand forecasting are to develop suitable production policies, reduce purchase costs, determine pricing strategies, and plan advertising. Common methods of demand forecasting include qualitative techniques like surveys and quantitative techniques like time series analysis, regression analysis, and barometric forecasting which analyze past demand data and economic indicators. Accurate demand forecasting requires in-depth knowledge of products, customers, and the business environment.
In this paper we attempt to review the models, process, qualitative and quantitative methods of forecasting. We also review the needs and reasons for forecasting and what methods and approaches are employed for forecasting, requirements for forecasting, what are the shortcomings and business implications of forecasting.
1. Demand forecasting is used to estimate future demand for products over specific time periods and is important for planning operations.
2. Demand can be categorized by the type of goods (consumer vs capital) and time period (short, medium, long term). Quantitative forecasting techniques include trend projection methods like time series analysis and regression.
3. Techniques like ARIMA combine moving averages and autoregressive methods to model trends and differences in time series data. Regression analysis uses statistical methods to model relationships between demand and influencing factors.
This document discusses various demand forecasting methods. Demand forecasting is predicting future demand based on past patterns. It is important for production planning, sales forecasting, inventory control, and other business and economic decisions. Forecasting methods include qualitative techniques like opinion polling and quantitative techniques like statistical analysis. Opinion polling involves consumer surveys, sales force opinions, or expert panels. Statistical methods include trend projection, regression analysis, and econometric modeling to establish relationships between demand and influencing factors.
Marketing Experiment - Part II: Analysis Minha Hwang
This document outlines an experiment conducted for a marketing research class. It discusses different types of experimental designs, including between-subjects and within-subjects designs. It also covers the trade-offs between field and lab experiments. Finally, it provides an overview of statistical analysis techniques like ANOVA and regression that can be used to analyze experimental data, including an example experiment involving different advertising themes and price levels.
This document provides information about demand forecasting and estimation techniques. It begins with an overview of why forecasting is important for strategic planning, finance, marketing, and production. It then discusses different forecasting techniques like using historical data, test markets, and statistical methods. It covers how forecasting impacts inventory management and considerations like accuracy over long time periods and unforeseen factors. Overall, the document outlines the purpose and importance of demand forecasting for business decision making, as well as various techniques and their pros and cons.
Demand forecasting estimates future demand for a product over a period of time. Engro Foods uses a combination of qualitative, causal, and opinion poll methods for sales and demand forecasting. The qualitative method uses historical data and management judgment. The causal method links demand to economic and pricing factors. The opinion poll method collects insights from sales experts. Engro Foods could improve forecasts by also using trend and smoothing techniques.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document provides an overview of a strategic management course. The objectives are to familiarize students with strategic management concepts and frameworks, and develop their ability to apply these concepts to understand business performance, generate strategy options, assess options under uncertainty, select and implement strategies. The course also aims to integrate previous learning and develop a general management perspective and judgment.
Empowering Innovation Portfolio Decision-Making through SimulationSopheon
New product development is a complex, high-risk endeavor for any organization. In order to execute a game-changing innovation program, leaders must be willing to engage the unknowns around future markets and the technologies that will serve them.
This webinar discusses how simulation and specialized business processes can provide a risk-free proving ground to challenge and compare innovation strategies, thereby empowering analysts and executives to confidently make difficult investment decisions.
To view this webinar, go to http://budurl.com/zgs5
The document provides an overview of an advanced strategic management course. The objectives are to understand strategic concepts and apply them to analyze enterprise performance, generate and evaluate strategic options, and implement strategies. The course also aims to integrate previous learning and develop general management skills. It then discusses the concept of strategy, defining it as determining long-term goals and adopting actions and allocating resources to achieve those goals. Different levels of strategy are described, from functional to business to corporate. Successful strategies are said to have effective implementation, understanding of the environment, objective resource appraisal, long-term objectives, and agreement. The document frames strategy as a quest for profit and discusses maximizing shareholder value over profit alone.
This document summarizes sales forecasting methods, including qualitative and quantitative approaches. Qualitative methods like opinion surveys, market trials, and the Delphi technique rely on human judgment for long-term or new product forecasting. Quantitative methods use past sales data and include moving average, exponential smoothing, and least squares regression for short to medium-term, old product forecasting. These data-driven techniques estimate future demand based on trends in historical data.
The document discusses various econometric methods used to study markets, including conjoint analysis, factor analysis, discriminant analysis, cost and return analysis, price spreads, producer price index, multiple regression analysis, and marketing efficiency measures like Shepherd's formula and marketing margin-cost ratio. Conjoint analysis determines how people value product attributes. Factor analysis groups correlated variables. Discriminant analysis predicts outcomes for categorical dependent variables. Multiple regression analyzes relationships between independent and dependent metrics. Other sections define key marketing terms and formulas.
Unilever uses a state-of-the-art customer demand planning system to forecast demand. It blends historical shipment data, promotional data, and current order data to generate statistical forecasts, which are then adjusted based on planned promotion predictions and point of sale data. This approach has helped Unilever reduce inventory levels and improve customer service. The document also discusses different forecasting techniques like time series analysis, causal methods, and judgmental forecasts, and how to measure forecast accuracy.
This study examines the relationship between customer relationship management (CRM), business strategy, and firm performance. It finds that CRM has an indirect effect on performance through its impact on differentiation and cost leadership strategies. Differentiation and cost leadership were found to fully mediate the relationship between CRM and performance. The impact of CRM on differentiation was greater for firms in highly commoditized industries than those in low commoditization industries, but industry commoditization did not affect the impact of CRM on cost leadership. The research provides recommendations for managers on focusing CRM efforts on business strategies and collaboration between CRM and strategy teams.
Review: trade level productivity measurement: critical challenges and solutionsRandi Ilhamm
This paper discusses the challenges of measuring productivity at the trade level for contractors and subcontractors. It identifies critical challenges such as those related to individual trades, firms, and the construction industry overall. The paper also proposes potential solutions to these challenges. Through interviews and questionnaires with 42 respondents, it analyzes the key challenges and rates potential solutions. The paper concludes that accurately measuring trade-level productivity is difficult due to challenges stemming from the nature of the industry and businesses within it. Better solutions are needed to help contractors more effectively track this important metric.
This document discusses demand estimation through regression analysis. It explains that regression analysis is used to model the relationship between a dependent variable (like quantity demanded) and independent variables (like price, income, etc.). By minimizing the errors between actual data points and the estimated regression line, regression analysis provides the "line of best fit" for estimating demand relationships. The document outlines different marketing research approaches used to collect demand data, including consumer surveys and market experiments. It also discusses the identification problem in directly observing demand from price-quantity data due to shifting supply curves.
Demand forecasting is predicting future demand for a firm's products. It helps with production planning and scheduling, acquiring inputs, financial planning, pricing strategies, and advertising planning. The key steps involve specifying objectives, determining timelines, choosing forecasting methods, collecting and adjusting data, estimating results, and interpreting them. Common techniques include surveys, statistical methods, opinion polls, trend projection methods, barometric methods, and econometric methods. Consumer survey techniques involve complete enumeration, sample surveys, and end-use methods. Expert opinion, Delphi methods, and managerial surveys are also used. Statistical techniques include trend projections, barometric indicators, and econometric regression and simultaneous equation models.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Demand forecasting involves predicting future demand to aid in production planning, resource allocation, and financial planning. The objectives of demand forecasting are to develop suitable production policies, reduce purchase costs, determine pricing strategies, and plan advertising. Common methods of demand forecasting include qualitative techniques like surveys and quantitative techniques like time series analysis, regression analysis, and barometric forecasting which analyze past demand data and economic indicators. Accurate demand forecasting requires in-depth knowledge of products, customers, and the business environment.
In this paper we attempt to review the models, process, qualitative and quantitative methods of forecasting. We also review the needs and reasons for forecasting and what methods and approaches are employed for forecasting, requirements for forecasting, what are the shortcomings and business implications of forecasting.
1. Demand forecasting is used to estimate future demand for products over specific time periods and is important for planning operations.
2. Demand can be categorized by the type of goods (consumer vs capital) and time period (short, medium, long term). Quantitative forecasting techniques include trend projection methods like time series analysis and regression.
3. Techniques like ARIMA combine moving averages and autoregressive methods to model trends and differences in time series data. Regression analysis uses statistical methods to model relationships between demand and influencing factors.
This document discusses various demand forecasting methods. Demand forecasting is predicting future demand based on past patterns. It is important for production planning, sales forecasting, inventory control, and other business and economic decisions. Forecasting methods include qualitative techniques like opinion polling and quantitative techniques like statistical analysis. Opinion polling involves consumer surveys, sales force opinions, or expert panels. Statistical methods include trend projection, regression analysis, and econometric modeling to establish relationships between demand and influencing factors.
Marketing Experiment - Part II: Analysis Minha Hwang
This document outlines an experiment conducted for a marketing research class. It discusses different types of experimental designs, including between-subjects and within-subjects designs. It also covers the trade-offs between field and lab experiments. Finally, it provides an overview of statistical analysis techniques like ANOVA and regression that can be used to analyze experimental data, including an example experiment involving different advertising themes and price levels.
This document provides information about demand forecasting and estimation techniques. It begins with an overview of why forecasting is important for strategic planning, finance, marketing, and production. It then discusses different forecasting techniques like using historical data, test markets, and statistical methods. It covers how forecasting impacts inventory management and considerations like accuracy over long time periods and unforeseen factors. Overall, the document outlines the purpose and importance of demand forecasting for business decision making, as well as various techniques and their pros and cons.
Demand forecasting estimates future demand for a product over a period of time. Engro Foods uses a combination of qualitative, causal, and opinion poll methods for sales and demand forecasting. The qualitative method uses historical data and management judgment. The causal method links demand to economic and pricing factors. The opinion poll method collects insights from sales experts. Engro Foods could improve forecasts by also using trend and smoothing techniques.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The document provides an overview of a strategic management course. The objectives are to familiarize students with strategic management concepts and frameworks, and develop their ability to apply these concepts to understand business performance, generate strategy options, assess options under uncertainty, select and implement strategies. The course also aims to integrate previous learning and develop a general management perspective and judgment.
Empowering Innovation Portfolio Decision-Making through SimulationSopheon
New product development is a complex, high-risk endeavor for any organization. In order to execute a game-changing innovation program, leaders must be willing to engage the unknowns around future markets and the technologies that will serve them.
This webinar discusses how simulation and specialized business processes can provide a risk-free proving ground to challenge and compare innovation strategies, thereby empowering analysts and executives to confidently make difficult investment decisions.
To view this webinar, go to http://budurl.com/zgs5
The document provides an overview of an advanced strategic management course. The objectives are to understand strategic concepts and apply them to analyze enterprise performance, generate and evaluate strategic options, and implement strategies. The course also aims to integrate previous learning and develop general management skills. It then discusses the concept of strategy, defining it as determining long-term goals and adopting actions and allocating resources to achieve those goals. Different levels of strategy are described, from functional to business to corporate. Successful strategies are said to have effective implementation, understanding of the environment, objective resource appraisal, long-term objectives, and agreement. The document frames strategy as a quest for profit and discusses maximizing shareholder value over profit alone.
The document provides an overview of pharmaceutical portfolio management. It discusses the importance of portfolio management, including maximizing value, maintaining the right number and balance of projects, and aligning with strategic direction. It outlines techniques for evaluating and optimizing portfolios, such as calculating net present value, expected commercial value, and using matrices like BCG and risk-reward to balance risk and reward across projects. Monte Carlo simulation and strategic "buckets" are also summarized as tools to incorporate risk and align projects with strategic goals respectively.
The document summarizes the operations of the Mumbai Dabbawalas, who deliver home-cooked lunches to office workers in Mumbai. It outlines their highly efficient system for sorting and delivering over 200,000 lunch boxes per day across Mumbai within 3 hours, with an extremely low error rate of 1 in 16 million transactions, equivalent to Six Sigma quality levels. The Dabbawalas have no formal education or technology, yet achieve world-class reliability through their simple coding system and cultural values like trust and ownership.
Rapid Optimization Application Development Using Excel and SolverMichael Mina
Marketing optimization is the process of determining how to allocate marketing dollars in order to achieve specific goals (e.g., maximize profit), subject to certain constraints (e.g., a fixed marketing budget). This often takes the form of using mathematical techniques to determine who to target, through which channel, and with what message or offer.
A number of optimization applications are commercially available. However, many of them require changes to data and computational infrastructure that are labor-intensive and cost-prohibitive. This presentation will demonstrate how optimization applications can be developed easily and quickly using Excel combined with Excel Solver, even for large marketing campaigns.
This presentation will discuss how segmentation can be used to reduce the complexity of large optimization problems, and how to quickly develop a simple but effective optimization application using Excel combined with Excel Solver.
This presentation will be of interest to those seeking to optimize marketing campaigns of any size while managing operational and computational complexity.
An electronic copy of the Excel worksheet used for optimization is this presentation is available at tinyurl.com/mina2018artforum.
This document provides an overview and introduction to strategic management concepts. It discusses the objectives of the course which are to understand strategic management frameworks and apply them to understand business performance, generate strategy options, assess options under uncertainty, select and implement strategies. It also defines key strategic management terms like strategy, levels of strategy at the corporate, business and functional levels, and competitive advantage. Sources of competitive advantage like cost advantage and differentiation advantage are introduced.
This document provides an overview of a strategic management course, including its objectives and concepts to be covered. The objectives are to familiarize students with strategic management frameworks and techniques, and to develop their ability to apply these concepts to understand business performance, generate and assess strategy options, and recommend effective strategy implementation. Key concepts that will be addressed include the levels of strategy (corporate, business, functional), sources of competitive advantage, profit maximization and value maximization, and frameworks for analyzing costs and identifying cost drivers across a company's value chain.
The document is a mark scheme for a GCE Advanced Level exam in Business Studies. It provides the requirements and basis for how examiners will award marks to different parts of student responses. For each question, it outlines the knowledge, application, analysis, and evaluation requirements to achieve different levels of marks. It also provides example answer responses that students could provide to receive marks. The mark scheme is intended to be used alongside the exam paper and examiner report to guide examiners in fairly and consistently assessing student answers.
The document is a mark scheme for a GCE Advanced Level exam in Business Studies. It provides the requirements and basis for how examiners will award marks to answers on the exam. For each question, it outlines the knowledge, application, analysis, and evaluation requirements to achieve marks at each level. It also provides example answers for each question that discuss concepts like benefits of ethical trading, calculating profit margins, impact of technology, investment appraisal, and revising an organizational structure. The mark scheme aims to guide teachers and students on what is required to receive marks for the different questions.
This document discusses strategies for integrating segmentation and predictive modeling. It begins by outlining a typical agenda, including whether to use segmentation, modeling, or both. It then covers strategic approaches like value-based behavioral segmentation and clustering to define customer segments. Tactical segmentation involves using outcomes from predictive models to segment customers. The document provides examples of integrating segmentation with different modeling techniques and discusses how segmented models can outperform single models. It emphasizes that both strategic and tactical approaches are useful but strategic provides more insights for improving communications.
This document discusses various concepts related to pricing decisions and cost management. It begins by outlining three major factors that affect pricing decisions: customers, competitors, and costs. It then distinguishes between short-run and long-run pricing decisions based on their time horizons. Short-run decisions consider relevant variable costs, while long-run decisions consider all future fixed and variable costs to earn a target return. The document provides examples of using target costing for both short-run and long-run pricing analyses. It also references using activity-based costing to calculate full manufacturing costs for setting long-run prices. Finally, it briefly mentions some alternative long-run pricing approaches like market-based, target return, and cost-
This document provides a summary of the sales incentive plan design process. It outlines four key steps:
1) Defining the sales platform by product strategy, sales type, complexity, and level of persuasion required.
2) Determining the appropriate mix of base pay and incentive pay based on the sales platform and market practices. Common mixes range from 100% base pay to 100% incentive pay.
3) Setting the on-target earnings and defining leverage to reward performance above target, typically 3x target pay at an excellence level.
4) Choosing a payment formula such as commission, salary plus bonus, or a matrix to determine payouts.
The document discusses several factors to consider in product design, including marketing, economics, production, and profitability. It outlines aspects of marketing like satisfying customer needs and demand creation. Economic analysis considers capital costs, production costs, expected profits, competitiveness, and sales projections. Production factors include selecting processes, materials, components, workmanship standards, and tolerances. The overall goal is to design profitable products that can be efficiently manufactured and are competitive in the market.
Strategicmanagement 120205221455-phpapp01 (1)Ahmed Zidan
This document provides an overview of a strategic management course. It aims to help students understand strategic concepts and frameworks, apply them to understand enterprise performance and generate strategy options, and integrate knowledge from previous courses. The document also covers the concept of strategy, defining it as determining long-term goals and allocating resources to achieve them. It discusses levels of strategy, sources of competitive advantage like differentiation and cost leadership, and tools for analyzing strategy such as experience curves and the value chain.
The document discusses various factors to consider during the product design stage including marketing, product characteristics, economic analysis, profitability, and production aspects. It emphasizes the importance of analyzing costs, expected sales, prices, and competition to determine the economic feasibility of a new product. The production aspects that must be considered are selection of processes, materials, components, workmanship, and tolerances.
Opportunities And Threats Of Entering New Markets New Geos Powerpoint Present...SlideTeam
Introducing our Opportunities And Threats Of Entering New Markets New Geos PowerPoint Presentation Slides to help you create a successful business expansion plan step-by-step. Identify the available geographic strategic options best-suited to widen your market base by taking the help of these entry strategy PPT slides. Use this swot analysis PPT template to elaborate on the plan of action for business growth, like expansion in successful existing geos, entering new geos, and dropping unsuccessful geos. Employ these content-specific market entry PPT layouts to carry out effective market research for your business. Highlight the process as well as the importance of value proposition analysis by taking the aid of these commercialization strategy PPT designs. Take advantage of our matrix template for this geographic expansion strategy PPT presentation to score each potential geo on the criteria of market opportunities like growth potential, competition level, investment, risk, and legal aspects. Download this global marketing effort PPT deck and create a roadmap for successful business expansion in the global market. https://bit.ly/3cJ7cx9
Six Sigma is a data-driven methodology for improving processes by eliminating defects. It involves the following key aspects:
- A structured DMAIC methodology of Define, Measure, Analyze, Improve, and Control phases to systematically solve problems.
- A focus on processes capable of producing no more than 3.4 defects per million opportunities. This is derived from operating processes with no more than six standard deviations from the mean.
- Use of statistical tools during the Analyze phase to determine root causes of defects and during the Improve phase to develop and test solutions.
- An emphasis on controlling performance through statistical process controls after improvements are made to maintain results.
Adventures in Business Analytics – Optimization and the Organization Garry, s...Tin Ho
Adventures in Business
Analytics – Optimization
and the Organization
Steve Garry
Marketing Optimization and the Organization
November 2014
Generating Better Business
Results Through Analytics
This document provides an overview of a course on advanced strategic management. The objectives of the course are to familiarize students with strategic management concepts, frameworks, and techniques. It aims to help students understand business performance, generate and assess strategic options, and select and implement strategies. The document discusses levels of strategy, definitions of strategy, elements of successful strategies, and how strategy relates to profit maximization and shareholder value. It also introduces concepts like sources of competitive advantage, experience curves, economies of scale, and using the value chain to analyze costs.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Data Control Language.pptx Data Control Language.pptx
Strategic approachppg v02
1. PPG: Strategic Decision Making
proposed to overcome
challenges in its paint
products. Pittsburgh, PA
Mar 26, 2017
By: Daniel Espinoza
CMU - Tepper
2. Motivation
• Present a rounded framework of analysis (making an exercise) to study
carefully the strategic options available for PPG regarding to its paint
products, in order to make the best decision today considering the
information up to the moment.
• Use of simulated data to test the method and understand possible
ramifications in this business challenge.
• Synthesize the course of action and apply stress tests over the results
achieved.
• Important note: not to take the conclusions as a recommended course
of action. The present results are consequence of assumptions that
must be refined and challenged with real data from the field.
3. AGENDA
• Objectives
• Possible Strategies
• Deterministic considerations
• Probabilistic Analysis
• Stress Tests of Strategies
• Conclusions
4. Objectives
Main Objective:
• Use of the Decision Analysis framework to study carefully the strategic options
for PPG regarding to its paint products, in order to make the best business
decision today with the available information.
Secondary Objectives:
• Take advantage of operational and market opportunities to boost paint products
of PPG and explore possible ramifications in this business challenge.
• Make an exercise with simulated data to test the methodology which will be the
main strategic tool to be used to improve significantly the market share and
sales in the current business scenario.
5. Possible strategies
DESCRIPCION STRATEGIES
1 2 3
Number of Strategy
Name of Strategy Beauty aware practicals Protective beauty
DESCRIPTION VALUE IN USENAME STRATEGIES
1 1 2 3
Consumer Focus Beauty Fans Market Beauty Fans No protection facnsBeauty and protection fans
Production Technology Variety Technology Variety Volume Variety
Commercial Channels Retail Sales Retail Wholesales Retail
Geographies Latam Countries Latam EMEA US-Canada
• After exploring several alternatives that were presented in previous ppt
deliveries, we define 3 strategies: (1) Beauty aware: set of consumer that
prefer a product with good surface finish. (2) Practical: only want a bulk
product without considering quality or protection. (3) Protective & Beauty:
people that want to obtain these features in one single product.
• There are strategic options regarding to market, technology (volume -
variety), sales (retail – wholesale) and geographies (Latam-EMEA-US-Canada).
This list is not exhaustive and can be modified or increased.
6. Possible strategies (metrics of uncertainty)
DESCRIPTION Strategy 1 Strategy 2 Strategy 3 INDEX NOMBRE UNIT OF
Low Median High Low Median High Low Median High INDEX MEASURE
Income
Size of the Market 550 650 730 630 700 790 810 1000 1100 2 Size USD Mill.
Incremental Market Share 0.7% 1.5% 1.6% 1.0% 2.0% 2.1% 1.5% 2.5% 2.6% 2 IncMark %
Costs
Production Cost 55% 60% 65% 50% 55% 65% 45% 50% 55% 2 CostProd %
Administrative Costs 13% 14% 18% 8% 10% 12% 10% 12% 14% 2 CostAdm %
Commercial Costs 10% 12% 14% 13% 14% 17% 17% 18% 23% 2 CostCom %
Investments
CAPEX 5 6 8 8 9 11 9 10 12 2 InvCapex US$/TM
Workcap 0.5 1 3 1 3 4 1 4 5 2 cost_perfvolUS$/TM
• Along to the strategies were defined metrics for every Strategy. We used a
high level financial model with the following variables: Size of the Market,
Incremental Market Share, Production Cost, Administrative Cost, Commercial
Cost, Capex, and Working capital. (The values above are assumptions).
7. Results of the Financial Model (Strategy 3)
• After making the financial calculations, we obtained the results for the NPV
for the Strategies 1, 2, and 3. Above are presented the results for Strategy 3
NPV = 9.66 $Mill.
DESCRIPTION US$ 0 1 2 3 4 5 6 7 8 9 10
Inflow 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00
Cost of Production 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50
Administrative Expenses 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00
Commercialization Expenses 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50
Taxes 1.40 1.40 1.40 1.40 1.40 1.40 1.40 1.40 1.40 1.40
Total Outflow 21.40 21.40 21.40 21.40 21.40 21.40 21.40 21.40 21.40 21.40
Operational Cash Flow 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60
Flow of Investments
Investment in machinery & Equipment -10 0 0 0 0 0 0 0 0 0 0
Variation of Working Capital -4 0 0 0 0 0 0 0 0 0 4
ECONOMIC CASH FLOW -14 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 7.60
NET FINANCING CASH 0 0 0 0 0 0 0 0 0 0 0
FINANCIAL CASH FLOW -14 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 7.60
NPV
$9.66
8. Deterministic considerations: Comparative results of
the Strategies measured by the NPV(10%)
• The valuation of the alternatives are showed above. Strategy 3 is the
preferred under deterministic analysis in the base case.
$0.00
$2.00
$4.00
$6.00
$8.00
$10.00
Strategy 1 Strategy 2 Strategy 3
$0.13
$2.83
$9.66
PPG Project: PROFITABILITY OF THE STRATEGIES
IN THE BASE CASE
9. Deterministic considerations: Strategy 1: Beauty aware
• Following this strategy, the main variables that generate variability in the
results are: Production Cost, followed by Capital Expenditure and
Administrative Cost. These variables explain 77.6% of the variations of the
NPV which is 0.1 $Mill.
Base
Value
0.6
6
0.14
0.12
1
650
0.015
% Swing
Explained
48.0
17.6
12.0
7.7
7.5
7.2
0.0
Production Cost
Capital Expenditure
Administrative Cost
Commercial Cost
Working Capital
Size of Market
Incremental Market Share
Value
–2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Base Value: 0.1
0.65 0.55
8 5
0.18 0.13
0.14 0.10
3 0.5
550 730
0.016 0.007
10. Deterministic considerations: Strategy 2: Practical
• This Strategy 2 shows that the main variables that generate variability in the
results are: Production Cost, Market, and Capital expenditure. These
variables explain 86.1% of the variations of the NPV which is 2.8 $Mill.
Base
Value
0.55
700
9
0.10
0.14
3
0.02
% Swing
Explained
72.9
7.5
5.7
5.2
5.2
3.5
0.0
Production Cost
Size of Market
Capital Expenditure
Administrative Cost
Commercial Cost
Working Capital
Incremental Market Share
Value
–3.0 –2.1 –1.2 –0.3 0.6 1.5 2.4 3.3 4.2 5.1 6.0
Base Value: 2.8
0.65 0.50
630 790
11 8
0.12 0.08
0.17 0.13
4 1
0.021 0.01
11. Base
Value
0.5
0.18
1000
0.12
4
10
0.025
% Swing
Explained
50.7
18.2
17.1
8.1
3.1
2.8
0.0
Production Cost
Commercial Cost
Size of Market
Administrative Cost
Working Capital
Capital Expenditure
Incremental Market Share
Value
4 5 6 7 8 10 11 12 13 14 15
Base Value: 10
0.55 0.45
0.23 0.17
810 1100
0.14 0.10
5 1
12 9
0.026 0.015
Deterministic considerations: Strategy 3: Beauty & protection
• This analysis under deterministic considerations indicates that the variability
of the NPV for the Strategy 3 is influenced by: Production Cost, Commercial
Cost, and Size of the Market. These variables explain 86.0% of the variations
of the NPV = 10 $Mill.
12. Value
Cumulative
Probability
0
.1
.2
.3
.4
.5
.6
.7
.8
.9
1.0
–20 –16 –12 –8 –4 0 4 8 12 16 20
1;EV=0 2;EV=–1
3;EV=1
• This is the core result of all the method applied regarding to the strategic
options available for PPG (paints). This graph means that Strategy 3
maximizes statistically the probability of success for PPG (paints).
(green line at the right)
Probabilistic Analysis:
13. Stress Tests of Strategies
Probability
Value
–3.0
–2.5
–2.0
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0
1 2
3
• If the assumptions regarding to the Production Costs change, the Strategy 3
continue being the preferred. (green line above)
• This means that the Strategy 3 is robust to provide expected profits and
accomplish the PPG objective which are to increase market share & sales,
although the market is risky.
14. Conclusions
• We demonstrated that the Decision Analysis framework can be used to study
carefully the strategic options for PPG regarding to its paint products. Also, to
deal with opened questions (quantitative and qualitative) to be answered in a
quantitative way.
• There are opportunities at operational and market level to boost the paint
products in PPG. These opportunities require to be systematically structured to
explore ramifications in this business challenge.
• It is recommendable to make today critical strategic decisions in a prospective
way with high level existent tools. This will improve significantly the probability
of success related to market share and sales and build a desired business
scenario for PPG.