Reducing Structural Bias in Technology Mappingsatrajit
The document discusses techniques to reduce structural bias in technology mapping. It proposes using supergates, which combine multiple library gates, to allow matches that intermediate points not present in the original circuit. It also describes performing lossless synthesis to merge equivalent networks and add choice nodes. Experimental results show the combined approach of supergates and lossless synthesis improves delay and area over the baseline.
1. The email discusses improving inventory management of slow moving products that make up 30% of inventory but less than 1% of sales per period.
2. It provides three ideas to review: re-forecasting too often for low demand products, failing to identify seasonal or market trends, and using the wrong data for forecasting regular demand.
3. Improving demand forecasting through accounting for different types of demand like regular, promotional, and closeout sales can help reduce out-of-stock issues compared to just sales forecasting.
Forecast NOW! прогнозирование спроса и управление запасамиAlexander Gritsay
Оптимизируем складские запасы и автоматизируем расчет заказов торговых компаний
Сокращаем дефицит и излишние запасы, повышаем прибыль торговых компаний с размерами от одного склада до федеральных дистрибьюторских сетей.
Система управления запасами Forecast NOW!Макс Раевский
Forecast NOW! - это система управления запасами, которая предназначена для розничных и оптовых торговых предприятий, а также аутсорсинговых компаний в сфере управления складскими запасами.
www.fnow.ru
Detecting logged in user's abnormal activityArvids Godjuks
Detection of abnormal user's activity is currently not performed in most popular Intrusion Detection Systems (IDS). However, it's not so rare when one user credentials are used by another user (for example, when password was stolen or watched). Also there are more and more sensitive data available through Internet.
To prevent this type of attacks we've developed an algorithm of building preferences based user behavior model.
It is using Markov chains to represent user behavioral information. For the time being, an experimental system that allows to analyze such method efficiency and detect irregular access to medical data is under development.
Since systems protected are a set of webservices, popular open source tools such as PHP, MySQL, GraphML, and Flare were used to implent it.
Reducing Structural Bias in Technology Mappingsatrajit
The document discusses techniques to reduce structural bias in technology mapping. It proposes using supergates, which combine multiple library gates, to allow matches that intermediate points not present in the original circuit. It also describes performing lossless synthesis to merge equivalent networks and add choice nodes. Experimental results show the combined approach of supergates and lossless synthesis improves delay and area over the baseline.
1. The email discusses improving inventory management of slow moving products that make up 30% of inventory but less than 1% of sales per period.
2. It provides three ideas to review: re-forecasting too often for low demand products, failing to identify seasonal or market trends, and using the wrong data for forecasting regular demand.
3. Improving demand forecasting through accounting for different types of demand like regular, promotional, and closeout sales can help reduce out-of-stock issues compared to just sales forecasting.
Forecast NOW! прогнозирование спроса и управление запасамиAlexander Gritsay
Оптимизируем складские запасы и автоматизируем расчет заказов торговых компаний
Сокращаем дефицит и излишние запасы, повышаем прибыль торговых компаний с размерами от одного склада до федеральных дистрибьюторских сетей.
Система управления запасами Forecast NOW!Макс Раевский
Forecast NOW! - это система управления запасами, которая предназначена для розничных и оптовых торговых предприятий, а также аутсорсинговых компаний в сфере управления складскими запасами.
www.fnow.ru
Detecting logged in user's abnormal activityArvids Godjuks
Detection of abnormal user's activity is currently not performed in most popular Intrusion Detection Systems (IDS). However, it's not so rare when one user credentials are used by another user (for example, when password was stolen or watched). Also there are more and more sensitive data available through Internet.
To prevent this type of attacks we've developed an algorithm of building preferences based user behavior model.
It is using Markov chains to represent user behavioral information. For the time being, an experimental system that allows to analyze such method efficiency and detect irregular access to medical data is under development.
Since systems protected are a set of webservices, popular open source tools such as PHP, MySQL, GraphML, and Flare were used to implent it.
Trend, myths and realities in logistics in russiaSerge Rivet
2008 April 29th , Marriott Tverskaya Hotel,
Moscow
Conference: “Warehouse Property development / Logistics Real Estate",
organized by LBS International
Presentation IN RUSSIAN
FDA 2013 Clinical Investigator Training Course: The Analysis of Investigator ...MedicReS
FDA 2013 Clinical Investigator Training Course: The Analysis of Investigator Data, Sources of Bias and Error
Susan Ellenberg, Ph.D.,(University of Pennsylvania)
Маркетинговое исследование "Продуктовый ритейл 2015"scoutmr
Продуктовый ритейл 2015: результаты интернет-опроса компании "Скаут Маркетинг". Июнь 2015 г.
Компания «Скаут Маркетинг» работает на рынке маркетинговых исследований с 2006 года. На данный момент приоритетными направлениями развития нашей компании являются исследования «Тайный покупатель», разработка стандартов обслуживания, проведение опросов, конкурентная разведка, анализ лояльности клиентов, брендинг.
Адрес: 105043, Российская Федерация, г.Москва, Измайловский проспект, дом 75/1, м. Измайловская
+7 (495) 646-63-26
Электронная почта: info@scoutmr.ru
Skype: scoutmr
Сайт: www.scoutmr.ru
View Related videos:-
Truth about Supply Demand Planning:-
http://www.youtube.com/watch?v=K66q2o1ED3c
Demantra Vs Oracle Demand Planning
http://www.youtube.com/watch?v=QwAzP3T6ut4
Another slideshare PPT:-
http://www.slideshare.net/amitforu78/demantra-vs-oracle-demand-planning
Contact me at www.ezdia.com
<a>AsiaLinks</a>
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
This document provides an overview of Demantra Demand Planning capabilities and the standard demand planning process using Demantra. It discusses how Demantra can help solve common demand planning problems by providing statistical forecasting, handling multiple demand signals and hierarchies, and enabling collaboration. It then outlines the standard process which involves automatically downloading customer demand forecasts, generating statistical forecasts, approving and finalizing forecasts, and loading the final forecast into ASCP for supply planning.
This document discusses different types of error and bias that can occur in epidemiological studies. It defines random error as occurring due to chance and resulting in imprecise measures, while systematic error or bias results in invalid measures that are not true. Types of bias discussed include selection bias, information bias, and confounding. Selection bias can arise from how cases and controls are selected, while information bias occurs when exposure or disease status is incorrectly classified. The document emphasizes the importance of reducing both random and systematic errors to obtain valid study results.
This document summarizes the outline for a supply chain management course. The course will cover topics such as strategic fit and scope, demand management, aggregate planning, network operations, inventory management, sourcing, transportation, and information technology. Students will be graded based on homework assignments, a beer game simulation, a midterm exam, and a final exam. Homework must be completed on time or grades will be reduced. Classes will be held twice a week from 6-8pm.
1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
2015 12-05 Александр Шиповалов - Инструмент для тестирования Sikuli scriptHappyDev
This document provides an overview of SikuliX, an automation tool that uses image recognition to control and interact with graphical user interfaces. It describes the main classes in SikuliX including App, Region, Screen, Offset, Math, Similarity, and Pattern. Methods for these classes are also outlined for performing actions like opening applications, finding regions on the screen, mouse and keyboard input, and image matching.
Trend, myths and realities in logistics in russiaSerge Rivet
2008 April 29th , Marriott Tverskaya Hotel,
Moscow
Conference: “Warehouse Property development / Logistics Real Estate",
organized by LBS International
Presentation IN RUSSIAN
FDA 2013 Clinical Investigator Training Course: The Analysis of Investigator ...MedicReS
FDA 2013 Clinical Investigator Training Course: The Analysis of Investigator Data, Sources of Bias and Error
Susan Ellenberg, Ph.D.,(University of Pennsylvania)
Маркетинговое исследование "Продуктовый ритейл 2015"scoutmr
Продуктовый ритейл 2015: результаты интернет-опроса компании "Скаут Маркетинг". Июнь 2015 г.
Компания «Скаут Маркетинг» работает на рынке маркетинговых исследований с 2006 года. На данный момент приоритетными направлениями развития нашей компании являются исследования «Тайный покупатель», разработка стандартов обслуживания, проведение опросов, конкурентная разведка, анализ лояльности клиентов, брендинг.
Адрес: 105043, Российская Федерация, г.Москва, Измайловский проспект, дом 75/1, м. Измайловская
+7 (495) 646-63-26
Электронная почта: info@scoutmr.ru
Skype: scoutmr
Сайт: www.scoutmr.ru
View Related videos:-
Truth about Supply Demand Planning:-
http://www.youtube.com/watch?v=K66q2o1ED3c
Demantra Vs Oracle Demand Planning
http://www.youtube.com/watch?v=QwAzP3T6ut4
Another slideshare PPT:-
http://www.slideshare.net/amitforu78/demantra-vs-oracle-demand-planning
Contact me at www.ezdia.com
<a>AsiaLinks</a>
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
This document provides an overview of Demantra Demand Planning capabilities and the standard demand planning process using Demantra. It discusses how Demantra can help solve common demand planning problems by providing statistical forecasting, handling multiple demand signals and hierarchies, and enabling collaboration. It then outlines the standard process which involves automatically downloading customer demand forecasts, generating statistical forecasts, approving and finalizing forecasts, and loading the final forecast into ASCP for supply planning.
This document discusses different types of error and bias that can occur in epidemiological studies. It defines random error as occurring due to chance and resulting in imprecise measures, while systematic error or bias results in invalid measures that are not true. Types of bias discussed include selection bias, information bias, and confounding. Selection bias can arise from how cases and controls are selected, while information bias occurs when exposure or disease status is incorrectly classified. The document emphasizes the importance of reducing both random and systematic errors to obtain valid study results.
This document summarizes the outline for a supply chain management course. The course will cover topics such as strategic fit and scope, demand management, aggregate planning, network operations, inventory management, sourcing, transportation, and information technology. Students will be graded based on homework assignments, a beer game simulation, a midterm exam, and a final exam. Homework must be completed on time or grades will be reduced. Classes will be held twice a week from 6-8pm.
1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
2015 12-05 Александр Шиповалов - Инструмент для тестирования Sikuli scriptHappyDev
This document provides an overview of SikuliX, an automation tool that uses image recognition to control and interact with graphical user interfaces. It describes the main classes in SikuliX including App, Region, Screen, Offset, Math, Similarity, and Pattern. Methods for these classes are also outlined for performing actions like opening applications, finding regions on the screen, mouse and keyboard input, and image matching.
2015-12-06 Артем Зиненко - Что делать, если браузеры клиентов действуют проти...HappyDev
This document discusses common browser vulnerabilities that can allow hackers to access user data. It covers topics like cross-site scripting (XSS), cross-site request forgery (CSRF), unvalidated redirects, clickjacking, and cross-origin resource sharing (CORS) configuration issues. The document provides examples of how these vulnerabilities can be exploited, such as hijacking user sessions after login or changing user account details without consent. Proper validation of user input and access controls are necessary to prevent unauthorized access to user data and accounts.