مشکل اساسی دوستان hr (البته برخی ) ، و بیشتر در ایران ، این هست ، که انسان را با ربات اشتباه میگیرن .

در جهان امروز ، انسانی بودن والاترین معنا است . و درک این معنا باعث رشد بیزینس ها است .

از یاد نبریم : انسان اشتباه میکند - نامید میشود . خسته میشود . و تمامی صفات انسانی را شامل میشود .

حال وظیفه سرمایه انسانی بهبود این هاست . و تعادل بین اونها .
این را از یاد نبریم : فرایند یادگیری انسانی ،و رشد آن با اشتباه کردن هست . و اشتباه همیشه بد نیست .

به طور خلاصه :

اتفاقاً یکی از مهم‌ترین نقدهایی که به مدیریت منابع انسانی (به‌ویژه در ایران) وارد است، همین نگاه «ماشین‌وار» به کارمند یا همکار است. وقتی HR صرفاً به افراد مثل «منابع مصرفی» نگاه کند، فراموش می‌شود که انسان‌ها فقط نیروی کار نیستند؛ بلکه مجموعه‌ای از احساسات، انگیزه‌ها، خستگی‌ها، امیدها و حتی اشتباه‌ها هستند.

🔑 چند نکته کلیدی در ادامه:

انسان ≠ ربات
اگر نیروی انسانی صرفاً برای خروجی و KPI دیده شود، خلاقیت و وفاداری از بین می‌رود. چون انسان برای رشد، نیاز به دیده‌شدن، شنیده‌شدن و درک‌شدن دارد.

اشتباه = بخشی از یادگیری
فرهنگ‌هایی که اشتباه را نابخشودنی می‌دانند، در واقع جلوی نوآوری و یادگیری را می‌گیرند. بهترین سازمان‌ها آن‌هایی هستند که از اشتباه به‌عنوان «فرصت رشد» استفاده می‌کنند.

سرمایه انسانی = حمایت + تعادل
نقش اصلی HR این است که هم نیازهای سازمان و هم نیازهای انسانی کارکنان را ببیند. یعنی ایجاد تعادل بین فشار کاری و سلامت روانی، بین نظم سازمانی و آزادی فردی.

بیزینس انسانی‌تر = بیزینس پایدارتر
شرکت‌هایی که به انسانیت کارکنان بها می‌دهند (با ایجاد فضا برای یادگیری، استراحت، و حتی پذیرفتن خطا)، نه‌تنها عملکرد بهتری دارند، بلکه برند کارفرمایی قوی‌تری هم می‌سازند.

#hr #grow #market #business

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My name is Gil Cattelain, and I'm a Product Marketing Manager here at Red Hat. I joined the company over four years ago, and I manage the Red Hat Enterprise Linux (RHEL) marketing releases and launches. You've likely read some of my blog posts, which I typically publish with every minor or major release, but this article highlights the importance of upgrading. If you're still on RHEL 6, RHEL 7, or even RHEL 8, then you should consider upgrading to RHEL 9.6 or a later version to take advantage of all the cool new features included in those versions.Why should you seriously consider upgrading to

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Brian D. is a senior machine learning (ML) engineer on our AI Inference team, which is part of the broader AI Engineering team at Red Hat. Based remotely in Chicago, Brian helps maintain LLM Compressor, a key component of vLLM (an open source inference server originally developed at UC Berkeley, and now supported by a global community). vLLM is designed to make AI inference—in other words, responses from models—more efficient. Through LLM Compressor, Brian and his team make it possible to optimize and deploy LLMs so they run faster, consume less energy, and operate on fewer GPUs, without

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Securing the software supply chain is paramount in today’s digital world. As more organizations adopt practices like keyless signing to verify the integrity of their software artifacts, the need for robust monitoring against the systems that maintain the software supply chain infrastructure is essential. One such system when signing and verifying content where monitoring can be applied is within the transparency log. While logs are tamper-evident, they are not tamper-proof, making a monitoring solution essential for an immutable and append-only record. This is where the Rekor Monitor comes i

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Migration toolkit for virtualization 2.9 (MTV) release is here, and brings lots of new features to help make your virtual machine (VM) migration experience easier. This iteration introduces user experience enhancements designed to provide greater clarity, improved functionality, and a comprehensive view of your migration process.New features and highlightsThe enhanced UI of the migration toolkit 2.9 focuses on three pivotal areas:Enhanced overview page: Get a broader and more granular perspective on your migration progress.Clearer migration plan wizard: Your creation process will be smoother w

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To CIOs and CISOs overseeing critical production systems, AI can seem chaotic: It’s a dynamic, constantly evolving ecosystem, where current certifications and standards may not apply the same way, if at all. But AI can’t be ignored - properly implementing and integrating an AI strategy can be a massive boon to the enterprise, but the key word is “properly.” Red Hat has always been committed to having our platforms conform to the certification and standards requirements of enterprise IT across industries. AI is no different, so we’re pleased to announce that Red Hat OpenShift AI has s

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The utility industry is currently facing a multitude of hurdles that are driven by a combination of aging infrastructure, changing energy landscape and evolving customer expectations. The power grid, specifically, is undergoing its greatest transformation since inception, with new challenges imposing problems that our grid is not equipped to handle.As utilities adopt a software-defined infrastructure,bringing IT best practices to OT ecosystems becomes increasingly feasible. By virtualizing applications and taking advantage of advancements in edge computing platforms and AI, utilities can moder

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این تصویر روشی به نام تحلیل ۵ چرا (5 Whys) را نشان می‌دهد که برای یافتن ریشه اصلی مشکلات استفاده می‌شود. مثال مطرح‌شده درباره ترک کارکنان یک سازمان است:

1. چرا؟
چون حقوق پایین است.


2. چرا حقوق پایین است؟
چون نظام جبران خدمات به‌روز نیست.


3. چرا نظام جبران خدمات به‌روز نیست؟
چون داده‌های بازار جمع‌آوری نمی‌شود.


4. چرا داده‌ها جمع‌آوری نمی‌شود؟
چون واحد منابع انسانی ابزار و فرایند لازم را ندارد.


5. چرا ابزار و فرایند وجود ندارد؟
چون استراتژی منابع انسانی بر پایه داده تعریف نشده است.



🔎 نتیجه‌گیری:
مشکل اصلی، پایین بودن حقوق نیست، بلکه نبود رویکرد داده‌محور در منابع انسانی است.



#5why #why

مساله دیگر

مسأله: رضایت مشتریان پایین است.

1. چرا؟
چون شکایت‌های مشتریان زیاد است.
2. چرا شکایت‌های مشتریان زیاد است؟
چون کیفیت خدمات/محصول ثابت نیست.
3. چرا کیفیت خدمات/محصول ثابت نیست؟
چون فرآیند کنترل کیفیت ضعیف است.
4. چرا فرآیند کنترل کیفیت ضعیف است؟
چون استانداردهای مشخص و داده‌های کافی برای پایش کیفیت وجود ندارد.
5. چرا استاندارد و داده وجود ندارد؟
چون سیستم مدیریت کیفیت و بازخورد مشتریان به‌صورت داده‌محور تعریف نشده است
✅ نتیجه: مشکل اصلی، کیفیت پایین یا شکایت مشتری نیست، بلکه نبود سیستم مدیریت کیفیت داده‌محور است

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