2025年5月15日 星期四

諸種偏頗之心態如何阻礙個人與商業決策之變革

 

諸種偏頗之心態如何阻礙個人與商業決策之變革

夫變革之道,利弊相隨,然人心之幽微,常受諸種偏頗之見所蔽,致使臨事猶疑,裹足不前。茲列舉數端,以明其理:

一、損失厭惡 (Loss Aversion)

  • 阻變之由: 人心之常,失之痛甚於得之樂。故遇變革,常先慮其所失,而輕忽其所獲。此等心態,使人戀棧舊習,不願輕易更弦易轍。
  • 個人之例: 或有人固守舊式預算之法,雖繁瑣低效,然恐失掌控之感,故不願易新法。
  • 商業之例: 或有公司畏懼新技術投入之風險,縱知可增產能,亦裹足不前。

二、負面偏見 (Negativity Bias)

  • 阻變之由: 人性使然,惡聞勝於善聽。故變革之潛在弊端,易於在心頭放大,使人過於謹慎,凡有絲毫負面之虞,即生抗拒之心。
  • 個人之例: 或有人得新職於異地,然憂慮離別親友,不適新境,遂裹足不前,而忽略高升之利。
  • 商業之例: 或有公司議合併,然憂慮文化衝突,裁員之虞,遂躊躇不決,而輕視市場擴張之益。

三、樂觀偏見 (Optimism Bias)

  • 阻變之由: 雖云樂觀似可助推變革,然過於自信,則易低估變革之艱辛。遂不願竭力以赴,或輕忽以待,遇挫則生退意。
  • 個人之例: 或有人妄以為減重易如反掌,遂不願持之以恆,終致徒勞。
  • 商業之例: 或有公司新品上市,而未備周全之策,蓋因過於樂觀其成。一旦遇阻,則不願調整策略。

四、確認偏見 (Confirmation Bias)

  • 阻變之由: 人常尋求與己見相符之資訊,而排斥相悖之論。若先入為主,認定變革不利,則只關注負面之說,益增其抗拒之心。
  • 個人之例: 或有人先信線上教育無益,則只覽負評劣跡,而無視其優點,遂拒絕嘗試。
  • 商業之例: 或有公司主管先信遠程辦公低效,則只關注散漫之例,而無視高效之實,遂阻撓彈性工作之推行。

五、可得性捷思 (Availability Heuristic)

  • 阻變之由: 人常依賴易於回憶之資訊以作決斷。若變革之負面事例易於記起,則更易生抗拒之心,縱使此等事例乃屬罕見或偏頗。
  • 個人之例: 或有人因易憶股災之聞,遂拒絕投資股市,縱知長期而言,獲利者眾。
  • 商業之例: 或有公司因曾有軟件更替失敗之例,遂拒絕採用新系統,縱知新系統已大有改進,成功可期。

六、錨定效應 (Anchoring Bias)

  • 阻變之由: 最初所獲之資訊(錨點),對後續決策影響甚巨。若變革之初,所聞皆為負面之詞,則易於形成抗拒之錨點。
  • 個人之例: 或有人初聞新膳食之法,盡言其禁忌限制,遂先入為主,心生厭惡,縱知其益良多亦不願嘗試。
  • 商業之例: 或有公司初布改組之令,先言裁員之憂,則員工易受此恐懼所縛,縱知長遠有利亦難以接受。

七、框架效應 (Framing Effect)

  • 阻變之由: 變革之呈現方式,足以左右人心。若以損失之語框架之,則更易招致反對;若以獲益之詞述之,則較易為人所納。
  • 個人之例: 或有新醫療方案,若曰「削減某項福利」,則易遭反對;若曰「調整福利以提升整體照護」,則較易接受。
  • 商業之例: 或有新政,若曰「限縮員工自主權」,則易生阻力;若曰「增進團隊協作效率」,則較易推行。

八、稟賦效應 (Endowment Effect)

  • 阻變之由: 人常珍視已有所得之物,甚於未得之物。故凡需捨棄現有之變革,縱使能獲等價或更優之物,亦常生不捨之情。
  • 個人之例: 或有人舊車雖已不堪,維修費昂,然因持有已久,情有所繫,故不願售之而購新車。
  • 商業之例: 或有公司舊設備已落伍,新設備更有效率,然因已擁有舊物,不願輕易更換。

九、後見之明偏誤 (Hindsight Bias)

  • 阻變之由: 事後觀之,人常自以為早已預料結果,無論成敗皆然。若變革之結果不佳,則更易強化對未來變革之抗拒,蓋因自以為能預見失敗也。
  • 個人之例: 或有人試行新健身之法,未見速效,遂曰「吾早知此法無用」,日後更不願嘗試新法。
  • 商業之例: 或有公司新行銷策略失敗,主管遂曰「吾早知此策不妥」,日後更不願嘗試新策略,縱使新策有更佳之數據分析為據。

十、內群體偏見 (In-Group Bias)

  • 阻變之由: 人常偏袒己群之人。若變革被視為外群體所強加,則易遭強烈反對,縱使此變革實有益處。
  • 個人之例: 或有社區中心新政,若被視為異類所推行,則成員或生反感,縱使新政能提升服務。
  • 商業之例: 或有公司合併,一方之員工或抗拒他方管理層所施之策,視之為外人,不解己方之道。

十一、現時偏誤 (Present Bias)

  • 阻變之由: 人常重眼前之利,輕長遠之益。凡變革需付出短期之代價或不適,以換取長期之利益,則易因著眼於眼前之負面而生抗拒。
  • 個人之例: 或有人不願開始長期儲蓄計畫,蓋因更願將錢用於當下之享樂,縱知將來將更富足。
  • 商業之例: 或有公司不願投資員工培訓,蓋因短期需耗成本,而更重眼前利潤,縱知長遠而言,培訓能提升產能。

十二、沉沒成本謬誤 (Sunk Cost Fallacy)

  • 阻變之由: 人常執著於已投入之時間、金錢或努力,即使明知繼續投入並無益處,亦不願放棄。此使人難以改變方向,縱使轉舵乃明智之舉。
  • 個人之例: 或有人舊車屢修不止,耗費甚鉅,然因已投入甚多,故不願棄之而購新車。
  • 商業之例: 或有公司明知某項目已無望,然因已投入大量資源,故不願止損,而繼續投入,實非明智之舉。

綜上所述,人心之偏頗,猶如迷霧,障目而使人難以洞察變革之真諦。唯有明瞭此等心理之蔽,方能警惕自身,破除固執,以更開闊之胸襟,迎接變革之挑戰。

破三難之局:成本、品質與時效可兼得乎?

 

破三難之局:成本、品質與時效可兼得乎?

夫工事之道,經營供應之術,人多言「三角難全」,謂成本、品質、時效,三者不可兼得。若求其速與精,則費用必鉅;欲其廉與精,則時日必長;若圖其速而省,則品質不保。此說流傳既久,若成鐵律,無人敢易。

然《制約理論》(Theory of Constraints, TOC)有言:凡事制於瓶頸,若能辨明其制,則難題可破,三利可兼。是故不當以「不可得兼」為常理,當反求諸己,問曰:「此難局,果為天成乎?抑人自設限耳?」

一、析其爭端,以「雲圖」明之

欲破其局,先須見其形。《雲圖》法,能示二策相爭之理。蓋其要者如下:

  • 目的(A): 期於工事或供應之成,收效圓滿。

  • 需求(B): 謀其速成而省費也。

  • 需求(C): 求其品質精良,不失工也。

  • 手段(D): 減費速行,以應B。

  • 反手段(D'): 加時投資,以保C。

觀其所爭,D與D’顯為相悖:一速而廉,一慎而費,故成其局。

二、質其預設,破其根本

若止於表面,則無解。然TOC教人探本溯源,問曰:此等衝突,所憑何理?

  • 謂速則必疏,疏則質下。

  • 謂質必費時,非慢不可。

  • 謂資源有限,費省質損。

此等皆人之假設,非天之常理。多見工序雜亂、調度不明、制約不辨,非真有「三難不可兼」之命。

三、破局之道,從制約入手

得其病源,乃可投其藥方。TOC之法,謂之「釋雲」,求一策以兼三利。其法如左:

  1. 明瓶頸以調全局: 每系必有制約,識之、用之、服從之,可釋資源之虛耗,速成工事,而無增其費、損其質也。

  2. 簡其工序,定其法度: 繁則多錯,亂則多延。若簡化其流,制以標準,則返工少而成本降,亦不損其質。

  3. 預防勝於檢查: 與其事後糾錯,不若事前防失。若能於流程之中築質,則無待大費時金於檢驗矣。

  4. 忌其多務並舉: 工事多病於多任並行,致延宕錯漏。若專注於關鍵,按「關鍵鏈」調度,則速成而質可保,費用亦省。

四、總結

所謂「三角不可得兼」者,非天理也,人理也。若能換其思維,破其假設,以制約為導,則成本、品質與時效,三者可得而兼焉。

此非術數,實乃明理也。


Breaking the Impossible Triangle: Achieving Cost, Quality, and Time Together with Clear Thinking

 Breaking the Impossible Triangle: Achieving Cost, Quality, and Time Together with Clear Thinking

In the world of project management and supply chain operations, the "impossible triangle"—cost, quality, and time—is often portrayed as an inescapable trade-off. The conventional wisdom says: you can pick two, but not all three. If you want high quality and fast delivery, it’ll be expensive. If you want low cost and high quality, it will take more time. And if you want fast and cheap, you’ll have to compromise on quality.

But what if this conflict is not inherent, but rather the result of flawed assumptions?

The Theory of Constraints (TOC) offers a powerful lens to challenge and resolve this dilemma. Instead of accepting the trade-off at face value, TOC invites us to ask: What are the underlying assumptions that create this conflict? And more importantly: Can we break those assumptions to find a win-win-win solution?

Step 1: Surface the Conflict Using the Evaporating Cloud

Let’s use the TOC tool known as the Evaporating Cloud (EC) to structure the conflict:

  • Goal (A): Achieve a successful project (or efficient supply chain)

  • Need B: Ensure the project is delivered quickly and at low cost

  • Need C: Ensure the project is delivered with high quality

  • Action D: Minimize resources, accelerate execution (to fulfill B)

  • Action D': Allocate more time, money, and oversight to ensure quality (to fulfill C)

At first glance, D and D’ directly conflict. One says "do it faster and cheaper", the other says "take more time and spend more".

Step 2: Challenge the Assumptions

This is where traditional thinking stops. But TOC pushes forward. What are the assumptions behind D and D’?

  • That speed requires shortcuts which harm quality.

  • That quality always requires more time and money.

  • That resources are the main constraint.

Are these always true? Not necessarily. These are local optima, not global truths. Many times, inefficiencies, misalignments, or hidden constraints—not the nature of the triangle—are the real culprits.

Step 3: Break the Conflict

Once we identify flawed assumptions, we can look for injections—new ideas or changes that break the trade-off. Here are a few proven ones:

  1. Focus on the Constraint: TOC teaches us that every system has a constraint. When we align all activities to exploit and subordinate to this constraint, we often unlock massive hidden capacity—achieving faster delivery without extra cost or quality compromise.

  2. Simplify and Standardize: Many quality issues and delays come from unnecessary complexity. Streamlining processes and applying standard work can reduce rework and speed up delivery while lowering costs.

  3. Build Quality In (Don’t Inspect It In): Shift from after-the-fact quality control to process-based quality. When quality is designed into each step (e.g. with mistake-proofing, feedback loops, and training), it doesn't have to cost more—or take longer.

  4. Eliminate Multitasking: In project environments, multitasking creates delays and chaos. Focusing on fewer tasks at a time (critical chain scheduling) can reduce lead time and cost, while also reducing errors.

The Bottom Line

The "cost-quality-time" triangle appears unbreakable only when we treat it as a zero-sum game. TOC invites us to reject that mindset. By identifying and challenging the assumptions behind the perceived conflict, and by aligning operations around the true system constraint, it becomes not only possible—but practical—to achieve high quality, low cost, and fast delivery simultaneously.

This is not magic. It’s clear thinking.


傳統製造業導入機器智能之議——以六問技術審其利害

傳統製造業導入機器智能之議——以六問技術審其利害


緒論

今之世,機器智能(人工智慧)日新月異,漸變諸行百業。製造業素重人手與機器,其法多依成例。然若善用智能,則可加速生產、減省成本、提昇品質,為傳統工廠注入新機。然則導入之前,必先詳審利弊。是故依「六問技術」之理,試析其可行之道。


一曰:其術之力何在?

機器智能之力,在於助人決策,預測未然。於製造業可行之事如:

  • 預知機器之損壞,早為修護;

  • 察覺瑕疵,保障品質;

  • 精密排程,減少停滯;

  • 明察庫存,供需得宜;

  • 減用原料,節省能源。

此等皆使工廠運作更順、決策更明、反應更速。


二曰:其所可能,昔日所不能者為何?

往日管理,多賴經驗與臆測。今賴智能,則:

  • 機器之數據可預未來之故障;

  • 依實時情況調節生產之速;

  • 分析缺陷之本因,非人眼可及;

  • 機器學習,日臻成熟,能習新務。

是以工廠可更靈活,更善應變,於市競中占優。


三曰:其所便者為何?何者更速、更廉、更簡?

智能所便甚多:

  • 維修轉為預防,不待壞而修;

  • 品管自動,晝夜不息;

  • 庫存管理,時時更新;

  • 預測之務,無須人力苦算。

如此可省工本,減延誤,滿顧客之需。


四曰:其所難者為何?或更貴、更煩、更複?

然智能導入,亦有不便:

  • 初設之費,頗為高昂;

  • 數據不足,則智能無由施展;

  • 工人或懼其奪職;

  • 智能黑箱,人難明其理。

若無妥備,則或致管理更紛,不若以往。


五曰:其新禍為何?

新技既至,新患隨之:

  • 數據若誤,所判則謬;

  • 系統若壞,影響甚鉅;

  • 過賴機器,或廢人之斷;

  • 網路相聯,則資安成憂。

是故導之須慎,人機並用,不可全任其術。


六曰:此術使誰得益?使誰受損?

得益者:

  • 工廠之主,營運得利;

  • 顧客之人,獲品優而速;

  • 技術之工,可轉高職,擔任智能之役。

受損者:

  • 操作簡務之工,或遭替代;

  • 不解智能、不善數據之廠,或漸落後;

  • 供應之小商,或難應新制。

是以施之宜兼顧眾人,訓練工人,使之與術共進,則可無怨。


結語

機器智能,若用其正,足以振興傳統製造,增產提效。然導入非小事,當詳審六問之道,明其所益,察其所損。若規劃得宜,不但工廠可興,工人亦可與時俱進,共創未來之業也。

Using AI in Traditional Manufacturing: Answering Six Simple Questions

Using AI in Traditional Manufacturing: Answering Six Simple Questions


Introduction

Artificial Intelligence (AI) is changing the way many industries work, including manufacturing. Traditional manufacturing companies often rely on machines, people, and fixed processes. AI brings new tools that can make work faster, smarter, and more efficient. But before using AI, it is important to ask some key questions. This paper uses the Six Questions of Technology to explore how AI can be added to a traditional manufacturing company in a smart and helpful way.


1. What is the power of the technology?

AI can help a manufacturing company:

  • Predict when machines need repairs (so they don’t suddenly break).

  • Improve product quality by spotting defects early.

  • Plan production schedules better.

  • Control inventory more accurately.

  • Reduce waste and energy use.

The power of AI is that it can help factories run more smoothly, make better decisions, and respond faster to problems.


2. What does it make possible that was not possible before?

Before AI, workers and managers had to rely on experience, guesswork, or slow systems. With AI, the company can now:

  • Use data from machines to predict future problems.

  • Adjust production in real-time based on demand or supply.

  • Spot patterns in product defects that humans might miss.

  • Use robots that learn and adapt to new tasks.

This makes the factory smarter and more flexible, allowing it to compete better in a fast-changing market.


3. What does it make easier, cheaper, or faster?

AI can make many tasks easier and quicker:

  • Machine maintenance becomes predictive, not reactive.

  • Quality checks can be automatic and continuous.

  • Inventory and supply chain tracking becomes real-time.

  • Planning and forecasting require less manual work.

These changes can lower costs, reduce delays, and improve customer satisfaction.


4. What does it make harder, more expensive, or more complicated?

At the same time, AI can bring new challenges:

  • It can be expensive to set up AI systems and train staff.

  • It may take time to gather and organize enough data for AI to work well.

  • Workers may worry about losing jobs to machines.

  • AI tools may be hard to understand or explain.

Without proper planning, AI could make the workplace more confusing instead of more efficient.


5. What new problems might it bring?

AI can also cause new issues:

  • Mistakes in data or algorithms can lead to wrong decisions.

  • AI systems might fail or break, causing big delays.

  • Over-reliance on AI can reduce human judgment and creativity.

  • Security risks may increase if systems are connected to the internet.

These problems mean AI must be introduced carefully, with human oversight.


6. Who gains and who loses from this technology?

Gains:

  • The company, by becoming more efficient and competitive.

  • Customers, who get better products, faster.

  • Skilled workers, who can move into higher-value jobs (like working with AI systems).

Loses:

  • Workers doing simple or repetitive jobs may be replaced.

  • Companies that can’t afford or don’t understand AI might fall behind.

  • Small suppliers may struggle to keep up with new demands.

To make AI fair, companies should invest in training and support for workers so they can grow with the technology.


Conclusion

AI has great potential to improve traditional manufacturing—but it must be used wisely. By asking the Six Questions of Technology, companies can better understand the benefits and challenges of AI. With the right steps, AI can help factories become smarter, faster, and more successful, while also supporting the people who work in them.

論中學引入機器智能之法——六問技術以明其利害

論中學引入機器智能之法——六問技術以明其利害


緒論

機器智能,今之所謂「人工智慧」者,乃當代之奇技,能助人言談、搜資訊、辨病象、理萬務。然則,欲將此術導入中學教育,當審慎思之:其所能,其所便,其所弊,皆當問焉。今試依「六問技術」之法,略陳淺見,以備參考。


一曰:其術之力何在?

機器智能能因材施教,適應人之所學。其所能者如:

  • 解題改作,應時而判;

  • 教人依步就班,量力而進;

  • 助師備課、減其瑣事;

  • 翻譯文言,讀文助聽;

  • 推介資源,增益所學。

總而言之,此術能興學效,釋師力,使教與學皆更精當。


二曰:其所可能,昔日所不能者為何?

昔者,一師教眾,難照顧人人。今有智能,可:

  • 為生量身規劃課程;

  • 設問答之機,晝夜可問;

  • 視生之困難,及早輔助;

  • 創變教室,使之應人之需。

此皆昔之所不能,今之所可為也。


三曰:其所便者為何?何者更速、更廉、更簡?

其便者甚多:

  • 改作自動,不勞人力;

  • 學生得即時回應,無待教師;

  • 翻譯、朗讀,頃刻而成;

  • 教材推介,不假旁求。

用之得法,則節時、省財、易行,可謂利多。


四曰:其所難者為何?或更貴、更煩、更複?

然機器智能亦非全善:

  • 所需器具,頗費資財;

  • 師生皆需習之,非一蹴而就;

  • 家貧子弟,或無器用之具;

  • 數據隱私,亦所當慎。

若無完備之備,則此術亦可成困擾之源。


五曰:其新禍為何?

其或生之弊,如:

  • 過倚機器,而廢思慮;

  • 人與人之互動漸疏;

  • 算法偏失,或致不公;

  • 數據蒐集,或害隱私。

是以,引之必慎,毋以新利而致新害。


六曰:此術使誰得益?使誰受損?

得益者:

  • 學困之生,獲得助力;

  • 勤勉之師,減其瑣務,專於教學;

  • 欲興教育之校,得事半功倍。

受損者:

  • 無器無網之生,益失公平;

  • 未得訓練之師,難以施用;

  • 貧困之鄉,無以購之。

故導入機器智能,務求公平,慎防貧富之限,致人有得有失。


結語

機器智能,若用得其所,可大興學效,解師困,助生學。然用之須明其本末,審其利害,問其可行。依「六問技術」而思,可得其全貌。庶幾師生皆利,而無遺於人,斯為善導智能入教之道也。

Bringing AI into Secondary Schools: Answering Six Simple Questions

Bringing AI into Secondary Schools: Answering Six Simple Questions


Introduction

Artificial Intelligence (AI) is changing the way we live, work, and learn. It helps us search online, talk to smart assistants like Siri or Alexa, and even helps doctors find illnesses. But how can we include AI in secondary schools to help students learn better? This paper answers the Six Questions of Technology to explore how AI can be added to education in a smart and useful way.


1. What is the power of the technology?

AI can help students learn in more personalized and engaging ways. It can:

  • Give instant feedback on homework.

  • Help students practice at their own speed.

  • Support teachers with grading and lesson planning.

  • Translate languages or read texts aloud.

  • Suggest extra materials for students who need more help or want to go deeper.

The power of AI is that it can make learning more tailored to each student and help teachers focus more on teaching and less on routine tasks.


2. What does it make possible that was not possible before?

Before AI, teachers had to spend a lot of time grading and preparing lessons for large groups. With AI, schools can now:

  • Offer personalized learning paths for each student.

  • Use chatbots or virtual tutors to answer questions any time.

  • Detect when students are struggling before it becomes a big problem.

  • Create smart classrooms that adjust to how students learn best.

This means more support for every student, not just the fastest or slowest ones.


3. What does it make easier, cheaper, or faster?

AI makes many things faster and easier:

  • Automatic grading saves teachers hours of time.

  • Students can get immediate feedback instead of waiting for a teacher.

  • Translating texts or helping students with reading difficulties can happen instantly.

  • Finding the right resources for each student becomes easier.

In the long run, AI can save schools time and money, especially in large classes.


4. What does it make harder, more expensive, or more complicated?

Using AI can also bring challenges:

  • Schools need to buy new tools or devices, which can be costly.

  • Teachers need training to use AI tools well.

  • Not all students may have access to the internet or devices at home.

  • Some people may worry about privacy and how AI uses data.

Adding AI can be hard if schools are not prepared with the right support, budget, and planning.


5. What new problems might it bring?

AI can bring new problems, such as:

  • Over-reliance on technology, where students or teachers stop thinking critically.

  • Less human interaction in the classroom.

  • Bias in AI systems that may treat some students unfairly.

  • Concerns about student data and privacy.

If not managed carefully, AI might create new issues even while solving old ones.


6. Who gains and who loses from this technology?

Gains:

  • Students who need extra help or learn differently.

  • Teachers who can focus more on creative and personal teaching.

  • Schools that want better results with fewer resources.

Loses:

  • Students without access to digital tools.

  • Teachers who don’t get proper training.

  • Communities that can’t afford the technology.

To be fair, schools need to make sure no one is left behind when using AI.


Conclusion

AI has the power to transform learning in secondary schools—but only if we use it wisely. By asking the Six Questions of Technology, we can think clearly about the benefits, the risks, and what steps are needed to make AI a helpful part of education for everyone. With the right support, training, and fairness, AI can be a great partner in helping students and teachers succeed.

2025年5月14日 星期三

執偏之悖:行之愈篤,謬之愈遠

 

執偏之悖:行之愈篤,謬之愈遠

夫組織之制,猶人身之構也。臟腑各司其職,協調以成康泰。然有悖理之象,謂之執偏之悖。何謂也?乃下屬各部,戮力以赴,務求其所轄之降本增效之績效指標(KPI),精益求精,效能日著。然此局部之「善」,積累而觀,反噬全局之戰略,致使組織傾頹,此誠可謂行之愈篤,謬之愈遠之悖也。

試觀諸部之行:

營銷之司, 志在擴張銷量,此乃常設之績效也。然竭力而為,致使產能難以企及,庫存驟增。積貨既久,成本益高,資金周轉滯澀,困於滯銷之物。

生產之部, 務求降低單位成本,亦為常見之績效也。然往往趨於批量生產,重在設備利用率,不察市場之瞬息萬變,交貨週期遂延。顧客日趨求速求變,必將棄之而去。

質檢之員, 奉命維持低不良率,此亦其責也。然或專注於末端檢驗,而忽略源頭之管控,致使瑕疵之品積壓,返工之事繁多,徒增資源之耗損,延誤交付之時。

財務之官, 勤於優化運營,削減眼前之費,亦其常務也。見生產之單位成本降低,或沾沾自喜。然庫存日增,交期延長,客源流失,組織之財政終將受損,雖生產之效看似可觀,現金流反枯竭,豈不悖哉!

物流之吏, 欲求運輸成本之優化,力求滿載而行,亦其職責所在。然或為湊足貨量而延遲發運,無意間增長了顧客之等待時日

由此可見,局部之優化,並不能保證全局之優良。反之,猶如盲人摸象,各執一端,其效適得其反,甚或傾覆大廈。各部皆以其所轄之「善」為務,竭力而行,終成組織之禍患,豈不悲哉!

執偏之「成」與弱者之韌

或有論者曰,目標乖謬而執行力弱之組織,其壽命反較長久,此豈不怪哉?蓋其力有不逮,雖欲行惡而未能速也,反得苟延殘喘之機。其內在之低效與遲緩,使其難以快速地步入歧途,免於速亡之厄。

譬如駑馬劣車,雖方向不正,然其力弱速緩,尚有轉圜之時。反觀良駒駿車,若方向有誤,則疾馳之下,頃刻之間,已至懸崖。故曰,目標既謬,而執行力強者,其敗亡之速,亦愈甚也。其精勤之行,反成其速朽之引擎,豈不諷刺乎!

鑒往知來,革弊鼎新

是故,執偏之悖警示吾人,組織之設計,務在全局之考量,戰略之清晰。績效指標之設,須環環相扣,映照組織之宏圖遠略,而非局限於部門之狹隘目標。獎勵之策,亦當激勵協同合作,嘉獎全局之績效。

此外,組織尚須倡導跨部門之溝通協作。各行其是,唯內是瞻,則昧於全局之損。常設對話之機制,共享績效之衡量,並以顧客之體驗為重,方能打破部門之壁壘。

總而言之,「執偏之悖」惕厲吾人,運營之精進,固然重要,然非長治久安之策。真正之組織效能,在於執行與戰略之合一,使局部之成功,裨益於整體之康健與永續。執偏而得之「成」,乃危險之陷阱。而執行力弱而目標亦謬之組織,其苟延殘喘,則以反諷之姿,警示吾人,高效地奔向錯誤之方向,其禍尤烈