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2025年11月3日 星期一

打破計畫性報廢循環:以限制理論促進永續消費的思維

 打破計畫性報廢循環:以限制理論促進永續消費的思維


摘要

自1950年代以來,「計畫性報廢」(engineered obsolescence)—故意縮短產品壽命以促進重複購買—成為消費資本主義的結構特徵。
此現象雖推動了經濟成長,卻也造成資源浪費、環境惡化與消費者信任危機。
本文運用限制理論(TOC)雲圖(衝突解析法)五大聚焦步驟,探討如何化解「追求利潤」與「永續誠信」間的根本矛盾,促進具社會責任的商業模式。


一、核心衝突(Evaporating Cloud 雲圖)

元素說明
目標 A建立繁榮且永續的經濟體。
需求 B企業必須維持獲利與成長。
需求 C社會必須確保環境與資源的長期永續。
行動 D鼓勵頻繁產品汰換與消費。
行動 D’設計耐用、可維修、可回收的產品。
衝突D 滿足 B 卻破壞 C;D’ 滿足 C 卻犧牲 B。

二、隱含假設

  • 獲利必須仰賴不斷銷售新產品。

  • 消費者只有在產品失效或過時時才會再購。

  • 永續生產無法與高獲利並存。

運用TOC的「假設挑戰」思維,可揭露並消除這些矛盾。


三、找出真實限制

現代消費主義的真正系統限制,是「企業商業模式將收益與銷售量直接綁定」。
此限制迫使生產者與消費者陷入「消耗-再購-浪費」的循環。


四、利用與提升限制

利用限制:
在現有模式下,可藉由提升效率、模組化設計、或訂閱式升級方案,提高產品價值與顧客黏著度。

提升限制:
透過商業模式創新來改變約束條件,例如:

  • 「產品即服務」(Product as a Service)模式:租賃、維修積分制度。

  • 終身升級與再製方案。

  • 建立回收、翻新、再製循環供應鏈。

此舉可同時滿足「獲利」(B) 與「永續」(C)。


五、使其他策略從屬並持續改善

法規制定、消費者教育與市場行銷皆應從屬於新模式的目標
鼓勵耐用、透明與誠信設計。
當永續成為新常態後,下一個限制(例如供應鏈彈性、資源取得)將浮現,成為新一輪改善焦點。


結論

「計畫性報廢」並非技術問題,而是商業模式的結構性限制—即將利潤與銷售量掛鉤的思維。
限制理論提供一套方法,使企業能系統性轉型,在維持經濟活力的同時,促進環境保護與社會誠信。

生命的脆弱鏈條:運用限制理論理解人體如何走向死亡

 生命的脆弱鏈條:運用限制理論理解人體如何走向死亡


摘要

生命是一個由多個子系統(器官、生化途徑、細胞過程)互相依存的動態複雜系統。死亡並非因為所有部位同時失效,而是某個關鍵限制(constraint)—一個器官或生理功能—停止運作,導致整體系統崩潰。
本文以限制理論的「五大聚焦步驟」分析人體死亡的系統過程,並提出對醫療、早期偵測與健康管理的啟示。


一、識別系統限制

在生理層面上,限制即是那個限制整體生命維持能力的關鍵器官或功能。
例如:心臟若停止跳動,即使其他器官完全健康,生命仍無法延續。
因此,限制定義了生命的邊界

關鍵洞見:
在醫學上,真正的限制並不一定是第一個失效的器官,而是那個「一旦退化,最迅速導致全身功能崩潰」的薄弱環節。


二、充分利用限制(Exploit the Constraint)

當限制被識別後,醫療系統必須最大化該限制的效能與保護
例如:

  • 在加護病房中,維持心肺功能(以氧氣供應與循環支持)即是利用現行的限制。

  • 在慢性病照護中,維持腎臟或肝臟功能往往是延續生命的關鍵。

限制理論原則:
不要將資源浪費在非限制部分,應集中力量保護並優化當前的限制。


三、使其他系統從屬於限制(Subordinate to the Constraint)

其他所有生理過程與醫療處置應與保護限制的目標一致
例如:

  • 調整藥物劑量與飲食以減少對關鍵器官的負擔。

  • 將醫院資源(如ICU照護、監測設備)依限制重要性優先配置。


四、提升限制(Elevate the Constraint)

當條件允許時,應提升限制,即透過科技或醫學手段改善該薄弱環節:

  • 器官移植

  • 人工心肺與人工器官

  • 再生醫學與基因療法

這同時也解釋了器官捐贈之所以有效:藉由替換或增強已失效的限制,生命得以繼續,即使其他系統維持原狀。


五、防止慣性與持續改善(Avoid Inertia)

當前限制被解決或提升後,新的限制會自然浮現
醫療系統需持續監測,找出下一個生命維持的瓶頸(例如由腎臟轉為心臟、再轉為免疫系統)。
這種持續迴圈的思維,正是預防醫學與精準醫療的基礎。


對醫療管理的啟示

  • 從症狀治療轉向以限制為導向的系統優化

  • 建立跨系統監測與預測工具(AI診斷、生物標誌分析),提前發現限制。

  • 動態分配資源,依生命最受限的功能決定醫療優先順序。


結論

死亡並非全系統的崩潰,而是單一未被解除的限制之主導效應
若醫療能從被動修復轉為主動限制管理,不僅可延長壽命,更能提升生命品質。

Breaking the Cycle of Planned Obsolescence: A Theory of Constraints Approach to Sustainable Consumerism

Breaking the Cycle of Planned Obsolescence: A Theory of Constraints Approach to Sustainable Consumerism



Abstract

Since the 1950s, engineered obsolescence—the deliberate shortening of product life to drive repeat purchases—has been a structural feature of consumer capitalism. While it supports economic growth, it also fosters waste, environmental damage, and consumer distrust. Using the Evaporating Cloud (Conflict Resolution Diagram) and the Five Focusing Steps, this paper explores how the Theory of Constraints can help reconcile the conflict between profit-driven consumerism and sustainability with integrity.


1. The Core Conflict (Evaporating Cloud)

ElementDescription
Goal (A)Build a prosperous and sustainable economy.
Need BEnsure business profitability and growth.
Need CEnsure long-term environmental and social sustainability.
Action DEncourage frequent product replacement and consumption.
Action D’Design durable, repairable, and sustainable products.
ConflictD satisfies B but undermines C; D’ satisfies C but undermines B.

2. Underlying Assumptions

  • Profitability depends on continuous sales, not lasting value.

  • Consumers will only buy if products fail or become obsolete.

  • Sustainable production cannot be profitable.

By challenging these assumptions, TOC helps evaporate the conflict.


3. Breaking the Constraint

The real system constraint in modern consumerism is the business model that links revenue to volume of new product sales.
This constraint forces both producers and consumers into a cycle of waste.


4. Exploiting and Elevating the Constraint

Exploit: Use TOC thinking to maximize profitability within the constraint by improving efficiency, modular design, or subscription-based upgrades.
Elevate: Transform the constraint by shifting the business model—from product sales to service-based, circular economy models, e.g.:

  • “Product as a Service” (e.g., leasing, repair credits)

  • Lifetime upgrade programs

  • Reuse, refurbish, and remanufacture systems

This maintains profitability (B) while also achieving sustainability (C).


5. Subordinate and Repeat

Regulations, consumer education, and marketing should all subordinate to this new model—aligning incentives toward durability, transparency, and ethical design.
As sustainability becomes the new norm, the next constraint (e.g., supply chain resilience, resource scarcity) will emerge for further improvement.


Summary 

Planned obsolescence reflects a structural constraint—an outdated linkage between profit and consumption volume.
TOC enables a systemic transition to models that maintain business viability while promoting environmental and social integrity.

The Fragile Chain of Life: Applying the Theory of Constraints to Understanding How the Human Body Dies

 The Fragile Chain of Life: Applying the Theory of Constraints to Understanding How the Human Body Dies


Abstract

Life is a dynamic, complex system sustained by the interdependence of multiple subsystems (organs, biochemical pathways, cellular processes). Death occurs not necessarily because all parts fail, but because one critical constraint—an organ or system—ceases to perform its essential function, causing the collapse of the entire system. This paper applies the Five Focusing Steps of the Theory of Constraints (TOC) to the biological process of death, and proposes insights for healthcare improvement, early detection, and systemic health management.


1. Identifying the Constraint

In biological terms, the constraint is the organ or physiological function that limits the entire body’s ability to sustain life.
For example, if the heart stops pumping, blood circulation ceases, regardless of whether all other organs are healthy. Thus, the constraint defines the boundary of life.

Key Insight:
In medicine, the true constraint is not always the first failing organ, but the weakest link whose degradation most rapidly affects systemic viability.


2. Exploiting the Constraint

Once identified, the medical system must maximize the performance and protection of this constraint.
For instance:

  • In critical care, maintaining oxygen supply (via heart or lung support) exploits the current constraint.

  • In chronic illness management, preserving kidney or liver function often dictates survival.

TOC Principle: Do not waste efforts on non-constraints. Direct interventions to protect or enhance the current limiting system.


3. Subordinating Everything Else

All other physiological processes and medical treatments should be aligned to protect the constraint.
For example:

  • Adjust drug dosages or diets to reduce stress on the failing organ.

  • Allocate hospital resources (ICU attention, monitoring) according to constraint criticality.


4. Elevating the Constraint

When possible, elevate the constraint through technology or medical intervention:

  • Organ transplantation

  • Artificial organs or life-support systems

  • Regenerative medicine or gene therapy

This step also explains why organ donation works: by replacing or augmenting the failed constraint, life can continue even when other systems are unchanged.


5. Preventing Inertia (Back to Step 1)

Once the constraint is resolved or elevated, a new constraint will emerge.
Healthcare systems must continuously monitor which function is now the limiting factor (e.g., from kidney to heart to immune system).
This cyclical awareness aligns with preventive and predictive medicine.


Implications for Healthcare Management

  • Shift from symptom-based treatment to constraint-based system optimization.

  • Integrate systemic monitoring tools (AI diagnostics, predictive biomarkers) to detect emerging constraints early.

  • Allocate resources dynamically according to the most life-limiting organ or function at any given time.


Summary

Death can be seen not as total system failure, but as the dominance of one unrelieved constraint.
Healthcare can evolve from reactive repair to proactive constraint management, extending both life expectancy and quality.

2025年10月28日 星期二

解鎖你的瓶頸:注意力、信任與動機的「知行不一」困境

 

解鎖你的瓶頸:注意力、信任與動機的「知行不一」困境

一個普遍存在卻難以解決的人類困境:「知行不一」(The Know-Do Problem)——我們知道該做什麼,卻始終無法付諸行動。我們將運用「約束理論」(Theory of Constraints, TOC)的視角,結合 Dr. Alan Barnard 的洞察,來解構這個問題。


一、 識別人類的三大稀缺與不對稱約束 (Identifying Our Three Asymmetrical Constraints)

在 TOC 中,約束不僅僅是稀缺資源,它具備一種「不對稱性」:獲得極難,失去極易。我們身處數位時代,注意力不再是唯一受限的資源,還有兩個更關鍵的瓶頸:信任動機

1. 稀缺資源一:注意力 (Attention)

  • 不對稱性: 你的目光很難被吸引,但一旦被吸引,卻隨時會被拉走。

  • 例子: 想像你在瀏覽社群媒體。設計者深知,他們每隔 3 秒鐘就必須透過新的刺激(視覺、通知或內容)來「重新贏得」你的注意力。這不是保持,而是重新爭取

  • 突破點: 既然注意力有限,我們必須學會停止浪費確保所有專注都流向唯一能幫助你達成一個最重要目標的事情上。

2. 稀缺資源二:信任 (Trust)

  • 不對稱性: 信任極難建立極易失去幾乎不可能重建

  • 例子: 在親密關係中,當伴侶問:「我穿這件洋裝好看嗎?」你為了保護對方而說了善意的謊言(「很棒」)。但當真相揭露,引發的不是洋裝的爭吵,而是信任的崩塌——「如果你能對洋裝說謊,你還對什麼說謊?」

  • 突破點: 關係中的問題往往是信任衝突解決之道在於「雙重接受」:提問者必須承諾,不懲罰說出「你的真實」的人。

3. 稀缺資源三:動機 (Motivation)

  • 不對稱性: 努力激發的動機是短暫且轉瞬即逝的,尤其容易受到突發事件的影響。

  • 例子: 一位成功的行銷顧問 Rich 告訴 Dr. Barnard,他早上知道必須做一場網路研討會(外部承諾是克服拖延的方式),零動機他開始與 AI 對話,AI 沒有直接激勵他,而是問他:「你對哪個主題最充滿熱情?」結果,他在不知不覺中投入了工作。

  • 突破點: 我們需要的不是表面的「激勵」,而是「催化條件」(Catalytic Conditions)。即找出讓你開始行動的最小、最微不足道的步驟(例如:早上沒動機做 100 個伏地挺身?那就先做一個)


二、 AI 如何幫助我們克服「知行不一」? (AI as a Personal Constraint Solver)

AI 在解決人類內在約束方面展現出巨大潛力,因為它可以提供一種「無情感的真實」。

  • 優勢 1: 迅速建立信任: 人們開始信任 AI,不是因為公司,而是因為 AI 是「在人類迴圈中」為個人最大利益服務,它根據你的輸入學習,並給你最個人化的反饋。

  • 優勢 2: ProCon Cloud 創新方法: Dr. Barnard 訓練 AI 遵循他的 ProCon Cloud 方法來尋找「創新」。

    • 衝突定義: 你的困境(例如:該不該離婚/離職)是兩個對立選項(「改變」 vs.「不改變」)之間的衝突,每個選項都有其獨特的優點(Pros)和缺點(Cons)。

    • 現狀的回報: 你之所以停滯不前,是因為你害怕失去現狀的回報(即使現狀很糟,也有其好處)

    • 創新步驟: 創新是找到一個能同時兼具兩種選項的所有優點,卻沒有任何缺點的新方案。

      • 例子: 癮君子知道戒菸的好處,但害怕失去「吸菸帶來的情緒緩解」。創新方案不是「戒菸」(放棄緩解情緒),而是「戒菸 + 學習冥想或運動」,用新的健康方式取代舊的心理回報。

  • 優勢 3: 將潛意識轉為意識: 我們無法挑戰潛意識中的信念。AI 透過提出精確問題,將潛意識中的「恐懼」(例如,如果你成功了,會獲得什麼你不想要的東西?)轉化為意識,一旦寫下來,我們就能質疑其真實性。


2025年10月18日 星期六

凱撒模式:一個持續改進的約束理論案例研究

 

凱撒模式:一個持續改進的約束理論案例研究

由實業家亨利·J·凱撒(Henry J. Kaiser)領導的自由輪建造的非凡轉型,是約束理論(Theory of Constraints, 實際應用的一個強大真實案例。由艾利亞胡·M·戈德拉特博士(Dr. Eliyahu M. Goldratt)發展的  認為,每個複雜系統都有至少一個約束(瓶頸)限制其整體產出(吞吐量)。凱撒的成功不僅在於識別最初的瓶頸,更在於系統性地重複  流程,以實現持續、驚人的改進


初始約束:時間與工藝

盟軍最初面臨的問題是災難性的吞吐量不足:德國潛艇擊沉船隻的速度快於他們建造船隻的速度。傳統的造船是一個順序性的過程,依賴於高度熟練的工匠、手工鉚接以及在船臺上組裝整個船體。

  • 初始約束(230 天): 順序組裝熟練勞工的供應

  • 凱撒的核心創新(提升約束): 凱撒和他的總工程師克萊·貝德福德(Clay Bedford)將船隻重新定義為批量生產的產品。他們用模組化建造焊接取代了順序性的熟練勞動。他們引入了「裝配線」概念,在不同的區域同時建造船隻的不同部分,並迅速訓練非熟練工人執行單一、可重複的任務。

這種根本性的轉變提升了初始約束,將平均建造時間從估計的 230 天,縮短到 197 天的紀錄,並迅速降至平均 42 天


第二階段:TOC 的首次迭代(42 天  21 天)

一旦最初的勞動力和流程約束得到解決,瓶頸立即轉移到下一個限制因素。對於任何高吞吐量的製造業務來說,約束不可避免地會轉移到最終產品組裝的空間。

  • 識別新約束(步驟 1): 最終組裝船臺(船塢)。船臺一次只能容納一個船體進行最終焊接和下水,這決定了最大產出率。

  • 利用與配合(步驟 2 和 3): 為了最大限度地利用船臺,工作被嚴格控制。返工被移到船臺外執行,並隱性地使用了鼓-緩衝-繩 () 系統:船臺設定了「鼓」的節奏,預製模組構成了保護性「緩衝」。

  • 為了減少 50% 而提升(步驟 4): 為了實現 21 天周期的宏偉目標,唯一可行的解決方案是物理複製瓶頸。透過將最終組裝船臺數量增加一倍(增加一個平行的船塢),船廠立即將最終組裝的能力提高了一倍,理論上將吞吐時間減少了一半。


第三階段:加速至世界級吞吐量(21 天  10 天)

根據 TOC 的第五步,「不要讓慣性產生;回到第一步。」一旦最終組裝船臺不再是約束,瓶頸就會向後轉移到流程的上游。

  • 識別新約束(步驟 1): 預製車間吞吐量。負責建造大型複雜模組(機艙、甲板室)的車間現在難以足夠快地為雙最終組裝線供料。它們的限制是空間、起重機可用性和複雜的焊接/裝配時間

  • 利用與配合(步驟 2 和 3): 車間將執行全面品質管理 () 和標準化以避免後續昂貴的返工。主緩衝(在製品庫存)被放置在這些車間之前,以確保它們永遠不會因為材料短缺而閒置。為配合車間的產出時間表,實行了專門的即時 () 運輸,以確保物流服從於生產。

  • 為了 10 天目標而提升(步驟 4): 實現 10 天的周期需要透過平行化進行大規模的提升:

    • 平行子模組化: 將複雜的模組(如機艙)分解成三個子組裝部分,在平行的組裝區同時建造。

    • 基礎設施複製: 建造一個額外的平行預製設施,專門用於最高產量的模組(船中貨艙),從而將車間的地面空間和起重機容量增加一倍。

透過重複應用 TOC——識別約束、最大限度地利用它、使系統的其餘部分與其節奏保持一致,並最終提升其容量——凱撒的船廠展示了持續改進如何從根本上改變生產的規律,將一個耗時數月的流程轉變為只需幾天。


自由輪案例研究要點總結:

  • 原始問題: 傳統造船需要 6 至 8 個月(長達 230 天),落後於德國潛艇的擊沉速度。

  • 核心創新: 亨利·J·凱撒和克萊·貝德福德將批量生產技術(類似於福特的裝配線)應用於造船。

  • 關鍵流程變革: 焊接取代了鉚接模組化建造允許將單獨的船體部分(船首、船尾、機艙)平行建造。

  • 勞動力: 招募並培訓了數千名沒有經驗的工人,讓他們執行一個特定的、簡單的任務。

  • 結果時間表:

    • 第一艘船:197 天。

    • 1942 年春平均:70 天。

    • 紀錄時間(SS Robert E. Peary):4 天 15 小時 29 分鐘

    • 1943 年全國平均:42 天

  • 遺產: 高產出使美國每天能建造三艘船,超過了德國潛艇的擊沉速度,成為戰爭取勝的關鍵因素。

The Kaiser Method: A Theory of Constraints Case Study in Continuous Improvement

 

The Kaiser Method: A Theory of Constraints Case Study in Continuous Improvement

The remarkable transformation of Liberty Ship construction during World War II, driven by industrialist Henry J. Kaiser, serves as a powerful, real-world case study in the Theory of Constraints ()TOC, developed by Dr. Eliyahu M. Goldratt, posits that every complex system has at least one constraint (a bottleneck) that limits its overall output (throughput). Kaiser’s success lay not just in identifying the initial bottleneck, but in systematically repeating the TOCprocess to achieve continuous, staggering improvement.


The Original Constraint: Time and Craftsmanship

The initial problem facing the Allies was a catastrophic throughput deficit: German U-boats were sinking ships faster than they could be built. Traditional shipbuilding was a sequential process, relying on highly skilled tradesmen, manual riveting, and assembly of the entire vessel on the slipway.

  • Original Constraint (230 Days): Sequential Assembly and Skilled Labor Availability.

  • Kaiser's Core Innovation (Elevating the Constraint): Kaiser and his chief engineer, Clay Bedford, redefined the ship as a product of mass production. They substituted sequential, skilled labor with modular construction and welding. They introduced an "assembly line" concept where different ship sections were built in parallel, and unskilled workers were quickly trained for single, repeatable tasks.

This radical shift elevated the initial constraints, slashing the average build time from an estimated 230 days to a 197-day record, and quickly down to an average of 42 days.


Phase II: The First Iteration of TOC (42 Days  21 Days)

Once the original labor and process constraints were resolved, the bottleneck immediately shifted to the next limiting factor. For any high-throughput manufacturing operation, the constraint invariably moves to the space where the final product is constructed.

  • New Constraint Identified (Step 1): The Final Assembly Ways (The Slip). Only one hull could occupy the slipway at a time for final hull welding and launch. This dictated the maximum output rate.

  • Exploit & Subordinate (Steps 2 & 3): To maximize the ways, work was strictly controlled. Rework was moved off the slipway, and a Drum-Buffer-Rope () system was implicitly used: the ways set the "Drum" pace, and pre-fabricated modules formed the protective "Buffer."

  • Elevation for 50% Reduction (Step 4): To meet the ambitious goal of a 21-day cycle, the only viable solution was to physically replicate the bottleneck. By doubling the number of Final Assembly Ways (adding a twin slip), the yard instantly doubled its capacity for final assembly, theoretically cutting the throughput time in half.


Phase III: The Second Iteration of TOC (21 Days  10 Days)

According to TOC's fifth step, "Don't let inertia set in; go back to step one." Once the Final Assembly Ways were no longer the constraint, the bottleneck migrated backward in the process flow.

  • New Constraint Identified (Step 1): Pre-Fabrication Shop Throughput. The shops that built the massive modular sections (engine rooms, deckhouses) now struggled to feed the dual final assembly lines fast enough. Their limits were space, crane availability, and complex welding/fitting time.

  • Exploit & Subordinate (Steps 2 & 3): Shops would enforce Total Quality Management () and Standardization to avoid costly rework later. The Buffer of ready-to-cut steel was placed before these shops to ensure they never ran idle. Dedicated, Just-in-Time () transportation was instituted to subordinate logistics to the shops' output schedule.

  • Elevation for 10-Day Goal (Step 4): Achieving a 10-day cycle demanded massive elevation through parallelization:

    • Parallel Sub-Modularization: Breaking complex modules (like the engine room) into three sub-assembly sections to be built simultaneously in parallel bays.

    • Infrastructure Replication: Building a parallel Pre-Fabrication facility dedicated to the highest-volume modules, thereby doubling the floor space and crane capacity in the shops.

By applying TOC repeatedly—identifying the constraint, maximizing its use, aligning the rest of the system to its pace, and finally elevating its capacity—Kaiser's yard demonstrated how continuous improvement can fundamentally change the physics of production, transforming a months-long process into a matter of days.


Summary of Liberty Ship Case Study Points:

  • Original Problem: Traditional shipbuilding took 6–8 months (up to 230 days), falling behind the rate of German U-boat attacks.

  • Core Innovation: Henry J. Kaiser and Clay Bedford applied mass production techniques (like Ford's assembly line) to shipbuilding.

  • Key Process Changes: Welding replaced riveting, and Modular Construction allowed separate sections (bow, stern, engine room) to be built in parallel.

  • Workforce: Recruited and trained thousands of inexperienced workers to perform one specific, simple task.

  • Results Timeline:

    • First ship: 197 days.

    • Spring 1942 average: 70 days.

    • Record time (SS Robert E. Peary): 4 days, 15 hours, 29 minutes.

    • National average by 1943: 42 days.

  • Legacy: The high output allowed the US to build three ships a day, surpassing German U-boat losses and proving a key factor in the war.



2025年10月7日 星期二

Melangkaui '5 Mengapa': Bagaimana Deduksi Saintifik Menyelesaikan Masalah Paling Kronik Syarikat Anda

 

Melangkaui '5 Mengapa': Bagaimana Deduksi Saintifik Menyelesaikan Masalah Paling Kronik Syarikat Anda

Dunia perniagaan sering berdepan dengan masalah kronik atau "masalah rumit" (wicked problems): isu yang berterusan walaupun usaha berulang kali untuk menyelesaikannya telah dilakukan. Contohnya termasuk jualan yang mendatar, pertikaian inventori yang tidak berkesudahan, atau kesilapan kualiti yang berulang.

Kaedah penyelesaian masalah tradisional, seperti Rajah Tulang Ikan (Fishbone Diagram) atau 5 Mengapa (5 Whys), sering gagal menangani isu-isu kompleks ini. Ia cenderung menghasilkan pendapat yang bercanggah dan berat sebelah—jualan menyalahkan operasi, operasi menyalahkan peramalan—dan mudah dimanipulasi oleh data terpilih.

Untuk mendiagnosis dan menyelesaikan masalah yang berlarutan ini dengan berkesan, pengurusan perlu mengadaptasi ketegasan metodologi sains, khususnya meminjam teknik dari Sains Bidang Sejarah (Historical Field Sciences), seperti penyiasatan forensik atau paleontologi: Kaedah Deduktif Hipotetikal (Hypothetical Deductive Method - HDM).


Tiga Langkah Deduksi Hipotetikal

HDM berfungsi berdasarkan prinsip bahawa satu punca akan sentiasa menghasilkan pelbagai kesan yang berbeza. Dengan memerhati satu kesan dan menyimpulkan kesan lain yang secara logiknya mesti wujud, pengurus boleh menguji kesahihan andaian awal mereka tanpa bergantung pada data yang berpotensi berat sebelah.

  1. Perhatikan Kesan dan Hipotesis Punca: Mulakan dengan masalah yang jelas (Kesan A), seperti "Penghantaran adalah isu yang diketahui." Hipotesis punca utama (Punca X), contohnya "Loji beroperasi tidak cekap."

  2. Ramalkan Kesan Lain: Jika Punca X adalah sebab yang sebenar, simpulkan secara logik kesan-kesan lain yang kurang jelas (Kesan B, C, D) yang mesti hadir. Contohnya, jika penghantaran teruk, anda juga perlu menjangka:

    • Kadar permintaan pelanggan untuk menjadualkan semula pesanan yang tinggi.

    • Pincangan besar dalam penghantaran yang tertumpu pada hujung bulan atau hujung suku tahun.

  3. Sahkan Secara Empirikal Kesan yang Diramalkan: Periksa organisasi untuk Kesan B, C, dan D. Jika ia wujud, punca yang dihipotesiskan berkemungkinan betul. Jika data menunjukkan penghantaran lancar dan tiada permintaan penjadualan semula, maka hipotesis asal—bahawa loji beroperasi tidak cekap—adalah salah, tidak kira apa yang dicadangkan oleh data awal.

Metodologi ini menyediakan rangka kerja yang ketat untuk menembusi bias organisasi dan menentukan akar masalah yang sebenar.


Tiga Garis Panduan untuk Ketegasan Saintifik

Untuk mengaplikasikan HDM secara berkesan dan memastikan diagnosis tidak berdasarkan generalisasi yang samar-samar, tiga garis panduan ketat mesti diikuti:

1. Tuntut Ketepatan dan Bahasa Literal

Elakkan menggunakan istilah yang luas, metafora, atau subjektif seperti "tidak cekap," "kurang motivasi," atau "budaya buruk." Semasa mendiagnosis, setiap perkataan mesti mempunyai definisi literal yang tepat. Contohnya, jika sesebuah pasukan mengatakan kesilapan adalah disebabkan "ketidakcekapan," anda mesti menentukan dengan tepat maksudnya. Jika kesilapan turut berlaku pada prosedur mudah yang didokumenkan dengan baik, maka definisi punca (kecekapan) anda adalah salah.

2. Tanya "Mengapa" dan "Bagaimana" untuk Mencari Mekanisme

Tidak cukup hanya mengetahui mengapa masalah wujud; anda mesti memahami bagaimana punca itu mencipta kesan. Ini mendedahkan mekanisme sebab-akibat dan meningkatkan keupayaan ramalan.

Dalam perniagaan, memahami mekanisme sebab-akibat di sebalik kesilapan makmal mungkin mendedahkan bahawa isu itu bukan kecekapan, tetapi penganalisis dibebani dengan pelbagai tugasan. Mekanismenya ialah bebanan ini membebankan memori jangka pendek mereka, yang membawa kepada kecuaian. Menemui bagaimana ini membawa kepada penyelesaian yang jauh lebih berkesan (mengurangkan beban kerja) daripada hanya menangani mengapa (latihan semula).

3. Jangan Sesekali Menyalahkan Masalah pada "Kekurangan Penyelesaian"

Jangan mentakrifkan masalah dengan penyelesaian yang anda tidak miliki. Elakkan penaakulan seperti: "Produktiviti jualan saya rendah kerana saya tidak mempunyai perisian CRM" atau "Pembangunan produk lambat kerana kami tidak mempunyai bajet R&D yang mencukupi."

Logik yang cacat ini membawa kepada pelaksanaan produk dan alatan IT yang mahal tanpa pernah memberikan hasil penuh. Sebaliknya, fokus pada apa yang anda lakukan dengan apa yang sudah anda miliki. Dengan mendalami proses dan sumber sedia ada, anda pasti akan menemui pandangan sebenar yang diperlukan untuk menyelesaikan masalah kronik.

Gabungan Pemikiran Saintifik yang ketat (HDM) dan Pemikiran Holistik (memahami bagaimana semua masalah rumit saling berkaitan) adalah satu-satunya jalan yang boleh dipercayai untuk menyelesaikan cabaran perniagaan yang paling berterusan.

Khoa Học Quản Trị: Dùng Phương Pháp Suy Luận Giả Thuyết Để Giải Quyết Các Vấn Đề "Nan Giải" Mạn Tính Của Công Ty

 

Khoa Học Quản Trị: Dùng Phương Pháp Suy Luận Giả Thuyết Để Giải Quyết Các Vấn Đề "Nan Giải" Mạn Tính Của Công Ty

Thế giới kinh doanh đầy rẫy các vấn đề mạn tính hay còn gọi là "vấn đề nan giải": những trục trặc cứ lặp đi lặp lại dù đã tốn nhiều công sức để giải quyết, ví dụ như doanh số trì trệ, mâu thuẫn tồn kho không hồi kết, hoặc lỗi chất lượng tái diễn.

Các phương pháp giải quyết vấn đề truyền thống, như Sơ đồ Xương Cá (Fishbone Diagram) hay 5 Tại Sao (5 Whys), thường thất bại trong việc xử lý những vấn đề phức tạp này. Chúng dễ dẫn đến những ý kiến mâu thuẫn, thiên vị—bộ phận bán hàng đổ lỗi cho vận hành, vận hành đổ lỗi cho dự báo—và dễ bị thao túng bởi dữ liệu chọn lọc.

Để thực sự chẩn đoán và giải quyết những vấn đề dai dẳng này, ban quản lý cần áp dụng sự chặt chẽ về phương pháp luận của khoa học tự nhiên, cụ thể là mượn một kỹ thuật từ Khoa học Lịch sử Hiện trường (như điều tra pháp y hoặc cổ sinh vật học): Phương pháp Suy luận Giả thuyết (Hypothetical Deductive Method - HDM).


Ba Bước Của Suy Luận Giả Thuyết

HDM hoạt động dựa trên nguyên tắc: một nguyên nhân duy nhất sẽ luôn tạo ra nhiều hệ quả khác nhau. Bằng cách quan sát một hệ quả và suy luận những hệ quả khác nào phải tồn tại một cách hợp lý, nhà quản lý có thể kiểm tra tính đúng đắn của giả định ban đầu mà không phụ thuộc vào dữ liệu có khả năng thiên vị.

  1. Quan sát Hệ quả và Đưa ra Giả thuyết Nguyên nhân: Bắt đầu bằng vấn đề rõ ràng (Hệ quả A), chẳng hạn như "Việc giao hàng có vấn đề." Đưa ra một nguyên nhân chính (Nguyên nhân X), ví dụ "Nhà máy đang hoạt động kém hiệu quả."

  2. Dự đoán các Hệ quả Khác: Nếu Nguyên nhân X là nguyên nhân thực sự, hãy suy luận hợp lý các hệ quả khác, ít rõ ràng hơn (Hệ quả B, C, D), mà buộc phải tồn tại. Ví dụ, nếu việc giao hàng kém, bạn cũng phải kỳ vọng thấy:

    • Tỷ lệ yêu cầu đổi lịch giao hàng từ khách hàng cao.

    • Sự mất cân đối lớn trong việc xuất hàng dồn vào cuối tháng hoặc cuối quý.

  3. Xác minh Thực nghiệm các Hệ quả Đã Dự đoán: Kiểm tra tổ chức để tìm kiếm các Hệ quả B, C và D. Nếu chúng tồn tại, nguyên nhân giả định có khả năng là đúng. Nếu dữ liệu cho thấy việc xuất hàng rất đều và không có yêu cầu đổi lịch, thì giả thuyết ban đầu—rằng nhà máy hoạt động kém hiệu quả—là sai, bất kể dữ liệu ban đầu gợi ý điều gì.

Phương pháp này cung cấp một khuôn khổ nghiêm ngặt để cắt xuyên qua những thiên vị tổ chức và xác định căn nguyên thực sự của vấn đề.


Ba Nguyên Tắc Để Chẩn Đoán Khoa Học

Để áp dụng HDM một cách hiệu quả và đảm bảo chẩn đoán không dựa trên những khái quát mơ hồ, cần tuân thủ ba nguyên tắc chặt chẽ:

1. Yêu cầu Độ Chính Xác và Ngôn Ngữ Theo Nghĩa Đen

Tránh sử dụng các thuật ngữ rộng, ẩn dụ hoặc chủ quan như "thiếu năng lực," "thiếu động lực," hay "văn hóa tồi."Khi chẩn đoán, mỗi từ phải có một định nghĩa theo nghĩa đen, chính xác. Ví dụ, nếu một nhóm nói lỗi là do "thiếu năng lực," bạn phải xác định chính xác ý nghĩa của nó. Nếu lỗi xảy ra ngay cả trong các quy trình đơn giản, có tài liệu rõ ràng, thì định nghĩa nguyên nhân (năng lực) của bạn là sai.

2. Hỏi "Tại Sao" và "Bằng Cách Nào" để Tìm Cơ Chế

Không đủ chỉ biết tại sao vấn đề tồn tại; bạn phải hiểu bằng cách nào nguyên nhân tạo ra hệ quả. Điều này tiết lộ cơ chế nhân quả và cải thiện đáng kể khả năng dự đoán.

Trong kinh doanh, hiểu cơ chế nhân quả đằng sau lỗi phòng thí nghiệm có thể cho thấy vấn đề không phải là năng lực, mà là do các nhà phân tích bị quá tải với nhiều nhiệm vụ. Cơ chế là sự quá tải này làm căng thẳng bộ nhớ ngắn hạn của họ, dẫn đến việc bỏ sót. Tìm ra bằng cách nào này sẽ dẫn đến một giải pháp hiệu quả hơn nhiều (giảm tải công việc) so với việc chỉ nói tại sao (tái đào tạo).

3. Không Bao Giờ Đổ Lỗi Vấn Đề Cho Việc "Thiếu Giải Pháp"

Đừng định nghĩa vấn đề bằng giải pháp bạn không có. Tránh lý luận kiểu: "Năng suất bán hàng thấp vì tôi không có phần mềm CRM" hay "Phát triển sản phẩm chậm vì chúng tôi không có đủ ngân sách R&D."

Lối suy luận sai lầm này dẫn đến việc triển khai các sản phẩm và công cụ CNTT đắt đỏ nhưng không bao giờ mang lại kết quả đầy đủ. Thay vào đó, hãy tập trung vào những gì bạn đang làm với những gì bạn đã có. Bằng cách đào sâu vào các quy trình và nguồn lực hiện có, bạn sẽ tìm thấy những hiểu biết sâu sắc thực sự cần thiết để giải quyết vấn đề mạn tính.

Việc kết hợp Tư duy Khoa học nghiêm ngặt (HDM) và Tư duy Toàn diện (hiểu sự liên kết giữa tất cả các vấn đề nan giải) là con đường đáng tin cậy duy nhất để giải quyết các thách thức kinh doanh dai dẳng nhất.

讓科學家來當偵探:用「假設-演繹法」揪出公司慢性病的真兇



讓科學家來當偵探:用「假設-演繹法」揪出公司慢性病的真兇


一、別再讓數據和爭吵模糊了焦點!

在企業裡,有些問題就像**「慢性病」「爛攤子」**一樣,怎麼治都治不好:業務停滯、庫存老是出錯、品質老是抓不到原因。

傳統的管理方法,像是畫魚骨圖或問**「五個為什麼」**,往往只會讓情況更糟。因為:

  • 意見矛盾: 銷售部門怪營運部門,營運部門怪預測部門,你根本分不清誰說的是事實,誰只是在推卸責任。

  • 數據誤導: 數據常常是片面的、不完整的,甚至會被用來證明各種立場偏見,讓大家各說各話,問題始終無解。

為了解決這些棘手的問題,我們必須放下老方法,像偵探古生物學家一樣思考,運用一套來自嚴謹科學的推理方法:假設-演繹法(Hypothetical Deductive Method, HDM)


二、科學偵探三部曲:用「預測效應」驗證真相

假設-演繹法的核心精神很簡單:一個真正的原因,必然會帶來一系列的連鎖反應。我們觀察到一個結果(A),然後大膽猜測一個原因(X),接著最重要的一步是:根據這個原因(X),預測出其他必定會發生的連帶結果(B、C、D)

如果這些被預測的結果(B、C、D)不存在,那你的原因(X)就是錯的!

【實戰範例】

  1. 觀察與假設: 你的問題是**「交貨老是出問題」(結果 A)。你猜測原因是「工廠運作效率太差」**(原因 X)。

  2. 預測連帶結果: 如果工廠效率真的太差,那麼你一定會看到(預測 B、C、D):

    • B:客戶每天打電話要求改期(大量重排訂單請求)。

    • C:出貨量極度不平均(八成以上的貨都擠在月底出)。

  3. 驗證真相: 假設你發現,客戶幾乎沒有要求改期,且出貨時間分散得很平均。這表示你的假設「工廠效率太差」是錯誤的

透過這種方法,你根本不需要去翻閱那些混亂的工廠效率報告,就能直接鎖定:問題不在工廠效率本身,而在於你對「交貨問題」的定義或「效率數據」的計算方式有誤。


三、三個原則,讓你的診斷精準無比

為了讓「假設-演繹法」有效運作,你的思考和語言必須極度嚴謹:

1. 語言必須精確,遠離空泛的比喻

永遠不要使用模糊、主觀帶有情緒的詞彙來定義問題。

例如,部門主管說問題是「員工能力不足」。你必須追問:「能力不足具體是什麼意思?」如果「能力不足」是指缺乏書面程序,那麼錯誤只會發生在需要經驗的複雜工作上。如果連簡單的、有明確 SOP 的工作都出錯,則證明能力不足並非真正的原因

2. 追問「為什麼」也要追問「如何發生」

光知道為什麼還不夠,你必須理解機制。找出「原因」和「結果」之間是如何運作的(How),這才能讓你做出精準的預測。

例如:你知道太陽從東邊升起(Why),因為你知道地球是從西向東自轉(How)。理解這個機制後,你就能預測:在一個反向自轉的星球上,太陽就會從西邊升起。

在企業裡,你發現實驗室錯誤頻傳,不只是「為什麼」要再訓練,而是要找出「如何」出錯的機制。你可能會發現,原因是分析師被超載的多重任務打斷,導致短期記憶超負荷,而不是能力問題。找到這個「機制」,才能對症下藥。

3. 絕不將「缺乏解方」當作問題的原因

不要將問題歸咎於你沒有的東西。

錯誤的邏輯是:「我的銷售效率低,因為我們沒有 CRM 軟體」或「我瘦不下來,因為家裡沒有跑步機」。

這種思維會讓你花大錢買一堆無效的軟體或設備。正確的思維應該是:**你如何利用你「已經擁有」的資源和流程?**專注於現有資源,才能真正找出問題的根本洞察。

唯有將這種嚴謹的科學思維整體思維(理解所有問題間的連動關係)結合起來,你才能真正解決企業中那些久治不癒的頑強慢性病。


How Scientific Deduction Solves Your Company's Most Chronic Problems

 

Beyond the 5 Whys: How Scientific Deduction Solves Your Company's Most Chronic Problems

The business world is plagued by "wicked" or chronic problems: issues that persist despite repeated efforts to solve them. Think of flat sales, endless inventory disputes, or recurring quality errors. Traditional problem-solving methods, such as the famous Fishbone Diagram or the 5 Whys, often fail to resolve these complex issues. They tend to produce contradictory, biased opinions—sales blames operations, operations blames sales—and can be easily manipulated by selective data.

To truly diagnose and solve these sticky problems, management must adopt the methodological rigor of a hard science, specifically borrowing a technique from Historical Field Sciences, like forensic investigation or paleontology.


The Problem with Business Diagnosis

Unlike laboratory sciences, management cannot easily run controlled experiments. A company can't change its pricing to observe the demand impact while simultaneously guaranteeing that competitors will remain static. This lack of control and the polarization of data mean a better approach is needed to sort fact from fiction.

The solution lies in the Hypothetical Deductive Method (HDM).

The Three Steps of Hypothetical Deduction

HDM works on the principle that a single cause will always produce multiple effects. By observing one effect and deducing what other effects must logically exist, managers can test the validity of their initial assumptions without relying on potentially biased data.

  1. Observe the Effect and Hypothesize the Cause: Start with the obvious problem (Effect A), such as "Delivery is a known issue." Hypothesize a primary cause (Cause X), such as "The plant is running inefficiently."

  2. Predict Other Effects: If Cause X is the true reason, logically deduce other, less obvious effects (Effect B, C, D) that must be present. For example, if the plant is inefficient, you should also expect:

    • High rates of customer requests to reschedule orders.

    • A massive skew in dispatches toward the month-end or quarter-end.

  3. Empirically Verify the Predicted Effects: Check the organization for Effects B, C, and D. If they exist, the hypothesized cause is likely correct. If the data shows dispatches are perfectly smooth and there are no rescheduling requests, then the original hypothesis—that the plant is running inefficiently—is false, regardless of what the initial data suggested.

This methodology provides a rigorous framework to cut through organizational bias and pinpoint the genuine root of the problem.


Three Guidelines for Scientific Rigor

To apply HDM effectively and ensure the diagnosis is not based on vague generalities, three strict guidelines must be followed:

1. Demand Precision and Literal Language

Avoid using broad, metaphorical, or subjective terms like "incompetent," "lack of motivation," or "bad culture." When diagnosing a problem, every word must have a precise, literal definition. For example, if a team says errors are due to "incompetency," you must precisely define what that means. If it means "lack of written procedures," then you should only see errors on complex tests where tacit knowledge is required. If errors are also happening on simple, well-documented tests, your definition of the cause (competency) is wrong.

2. Ask "Why" and "How" to Find the Mechanism

It's not enough to know why a problem exists; you must understand how the cause creates the effect. This reveals the causal mechanism and greatly improves predictability. For instance, knowing the Sun rises in the East because of the Earth's west-to-east rotation (the how) allows you to predict that on a planet with an opposite rotation, the Sun would rise in the West.

In business, understanding the causal mechanism behind lab errors may reveal that the issue isn't competence, but that analysts are overloaded with multiple work-fronts. The mechanism is that this overload taxes their short-term memory, leading to skips and misses on detailed tasks. Finding this how leads to a far more effective solution (reducing workload) than the superficial why (retraining).

3. Never Blame the Problem on a "Lack of Solution"

Do not define a problem by the solution you don't have. Avoid reasoning like: "My sales team has low productivity because I don't have CRM software" or "Our product development is slow because we don't have enough R&D budget."

This faulty logic leads to expensive IT products and tools being implemented without ever delivering full results. Instead, focus on what you are doing with what you already have. By diving deeper into existing processes and resources, you will invariably find the genuine insights needed to resolve the chronic problem.

The combination of rigorous Scientific Thinking (HDM) and Holistic Thinking (understanding how all wicked problems are interlinked) is the only reliable path to solving the most persistent business challenges.


系統思考真功夫:用「抓流程」解決公司裡所有「搞不定」的爛攤子

系統思考真功夫:用「抓流程」解決公司裡所有「搞不定」的爛攤子


一、別再說公司太複雜了

很多老闆或主管遇到問題時,總愛歎氣說:「我們公司部門太多、結構太複雜了,問題當然難解!」他們認為公司越大,問題就越像一團亂麻。這句話常常變成專案失敗時的藉口

但真正厲害的管理心法告訴我們:公司一點都不複雜!它只是被錯誤地連接起來了

把你的公司想像成一個人體:身體裡有消化系統、循環系統、呼吸系統,它們各自負責一塊,但都連在一起,為了一個共同目標——讓你活著。公司裡的各個部門(採購、生產、銷售)也是一樣的一套系統


二、血流不順,全身都痛

要了解這套系統,我們得看中間流動的介質

就像人體裡流的是血液一樣,公司裡流動的是什麼呢?在工廠裡是物料;在設計部門是圖紙;在專案公司是工作進度;在行銷部門是客戶詢問

當這條「血路」在某個環節被卡住扭曲了,問題就產生了。

舉個最常見的例子:採購部門為了省錢,決定「嚴格控管」物料庫存(這就是一個卡點)。結果呢?

  • 生產線常常缺料停工(利用率下降)。

  • 訂單交不出去,成品積壓(影響出貨)。

  • 為了救火,大家開始跨部門吵架

一個採購部門的小小決策,瞬間讓整個公司從生產到銷售都亂了套。流程被扭曲後,影響會像漣漪一樣,傳遍整個組織。


三、抓出流程圖上的「波浪」

要知道問題出在哪裡,光看報表沒用,要畫出這條「血路」的流程圖

我們把每週或每個月的物料/訂單/工作量畫出來,如果曲線像波浪一樣上上下下,就代表流程已經嚴重扭曲了。這些波浪(或「流程干擾」)會帶來實實在在的損失:

  1. 流程太滿時(波峰):

    • 部門忙到爆炸,狂加班,成本飆高。

    • 主管會誤以為「人手或設備不夠」,結果亂花錢投資(錯誤的資本支出)。

  2. 流程太空時(波谷):

    • 線體或員工閒得發慌,產能浪費。

    • 之前投入的設備和人力都在空轉,浪費了投資。

這些額外花費,就是流程扭曲給公司帶來的「隱性病痛」。


四、找到「震源」:問題到底從哪裡來?

解決問題的關鍵,就是要確認這個「流程扭曲」的震源在哪裡。我們需要檢驗兩個可能性:

  1. 禍從天降(非局部性原因): 流程圖上這個部門的扭曲,其實是上一個或更上一個部門的錯誤行為傳染過來的。

  2. 自己人搞鬼(局部性原因): 扭曲就是這個部門自己的錯誤決策造成的,然後它再把問題傳染給下一個部門。

很多主管往往會搞錯。例如,他們看到鑄造廠模具裂開,就馬上認定是砂的品質有問題(認定是上游的禍)。但經過科學分析後才發現,問題根本出在鑄造線設備本身對不齊(問題其實在自己部門)。

因此,解決問題的兩大步驟就是:

  1. 先畫出所有部門的流程圖。

  2. 再交叉比對,確定那個「因果影響」是從哪裡開始流出的。

只要能精準地鎖定源頭,就能用一套方案,解決掉一大堆部門的問題。公司並不是「許多部門的集合」,而是「許多部門的連接」。當你把這個連接點修好,所有難題都會迎刃而解。


Beyond Symptoms: Using Flow Analysis to Demystify Organizational Complexity

 

Systems Thinking: How Flow Analysis Identifies the Single Root Cause of Chronic Business Problems

Many organizations incorrectly define their complexity by the sheer number of departments, divisions, or resources they possess. This perception—that "larger equals more complex"—often serves as an excuse when improvement initiatives fail. However, a systems-thinking approach argues that complexity is not inherent, but rather a function of systemic connections and distorted flow.

Just as the human body is a system of interconnected organs (respiratory, digestive, circulatory), a business is a system of connected entities (departments and functions) that work in tandem toward a common goal.


The Analogy of Flow and Disruption

To understand systemic connections, consider the flow of a crucial medium through the system. In the human body, the medium is blood. If the blood flow is restricted—a constriction in a vessel—the resulting disruption travels throughout the entire body, leading to effects like increased blood pressure, organ damage, or heart failure.

In a business or supply chain, the flowing medium could be material (in manufacturing), drawings (in engineering and design), work (in project management), or sales inquiries (in marketing). When a constraint or action in one department disrupts this flow, the distortion travels across the entire organization.

For instance, a seemingly isolated decision by the procurement department to tightly control material inventory can disrupt the flow of material, consequently affecting:

  • The utilization of production lines.

  • The dispatches of finished goods.

  • The inventory levels across the supply chain.

A localized constriction can thus cause widespread problems across all departments.


Peaks, Troughs, and Financial Implications

Organizations can diagnose these flow disruptions by mapping the flow pattern of the core medium over a certain time horizon (ee.g., weekly). If the resulting pattern is wavy or curvy, it indicates a flow distortion with significant financial consequences:

  1. Peaks (Overload): When flow peaks, the department experiences an overload, leading to:

  2. Troughs (Underload): When flow hits a trough, the department experiences underload, leading to:

These effects travel through the system even though the departments may not be physically connected. The material itself is the carrier of the causal influence.


Locating the Root Cause: Local vs. Non-Local

The key to solving a problem is invalidating the wrong hypothesis about the cause's location:

  1. Non-Local Cause: The distortion observed in a department was caused by a disturbance that traveled from another place.

  2. Local Cause (Epicenter): The distortion was caused by an action or issue within the department itself, which then creates shockwaves that travel to other areas.

Managers often incorrectly assume the cause is local (e.g., blaming poor mold cracking on the sand quality), when the true cause might be non-local (a process issue earlier in the flow) or vice versa (as in the case where mold cracking was due to equipment misalignment, making the cause local).

The systematic approach is a two-step process: first, map the flow patterns across all departments; second, determine where the causal influence is truly flowing from by evaluating the two hypotheses. This pinpoints the single location of the root cause, demystifying complexity and simplifying the problem-solving effort. The organization should realize it is not a collection of entities, but a connection of entities.


超越藝術與科學:框架與解決長期管理問題的三個標準

超越藝術與科學:框架與解決長期管理問題的三個標準

關於管理究竟是「藝術」還是「科學」的爭論,通常以一個令人不滿意的陳腔濫調告終:它是「兩者的結合」。然而,一種根植於系統思考的嚴謹方法則主張,要解決任何組織中最頑固、最「棘手」的問題,管理必須被視為一門明確的科學。

這門科學並非關乎缺乏情感的流程,而是關乎有效的診斷。它認為,衡量一個解決方案的真正標準,在於其能否滿足特定的、可證偽的準則,從而將長期困擾組織的問題轉變為可解決的挑戰。


管理學:永遠是一門科學

管理是「藝術」的觀念,通常是因為組織充滿了複雜、帶有情感的人。處理不同個性和爭取認同似乎需要技巧,即「藝術」。然而,核心的業務問題——例如庫存為何激增或銷售為何停滯不前——需要以證據為基礎的、邏輯性的診斷,這完全屬於科學的範疇。

根據科學哲學的定義,一個解釋如果既可檢驗可證偽,就被認為是科學的,這意味著它必須有明確的邊界條件,說明在何種情況下它將失效。將此應用於商業領域,就定義了良構問題(Well-Posed Problem):一個具有清晰參數的問題,能夠邏輯地排除不良的解決方案。

以這種方式框架問題的優勢是顯而易見的:它促進了輕鬆的認同,通過限制競爭方案的數量使執行更為精確,如果解決方案失敗,也能準確識別出是哪裡出了問題。


良構問題的三個核心準則

對於組織中長期存在的頑疾——那些儘管多次嘗試解決卻仍然反覆出現的問題——問題的定義必須滿足以下三個核心準則:

1. 理解相互關聯性並找出槓桿點

在一個系統中,問題絕非孤立存在。銷售平平不只是「銷售問題」;它與員工士氣低落、營運利用率低以及成本上升等問題有著因果關聯

第一步是繪製這些跨領域和跨部門的因果鏈接圖,以找出槓桿點(Leverage Point)熱點。這個熱點是所有其他症狀的根源。透過識別這個核心,你可以將焦點從處理症狀轉向解決機能障礙的真正源頭。

2. 解決隱藏的悖論(雙贏方案)

長期問題持續存在的主要原因是一個隱藏的衝突悖論。管理者通常憑直覺知道槓桿點在哪裡,但卻陷入僵局,擔心以直覺的方式解決核心問題會危及另一個領域的關鍵需求。

例如,增加庫存可以提高產品可用性(銷售勝利),但同時增加了成本(財務損失)。一個簡單、片面的解決方案總會被否決或在其他地方造成損害。一個真正科學的、良構的問題要求闡明這個悖論,並制定出能同時滿足雙方需求雙贏解決方案。這通常意味著打破一個根深蒂固但錯誤的假設,從而跳出衝突的循環。

3. 釋放管理能力

一個強大解決方案的最終檢驗是其系統性影響。由於該解決方案針對了核心問題(槓桿點)並解決了隱藏的衝突(悖論),它應該像**「銀彈」**一樣發揮作用,引發一連串的正面效應,消除許多原有的症狀。

當症狀消失時,結果是巨大管理能力的釋放——原本用於救火、管理內部衝突,以及處理與這些邊緣問題相關的行政管理的時間、精力和資源都得以解放。如果一個解決方案沒有釋放管理能力,那麼它就沒有真正解決那個長期問題。


最具同理心的行動

最終,運用科學方法解決長期問題,是管理者可以執行的最具同理心的行動。儘管個人化的同理心對於單獨互動很重要,但組織中大多數**「人的問題」——例如跨職能衝突、部門間的權力鬥爭,以及季末趕工造成的高壓——都源於系統性根源**。

比起只會溫言軟語或個人安慰的管理者,那位解決了系統性問題(例如,消除了員工在季度末必須工作 90 小時的需求)的管理者,能夠更持續地大規模改善人們的生活。透過戴上科學家的帽子,管理者可以做出真正持久的影響,從而轉變組織環境,培養出一個衝突更少、效率更高的文化。