1. The bullwhip effect occurs in supply chains where demand appears more variable upstream than downstream due to factors like demand forecasting, lead times, batch ordering, price variability, and supply issues.
2. A study by Procter & Gamble found that retailer sales were fairly stable but orders to factories fluctuated more, showing the bullwhip effect.
3. Ways to mitigate the bullwhip effect include reducing forecast error and lead times, continuous replenishment, centralized demand information, and allocating supply based on sales rather than orders.
This document discusses the bullwhip effect in supply chain management. It begins by defining the bullwhip effect as increased variability in orders placed by retailers and distributors compared to actual consumer demand. The document then provides explanations for why the bullwhip effect occurs, including demand forecasting, lead times, batch ordering, price variability, and supply allocation. It also quantifies the bullwhip effect mathematically using models of demand, ordering quantities, inventory, and variance. Finally, it discusses how to cope with the bullwhip effect by reducing demand uncertainty, lead times, batch sizes, and improving information sharing.
Sequential Selection of Correlated Ads by POMDPsShuai Yuan
This document summarizes a research paper that proposes a framework for sequential ad selection to optimize online advertising revenue. It formulates the problem as a partially observable Markov decision process (POMDP) and provides exact and approximate solutions. The paper evaluates the proposed approach on a public dataset and finds that considering dependencies between ads and using a POMDP formulation outperforms baseline methods like selecting ads randomly or based only on immediate reward.
1. This document provides 20 multi-step physics problems involving heat, temperature, thermal expansion, and their relationships. The problems cover concepts such as calculating temperature from pressure measurements, determining pressure from known temperature, calculating changes in length of materials due to temperature changes, and more.
2. The key concepts covered include the definitions of ice point, steam point, and their use in determining temperature, the relationships between pressure, volume, and temperature in gases, using thermal expansion coefficients to calculate changes in dimensions, and applying these concepts to problems involving materials like steel, aluminum, and others.
3. A variety of equations are used and solved, including the ideal gas law, definitions of thermal expansion coefficients, and formulas relating changes
The document discusses several machine learning algorithms and techniques. It introduces classification, pattern recognition, clustering, association rule learning. It then covers decision trees in more detail, explaining the exact cover by 3-set problem, ID3 algorithm, CART, and C4.5 decision tree induction. Random forests are also mentioned briefly. Examples are provided to illustrate calculation of information gain and entropy measures.
This document provides an overview of linear programming models, including how to formulate an LP model, solve it, and interpret the results. It discusses key properties of LP models, such as seeking to maximize or minimize an objective function subject to constraints. The document then presents an example LP model for a product mix problem at Flair Furniture Company, showing how to set up the decision variables, objective function, and constraints to maximize profit. It also provides a graphical representation of the Flair Furniture LP model and describes the optimal solution.
(1) The document discusses several machine learning algorithms including classification, pattern recognition, clustering, and association rule mining.
(2) It then focuses on exact cover by 3-set, describing it as an NP-complete problem. Several examples are provided to illustrate exact cover by 3-set.
(3) Finally, information gain is introduced as a metric for selecting the best attributes for decision tree learning algorithms like ID3 and C4.5. Formulas are given for calculating information gain.
This document discusses an integrate-and-dump detector used in digital communications. It describes the operation of the integrate-and-dump detector, showing how it integrates the received signal plus noise over each symbol interval. The output of the integrator is used to detect whether a 1 or 0 was transmitted. An expression is derived for the probability of detection error in terms of the signal amplitude, noise power spectral density, and symbol interval. An example is also provided to calculate the error probability for a given binary signaling scheme and system parameters.
1. The document discusses the concept of tangent lines and slope. It provides 5 examples of calculating the slope of a function at different points to derive the equation of the tangent line.
2. The slopes are calculated by taking the limit as h approaches 0 of the change in y over the change in x.
3. The slopes found were 2, 0, -1/2, 4, and 1/2, leading to tangent lines of y=2x-3, y=-2, y=-x/2+1, y=4x+2, and y=x/2+1 respectively.
This document discusses the bullwhip effect in supply chain management. It begins by defining the bullwhip effect as increased variability in orders placed by retailers and distributors compared to actual consumer demand. The document then provides explanations for why the bullwhip effect occurs, including demand forecasting, lead times, batch ordering, price variability, and supply allocation. It also quantifies the bullwhip effect mathematically using models of demand, ordering quantities, inventory, and variance. Finally, it discusses how to cope with the bullwhip effect by reducing demand uncertainty, lead times, batch sizes, and improving information sharing.
Sequential Selection of Correlated Ads by POMDPsShuai Yuan
This document summarizes a research paper that proposes a framework for sequential ad selection to optimize online advertising revenue. It formulates the problem as a partially observable Markov decision process (POMDP) and provides exact and approximate solutions. The paper evaluates the proposed approach on a public dataset and finds that considering dependencies between ads and using a POMDP formulation outperforms baseline methods like selecting ads randomly or based only on immediate reward.
1. This document provides 20 multi-step physics problems involving heat, temperature, thermal expansion, and their relationships. The problems cover concepts such as calculating temperature from pressure measurements, determining pressure from known temperature, calculating changes in length of materials due to temperature changes, and more.
2. The key concepts covered include the definitions of ice point, steam point, and their use in determining temperature, the relationships between pressure, volume, and temperature in gases, using thermal expansion coefficients to calculate changes in dimensions, and applying these concepts to problems involving materials like steel, aluminum, and others.
3. A variety of equations are used and solved, including the ideal gas law, definitions of thermal expansion coefficients, and formulas relating changes
The document discusses several machine learning algorithms and techniques. It introduces classification, pattern recognition, clustering, association rule learning. It then covers decision trees in more detail, explaining the exact cover by 3-set problem, ID3 algorithm, CART, and C4.5 decision tree induction. Random forests are also mentioned briefly. Examples are provided to illustrate calculation of information gain and entropy measures.
This document provides an overview of linear programming models, including how to formulate an LP model, solve it, and interpret the results. It discusses key properties of LP models, such as seeking to maximize or minimize an objective function subject to constraints. The document then presents an example LP model for a product mix problem at Flair Furniture Company, showing how to set up the decision variables, objective function, and constraints to maximize profit. It also provides a graphical representation of the Flair Furniture LP model and describes the optimal solution.
(1) The document discusses several machine learning algorithms including classification, pattern recognition, clustering, and association rule mining.
(2) It then focuses on exact cover by 3-set, describing it as an NP-complete problem. Several examples are provided to illustrate exact cover by 3-set.
(3) Finally, information gain is introduced as a metric for selecting the best attributes for decision tree learning algorithms like ID3 and C4.5. Formulas are given for calculating information gain.
This document discusses an integrate-and-dump detector used in digital communications. It describes the operation of the integrate-and-dump detector, showing how it integrates the received signal plus noise over each symbol interval. The output of the integrator is used to detect whether a 1 or 0 was transmitted. An expression is derived for the probability of detection error in terms of the signal amplitude, noise power spectral density, and symbol interval. An example is also provided to calculate the error probability for a given binary signaling scheme and system parameters.
1. The document discusses the concept of tangent lines and slope. It provides 5 examples of calculating the slope of a function at different points to derive the equation of the tangent line.
2. The slopes are calculated by taking the limit as h approaches 0 of the change in y over the change in x.
3. The slopes found were 2, 0, -1/2, 4, and 1/2, leading to tangent lines of y=2x-3, y=-2, y=-x/2+1, y=4x+2, and y=x/2+1 respectively.
This document discusses the bullwhip effect in supply chain management. It begins by defining the bullwhip effect as increased variability in orders placed by retailers and distributors compared to actual consumer demand. The document then provides explanations for why the bullwhip effect occurs, including demand forecasting, lead times, batch ordering, price variability, and supply allocation. It also quantifies the bullwhip effect mathematically using models of demand, ordering quantities, inventory, and variance. The document concludes by noting how the bullwhip effect can be magnified by longer lead times and smaller forecast windows, and reduced by positive demand correlation.
This document provides formulas and definitions for operations management concepts related to inventory management, forecasting, aggregate production planning, material requirements planning, scheduling, quality management, statistical process control, and service levels. Key formulas include economic order quantity, economic production quantity, safety stock calculation, various forecasting methods, inventory balance equation for aggregate production planning, net requirements calculation for MRP, job flow time and makespan for scheduling, quality indices, control charts, and service level determination based on z-values.
1. The document discusses operations that can be performed on continuous-time signals, including time reversal, time shifting, amplitude scaling, addition, multiplication, and time scaling.
2. It provides examples of each operation using the unit step function u(t) and illustrates the effect graphically. Combinations of operations are also demonstrated through examples.
3. Key operations include time shifting which delays a signal, time scaling which speeds up or slows down a signal, and their combination which first performs one operation and then the other.
1. The document discusses operations that can be performed on continuous-time signals, including time reversal, time shifting, amplitude scaling, addition, multiplication, and time scaling.
2. It provides examples of each operation using the unit step function u(t) and illustrates the effect graphically. Combinations of operations are also demonstrated through examples.
3. Key operations include time shifting which delays a signal, time scaling which speeds up or slows down a signal, and their combination which first performs one operation and then the other.
An Inventory Management System for Deteriorating Items with Ramp Type and Qua...ijsc
The present paper deals with an inventory management system with ramp type and quadratic demand rates. A constant deterioration rate is considered into the model. In the two types models, the optimum time and total cost are derived when demand is ramp type and quadratic. A structural comparative study is demonstrated here by illustrating the model with sensitivity analysis.
The document discusses time series decomposition and smoothing. It introduces the components of time series patterns including trends, cycles, and seasonality. Time series can be decomposed into trend, seasonal, and remainder components using either additive or multiplicative models. Simple exponential smoothing is presented as a method to smooth time series data by applying weighted averages with weights that decrease exponentially. Autocorrelation and differencing methods are discussed to make time series stationary for ARIMA modeling.
Dan Towner of ACCU Bristol & Bath, presenting at the Bristol IT MegaMeet 2013
This talk aims to demystify the clever parts of compilers that nobody ever told you about, explaining their inner secrets in simple terms. Come along to find out what induction variables do, what software pipelining is, how vectorisation works, how code scheduling is done, and how the debugger makes sense of it all.
See the video of the presentation here: http://www.youtube.com/watch?v=aeyf6wfxbL4
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document provides an introduction to signals and systems. It begins by classifying different types of signals as continuous-time/discrete-time, analog/digital, deterministic/random, periodic/aperiodic, power/energy. It then discusses representations of signals in the time and frequency domains, including the Fourier series representation of periodic signals. Key concepts covered include the unit step, rectangular, triangular and sinc functions, as well as signal operations like time shifting, scaling and inversion. The document concludes by introducing Parseval's theorem relating the power of a signal to the power of its Fourier coefficients.
This document provides an introduction to signals and systems. It begins by classifying different types of signals as continuous-time/discrete-time, analog/digital, deterministic/random, periodic/aperiodic, power/energy. It then discusses representations of signals in the time and frequency domains, including the Fourier series representation of periodic signals. Key concepts covered include the unit step, rectangular, triangular and sinc functions, as well as signal operations like time shifting, scaling and inversion. The document concludes by introducing Parseval's theorem relating the power of a signal to the power of its Fourier coefficients.
The document describes a two tank heating process and provides information about the tank properties. It then asks several questions:
1) Develop block diagrams relating outlet temperatures to inlet temperatures and heat flow rates. Expressions for the Laplace transforms of the outlet temperatures T1(s) and T2(s) are obtained.
2) Determine the expression for T2'(s) and use it to find T2(2) and T2(∞).
3) For a system with heat transfer between two nested tanks, an expression is obtained for the Laplace transform of the inner tank temperature T1(s). This is inverted to find T1(0), T1(5), T1(10
This document discusses key challenges for high volume thermo-compression bonding (TCB) in three-dimensional integrated circuits (3D ICs) with through-silicon vias (TSVs). It identifies that capabilities, tool-to-tool repeatability, and monitoring and alarming are crucial factors for high volume TCB production. It presents methods to reduce bond control complexity, improve thermal repeatability, and verify tool-to-tool repeatability through metrics. Integrated monitoring and alarming requires portable alarm windows which depend on consistent tool repeatability.
Reducing the time of heuristic algorithms for the Symmetric TSPgpolo
The document discusses improving the time efficiency of greedy heuristic algorithms for the symmetric traveling salesman problem (STSP). It presents a naive greedy nearest neighbor algorithm and identifies problems with its runtime. An improved algorithm is proposed using a k-d tree data structure to efficiently search for nearest neighbors. Testing on TSPLIB instances shows the improved algorithm provides speedups of over 100 times compared to the naive approach.
This document discusses uncertainty propagation techniques for determining statistics of model outputs given uncertain model inputs. It covers analytic approaches for linear models, perturbation methods for nonlinear models, and direct sampling methods. It also discusses computing moments using stochastic spectral methods like stochastic Galerkin with polynomial chaos. The document provides an example of applying perturbation and sampling methods to a nonlinear oscillator model with uncertain parameters. It compares the results from both approaches to the true natural frequency. Finally, it discusses uncertainty quantification for a HIV model and the use of prediction intervals in nuclear power plant design.
This document discusses the history and development of predictive functional control (PFC). It begins by discussing how Jean Piaget's work on cognitive psychology and learning influenced the concepts of internal models, reference trajectories, and error compensation, which are key principles of PFC. It then states that PFC is not an invention but a discovery based on these natural principles of control. The document provides several examples of implementing PFC to control different industrial processes like reactors, casting, and pumping. It emphasizes that PFC allows transparent control that transfers constraints to help control complex nonlinear systems.
The document discusses equations for open channel flow and methods to calculate parameters for different channel cross section shapes including trapezoidal, circular, parabolic, and natural channels. It provides equations for area, perimeter, and their derivatives with respect to depth (y) for each cross section shape. Methods using the Newton-Raphson technique are presented to calculate the normal depth (y) given inputs of discharge (q), channel properties, slope and Manning's roughness coefficient. Outputs from the methods include normal depth, area, wetted perimeter, hydraulic radius, mean depth, mean velocity, energy, Froude number and more. Python code implementing the Newton-Raphson method for each cross section shape is also presented.
The document describes a simulation of a PMSM motor control system for electric power steering controllers. It includes:
1) A system block diagram showing the main components of an EPS system including a PMSM motor, steering mechanism, and EPS control unit.
2) Simulink models of the key system elements - the PMSM motor, position sensor, current sensing, PI controller, and inverse Park and space vector modulation models.
3) Simulation and experimental results showing the effects of position sensor resolution and current sensing errors on torque ripple, and validating the simulated d-axis step response with experimental measurements.
4) A conclusion that the complete PMSM drive model and experimental validation can
This document discusses key concepts relating to displacement, velocity, and acceleration. It provides formulas for calculating these values, including:
Displacement (x) is distance in a certain direction. Velocity (v) is the change in displacement over time. Acceleration (a) is the change in velocity over time. Examples are given to demonstrate calculating displacement, velocity, and acceleration at various time intervals using position-time data. The document also shows setting velocity equal to zero to find the time when an object momentarily stops moving.
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
This document discusses the bullwhip effect in supply chain management. It begins by defining the bullwhip effect as increased variability in orders placed by retailers and distributors compared to actual consumer demand. The document then provides explanations for why the bullwhip effect occurs, including demand forecasting, lead times, batch ordering, price variability, and supply allocation. It also quantifies the bullwhip effect mathematically using models of demand, ordering quantities, inventory, and variance. The document concludes by noting how the bullwhip effect can be magnified by longer lead times and smaller forecast windows, and reduced by positive demand correlation.
This document provides formulas and definitions for operations management concepts related to inventory management, forecasting, aggregate production planning, material requirements planning, scheduling, quality management, statistical process control, and service levels. Key formulas include economic order quantity, economic production quantity, safety stock calculation, various forecasting methods, inventory balance equation for aggregate production planning, net requirements calculation for MRP, job flow time and makespan for scheduling, quality indices, control charts, and service level determination based on z-values.
1. The document discusses operations that can be performed on continuous-time signals, including time reversal, time shifting, amplitude scaling, addition, multiplication, and time scaling.
2. It provides examples of each operation using the unit step function u(t) and illustrates the effect graphically. Combinations of operations are also demonstrated through examples.
3. Key operations include time shifting which delays a signal, time scaling which speeds up or slows down a signal, and their combination which first performs one operation and then the other.
1. The document discusses operations that can be performed on continuous-time signals, including time reversal, time shifting, amplitude scaling, addition, multiplication, and time scaling.
2. It provides examples of each operation using the unit step function u(t) and illustrates the effect graphically. Combinations of operations are also demonstrated through examples.
3. Key operations include time shifting which delays a signal, time scaling which speeds up or slows down a signal, and their combination which first performs one operation and then the other.
An Inventory Management System for Deteriorating Items with Ramp Type and Qua...ijsc
The present paper deals with an inventory management system with ramp type and quadratic demand rates. A constant deterioration rate is considered into the model. In the two types models, the optimum time and total cost are derived when demand is ramp type and quadratic. A structural comparative study is demonstrated here by illustrating the model with sensitivity analysis.
The document discusses time series decomposition and smoothing. It introduces the components of time series patterns including trends, cycles, and seasonality. Time series can be decomposed into trend, seasonal, and remainder components using either additive or multiplicative models. Simple exponential smoothing is presented as a method to smooth time series data by applying weighted averages with weights that decrease exponentially. Autocorrelation and differencing methods are discussed to make time series stationary for ARIMA modeling.
Dan Towner of ACCU Bristol & Bath, presenting at the Bristol IT MegaMeet 2013
This talk aims to demystify the clever parts of compilers that nobody ever told you about, explaining their inner secrets in simple terms. Come along to find out what induction variables do, what software pipelining is, how vectorisation works, how code scheduling is done, and how the debugger makes sense of it all.
See the video of the presentation here: http://www.youtube.com/watch?v=aeyf6wfxbL4
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document provides an introduction to signals and systems. It begins by classifying different types of signals as continuous-time/discrete-time, analog/digital, deterministic/random, periodic/aperiodic, power/energy. It then discusses representations of signals in the time and frequency domains, including the Fourier series representation of periodic signals. Key concepts covered include the unit step, rectangular, triangular and sinc functions, as well as signal operations like time shifting, scaling and inversion. The document concludes by introducing Parseval's theorem relating the power of a signal to the power of its Fourier coefficients.
This document provides an introduction to signals and systems. It begins by classifying different types of signals as continuous-time/discrete-time, analog/digital, deterministic/random, periodic/aperiodic, power/energy. It then discusses representations of signals in the time and frequency domains, including the Fourier series representation of periodic signals. Key concepts covered include the unit step, rectangular, triangular and sinc functions, as well as signal operations like time shifting, scaling and inversion. The document concludes by introducing Parseval's theorem relating the power of a signal to the power of its Fourier coefficients.
The document describes a two tank heating process and provides information about the tank properties. It then asks several questions:
1) Develop block diagrams relating outlet temperatures to inlet temperatures and heat flow rates. Expressions for the Laplace transforms of the outlet temperatures T1(s) and T2(s) are obtained.
2) Determine the expression for T2'(s) and use it to find T2(2) and T2(∞).
3) For a system with heat transfer between two nested tanks, an expression is obtained for the Laplace transform of the inner tank temperature T1(s). This is inverted to find T1(0), T1(5), T1(10
This document discusses key challenges for high volume thermo-compression bonding (TCB) in three-dimensional integrated circuits (3D ICs) with through-silicon vias (TSVs). It identifies that capabilities, tool-to-tool repeatability, and monitoring and alarming are crucial factors for high volume TCB production. It presents methods to reduce bond control complexity, improve thermal repeatability, and verify tool-to-tool repeatability through metrics. Integrated monitoring and alarming requires portable alarm windows which depend on consistent tool repeatability.
Reducing the time of heuristic algorithms for the Symmetric TSPgpolo
The document discusses improving the time efficiency of greedy heuristic algorithms for the symmetric traveling salesman problem (STSP). It presents a naive greedy nearest neighbor algorithm and identifies problems with its runtime. An improved algorithm is proposed using a k-d tree data structure to efficiently search for nearest neighbors. Testing on TSPLIB instances shows the improved algorithm provides speedups of over 100 times compared to the naive approach.
This document discusses uncertainty propagation techniques for determining statistics of model outputs given uncertain model inputs. It covers analytic approaches for linear models, perturbation methods for nonlinear models, and direct sampling methods. It also discusses computing moments using stochastic spectral methods like stochastic Galerkin with polynomial chaos. The document provides an example of applying perturbation and sampling methods to a nonlinear oscillator model with uncertain parameters. It compares the results from both approaches to the true natural frequency. Finally, it discusses uncertainty quantification for a HIV model and the use of prediction intervals in nuclear power plant design.
This document discusses the history and development of predictive functional control (PFC). It begins by discussing how Jean Piaget's work on cognitive psychology and learning influenced the concepts of internal models, reference trajectories, and error compensation, which are key principles of PFC. It then states that PFC is not an invention but a discovery based on these natural principles of control. The document provides several examples of implementing PFC to control different industrial processes like reactors, casting, and pumping. It emphasizes that PFC allows transparent control that transfers constraints to help control complex nonlinear systems.
The document discusses equations for open channel flow and methods to calculate parameters for different channel cross section shapes including trapezoidal, circular, parabolic, and natural channels. It provides equations for area, perimeter, and their derivatives with respect to depth (y) for each cross section shape. Methods using the Newton-Raphson technique are presented to calculate the normal depth (y) given inputs of discharge (q), channel properties, slope and Manning's roughness coefficient. Outputs from the methods include normal depth, area, wetted perimeter, hydraulic radius, mean depth, mean velocity, energy, Froude number and more. Python code implementing the Newton-Raphson method for each cross section shape is also presented.
The document describes a simulation of a PMSM motor control system for electric power steering controllers. It includes:
1) A system block diagram showing the main components of an EPS system including a PMSM motor, steering mechanism, and EPS control unit.
2) Simulink models of the key system elements - the PMSM motor, position sensor, current sensing, PI controller, and inverse Park and space vector modulation models.
3) Simulation and experimental results showing the effects of position sensor resolution and current sensing errors on torque ripple, and validating the simulated d-axis step response with experimental measurements.
4) A conclusion that the complete PMSM drive model and experimental validation can
This document discusses key concepts relating to displacement, velocity, and acceleration. It provides formulas for calculating these values, including:
Displacement (x) is distance in a certain direction. Velocity (v) is the change in displacement over time. Acceleration (a) is the change in velocity over time. Examples are given to demonstrate calculating displacement, velocity, and acceleration at various time intervals using position-time data. The document also shows setting velocity equal to zero to find the time when an object momentarily stops moving.
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
3 Simple Steps To Buy Verified Payoneer Account In 2024SEOSMMEARTH
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The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
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Building Your Employer Brand with Social MediaLuanWise
Presented at The Global HR Summit, 6th June 2024
In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement – helping to position your organization as an employer of choice in today's competitive talent landscape.
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challengesHolger Mueller
Holger Mueller of Constellation Research shares his key takeaways from SAP's Sapphire confernece, held in Orlando, June 3rd till 5th 2024, in the Orange Convention Center.
Structural Design Process: Step-by-Step Guide for BuildingsChandresh Chudasama
The structural design process is explained: Follow our step-by-step guide to understand building design intricacies and ensure structural integrity. Learn how to build wonderful buildings with the help of our detailed information. Learn how to create structures with durability and reliability and also gain insights on ways of managing structures.
Digital Marketing with a Focus on Sustainabilitysssourabhsharma
Digital Marketing best practices including influencer marketing, content creators, and omnichannel marketing for Sustainable Brands at the Sustainable Cosmetics Summit 2024 in New York
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
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Zodiac Signs and Food Preferences_ What Your Sign Says About Your Tastemy Pandit
Know what your zodiac sign says about your taste in food! Explore how the 12 zodiac signs influence your culinary preferences with insights from MyPandit. Dive into astrology and flavors!
How to Implement a Real Estate CRM SoftwareSalesTown
To implement a CRM for real estate, set clear goals, choose a CRM with key real estate features, and customize it to your needs. Migrate your data, train your team, and use automation to save time. Monitor performance, ensure data security, and use the CRM to enhance marketing. Regularly check its effectiveness to improve your business.
1. Bullwhip Effect and Risk Pooling
Tokyo University of
Marine Science and Technology
Mikio Kubo
2. Bullwhip effect
• Key concept for understanding the SCM
• Procter & Gamble noticed an interesting
phenomenon that retail sales of the
product were fairly uniform, but
distributors’ orders placed to the factory
fluctuated much more than retail sales.
3. Why the bullwhip effect occurs?
1. Demand Forecasting
• One day, the manager of a retailer observed a
larger demand (sales) than expected.
• He increased the inventory level because he
expected more demand in the future (forecasting).
• The manager of his wholesaler observed more
demand (some of which are not actual demand)
than usual and increased his inventory.
• This caused more (non-real) demand to his maker;
the manager of the maker increased his inventory,
and so on. This is the basic reason of the bull
whip effect.
4. Why the bullwhip effect occurs?
2. Lead time
• With longer lead times, a small change
in the estimate of demand variability
implies a significant change in safety
stock, reorder level, and thus in order
quantities.
• Thus a longer lead time leads to an
increase in variability and the bull whip
effect.
5. Why the bullwhip effect occurs?
3. Batch Ordering
• When using a min-max inventory policy, then
the wholesaler will observe a large order,
followed by several periods of no orders,
followed by another large order, and so on.
• The wholesaler sees a distorted and highly
variable pattern of orders.
• Thus, batch ordering increases the bull whip
effect.
6. Why the bullwhip effect occurs?
4. Variability of Price
• Retailers (or wholesalers or makers)
offer promotions and discounts at
certain times or for certain quantities.
• Retailers (or customers) often attempt
to stock up when prices are lower.
• It increases the variability of demands and
the bull whip effect.
7. Why the bullwhip effect occurs?
5. Lack of supply and supply
allocation
• When retailers suspect that a product
will be in short supply, and therefore
anticipate receiving supply proportional
to the amount ordered (supply
allocation).
• When the period of shortage is over,
the retailer goes back to its standard
orders, leading to all kinds of distortions
8. Quantifying the Bullwhip
Effect
One stage model
For each period t=1,2…, let
Retailer Customer
Ordering
quantity q[t] Inventory I[t] Demand D[t]
9. Discrete time model
(Periodic ordering system)
Lead time L
Items ordered at the end of period t will arrive at the
beginning of period t+L+1.
2)
Demand
D[t]
occurs
t t+1 t+2 t+3 t+4
1) Arrive the 3) Forecast demand F[t+1]
items ordered 4) Order q[t] Arrive the items
in period t-L-1 in period t+L+1 ( L=3)
10. Demand process
• d: a constant term of the demand process
• ρ: a parameter that represents the correlation
between two consecutive periods ρ 1 < ρ < 1)
(−
• ε t = 1,2, ) : An error parameter in period t; it
(t
has an independent distribution with mean 0 and
standard deviation σ
• Dt: the demand in period t
Dt = d + ρDt −1 + ε t
12. Ordering quantity q[t]
• Forecasting ( p period moving average )
p
∑D
j =1
t− j
ˆ
dt =
p
ˆ
We denote d t and Dt by F [t ] and D[t ], respectively.
• Ordering quantity q[t] of period t is:
q[t]=D[t]+L (F[t+1]-F[t]) ,t=1,2,…
16. Asymptotic analysis:
expectation,variance, and Covariance)
d
E ( D[t ]) = By solving E[D]=d+ρE[D]
1− ρ
σ 2
Var ( D[t ]) = By solving
1− ρ 2 Var[D]=ρ2 Var[D]+σ2
ρ σ
p 2
Cov ( D[t ], D[t − p ]) =
1− ρ 2
17. Expansion of ordering quantity
q[t ] = D[t ] + LF [t + 1] − LF [t ]
p p
L ∑ D[t + 1 − j ] L ∑ D[t − j ]
j =1 j =1
= D[t ] + −
p p
L L
= (1 + ) D[t ] − D[t − p ]
p p
18. Variance of ordering quantity
L 2 L 2
Var ( q[t ]) = (1 + ) Var ( D[t ]) + ( ) Var ( D[t − p ])
p p
L L
− 2(1 + )( )Cov ( D[t ], D[t − p ])
p p
2 L 2 L2
= p + p 2 (1 − ρ ) Var ( D[t ])
1 +
2
Var ( q[t ]) 2 L 2 L2
=1+
p + 2 (1 − ρ ) 2
Var ( D[t ]) p
19. Observations
Var (q[t ]) 2 L 2 L2
= 1+ + 2 (1 − ρ ) 2
Var ( D[t ]) p p
• When p is large, and L is small, the bullwhip
effect due to forecasting error is negligible.
• The bullwhip effect is magnified as we increase
the lead time and decrease p.
• A positive correlation DECRESES the bull
whip effect.
20. Coping with the Bullwhip Effect
1. Demand uncertainty
• Adjust the forecasting parameters, e.g.,
larger p for the moving average method.
• Centralizing demand information; by
providing each stage of the supply
chain with complete information on
actual customer demand (POS: Point-Of-
Sales data )
• Continuous replenishment
• VMI ( Vender Managed Inventory:
VMI )
21. Coping with the Bullwhip Effect
2. Lead time
• Lead time reduction
• Information lead time can be reduced ujsing
EDI ( Electric Data Interchange ) or
CAO ( Computer Assisted Ordering ) .
• QR ( Quick Response ) in apparel
industry
22. Coping with the Bullwhip Effect
3. Batch ordering
• Reduction of fixed ordering cost using EDI
and CAO
• 3PL ( Third Party Logistics )
• VMI
23. Coping with the Bullwhip Effect
4. Variability of Price
• EDLP: Every Day Low Price ( P&G )
• Remark that the same strategy does not
work well in Japan.
24. Coping with the Bullwhip Effect
5. Lack of supply and supply
allocation
• Allocate the lacking demand due to sales
volume and/or market share instead of order
volume. ( General Motors , Saturn,
Hewlett-Packard )
• Share the inventory and production
information of makers with retailers and
wholesalers. ( Hewlett-
Packard , Motorola )