سيستم جامع مديريت توليد MTS در برگيرنده فعاليتهاي اصلي زنجيره توليد محصول و مديريت كارخانه بوده و در ارتباط با زير سيستمهاي حوزه مالي و بازرگاني به عنوان يك راه حل جامع سازماني مي تواند به عنوان ابزاري قدرتمند در خدمت صنايع توليدي قرار گيرد
اين سيستم شامل زير سيستمها و ماژول هاي ذيل مي باشد :
1. مهندسي ساخت يا محصول (تكنولوژي ساخت ) (Manufacturing Engineering)
2. برنامه ريزي و كنترل توليد (Production Planning & Control)
3. ورود اطلاعات وقايع حين توليد بصورت كاملا مكانيزه و با استفاده از دستگاههاي باركد خوان صورت مي پذيرد
4. برنامه ريزي و كنترل تعهدات (Commitment Planning & Control)
5. برنامه ريزي نيازمندي مواد (Material Requirement Planning - MRP)
6. ظرفيت سنجي منابع توليد (Rough Cut Capacity Planning - RCCP)
7. مديريت موارد عدم تطابق (Non Conformity Management)
8. مديريت فروش و گارانتي محصولات (Sale & Guarantee Management)
9. كنترل مصرف مواد و ضايعات (Material Consume Control)
10. مديريت درخواست كار (Work Request Management)
11. برنامه ريزي و كنترل كاليبراسيون (Calibration Planning & Control)
12. كنترل كيفيت و پارامترهاي توليد (QC & Process Criteria)
علاوه به موارد فوق نرم افزار مورد نياز جهت جمع آوري اطلاعات از خط توليد بصورت جداگانه پياده سازي گرديد كه بر روي دستگاه هاي باركد خوان پرتابل نصب بوده و وظيفه جمع آوري ريز وقايع خط توليد را بر عهده دارد.
سيستم جامع مديريت توليد MTS در برگيرنده فعاليتهاي اصلي زنجيره توليد محصول و مديريت كارخانه بوده و در ارتباط با زير سيستمهاي حوزه مالي و بازرگاني به عنوان يك راه حل جامع سازماني مي تواند به عنوان ابزاري قدرتمند در خدمت صنايع توليدي قرار گيرد
اين سيستم شامل زير سيستمها و ماژول هاي ذيل مي باشد :
1. مهندسي ساخت يا محصول (تكنولوژي ساخت ) (Manufacturing Engineering)
2. برنامه ريزي و كنترل توليد (Production Planning & Control)
3. ورود اطلاعات وقايع حين توليد بصورت كاملا مكانيزه و با استفاده از دستگاههاي باركد خوان صورت مي پذيرد
4. برنامه ريزي و كنترل تعهدات (Commitment Planning & Control)
5. برنامه ريزي نيازمندي مواد (Material Requirement Planning - MRP)
6. ظرفيت سنجي منابع توليد (Rough Cut Capacity Planning - RCCP)
7. مديريت موارد عدم تطابق (Non Conformity Management)
8. مديريت فروش و گارانتي محصولات (Sale & Guarantee Management)
9. كنترل مصرف مواد و ضايعات (Material Consume Control)
10. مديريت درخواست كار (Work Request Management)
11. برنامه ريزي و كنترل كاليبراسيون (Calibration Planning & Control)
12. كنترل كيفيت و پارامترهاي توليد (QC & Process Criteria)
علاوه به موارد فوق نرم افزار مورد نياز جهت جمع آوري اطلاعات از خط توليد بصورت جداگانه پياده سازي گرديد كه بر روي دستگاه هاي باركد خوان پرتابل نصب بوده و وظيفه جمع آوري ريز وقايع خط توليد را بر عهده دارد.
Understanding Students' Engagement with Personalised Feedback MessagesHamideh Iraj
Understanding Students' Engagement with Personalised Feedback Messages
Honourable Mention Full Paper
The 10th International Learning Analytics and Knowledge Conference (LAK20)
The alignment of e commerce strategies with corporate strategy a case studyHamideh Iraj
The Alignment of E-Commerce Strategies with Corporate Strategy: A case study
Abstract:
In this paper, we studied six international companies and investigated their strategies and how it supports corporate strategy. Corporate strategy is articulated according to Porter's generic strategies that are commonly used by businesses to achieve and maintain competitive advantage including Cost Leadership, Differentiation and Focus. Walmart, Apple and Southwest Airlines were selected to stand for aforementioned strategies respectively. In the second section we discussed about innovation, growth and alliance strategies. Amazon, SAS and Star Alliance were selected for aforementioned strategies respectively.
Persian presentation applying knowledge based education to reach knowledge ...Hamideh Iraj
آموزش دانش بنیان برای رسیدن به اقتصاد دانش بنیان
ارائه شده در هفتمین کنفرانس ملی و اولین کنفرانس بین المللی مدیریت دانش
28 و 29 بهمن ماه 1393
مرکز همایش های بین المللی دانشگاه شهید بهشتی
Membean Word Root Of The Day Archive Numbers 1-99
For MP3 files and more information visit:
http://www.membean.com/wrotds/archive
http://feeds.feedburner.com/membean/MembeanWROTD
Persian notes four paradigms of information systems developmentHamideh Iraj
خلاصه مقاله پارادایم های حاکم بر فرایند توسعه سیستم های اطلاعاتی
هیرشهایم و کلاین
Persian Notes- Four Paradigms of Information Systems Development
Rudy Hirschheim and Heinz K. Klein
Evaluation theories conclusion -English
Main Resources:
Integrated Series in Information Systems, Volume 28, Information Systems Theory Explaining and Predicting Our Digital Society, Vol. 1, Springer; 2012 edition
Integrated Series in Information Systems, Volume 29, Information Systems Theory Explaining and Predicting Our Digital Society, Vol. 2, Springer; 2012 edition
Understanding Students' Engagement with Personalised Feedback MessagesHamideh Iraj
Understanding Students' Engagement with Personalised Feedback Messages
Honourable Mention Full Paper
The 10th International Learning Analytics and Knowledge Conference (LAK20)
The alignment of e commerce strategies with corporate strategy a case studyHamideh Iraj
The Alignment of E-Commerce Strategies with Corporate Strategy: A case study
Abstract:
In this paper, we studied six international companies and investigated their strategies and how it supports corporate strategy. Corporate strategy is articulated according to Porter's generic strategies that are commonly used by businesses to achieve and maintain competitive advantage including Cost Leadership, Differentiation and Focus. Walmart, Apple and Southwest Airlines were selected to stand for aforementioned strategies respectively. In the second section we discussed about innovation, growth and alliance strategies. Amazon, SAS and Star Alliance were selected for aforementioned strategies respectively.
Persian presentation applying knowledge based education to reach knowledge ...Hamideh Iraj
آموزش دانش بنیان برای رسیدن به اقتصاد دانش بنیان
ارائه شده در هفتمین کنفرانس ملی و اولین کنفرانس بین المللی مدیریت دانش
28 و 29 بهمن ماه 1393
مرکز همایش های بین المللی دانشگاه شهید بهشتی
Membean Word Root Of The Day Archive Numbers 1-99
For MP3 files and more information visit:
http://www.membean.com/wrotds/archive
http://feeds.feedburner.com/membean/MembeanWROTD
Persian notes four paradigms of information systems developmentHamideh Iraj
خلاصه مقاله پارادایم های حاکم بر فرایند توسعه سیستم های اطلاعاتی
هیرشهایم و کلاین
Persian Notes- Four Paradigms of Information Systems Development
Rudy Hirschheim and Heinz K. Klein
Evaluation theories conclusion -English
Main Resources:
Integrated Series in Information Systems, Volume 28, Information Systems Theory Explaining and Predicting Our Digital Society, Vol. 1, Springer; 2012 edition
Integrated Series in Information Systems, Volume 29, Information Systems Theory Explaining and Predicting Our Digital Society, Vol. 2, Springer; 2012 edition
17. ﻣﻨﺒﻊ:
آﻧﺎﻟﻴﺰ ﺣﺎﻻت ﺑﺎﻟﻘﻮه ﺧﺮاﺑﻲ و آﺛﺎر آن – ﻣﻔﺎﻫﻴﻢ و روش ﭘﻴﺎده ﺳﺎزي ﮔﺮدآوري و ﺗﺪوﻳﻦ : اﻣﻮر ﻣﻬﻨﺪﺳﻲ
ﺗﺠﺮﻳﻪ و ﺗﺤﻠﻴﻞ ﻋﻮاﻣﻞ ﺷﻜﺴﺖ و آﺛﺎر آن ﻧﺸﺮﺳﺎﭘﻜﻮ
71
18. ﺗﻮﻓﺎن ﻓﻜﺮي
ﺗﻮﻓﺎن ﻓﻜﺮي " "BRAIN STORMINGﻳﻜـﻲ از ﺷـﻨﺎﺧﺘﻪ ﺷـﺪه ﺗـﺮﻳﻦ ﺷـﻴﻮه ﻫـﺎي ﺑﺮﮔـﺰاري
ﺟﻠﺴﺎت ﻫﻢ ﻓﻜﺮي و ﻣﺸﺎوره ﺑﻮده و ﻛﺎرﺑﺮد ﺟﻬﺎﻧﻲ دارد. اﻳﻦ روش داراي ﻣﺰاﻳﺎ ووﻳﮋﮔﻴﻬﺎﻳﻲ ﻣﻨﺤﺼﺮﺑﻪ ﻓـﺮد
اﺳﺖ . درواﻗﻊ ﺑﺴﻴﺎري از ﺗﻜﻨﻴﻚ ﻫﺎي دﻳﮕﺮ ﻣﻨﺸﻌﺐ از اﻳﻦ روش اﺳﺖ . در اﻳﻨﺠﺎ ﺿﻤﻦ ﻣﻌﺮﻓﻲ ﻛﻮﺗﺎﻫﻲ از
ﺗﺎرﻳﺨﭽﻪ و ﺗﻌﺮﻳﻒ ﺗﻮﻓﺎن ﻓﻜﺮي ﺑﻪ ﺑﺮرﺳـﻲ ﻗﻮاﻋـﺪاﻳﻦ روش ﻣـﻲ ﭘـﺮدازﻳﻢ . آﻧﮕـﺎه ﺗﺮﻛﻴـﺐ اﻋـﻀﺎ و ﮔـﺮوه
ﻣﺸﺨﺺ ﻣﻲ ﺷﻮد و ﭘﺲ از آن روﻧﺪﺑﺮﮔﺰاري ﻳﻚ ﺟﻠﺴﻪ ﺗﻮﻓﺎن ﻓﻜﺮي اراﺋـﻪ ﻣـﻲ ﮔـﺮدد. درﻧﻬﺎﻳـﺖ ﻣﺰاﻳـﺎ و
ﻣﻌﺎﻳﺐ اﻳﻦ روش ﻣﻌﺮﻓﻲ ﻣﻲ ﺷﻮد ﺗﺎ دﺑﻴﺮان و روﺳﺎي ﺟﻠﺴﺎت ﺑﺘﻮاﻧﻨﺪ داﻣﻨﻪ ﻛﺎرﺑﺮد آن را ارزﻳـﺎﺑﻲ ﻛـﺮده و
درﺟﺎي ﺧﻮداز آن اﺳﺘﻔﺎده ﻛﻨﻨﺪ.
اﻳﻦ روش ﺗﻮﺳﻂ اﻟﻜﺲ اﺳﺒﻮرن در ﺳﺎل 8891 ﻣﻌﺮﻓﻲ ﮔﺮدﻳﺪ. در آن زﻣﺎن ﺑﻨﻴﺎد ﻓﺮﻫﻨﮕـﻲ اﺳـﺒﻮرن اﻳـﻦ
روش را در ﭼﻨﺪﻳﻦ ﺷﺮﻛﺖ ﺗﺤﻘﻴﻘﺎﺗﻲ ، ﺑﺎزرﮔﺎﻧﻲ ، ﻋﻠﻤﻲ وﻓﻨﻲ ﺑﺮاي ﺣﻞ ﻣﺸﻜﻼت و ﻣﺴﺎﺋﻞ ﻣـﺪﻳﺮﻳﺖ ﺑـﻪ
ﻛﺎر ﮔﺮﻓﺖ . ﻣﻮﻓﻘﻴﺖ اﻳﻦ روش در ﻛﻤﻚ ﺑﻪ ﺣﻞ ﻣﺴﺎﺋﻞ آﻧﭽﻨﺎن ﺑﻮد ﻛﻪ ﻇﺮف ﻣـﺪت ﻛﻮﺗـﺎﻫﻲ ﺑـﻪ ﻋﻨـﻮان
روﺷﻲ ﻛﺎرآﻣﺪ ﺷﻨﺎﺧﺘﻪ ﺷﺪ. ﻓﺮﻫﻨﮓ ﻟﻐﺖ و ﺑﺴﺘﺮ ﺗﻌﺮﻳﻒ ﺗﻮﻓﺎن ﻓﻜﺮي را ﭼﻨﻴﻦ ﺑﻴﺎن ﻣـﻲ دارد: "ﺗﻜﻨﻴـﻚ
ﺑﺮﮔﺰاري ﻳﻚ ﻛﻨﻔﺮاﻧﺲ ﻛﻪ در آن ﺳﻌﻲ ﮔﺮوه ﺑﺮ اﻳﻦ اﺳﺖ ﺗﺎ راه ﺣـﻞ ﻣﺸﺨـﺼﻲ را ﺑﻴﺎﺑـﺪ". در اﻳـﻦ روش
ﻫﻤﻪ ﻧﻈﺮات در ﺟﻤﻊ ﺑﻨﺪي ﻣﻮرد اﺳﺘﻔﺎده ﻗﺮار ﻣﻲ ﮔﻴﺮﻧﺪ. روش ﺗﻮﻓﺎن ﻓﻜﺮي اﻣﺮوزه ﻳﻜﻲ ازﻣﺘﺪاول ﺗـﺮﻳﻦ
روﺷﻬﺎي ﺗﺼﻤﻴﻢ ﮔﻴﺮي ﮔﺮوﻫﻲ اﺳﺖ و ﻣﻮﺟﺐ ﮔﺴﺘﺮش و ﺗﺤـﻮل ﺑـﺴﻴﺎري ازروﺷـﻬﺎي ﻣـﺮﺗﺒﻂ و ﻣـﺸﺎﺑﻪ
ﮔﺮدﻳﺪه اﺳﺖ . داﻧﺸﻤﻨﺪان زﻳﺎدي ازﺟﻤﻠﻪ : اﺳﺒﻮرن ، ﻛﺎل وﻫﻤﻜﺎران ، ﺑﻮﭼﺎرد، ﮔﭽﻜﺎ و ﻫﻤﻜﺎران ، دﻟﺒـﮓ
و ﻫﻤﻜﺎران ، ﻟﻮﺋﺲ ، وﻧﮕﺎﻧﺪي و ﺳﻴﺞ درﻛﺘﺎﺑﻬﺎي ﺧﻮد ﺑﻪ اﻳﻦ روش ﭘﺮداﺧﺘﻪ اﻧﺪ و ﺟﻬﺖ ارﺗﻘﺎ آن ﻛﻮﺷﻴﺪه
اﻧﺪ.
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19. ﻗﻮاﻋﺪ ﺗﻮﻓﺎن ﻓﻜﺮي
اﺳﺒﻮرن ﻋﻨﻮان ﻣﻲ دارد: "ﭘﻴﺸﻨﻬﺎد اﻳﺠﺎد ﺷﺪه در ذﻫﻦ ﻳﻚ ﻓﺮد ﻋﺎدي در ﮔﺮوه ، 2 ﺑﺮاﺑﺮﭘﻴـﺸﻨﻬﺎد اﻳﺠـﺎد
ﺷﺪه درﺣﺎﻟﺖ اﻧﻔﺮادي اﺳﺖ ". درﺻﻮرﺗﻲ ﻛﻪ ﻗﻮاﻋﺪ و ﻣﻘﺮرات ﻣﺸﺨﺼﻲ ﺑـﺮاي ﺟﻠـﺴﺎت ﺗﻮﻓـﺎن ﻓﻜـﺮي در
ﻧﻈﺮ ﮔﺮﻓﺘﻪ و رﻋﺎﻳﺖ ﮔﺮدد، اﻳﻦ روش ﺑﺴﻴﺎر ﻛﺎرآﻣﺪﺗﺮﺧﻮاﻫﺪﺷﺪ. ﺗﻮﻓﺎن ﻓﻜﺮي ﺑﺮ دو اﺻـﻞ و ﭼﻬـﺎر ﻗﺎﻋـﺪه
اﺳﺎﺳﻲ اﺳﺘﻮار اﺳﺖ .اﺻﻞ اول ﻣﺒﺘﻨﻲ ﺑﺮ ﺗﻨﻮع ﻧﻈﺮات اﺳـﺖ . ﺗﻨـﻮع ﻧﻈـﺮات آن ﺑﺨـﺶ از ﻣﻐـﺰ را ﻛـﻪ ﺑـﻪ
ﺧﻼﻗﻴﺖ ﻣﺮﺑﻮط اﺳﺖ ﻓﻌﺎل ﺗﺮ ﻣـﻲ ﻛﻨـﺪ ﺗـﺎ ﺑـﺮ ﺗﻔﻜـﺮ ﻗـﻀﺎوﺗﻲ ""JUDJMENTAL THINKING
ﺧﻮدﻓﺎﺋﻖ آﻳﺪ. ﺗﻔﻜﺮ ﻗﻀﺎوﺗﻲ درواﻗﻊ ﺑﻪ ﻣﻌﻨﻲ ارزﻳﺎﺑﻴﻬﺎ و ﻧﻈﺮات ﺗﻜﻤﻴﻠﻲ ﻧﺴﺒﺖ ﺑـﻪ ﻣﻄﻠﺒـﻲ ﻣﻄـﺮح ﺷـﺪه
اﺳﺖ . ﺑﻪ اﻳﻦ ﻣﻨﻈﻮر ﺑﻌﺪ از آﻧﻜﻪ ﺗﻤﺎﻣﻲ ﭘﻴﺸﻨﻬﺎدات را ﺟﻤﻊ آوري ﮔﺮدﻳﺪ:ﺑﺮرﺳـﻲ و ارزﻳـﺎﺑﻲ ﭘﻴـﺸﻨﻬﺎدات
ﺻﻮرت ﻣﻲ ﮔﻴﺮد. اﺻﻞ دوم ﻛﻤﻴﺖ ، ﻓﺰاﻳﻨـﺪه ﻛﻴﻔﻴـﺖ اﺳـﺖ . ﻳﻌﻨـﻲ ﻫﺮﭼـﻪ ﺗﻌـﺪاد ﭘﻴـﺸﻨﻬﺎدات ﺑﻴـﺸﺘﺮ
ﺷﻮد،اﺣﺘﻤﺎل رﺳﻴﺪن ﺑﻪ ﻳﻚ راه ﺣﻞ ﺑﻬﺘﺮ اﻓﺰاﻳﺶ ﻣﻲ ﻳﺎﺑﺪ.
ﭼﻬﺎر ﻗﺎﻋﺪه اﺳﺎﺳﻲ ﺗﻮﻓﺎن ﻓﻜﺮي
1 - اﻧﺘﻘﺎل ﻣﻤﻨﻮع :
اﻳﻦ ﻣﻬﻤﺘﺮﻳﻦ ﻗﺎﻋﺪه اﺳﺖ و ﻻزم اﺳﺖ ﺗﻤﺎم اﻋﻀﺎ ﺑﻪ آن ﺗﻮﺟﻪ ﻛﺮده وﺑﺮرﺳﻲ و ارزﻳﺎﺑﻲ ﭘﻴﺸﻨﻬﺎد را ﺑﻪ آﺧﺮ
ﺟﻠﺴﻪ ﻣﻮﻛﻮل ﻛﻨﻨﺪ. ﺿﻤﻦ اﻳﻨﻜﻪ ﻣﻼﺣﻈﻪ ﺗﺒﻌﻴﺾ آﻣﻴﺰ ﭘﻴﺸﻨﻬﺎدات ﻧﻴﺰ ﻣﻤﻨﻮع اﺳﺖ .
2 - اﻇﻬﺎرﻧﻈﺮ آزاد و ﺑﻴﻮاﺳﻄﻪ :
91
20. اﻳﻦ ﻗﺎﻋﺪه ﺑﺮاي ﺟﺮات ﺑﺨﺸﻴﺪن ﺑﻪ ﺷﺮﻛﺖ ﻛﻨﻨﺪﮔﺎن ﺑﺮاي اراﺋﻪ ﭘﻴﺸﻨﻬﺎداﺗﻲ اﺳﺖ ﻛﻪ ﺑﻪ ذﻫﻦ آﻧﻬﺎ ﺧﻄﻮر
ﻣﻲ ﻛﻨﺪ. ﺑﻪ ﻋﺒﺎرت دﻳﮕﺮ در ﻳﻚ ﺟﻠﺴﻪ ﺗﻮﻓﺎن ﻓﻜﺮي ﺗﻤﺎم اﻋﻀﺎ ﺑﺎﻳﺪ ﺟﺴﺎرت و ﺷﻬﺎﻣﺖ اﻇﻬﺎرﻧﻈﺮ را ﭘﻴـﺪا
ﻛﺮده ﺑﺎﺷﻨﺪ و ﺑﺪون آﻧﻜﻪ ﺗﺮﺳﻲ از ارزﻳﺎﺑﻲ و ﺑﻌﻀﺎ اﻧﺘﻘﺎد ﻣﺴﺘﻘﻴﻢ داﺷﺘﻪ ﺑﺎﺷﻨﺪ; ﺑﺘﻮاﻧﻨـﺪ ﭘﻴـﺸﻨﻬﺎد و ﻧﻈـﺮ
ﺧﻮد را ﺑﻴﺎن ﻛﻨﻨﺪ.ﻫﺮﭼﻪ ﭘﻴﺸﻨﻬﺎدات ﺟﺴﻮراﻧﻪ ﺗﺮ ﺑﺎﺷﺪ ﻧﺸﺎن دﻫﻨﺪه اﺟﺮاي ﻣﻮﻓﻖ ﺗﺮ ﺟﻠﺴﻪ اﺳﺖ .
3 - ﺗﺎﻛﻴﺪ ﺑﺮ ﻛﻤﻴﺖ :
ﻫﺮﭼﻪ ﺗﻌﺪاد ﻧﻈﺮات ﺑﻴﺸﺘﺮ ﺑﺎﺷﺪ، اﺣﺘﻤﺎل وﺟﻮد ﭘﻴﺸﻨﻬﺎدات ﻣﻔﻴﺪ وﻛﺎرﺳﺎزﺗﺮ در ﺑﻴﻦ آﻧﻬﺎ ﺑﻴﺸﺘﺮ ﻣﻲ ﺷﻮد.
ﻣﻮﻓﻘﻴﺖ اﺟﺮاي روش ﺗﻮﻓﺎن ﻓﻜﺮي ﺑﺎ ﺗﻌﺪادﭘﻴﺸﻨﻬﺎدات ﻣﻄﺮح ﺷﺪه در ﺟﻠﺴﻪ راﺑﻄﻪ ﻣﺴﺘﻘﻴﻢ دارد. در اﻳـﻦ
روش اﻳﻦ ﮔﻮﻧﻪ ﻋﻨﻮان ﻣﻲ ﺷﻮدﻛﻪ ﻫﺮﭼﻪ ﺗﻌﺪاد ﭘﻴﺸﻨﻬﺎد ﺑﻴﺸﺘﺮ ﺑﺎﺷﺪ اﺣﺘﻤﺎل وﺟﻮد ﻃﺮح ﭘﻴﺸﻨﻬﺎد ﻛﻴﻔـﻲ
ﺑﻴﺸﺘﺮ اﺳﺖ .
4 - ﺗﻠﻔﻴﻖ و ﺑﻬﺒﻮد ﭘﻴﺸﻨﻬﺎدات :
اﻋﻀﺎ ﻣﻲ ﺗﻮاﻧﻨﺪ ﻋﻼوه ﺑﺮ اراﺋﻪ ﭘﻴﺸﻨﻬﺎد، ﻧﺴﺒﺖ ﺑﻪ ﺑﻬﺒﻮدﭘﻴﺸﻨﻬﺎد ﺧﻮد اﻗﺪام ﻛﻨﻨﺪ. روش ﺗﻮﻓﺎن ﻓﻜﺮي اﻳﻦ
اﻣﻜﺎن را ﺑﻪ اﻋﻀﺎ ﻣﻲ دﻫﺪ ﻛﻪ ﭘﺲ ازﺷﻨﻴﺪن ﭘﻴﺸﻨﻬﺎدات دﻳﮕﺮان ﭘﻴـﺸﻨﻬﺎد اوﻟﻴـﻪ ﺑﻬﺒـﻮد داده ﺷـﻮد. آﻧﻬـﺎ
ﻫﻤﭽﻨﻴﻦ ﻣﻲ ﺗﻮاﻧﻨﺪ ﭘﻴﺸﻨﻬﺎدﺧﻮد را ﺑﺎ ﭼﻨﺪ ﭘﻴﺸﻨﻬﺎد دﻳﮕﺮ ﺗﻠﻔﻴﻖ ﻛﺮده و ﭘﻴﺸﻨﻬﺎد ﺑﻬﺘﺮ و ﻛـﺎﻣﻠﺘﺮي را ﺑـﻪ
دﺳﺖ آورﻧﺪ.
ﺗﺮﻛﻴﺐ اﻋﻀﺎي ﮔﺮوه ﺗﻮﻓﺎن ﻓﻜﺮي
02
28. 8- ﻫﻤﻴﺸﻪ ﺑﺮاي ﺣﻞ ﻣﺸﻜﻞ از داده و اﻃﻼﻋﺎت ﺑﻪ روز اﺳﺘﻔﺎده ﻛﻨﻴﺪ.
9- ﺑﺮاي ﺣﻞ ﻣﺸﻜﻞ ﺑﻼﻓﺎﺻﻠﻪ ﺑﻪ دﻧﺒﺎل ﻫﺰﻳﻨﻪ ﻛﺮدن ﻧﺒﺎﺷﻴﺪ. ﺑﻠﻜﻪ از ﺧﺮد ﺧﻮد اﺳﺘﻔﺎده ﻛﻨﻴﺪ. اﮔﺮ ﻋﻘﻠﺘﺎن
ﺑﻪ ﺟﺎﻳﻲ ﻧﻤﻲ رﺳﺪ، آن را در ﻫﻤﻜﺎراﻧﺘﺎن ﺑﺠﻮﻳﻴﺪ و از ﺧﺮد ﺟﻤﻌﻲ اﺳﺘﻔﺎده ﻛﻨﻴﺪ.
01- ﻫﻴﭻ وﻗﺖ ﻧﻜﺎت رﻳﺰ ﻣﺴﺌﻠﻪ را ﻓﺮاﻣﻮش ﻧﻜﻨﻴﺪ. رﻳﺸﻪ ﺑﺴﻴﺎري از ﻣﺸﻜﻼت ﺑﺰرگ ﻫﻤﻴﻦ ﻧﻜﺎت رﻳﺰ
اﺳﺖ.
11- ﺣﻤﺎﻳﺖ ﻣﺪﻳﺮﻳﺖ ارﺷﺪ ﻣﻨﺤﺼﺮ ﺑﻪ ﻗﻮل و ﻛﻼم ﻧﻴﺴﺖ. ﻣﺪﻳﺮﻳﺖ ﺑﺎﻳﺪ ﺣﻀﻮر ﻣﺸﻬﻮد و ﻣﻠﻤﻮس داﺷﺘﻪ
ﺑﺎﺷﺪ.
21- ﺑﺮاي ﺣﻞ ﻣﺴﺎﺋﻞ ﻫﺮ ﺟﺎ ﻛﻪ اﻣﻜﺎن آن وﺟﻮد دارد از واﮔﺬاري اﺧﺘﻴﺎر ﺑﻪ زﻳﺮدﺳﺘﺎن اﺑﺎ ﻧﻜﻨﻴﺪ.
31- ﻫﻴﭻ وﻗﺖ ﺑﻪ دﻧﺒﺎل ﻣﻘﺼﺮ ﻧﮕﺮدﻳﺪ. ﻫﻴﭻ ﮔﺎه ﻋﺠﻮﻻﻧﻪ ﻗﻀﺎوت ﻧﻜﻨﻴﺪ.
41- ﻣﺪﻳﺮﻳﺖ دﻳﺪاري و اﻧﺘﻘﺎل اﻃﻼﻋﺎت ﺑﻬﺘﺮﻳﻦ اﺑﺰار ﺑﺮاي ﺣﻞ ﻣﺴﺌﻠﻪ ﺑﻪ ﺻﻮرت ﮔﺮوﻫﻲ اﺳﺖ.
51- ارﺗﺒﺎط ﻳﻚ ﻃﺮﻓﻪ دﺳﺘﻮري از ﺑﺎﻻ ﺑﻪ ﭘﺎﻳﻴﻦ ﻣﺸﻜﻼت ﺳﺎزﻣﺎن را ﭘﻴﭽﻴﺪه ﺗﺮ ﻣﻴﻜﻨﺪ. ﻣﺪﻳﺮﻳﺖ ارﺷﺪ
ﺑﺎﻳﺪ ﺑﺎ ﻻﻳﻪ ﻫﺎي ﭘﺎﻳﻴﻦ ﺗﺮ ﺳﺎزﻣﺎن ارﺗﺒﺎط دو ﺟﺎﻧﺒﻪ داﺷﺘﻪ ﺑﺎﺷﺪ.
61- اﻧﺴﺎﻧﻬﺎ ﺗﻮاﻧﺎﻳﻲ ﻫﺎي ﻓﺮاواﻧﻲ دارﻧﺪ. از اﻟﮕﻮﻫﺎي ﭼﻨﺪ ﻣﻬﺎرﺗﻲ و ﻏﻨﻲ ﺳﺎزي ﺷﻐﻠﻲ ﺑﺮاي ﺷﻜﻮﻓﺎ ﺷﺪن
آﻧﻬﺎ اﺳﺘﻔﺎده ﻛﻨﻴﺪ.
71- ﺗﻨﻬﺎ ﻓﻌﺎﻟﻴﺖ ﻫﺎﻳﻲ را اﻧﺠﺎم دﻫﻴﺪ ﻛﻪ ﺑﺮاي ﺳﺎزﻣﺎن ﺷﻤﺎ ارزش اﻓﺰوده اﻳﺠﺎد ﻣﻲ ﻛﻨﻨﺪ.
81- ﻓﺮاﻣﻮش ﻧﻜﻨﻴﺪ ﻛﻪ Five Sﭘﺎﻳﻪ و اﺳﺎس اﻳﺠﺎد ﻣﺤﺼﻮﻟﻲ ﺑﺎ ﻛﻴﻔﻴﺖ اﺳﺖ.
91- ﺑﺮ اﺳﺎس اﻟﮕﻮﻫﺎي ﻛﺎر ﮔﺮوﻫﻲ ، ﻣﺴﺎﺋﻞ ﻣﺤﻴﻂ ﻛﺎرﺗﺎن را ﺣﻞ ﻛﻨﻴﺪ.
02- ﺣﺬف ﻣﻮدا ) اﺗﻼف( ﻓﺮآﻳﻨﺪي ﭘﺎﻳﺎن ﻧﺎﭘﺬﻳﺮ اﺳﺖ. ﻫﺮﮔﺰ از اﻳﻦ ﻛﺎر ﺧﺴﺘﻪ ﻧﺸﻮﻳﺪ.
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29. Bayesian network
A Bayesian network or Bayesian belief network or just belief network is
a directed graph of nodes representing variables and arcs representing
dependence relations among the variables. If there is an arc from node A to
another node B, then we say that A is a parent of B. If a node has a known
value, it is said to be an evidence node. A node can represent any kind of
variable, be it an observed measurement, a parameter, a latent variable, or a
hypothesis. Nodes are not restricted to representing random variables; this is
what is "Bayesian" about a Bayesian network.
A Bayesian network is a representation of the joint distribution over all the
variables represented by nodes in the graph. Let the variables be X(1), ...,
X(n). Let parents(A) be the parents of the node A. Then the joint distribution
for X(1) through X(n) is represented as the product of the probability
distributions
for i = 1 to n. If X has no parents, its probability
distribution is said to be unconditional, otherwise it is conditional.
Questions about dependence among variables can be answered by studying
the graph alone. It can be shown that the graphical notion called d-separation
corresponds to the notion of conditional independence: if nodes X and Y are
d-separated (given specified evidence nodes), then variables X and Y are
independent given the evidence variables. The set of all other nodes that
node X can directly depend on is given by X's Markov blanket.
In order to fully specify the Bayesian network and to carry out numerical
calculations, it is necessary to further specify for each node X the probability
distribution for X conditional on its parents. The distribution of X given its
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30. parents may have any form. However, it is common to work with discrete or
Gaussian distributions, since that simplifies calculations. Sometimes only
constraints on a distribution are known; one commonly uses the principle of
maximum entropy to specify the distribution with the greatest information
entropy given these constraints. In this way the distribution makes the fewest
additional assumptions beyond what is known to be true as expressed by the
constraints. (Analogously, in the specific context of a dynamic Bayesian
network, one commonly specifies the conditional distribution for the hidden
state's temporal evolution to maximize the entropy rate of the implied
stochastic process.) Often these conditional distributions depend on
unknown parameters which must be estimated from data, ideally using
maximum likelihood. However, the likelihood maximization is often
intractable for many problems, leading to the use of iterative approximation
techniques, the most widespread being the expectation-maximization
algorithm.
The goal of inference is typically to find the conditional distribution of a
subset of the variables, conditional on known values for some other subset
(the evidence), and integrating over any other variables. This conditional
distribution is known as the posterior distribution of the subset of the
variables given the evidence. The posterior gives a universal sufficient
statistic for detection applications, when one wants to choose values for the
variable subset which minimize some expected loss function, for instance
the probability of decision error. A Bayesian network can thus be considered
a mechanism for automatically constructing extensions of Bayes' theorem to
more complex problems. The most common exact inference methods are
variable elimination that eliminates (by integration or summation) the non-
30
31. observed non-query variables one by one by distributing the sum over the
product, clique tree propagation that caches the computation so that the
many variables can be queried at one time, and new evidence can be
propagated quickly, recursive conditioning that allows for a space-time
tradeoff but still allowing for the efficiency of variable elimination when
enough space is used - all of these methods have complexity that is
exponential in tree width. The most common approximate inference
algorithms are stochastic simulation, mini-bucket elimination (which
generalizes loopy belief propagation) and variational methods.
Bayesian networks are used for modelling knowledge in gene regulatory
networks, medicine, engineering, text analysis, image processing, data
fusion, and decision support systems.
Learning the structure of a Bayesian network is a very important part of
machine learning. Given the information that the data is being generated by
a Bayesian network and that all the variables are visible in every iteration,
the following methods are used to learn the structure of the acyclic graph
and the conditional probability table associated with it. The elements of a
structure finding algorithm are a scoring function and a search strategy. An
exhaustive search returning back a structure that maximizes the score is one
implementation which is superexponential in the number of variables. A
local search algorithm makes incremental changes aimed at improving the
score of the structure. A global search algorithm like Markov chain Monte
Carlo can avoid getting trapped in local minima. Friedman et. al. talk about
using mutual information between variables and finding a structure that
maximizes this. They do this by restricting the parent candidate set to k
nodes and exhaustively searching therein.
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32. Latent variable
Latent variables, as opposed to observable variables, are those variables
that cannot be directly observed but are rather inferred from other variables
that can be observed and directly measured. Examples of latent variables
include quality of life, business confidence, morale, happiness,
conservatism. Latent variables are also called as hypothetical variables or
hypothetical constructs. The use of latent variables is common in social
sciences and to an extent in the economics domain. The exact definition of
latent variables varies in different domains.
One advantage of using latent variables is that it reduces the dimensionality
of data. A large number of observable variables can be aggregated to
represent an underlying concept, making it easier for human beings to
understand and assimilate information.
Bayesian probability
Bayesianism is the philosophical tenet that the mathematical theory of
probability applies to the degree of plausibility of a statement. This also
applies to the degree of believability contained within the rational agents of
a truth statement. Additionally, when a statement is used with Bayes'
theorem, it then becomes a Bayesian inference.
This is in contrast to frequentism, which rejects degree-of-belief
interpretations of mathematical probability, and assigns probabilities only to
random events according to their relative frequencies of occurrence. The
Bayesian interpretation of probability allows probabilities to be assigned to
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33. random events, but also allows the assignment of probabilities to any other
kind of statement.
Whereas a frequentist and a Bayesian might both assign a 1/2 probability to
the event of getting a head when a coin is tossed, only a Bayesian might
assign 1/1000 probability to a personal belief in the proposition that there
was life on Mars a billion years ago. This assertion is made without
intending to assert anything about relative frequency.
ﺷﺒﻜﻪ ﺑﺎﻳﺴﻲ
ﺷﺒﻜﻪ ﺑﺎﻳﺴﻲ ﻳﻚ ﮔﺮاف ﺟﻬﺖ دار اﺳﺖ ﻛﻪ در آن ﮔﺮه ﻫـﺎ ﻣﺘﻐﻴﺮﻫـﺎ را ﻧـﺸﺎن ﻣـﻲ دﻫﻨـﺪ و ﻳﺎﻟﻬـﺎ راﺑﻄـﻪ
وﺟﻮد داﺷﺘﻪ ﺑﺎﺷﺪ ﻣﻲ ﮔـﻮﻳﻴﻢB ﺑﻪ ﮔﺮهA واﺑﺴﺘﮕﻲ ﺑﻴﻦ ﻣﺘﻐﻴﺮﻫﺎ را ﻧﺸﺎن ﻣﻲ دﻫﻨﺪ.اﮔﺮ ﻛﻤﺎﻧﻲ از ﮔﺮه
اﺳﺖ. اﮔﺮ ﮔﺮه اي ﻣﻘﺪار ﺷﻨﺎﺧﺘﻪ ﺷﺪه اي داﺷﺘﻪ ﺑﺎﺷﺪ ﮔﻔﺘﻪ ﻣﻲ ﺷﻮد ﻛﻪ ﮔﺮه ﺷﺎﻫﺪ اﺳﺖ. ﻳﻚB واﻟﺪA
ﮔﺮه ﻣﻲ ﺗﻮاﻧﺪ ﻫﺮ ﻧﻮع ﻣﺘﻐﻴﺮ را ﻧﺸﺎن دﻫﺪ. ﻳﻚ ﻣﻘﺪار ﻣﺸﺎﻫﺪه ﺷﺪه ﻳﻚ ﭘﺎراﻣﺘﺮ، ﻳﻚ ﻣﺘﻐﻴﺮ ﺑﺎﻟﻘﻮه ﻳـﺎ ﻳـﻚ
.ﻓﺮض. ﮔﺮه ﻫﺎ ﻣﺤﺪود ﺑﻪ ﻧﻤﺎﻳﺶ ﻣﺘﻐﻴﺮﻫﺎي ﺗﺼﺎدﻓﻲ ﻧﻴﺴﺘﻨﺪ. اﻳﻦ ﻣﻌﻨﻲ ﺑﺎﻳﺴﻲ در ﻳﻚ ﺷﺒﻜﻪ ﺑﺎﻳﺴﻲ اﺳﺖ
33