Applying Goldratt’s thinking processes to prevent mistakes

Abstract

A wise person learns from others’ mistakes. Mistakes are common even though most are preventable! Serious mistakes occur in business, government, and even in personal lives. Frequently mistakes are caused by not recognizing the situation, environment or system (referred to as system hereafter). The tendency is to hide or dismiss mistakes. That in itself is a mistake! Many times, by examining system causalities, actions can be planned and taken to either prevent the mistake or to reduce its impact. Two types of common mistakes to thinking clearly (defined as recognizing the system causalities) are identified and studied: 1.the proposed action does not solve the system problem; 2. the proposed action solves the system problem but causes some unintended serious negative consequences. Eliyahu M. Goldratt developed the theory of constraints (TOC) thinking processes (TP) to apply the scientific method to solving business problems. Rather than select a personal or business problem, since science is based on causality within reality, three major scientific activities, inference (type 1 mistake), observation (type 2 mistake), and prediction (types 1 and 2 mistake prevention) are used to provide a tutorial on the TP and its use in addressing these types of mistakes.

Keywords

Causality thinking processes theory of constraints mistake proofing Goldratt scientific method

James F. Cox III, PhD, CFPIM, CIRM, TOCICO certifications, Professor Emeritus, was the Robert O. Arnold Professor of Business. Dr. Cox’s research has centered on the Theory of Constraints for 34 years. He has authored or co-authored five books, three on TOC and almost 100 peer reviewed articles. He was the co-editor of the APICS Dictionary, 7th through 11th editions; the Theory of Constraints Handbook (McGraw-Hill 2010); and the TOCICO Dictionary 2nd ed (McGraw-Hill 2012). In retirement Dr. Cox continues his research and service interests.

Emily Grace Robinson is a senior at Freedom High School in Morganton, North Carolina. She is currently the president of Anchor Club and secretary of the student body. She is taking AP courses for college credit and will be studying politics over the summer. Her studies focus on science, mathematics, and politics. She is an active volunteer in her community.

1 Introduction

Many times people make mistakes because they just are not educated or trained to think clearly. In fact people are trained to do the opposite. Why... ? Because... ! Children come into this world with a natural curiosity of wanting to know more about things they encounter. They ask their parents “Why... ?” dozens of times each day until their parents get tired of answering and deflect the question with: “Go ask your mother.” Or “You will know when you get a little older.” Or “Not now!” Goldratt [1] described these situations frequently when speaking to audiences of business professionals. He was first and foremost a scientist (a PhD physicist turned management consultant) and he frequently voiced concern about how parents blunted this natural curiosity in children, later teachers in students (“did you not read the text?”) and finally bosses in employees (“we’ve always done it this way!”).

Though a world renowned author and consultant, Goldratt’s life goal was to teach people to think clearly. In response to the question of what is needed for a person to think clearly Goldratt [2 pp. 34– 35] responded: “... what is needed is to accept the concept of inherent simplicity, not as an interesting speculation, but as the practical way of viewing reality, any reality... In a nutshell, it is at the foundation of all modern science as put by Newton ‘Natura valde simplex est et sibi consona.’ And, in simpler language, it means, ‘nature is exceedingly simple and harmonious with itself.”’ That was Newton’s belief about nature.” Goldratt [3 p. 49] concludes: “What I mean by inherent simplicity is that reality, any part of reality, is governed by very few elements, and that any existing conflict can be eliminated... If we take that as a given, as absolutely correct in every situation, we’ll find ourselves thinking clearly.” Goldratt viewed identifying and analyzing the inherent simplicity of a system (the causalities of the system) as fundamental to applying the scientific method to improving the system.

Over ten years ago my granddaughter, Emily (then a first grader), came home from school excited about what she had learned in science. I thought to myself: what is this excitement about? So I asked Emily. She provided such a logical explanation of the impact of using pesticides (DDT) on farming and on eagles (an unintended consequence). I was quite impressed with her knowledge and explanation! I thought Emily’s explanation sounded like a logical argument based on Goldratt’s thinking processes (TP)!

In the early 1990’s Eliyahu M. Goldratt, the developer of the theory of constraints (TOC) management philosophy, and a few colleagues devised a set of logic tools (TP) and rules (the categories of legitimate reservation, CLR) for applying the scientific method to organization problems. The TP provide a graphical depiction of the logical relationships in a system and provide a simple mechanism to identify, analyze, comprehend and communicate problems and develop solutions using cause-and-effect logic. The TP tools can be used independently or as a comprehensive study organized to answer three questions, called the change question sequence (CQS). To answer the first question (Q1: what to change), the current reality tree (CRT) is constructed by diving down from symptoms (called UDEs, undesirable effects in TOC terminology) to the core problem by continually asking “Why?” and validating the cause then by building upward by using “if-then” logic to connect the core problem to these UDEs. Solving the core problem generally eliminates or reduces the impact of the organization’s UDEs. Once the core problem and its system causalities are identified, the analysis continues by addressing the second question: to what to change. The researcher builds a future reality tree (FRT) to respond to this question by challenging system assumptions to identify and test potential solutions by predicting their impacts on the organization. During FRT construction, the solution is analyzed to identify and eliminate potential mistakes. Reality is also compared to the FRT during solution implementation to identify and correct mistakes as they happen. The third question, how to cause the change is used in the development of effective implementation plans (and is not discussed in this paper). These TP have much broader application than just organizations or business systems though, they provide a universal methodology for applying scientific thinking and the scientific method to improve any system.

In responding to the CQS, one must first have an understanding of systems and related definitions. Georgantzas [4] provides a brief but excellent discussion of the evolution of the definition of the term “system” starting with Aristotle to the present. Georgantzas [4 p. 155] uses Karl Ludwig von Bertalanffy’s simple definition: “an entity which maintains its existence through the mutual interaction of its parts.” Systems and other system terms useful in a TP analysis are discussed in the literature review section.

To achieve the paper purposes three major scientific activities (common to most academic and practical subjects) are used: inference, observation and prediction. Examples from science are chosen as science is based on causality, the foundation of responding to the question of why. The purposes of this paper are twofold: 1. to introduce researchers, practitioners, teachers and students to the Goldratt TP in hopes that its simplicity and graphic presentation of causality provide a methodology for better comprehension and communication about systems; and 2. to provide a methodology specifically for learning from serious mistakes (and maybe avoiding these mistakes before they happen by developing a prescriptive solution). Two types of mistakes to thinking clearly are analyzed: 1. the proposed action does not solve the problem; 2. the proposed action solves the problem but causes some unintended serious negative consequences. The type 1 mistake is illustrated using inference (building/testing hypotheses) with the current and future reality branches (CRB/FRB). The type 2 mistake is illustrated using observation: reading and building causal explanations using the current reality tree (CRT) and the categories of legitimate reservation (CLR: rules of logic). The prevention or minimization of both types 1 and 2 mistakes is illustrated using the future reality tree (FRT) to first predict both the positive and negative consequences of proposed actions and second to track the prediction against actual progress during implementation. The FRT prescribes a set of actions that should be taken and why. Examples include 1. inference: Semmelweis’s medical research on childbed fever; 2. observation: Emily’s pesticide explanation (the unintended consequences); and 3. prediction: Emily’s pesticide solution. A point should be made here: science is generally thought to be based on reality but as can be seen in these examples many times one’s perception of reality (frame) differs from the actual system reality (the causal relationships). It is hoped that these tools and examples provide evidence for the use of such logic tools in all aspects of decision making.

2 Literature review

This literature review is comprised of a brief discussion of systems concepts and of Goldratt related TP works.

2.1 Systems, system thinking and system dynamics

The systems literature [5] is both massive and impressive spanning over a hundred years. Using terminology and concepts from the systems, systems thinking and systems dynamics (SD) literature is not unusual for TOC researchers [6 –9]. Of particular interest, Mabin, Davies, and Cox [10] discuss how the TP may be utilized with the causal loop diagramming (CLD) method of SD in a multi-methodological intervention. While the CLD provides a holistic frame of how relationships interact over time the CRT provides a more detailed picture of the cause-and-effect relationships and specifically the assumptions (conditions) supporting the causal relationships. To better understand the TP applications, a few terms (system, system scope, system boundaries, detail and dynamic complexity and framing), definitions and concepts systems are needed.

For this research von Bertalanffy’s simple definition provided in the introduction is modified slightly: a system is defined as an organization or array of parts working together through cause-and-effect relationships, feedback loops, etc. to perform a purpose. The focus in systems thinking [11] is not the individual system part and how it works (a reductionist approach) but on the system as a whole in the process of achieving its purpose. A system is composed of other systems and these other systems composed of still other systems. For example, the human body (a system) is composed of a number of different body systems including the circulatory, digestive, endocrine, immune, lymphatic, muscular, nervous, reproductive, respiratory, skeletal, and urinary systems. Each body system is made up of organs (each a system), which is composed of cells, which is composed of molecules, which is composed of atoms. Each system in this chain is composed of a number of different parts with all parts working together to perform the purpose of that system in support of the human body system. Moving upward, humans are parts of numerous other systems (various organizations, ecology systems, economic system, upward to the highest system, the universe), each system with different purposes and each trying to achieve those purposes over time. Systems are neither good nor bad but actions taken within a system to influence the larger system can make the results good or bad with respect to that system purpose. When discussing a specific system, the larger system it is a part of might be considered the environment within which the system operates. An open system, the most common system, exchanges inputs and outputs with the environment. For example, humans may intervene into the eco-system by taking specific actions to influence or change a system and the surrounding environment (the larger system).

A system is defined by its scope, system boundaries, etc. In discussing the need to identify system boundaries (where the system starts and ends) and the time dimension (how far in time does the system extend) on systems, Senge [12 ch. 5] provides the distinction between detail complexity and dynamic complexity. Detail complexity exists where many variables are involved in a system; frequently researchers take a reductionist approach to analyzing such systems and many times the analysis and solution treats a symptom instead of the core problem. In contrast, dynamic complexity exists where cause-and-effect relationships may not be obvious as the cause and its resulting effect may be separated by long time intervals and cross system boundaries (as in an organization experiencing quality problems where the cause may be in the purchasing of a raw material and the effect may be found in customer dissatisfaction with the product). The researcher may not determine the causal relationships due to the separation of cause and effect by time and distance (location) interval. Both types of complexity present problems. The system perspective or frame may be inappropriate based on the system boundaries selected.

Russo and Shoemaker [13] define framing as “structuring the question. This means defining what must be decided and determining in a preliminary way what criteria would cause you to prefer one option over another... what aspects they (decision makers, my addition) consider important and which they do not. Thus they inevitably simplify the world.” The frame is the mental picture or window the researcher has in viewing the system. Mabin and Davies [7–9 , 14] have used framing in a wide variety of applications. The objective of framing in this hypothesis building/testing research is to identify the frame which provides the inherent simplicity, the underlying causality of the hypothesis. This frame determines the inherent classification of the system being studied.

2.2 Goldratt’s TP related works

Eliyahu M. Goldratt (1947–2011), PhD, an Israeli physicist authored over a dozen books, many written as business novels. His best seller (coauthored with Jeff Cox), The Goal [15], sold over 6 million copies and has been translated into over two dozen languages. In the introduction [15], Goldratt wrote of science: “I view science as nothing more than an understanding of the way the world is and why it is that way....” Throughout The Goal, manufacturing problems are surfaced and described in a Socratic manner leading the reader to grasp the cause-and-effect logic underlying the situation including situations where unintended negative results (mistakes) occurred caused by not fully comprehending the causal relationships or the system scope. In “It’s not Luck” (the sequel to The Goal) Goldratt [16] presents two TP: the CRT and EC using personal and organizational problems. Selected TP definitions (modified from [17]) are provided in Table 1. Goldratt’s TOC [18 –21] has been successfully applied by thousands of organizations (manufacturing, services, government, not-for-profits, hospitals, etc.) with most organizations using the TP to initially identify the organization’s core problem and then prescribe and implement an effective solution.

Table 1
Definitions of selected TOC thinking processes terms

mystery analysis – A sufficiency-logic (if-then) diagram used to illustrate the cause-and-effect relationships between an action (the cause) and its result (the effect). A major purpose of the analysis is to construct and test hypotheses to determine under what condition(s) (underlying assumption) a specific cause-and-effect relationship failed/holds.

negative branch – A sufficiency-logic (if-then) diagram (CRB) that describes through cause-and-effect linkage from an action to an unintended negative consequence(s) or effect. To eliminate the negative branch a secondary/trimming injection is required where the branch turns negative.

current reality branch (CRB) – A segment of a current reality tree providing the causal linkage from an action or the core problem to one or a few UDEs.

current reality tree (CRT) – A sufficiency-logic (if-then) diagram used to illustrate the cause-and-effect linkage from the core problem to the undesirable effects (UDEs) in a system. By acknowledging this causality, actions to eliminate the core problem can be identified and tested.

future reality branch (FRB) – A sufficiency-logic (if-then) diagram for examining the impact of potential actions.

future reality tree (FRT) – A sufficiency-logic (if-then) diagram that facilitates constructing and testing potential actions before they are taken. The causal linkages of the CRB/CRT provide a framework for linking the proposed actions to the desired effects.

Goldratt [22] describes the three stages of a science as classification, correlation and cause-and-effect and their role in advancing science. He provides examples from astronomy (classification the Greeks; correlation Ptolemy and Kepler; cause-and-effect Sir Isaac Newton); to disease (classification of diseases by symptoms; correlation smallpox vaccination by Edward Jenner; cause-and-effect Louis Pasteur germs cause disease). Years later, Goldratt revisited classification and its relationship to cause-and-effect by defining two concepts: inherent simplicity and inherent classification.

At the foundation of identifying the inherent simplicity is a complementary concept, inherent classification [3 ch. 35]. Goldratt describes inherent classification [23] as: “The most intuitive structure to a body of knowledge is a classification. But, as I already explained in the 2nd revised edition of The Goal [2 ch 38 my addition], classification is really meaningful only if it is the inherent classification – a classification that stem from the basic element of cause-effect. No problem; the mere fact that there is a guiding principle indicates that the inherent classification is at our grasp (probably already exists as one of the subjects)... Therefore a good frame will exist if the guiding principle is used to logically develop the sequence in which the classification emerges. And to explain the subjects at their proper place, the place provided by theclassification.”

The simplest example of the TP is also the newest, the mystery analysis. The mystery analysis had its origins when Goldratt [24, 25] discussed the use of the TP to compare what was expected to happen (hypothesis) to what actually happened (observation) and to investigate the reason or cause (the unspoken assumption/condition) of differences (the mistake) between the expected and observed conclusions. Barnard and Morgenstern [26] and Schragenheim [27 –29] also discussed the use of the TP to analyze “unexpected” results. Kishira and Yamanaka [30] extended these applications by providing an investigative process called the “mystery” analysis, which is described as learning from mistakes. In addition to Kishira and Yamanaka introducing the mystery analysis, Izumi and Sasagawa [31] and Nakayama [32] provide business applications of the process. The mystery analysis process proposed by Kishira and Yamanaka [30] is:

What problems are present?

What was the expected effect?

What actions did you take to cause the P-DE (predicted desirable effect)?

What UDE happened in reality instead?

What was the cause of the UDE to happen?

Is there any good idea to dissolve it?

Do you think the expected effect will happen in reality?

A diagram for this process is provided later after some basic concepts are introduced.

3 Thinking processes methodology/applications

The use of logic dates back over two thousand years. The ancient Greek philosopher Aristotle (384-322 B.C.) wrote the first known treatises of logic and began teaching the first logic classes in history. Numerous books and articles on logic have been authored since Aristotle. But how does logic relate to science? Briefly, science [33] deals with inferences, observation and prediction. Goldratt’s TP are applied to these activities with each building upon the proceeding discussion. Sections 3.1 and 3.2 present the TP methodology and an application for hypothesis building/testing using the CRB/FRB. Sections 3.3 and 3.4 present the TP methodology for observation and an application using the CRT. Sections 3.5 and 3.6 present the TP methodology for prediction and an application using the CRT/FRT.

3.1 Inference methodology: TP hypothesis building/testing

Kuhn [34 pp. 37–39] describes science as puzzle solving. Inference, the process of arriving at a conclusion using known evidence or premises is problem solving. Misiti [35 pp. 38-39] states: “... help your students think of a hypothesis as an if-then statement that is used to guide the development of an inquiry... Hypotheses, however, can be proven wrong. In fact, often the greatest value of testing a hypothesis is the potential for finding a factor that does not cause an expected change to occur in the investigation.” Using the concepts of inherent classification and inherent simplicity and the TP as a puzzle-solving framework to conduct scientific experiments is quite simple but highly effective. A hypothesis consists of a premise(s) and a conclusion. The hypothesis building/testing process is presented:

What is the situation to be analyzed?

What is the frame of the study?

What is the hypothesis of the study?

What is the premise (cause) of the hypothesis?

What is the conclusion (effect) of the hypothesis?

Under what condition (assumption) is the hypothesis valid?

What is the observation (result) from testing the hypothesis?

Did the experiment observation match the hypothesis conclusion?

If a match then the hypothesis is validated.

If not a match then how should the frame, premise and/or assumption be modified to correct the mistake? Go to step 2.

On Fig. 1a1, a logical argument in the TP structure is provided with its two components: the premise (P) and the conclusion (C). An argument can be comprised of one (see Fig. 1a1) or more premises (see Fig. 1a2) independently causing the conclusion. The conclusion is derived from the premise(s) by causality (either hypothesized or actual). Fig. 1a1 is read as: if premise then conclusion. To eliminate the conclusion in this structure the premise must be addressed. Figure 1a2 is read if premise 1 then conclusion; if premise 2 then conclusion. To eliminate the conclusion in this structure (magnitudinal and) both premise 1 and premise 2 must be addressed.

Fig.1

Premise and conclusion argument formats.

In examining Fig. 1a1 more closely to determine how to change the conclusion an important unanswered question is: When does the premise cause the conclusion? Under what limiting condition does the argument hold true? This limiting condition represents the “unspoken assumption” and is based on the inherent classification (how can the population be segmented to only that condition or segment that produced the conclusion?) This type of connector is called a “conceptual and” connector and is indicated by a line across incoming arrows. Both the premise and condition (unspoken assumption) must exist for the conclusion to result. The causality can be eliminated by changing either the premise or the assumption. In many hypotheses when the result is not what was expected, the scientist must try to determine why the observed conclusion did not match the hypothesized conclusion. What was wrong with the frame? The cause for the difference in results is generally the unspoken assumption, the inherent classification. To surface an unspoken assumption, use the statement on Fig. 1b1: If premise then conclusion because unspoken assumption. This argument is shown on Fig. 1b2 and read as: if premise and assumption then conclusion. This logical structure represents the structure of the hypothesis. On Fig. 1c, the argument includes an injection or action taken to change the unspoken assumption and bring about the desired conclusion. It is read as: if premise(s) and injection then conclusion.

Given this basic structure of hypothesis building/testing, the mystery analysis [30] template is provided on Fig. 1d the steps in the process can now be related to the various entities in the template. Once the template is filled in with entities responding to the questions (1-7) it is read as follows: If 5 Assumption and 3 Injection then 2 Expected outcome. But 4 occurred. Why? (Either 3 or 5 did not exist or the causal arrows did not exist.) If 5 and 3 then 4 resulted. Note the hypothesis expectation 2 did not match the observation 4. If proposed action 6 and assumption 5 then expected results 2. Question 7 should be answered. If answered no then fill in the template with a new hypothesis.

A simple but true example is used to illustrate building/testing hypotheses across different frames to identify and learn from mistakes. The Semmelweis story is a composite of several references [36 –39] but primarily the Lessler interview [36]. The type 1 mistake is illustrated below.

3.2 TP hypothesis building/testing application: Medicine

In 1846, Ignaz Semmelweis (1818-65), a Hungarian doctor was hired to deliver babies in the maternity ward in a Venusian hospital. At this time physicians had some scientific training such as performing autopsies to determine the patient’s cause of death. Physicians also collected data for various research studies to improve medical practice. On starting his new job, Semmelweis noticed and became interested in a particular problem in the two maternity wards. He wanted to determine why so many women in hospital maternity wards died soon after childbirth from something called childbed fever disease (puerperal infection). Semmelweis hypothesized that only one cause existed for the disease. What could be the situation (frame) that causes this high death rate percentage (two to ten times the death rate of women delivering babies at home)? To determine the cause of the deaths, Semmelweis collected data on the percentage of women dying after childbirth due to childbed fever for the hospital over the objections of his supervisors.

The hypothesis building/testing process presented in section 3.1 is applied to this problem to illustrate its use in research and hopefully as a methodology for reducing mistakes by forcing one to verbalize the hidden assumption. Responses to each question are inter-disbursed in the discussion below.

What is the situation to be analyzed? The situation to be analyzed is the high percentage of mothers’ deaths following childbirth.

What is the frame of the study? All medical staff.

To respond to question 3, see Fig. 2a-b. On Fig. 2a, the logical argument for this problem is: if P Medical staff delivers babies then C A high percentage (25–30%) of new mothers die of childbed fever. Given this hypothesis, the search for inherent classification begins. Under what condition does the high death rate exist? The unspoken assumption is surfaced as: if P Medical staff delivers babies then C A high percentage of new mothers die of childbed fever because A All medical staff death rate percentages are the same. This argument presented as an hypothesis is provided on Fig. 2b, and is read as: if P1 Medical staff delivers babies and A All medical staff death rate percentages are the same then C A high percentage of new mothers die of childbed fever. To test this hypothesis, Semmelweis separated the childbirths and mother outcomes by wards (the proposed inherent classification that distinguishes between death rates) and compared the two maternity wards in the hospital; one was serviced by midwives and the other by physicians and medical students to determine if the death rates differed. He computed the percentage of mothers dying in each ward and to his surprise the percentage was much higher in the ward serviced by the physicians. Mothers in the ward serviced by doctors died at a rate nearly five times (some websites indicate ten times) higher than mothers in the midwives’ ward! To respond to question 4, the experimental observations for midwives and doctors are provided on Fig. 2c1 and c2 respectively. In response to question 5 these conclusions falsify the hypothesis that there is no difference between the percentages of mothers’ deaths based on medical staff. The assumption, A All medical staff death rate percentages are the same, is invalidated (question 6). Semmelweis was mistaken; the death rate differed by ward! The frame of examining the system by ward was correct but provided little useful information as to cause.

Fig.2

Current situation, hypothesis based on unspoken assumption and experiment observation related to A All medical staff death rate percentages are the same.

Given these results (question 7), Semmelweis pondered: why was there a significant difference in death rates between the wards staffed by different employee types (midwives versus doctors)? He must start again at question 2. This current situation is illustrated on Fig. 3a1 and a2. Using a different frame (medical procedures), Semmelweis studied the procedures used in each ward to identify any possible procedure differences (inherent classification) as the cause of the difference in death rates. A major difference identified by Semmelweis was that mothers gave birth lying on their sides in the midwives’ ward and mothers gave birth lying on their backs in the physicians’ ward. This inherent classification, the difference in procedures is presented on Fig. 3b1 and b2. The reader is left to see that the underlying assumption (A1 or A2) in each is based on the different procedures used by physicians and midwives. To validate this hypothesis doctors were asked to deliver babies with the mother lying on their sides. Based on question 3 the hypothesis is presented on Fig. 3c1 and the observation (question 3) based on the experiment on Fig. 3c2. The hypothesis becomes: If P1 Doctors delivered babies and I (injection) Mothers give birth lying on their sides then C1 A low percentage of new mothers died of childbed fever. The experiment is then conducted. The experiment observation is: If P1 Doctors delivered babies and I Mothers give birth lying on their sides then C2 An unusually high percentage of new mothers died of childbed fever. Comparing the hypothesis to the observation (question 5) shows that the unspoken assumption is invalidated (question 7); mothers’ position in childbirth does not cause the difference in death rate. The puzzle still remained (the assumption was incorrect meaning start back atquestion 2)!

Fig.3

Current situation, hypothesis based on unspoken assumption and experiment observation related to differences in mothers’ position when giving birth.

Again, Semmelweis pondered: “why is there the significant difference in death rates between the wards?” Another major difference (not shown in a figure) he observed in the procedures used in the two wards was that when a mother died in the physicians’ ward, a priest and attendant paraded slowly through the physicians’ ward ringing a bell loudly. Semmelweis hypothesized that the loud noise caused the mothers fright while in a fragile state which caused their later death. His assumption was that the difference in death rates was caused by the loud clanging of the bell scaring women in the physicians’ ward. He suspended this bell-ringing practice but the high death rate remained. That hypothesis proved to be invalid. Classifying the population by bell ringing was incorrect and did not cause the difference. The puzzle still remained unsolved.

The current situation is portrayed on Fig. 4a1 and a2. If P1 Midwives delivered babies then C1 A low percentage of new mothers died of childbed fever. If P2 Doctors delivered babies then C2 An unusually high percentage of new mothers died of childbed fever. BUT why? What was the inherent classification, what difference in procedures between the maternity wards was causing the difference in death rates?

Fig.4

Current situation, hypothesis based on unspoken assumption and experiment observation related to differences in cleanliness of hands when delivering babies.

Semmelweis was stumped again. Feeling such frustration with his research efforts, he went on vacation. Upon returning to work, he found that a close friend, a pathologist, had died. On investigating the friend’s death he discovered his friend had died with similar symptoms of childbed fever. Upon further study, Semmelweis found that many pathologists died with similar symptoms. This caused Semmelweis to realize that childbed fever disease was not limited to mothers giving birth (his unspoken mistaken assumption) but was more widespread than originally thought. The system boundaries and frame under study being studied had to be expanded. Semmelweis then hypothesized that the physicians delivering babies in the maternity ward also performed autopsies and carried cadaver tissue back to their maternity ward. Additionally, physicians performing autopsies may have had a cut and contaminated the cut with cadaver tissue. Upon closer examination of physician procedures he found that frequently physicians did not wash their hands between autopsies and baby deliveries.

The inherent classification, the differences in hand cleanliness, is now revealed and provides the inherent simplicity causing the differences in mother’s death rates. This situation is portrayed on Fig. 4b1 and b2. If P1 Midwives delivered babies and A1 Midwives have clean hands when delivering babies then C1 A low percentage of new mothers died of childbed fever. If P2 Doctors delivered babies and A2 Doctors have unclean hands from previous surgeries when delivering babies then C2 An unusually high percentage of new mothers died of childbed fever. Using this information the hypothesis is illustrated on Fig. 4c1: If P2 Doctors delivered babies and I Doctors wash their hands thoroughly with a strong disinfecting agent between surgeries then C1 A low percentage of new mothers died of childbed fever. The experiment observation is given on Fig. 4c2: If P2 Doctors delivered babies and I Doctors wash their hands thoroughly with a strong disinfecting agent between surgeries then C1 A low percentage of new mothers died of childbed fever. The hypothesis expectation and the experiment observation are the same; the hypothesis cannot be invalidated! Physicians’ uncleaned hands were the culprit. The puzzle is solved through applying the hypothesis building/testing process and learning from previous mistaken assumptions! Instituting handwashing immediately reduced the percentage of deaths from 10 percent (range 5–30 percent) to about 1–2 percent. While results of this analysis seem just common sense today that was not the case in Semmelweis’ day. His research result was not well received (The Semmelweis effect named for Ignaz Semmelweis, is defined as the tendency to reject new knowledge or contradictory evidence to the existing paradigm.) as physicians felt Semmelweis was blaming them for the large number of childbed fever deaths rather than the widespread practice of not washing their hands. Instead of being recognized for his significant contribution to medical science, Semmelweis continued to promote his results, the use of antisepsis, with little success and later died in an asylum of the very disease he had found the cause and cure of.

In search for inherent simplicity Semmelweis could have used frames based on population characteristics i.e. mother’s age, race, health condition, etc. in searching for the inherent classification to determine the cause of the high death rate. Some hospital staff felt the difference in death rates may have been caused by hospital overcrowding or poor ventilation but both conditions existed in each wards.

Like many other pioneers of science, Semmelweis’s findings were treated with skepticism and distrust (For an extensive discussion of this phenomenon see: Kuhn, in The Structure of Scientific Revolutions). Hopefully the use of Goldratt’s TP and specifically the concepts of inherent classification and inherent simplicity in constructing, communicating and executing scientific experiments will provide clarity and objectivity to scientific research.

3.3 Observation methodology: Current reality tree

The second type of mistake is described as taking an action to solve a problem but the action creates a major unintended negative consequence. The science activity of observation is used to illustrate this type of mistake. The CRT is an effective tool for identifying, analyzing and communicating scientific observation in a logical manner. The next two sections describe how to read a CRT and an application of reading a CRT.

3.3.1 Reading methodology: A current reality tree

The statements (entities in the CRT) are read using if-then and if-and-then connectors. The base of the arrow is the premise and the tip of the arrow is the conclusion or inference. A line across two (or more) incoming arrows represents a “conceptual and” connector. CRTs are read from the bottom to the top of the page except where a “conceptual and” connector is encountered. In that instance the reader must go back to the base of the other side of the “and connector” and read upward to the “and connector” before proceeding above the “and connector”. The numbers are meaningless except to assist listeners to quickly identify where the reader is in the CRT. Where two or three incoming arrows enter an effect (with no line across them) it means that each incoming entity contributes independently to causing the effect. Similar to the conceptual “and connector” the reader should go to the base of the other incoming arrows and read upward to the joining entity prior to proceeding upward. Only the top two or three major causes for an effect are provided, which usually accounts for about 70–80% of the magnitude of the effect. Recall this situation of multiple independent incoming arrows is called a “magnitudinal and” connector.

3.3.2 Reading application: A current reality tree of Emily’s pesticide problem

Background (storyline): Emily was a first grader and had just had an enjoyable day at school studying science (the environment). In describing why she was so excited she provided such a logical argument that I thought the teacher had been teaching Goldratt’s TP. Notice in this example Emily is actually examining the system using two frames: an economic frame and an environment frame. Note also Senge’s principles related to time/location separation of cause and effect and system boundaries.On Fig. 5a the CRT provides a simple description of the economic benefits of using pesticides to kill insects. Each entry point when encountered should be read for clarity and its existence before proceeding upward. 5 Farmers plant crops to provide food for people and animals. 10 Farmers have bad bug problems. 15 Farmers use strong poisons (pesticides) to kill the bugs. The pointed-edged rectangles (entities 5 and 15) represent actions (injections) taken by someone (the farmers in this instance). If 5 Farmers plant crops to provide food for people and animals and 10 Farmers have bad bug problems and 15 Farmers use strong poisons (pesticides) to kill the bugs then 20 Farmers have few bug problems. If 20 Farmers have few bug problems then 25 Crop yield is high. If 25 Crop yield is high then 30 Most farms are profitable. If 30 Most farms are profitable then 35 Farmers/workers are fully employed. If 35 Farmers/workers are fully employed then 90 Many families’ standard of living increases. Both farmers and consumers have a strong incentive for farmers to use pesticides to increase crop production. The economic benefits are significant and are felt even in the first year. But that is not the full story. There are significant unintended negative consequences to this action.

Fig.5

(a) Emily’s scrutinized CRT describing the economic benefits of the use of pesticides. (b) Emily’s scrutinized CRT describing eagles’ endangerment (the unintended consequences) of the use of pesticides.

On Fig. 5b the CRT provides is a simple description of the environmental problem of using pesticides using cause-and-effect logic. The logic is the same for the base of the diagram but entities 5, 10 and 15 also cause the start of a negative effect. If 5 Farmers plant crops to provide food for people and animals and 10 Farmers have bad bug problems and 15 Farmers use strong poisons (pesticides) to kill the bugs then 20 The poison gets in the soil. 25 Rains carry the poison to the streams and lakes. If 20 The poison gets in the soil and 25 Rains carry the poison to the streams and lakes then 30 Poison gets in streams and lakes. If 30 Poison gets in streams and lakes then 35 Many fish are poisoned. 37 Fish are a primary food source for eagles. If 35 Many fish are poisoned and 37 Fish are a primary food source for eagles then 40 Eagles frequently eat the poisoned fish and drink the poisoned water. If 30 Poison gets in streams and lakes then 40 Eagles frequently eat the poisoned fish and drink the poisoned water. Go back to the CRT base before proceeding upward. 18 The poison is toxic to eagles. If 15 Farmers use strong poisons (pesticides) to kill the bugs and 18 The poison is toxic to eagles then 45 The poison causes the eagles’ egg shells to be soft and weak. If 15 Farmers use strong poisons (pesticides) to kill the bugs and 18 The poison is toxic to eagles then 55 Continued poisoning is harmful to eagles. If 40 Eagles frequently eat the poisoned fish and drink the poisoned water and 45 The poison causes the eagles’ egg shells to be soft and weak and 55 Continued poisoning is harmful to eagles then 65 Frequently the eggs of poisoned eagles are soft and crack easily before hatching. If 65 Frequently the eggs of poisoned eagles are soft and crack easily before hatching then 80 Fewer eagles are born. If 40 Eagles frequently eat the poisoned fish and drink the poisoned water and 55 Continued poisoning is harmful to eagles then 70 Many poisoned male eagles are sterile. If 70 Many poisoned male eagles are sterile then 80 Fewer eagles are born. If 80 Fewer eagles are born then 85 Eagles are endangered. If 40 Eagles frequently eat the poisoned fish and drink the poisoned water and 55 Continued poisoning is harmful to eagles then 60 Many poisoned eagles die prematurely. If 60 Many poisoned eagles die prematurely then 85 Eagles are endangered.

In the environment frame the widespread use of DDT in agriculture started after WWII however the effects on wildlife were subtle and built up over time. Rachael Carlson’s book, Silent Spring, published in 1962 publicized the pesticide’s harm to wildlife. The use of DDT was banned in the US in 1972. Again note Senge’s principles related to time/location separation of cause and effect and system boundaries. The story of DDT illustrates mistake 2, unintended consequences. Dr. Paul Mueller, the inventor of DDT was awarded a Nobel Prize in 1948 for his invention of DDT as a significant contribution to agriculture. The unintended consequences came in another frame (the environment) and over a decade later!

3.4 Observation methodology: Using the CLR to scrutinize a CRT

Start with the situation where: 10 Farmers have bad bug problems and build upward causally to 65 Eagles are endangered. These two entities are connected by using cause-and-effect logic: building upward using if-then logic and diving downward asking “why?”. To provide a starting point for illustrating how to apply the CLR let’s assume the CRT on Fig. 6 is built in this manner. For ease of discussion in applying the CLR, note these entries 10, 30, 40 50, 60, and 65 (note 50 was rewritten as 65 for clarity and 65 was renumbered as 85 based on expansion of the diagram) from Fig. 6 have been italicized on Fig. 5 and the succeeding discussion. The CLR are used to improve this CRT. Let’s read the tree starting at the bottom on the figure and read the whole logical argument before scrutinizing it. 10 Farmers have bad bug problems. If 10 Farmers have bad bug problems then 30 Poison gets in streams and lakes. If 30 Poison gets in streams and lakes then 40 Eagles eat the fish and drink the water. If 40 Eagles eat the fish and drink the water then 50 Many eagles’ babies die before hatching. If 50 Many eagles’ babies die before hatching then 65 Eagles are endangered. If 40 Eagles eat the fish and drink the water then 60 Many eagles die. If 60 Many eagles die then 65 Eagles are endangered.

Fig.6

First draft of CRT of Emily’s description of pesticides use and eagle endangerment.

To develop a CRT having solid logic the CLR are applied to this tree. The CLR may seem difficult concepts but once understood they can provide an effective methodology to help one think and communicate logically in science, other academic subjects and in life in general. Goldratt provided the CLR to scrutinize and improve the logic of a TP diagram. These CLR should be thought of as rules of logic (similar to the rules of grammar). The CLR are used in two different situations: first, the diagram developer (DD) can use the rules to construct, question and strengthen her logic; and second, the diagram scrutinizer (DS) can use the rules to specifically identify and eliminate poor logical connections to clarify and better grasp another’s diagram. The DD and DS work together to improve the logical explanation. One should always assume the DD is correct but the diagram may need clarity in presenting her argument.

The CLR are comprised of three hierarchal levels of inquiry, which contain seven types of challenges. See Fig. 7. The levels contain reservations (with a brief suggested dialog to present the specific concern to the DD) ranging from very general concerns (L1: level 1) to very specific concerns (L3: level 3). In analyzing a part of the diagram, the DS always starts with the first level of questioning: the clarity reservation but should already have identified the specific concern at level 3. If the DD response improves the logical explanation of the situation then this information is added to the diagram. If the DD response to this high level of reservation is unsatisfactory, specifically to clarifying what is written on the diagram then the DS should be ready to move to one of the two challenges at the second level of reservation—the entity existence or causality existence reservation (and then further to the specific challenge at the third level if needed). At level 2, the DD now accepts that either an entity (ies) is unclear or the causal arrow between the entities is unclear and responds appropriately. If the DD’s explanation clarifies the concern then this explanation is used to modify the diagram. If at this second level of inquiry the DD’s response is still inadequate then the DS should specifically point out the logic problem using the third level of the CLR. If the problem related to the entity existence reservation at level 2 then the DS should proceed either to the predicted effect reservation or the additional cause reservation at level 3. If the problem related to causality existence at level 2 then the DS should proceed to predicted effect, cause insufficiency, or house on fire at level 3.

Fig.7

Roadmap of the Categories of Legitimate Reservation (CLR).

Note: predicted effect reservation can be used to challenge the existence of an entity or the causal linkage between two entities. It is stating that if the entity (or causality) exists then some predicted effect must also exist. Since the predicted effect does not exist, the entity (or causality) in question does not exist. Please read each reservation carefully on Fig. 7. The details of applying the CLRs to Fig. 6 to create Fig. 5b are provided in Appendix A. Please use Fig. 7 as you study Appendix A.

3.5 Prediction methodology: Using the CRT to predict the future

In order to make a prediction (and not a pure guess) of the future, one must have a knowledge of the current environment with its existing causalities and any anticipated changes (build the CRT first). The FRT (FRB) is a prediction of the consequences of initiating actions to eliminate UDEs in the current situation before the proposed actions are taken. An additional purpose is to identify and eliminate potential new problems (type 2 mistakes) from being created by the primary and supporting injections. The CRT (CRB) provides a template of the existing inherent simplicity of the environment in building the FRT(FRB). The FRT prescribes the actions / plan needed to cause the desired state.

The method used here for constructing a FRT starts with an injection at the base of the CRT and logically builds upward from the proposed action to ensure that the action eliminates the UDEs and replaces them with DEs and no new UDEs are caused.

3.6 Prediction application: CRT➔FRT Emily’s pesticide problem

Please review the CRT of Emily’s pesticide problem on Fig. 5b which shows the negative effects of poisoning bugs. Core drivers are defined as entry points into the CRT and dictate the causality in the environment. These include 5, 10, 15, 18, 25, and 37.

For this exercise shown on Fig. 8 let’s assume entities 5, 10, and 37 are core drivers that cannot be changed. The remaining entry points are subject to taking actions to change the UDEs above that point in the diagram. Let’s examine one of the remaining entry points to determine how to change the CRT. Suppose entity 15 is replaced with Inj. 1 Farmers use LT (less toxic) poisons (pesticides) to kill the bugs. Tracing the impact of this proposed action using the structure of the CRT creates the FRT, a prediction of the consequences of the action in the ecology frame. The entry points into the diagram are 5, 10, 15, 18, 25, and 37. Read each for clarity and entity existence and then the causal linkages upward in the FRT. Please read Fig. 8 now. In summary, the LT poison has less of an impact on the fish and water which is still ingested by eagles but the poison has less impact on eagle eggs, male eagles’ sterility and eagle longevity which in turn means more eagles are born and eagles are not endangered. This looks to be a viable solution to the bug problem but remember the second purpose of the FRT is to identify any possible negative consequences (mistakes) of taking this action.

Fig.8

Emily’s scrutinized FRT describing the impact of less toxic pesticide use on eagles’ endangerment.

On Fig. 9 the logic for the economic impact frame is provided for the negative branch for Inj 1 Farmers use LT (pesticides) poisons to kill the bugs with the core drivers of 5 and 10. The causality indicates that farmers still have some bug problems which reduces crop yield causing some marginally profitable farms to bankrupt and ultimately causing some unemployment and a reduction of these families’ standard of living. Additionally this reduced crop yield causes higher food prices. New core drivers related to the LT poison are 40 Research to develop new LT poison is expensive and 50 EPA bands low-cost, effective poisons. These cause 55 production costs to increase which causes 60 food prices to rise which 30 bankrupts marginally profitable farms and 90 reduces the standard of living for manyfamilies.

Fig.9

Negative branch of Emily’s description of less toxic (LT) poisons use and eagle endangerment.

Other negative and positive branches should be examined for validity by knowledgeable scientists before any proposed actions are taken to identify and address potential negative effects. Thus the scientists are predicting the impact of a proposed solution before it is implemented. More so as the action is taken the scientists have a roadmap of the effects and underlying assumptions to check if reality (observation) differs from the roadmap (expectation) and a mistake in thinking has occurred. Recognize that a several billion dollar industry has emerged in attempting to find less harmful pesticides.

Are there other potential solutions to the bug problem? Check the entry points on the CRT. Entity 15 could be replaced with farmers using insect traps, bug zappers, green house farming, container farming, etc.? Each of these solutions is currently used to some extent but has not been deemed financially viable for large commercial farms. Emerging solutions include genetically modified bug resistant crops, crop rotation and integrated pest management. See for example: http://www.sustainabletable.org/263/pesticides. These emerging solutions offer promise but may also have negative consequences. For example, genetically modified crops may have a negative stigma in the consumer market.

4 Discussion

In The Goal (1984) introduction, in describing why he wrote the book as a novel, Goldratt wrote:

“Finally, and most importantly, I wanted to show that we can all be outstanding scientists. The secret of being a good scientist, I believe, lies not in our brain power. We have enough. We simply need to look at reality and think logically and precisely about what we see. The key ingredient is to have the courage to face inconsistencies between what we see and deduce and the way things are done. This challenging of basic assumptions is essential tobreakthroughs....”

In this paper the TOC concepts are introduced and applied to the three major scientific activities of experimentation (hypothesis building/testing), observation and prediction. Management academics have long desired to be more scientific, to emulate the physical sciences in hopes of gaining respectability. See for example the early articles [40 –46] in the academic journals such as Management Science and Operations Research. This preoccupation with making management more “scientific” has led to the use of more and more mathematically sophisticated modeling but this approach requires a reductionism approach to simplifying the system based on the limitations of mathematics. Goldratt recognized that simplifying was not the same as inherent classification or inherent simplicity. He did not invent these concepts. He discovered their usefulness in addressing organization problems. Inherent classification is the classification system of the population under study based on the causality of the purpose of the study. In business, Goldratt used this concept to classify financial data (into the classifications of throughput, inventory and operating expenses) for the purpose of financial decision making; to classify resources (constraint and non-constraints) to better manage complex production environments; to classify the numerous part flows (V, A, T and I structures) to identify the control points in any manufacturing facility to better schedule and control production; to classify activities (critical chain and non-critical chain activities) to better manage projects, to classify decision-making analysis into the CQS to organize analysis and discussion, etc. Based on these inherent classifications, Goldratt identified the inherent simplicity in very different organizations for the purpose of better management.

Similarly, in science a number of inherent classification systems exist and are used for quite useful purposes. The ancient Greeks classified objects in the sky (stars, planets, comets, suns, moons, etc.) for the prediction of their movements. Dmitri Mendeleev, a Russian chemist, developed the first widely recognized periodic table of elements to derive and explain relationships among the properties of elements. Mendeleev also predicted the properties of then-unknown elements that later filled gaps in this table. The modern taxonomic system (species, genus, family, order, class, phylum, and kingdom) was developed by the Swedish botanist Carolus Linneaeus (1707–1788) to provide an explanation of the relationships of living things based on physical characteristics (from general to specific by similarity of members).

In this paper the field of science is used to illustrate inherent simplicity and inherent classification, causality, etc. The scientific activities of hypothesis building/testing, observation and prediction exist in all systems. The TP can be quite effective in analyzing problems in any system. Additionally system concepts of boundaries, framing, and time delays and location distances between cause and effect are useful. The mystery analysis process developed by Kishira (a TOC business consultant) and Yamanaka (a Japanese Nobel Prize-winning stem cell scientist) is emerging as a powerful tool in both business and scientific research in Japan.

The solution for addressing type 1 mistakes was described in examining the story of Semmelweis in his search for the cause of the high incident of childbed fever deaths. An interesting point here is that Semmelweis extended his system boundaries and frame as he searched for the inherent simplicity. If Semmelweis had not investigated his pathologist friend’s death and determined that it was also caused by childbed fever he might have eventually discovered the cause and solution but missed the opportunity of applying his solution to a whole class of diseases eliminated by physicians and staff washing their hands. Mistakes can happen but major opportunities can also happen based on fully comprehending the system and its unspoken assumptions. Goldratt’s tools of inherent classification and inherent simplicity and the hypothesis building/testing process could have been useful in developing, analyzing, testing and communicating Semmelweis’s hypotheses and learning from his mistakes. The use of the TP to explain scientific experiments using the concept of inherent simplicity (causality) and the CRT/CRB logic diagram offers a graphic and logical tool to better comprehend and communicate scientific experiments and to identify and eliminate the causes of unexpected results (negative or positive).

Type 2 mistakes are unintended consequences and occur in more complex systems or where significant time delays between cause and effect exist or distance between cause and effect exists, a CRT provides an excellent framework for identifying, examining, and communicating the inherent simplicity of causality. The CLR provide a check on the clarity of entities and their causal relationships. Emily’s pesticide example provides a framework for learning how to use the CLR. Once one becomes familiar with the logic rules by building and scrutinizing a couple CRTs, the rules become second nature.

Certainly other types of mistakes exist and are prevalent; one major type not studied is the mistake of “solving” an UDE instead of the underlying cause. This type of mistake is easily solved by diving down from the UDE to its underlying cause (by asking: why?) and validating entity and causality existence (shown in previous examples).

Prediction is an important activity in science and in systems. Building the CRT/CRB of the current system provides a framework for discussion among scientists and professionals prior to predicting the results of proposed actions. The FRT/FRB clarifies what each person thinks is important (the injections and unspoken assumptions) in predicting outcomes and provides a framework for an educated discussion of both positive and negative consequences. In contrast, today the newest fad in research methodology is data analytics (the collection, classification and correlation analysis of huge volumes of data) to predict events. In a number of data analytic studies such as outpatient appointment scheduling the UDE high patient no-show rates is analyzed to determine that young low-income, male, minority patients have a much higher no-show rate than other patients. These patient types are identified and where needed overbooking is used at these appointment slots to insure high utilization of the provider. Many times both patients show up for the appointment and all succeeding patient appointments are delayed. In contrast to this use of data analytics, in using the TP the researcher would identify and address the causes of the no-shows (wrong cell phone number, no emphasis on the need to cancel unneeded appointments, reminders, etc.) and the core problem (a poor appointment scheduling and execution process).

Other TP tools offer significant opportunities to support scientific and other activities. In all environments inherent simplicity (causality) exists which links most outcomes to its source(s). Core drivers (an entity not having an incoming arrow, a cause) determine or at least influence the current and future environments. TOC focuses on identifying these core drivers to further identify the core problem(s) in a system. These core problems (generally one to three) cause 70–80% of the UDEs in a system.

Of course, uncertainty exists everywhere, especially in predictions (27–29). The experiment might produce a false positive, delayed response, etc. Is there a cause or just chance or “bad luck”? Replication helps answer this question. Researchers can take actions to reduce the uncertainty but uncertainty still exists, except in possibly controlled laboratory experiments. Otherwise things don’t always go as planned particularly in the social sciences. Murphy is alive and well. Expanding the system boundaries under study might incorporate the variable thought to be chance. Using terms like frequently, many times, sometimes, seldom, etc. in the CRT provides some measure of the causality. Seldom can a social science or environmental experiment be controlled; it is an observational experiment. Even in the somewhat controlled series of experiments conducted by Seimmelweis, he could have asked: Is the high death rate of mothers after childbirth a natural phenomenon or is there a cause? Prior to Seimmelweis’ investigation, no actions were taken no one noticed the high rate. If statistics were kept perhaps Seimmelweis could have noticed the spike in death rates after physicians started to perform autopsies. Ironically, the higher death rate was a result of physicians conducting scientific research (autopsies) into the causes of patients’ deaths (an unintended consequence).

Other subjects such as history, social studies, and business display inherent simplicity and might best be examined, analyzed, and communicated using these TPs. Using the common framework of logic across academic subjects might provide an integrative process of teaching and learning. Instead of learning being independent across subjects, students and teachers can better discern commonalities across subjects.

In trying to manage systems, it would seem only common sense before introducing changes in any complex system (ecological, economical, business, personal, etc.) that one would assemble experts in various components of the system to map the current reality of the system surfacing its causalities and unspoken assumptions and then predict the impact of the introduction of the change(s) before the change(s) is implemented.

One last point, the TP adds significant rigor to qualitative research (case studies, and action research). A literature review/critique focused on showing that the UDEs identified in the case study exist across a wider spectrum of organizations (entity existence) would increase the validity of the research and the generalizability of the findings (for both the core problem identification and the prescriptive solution) in both case and action research. In quantitative research such as survey research conducting the TP on relevant cases prior to developing the survey instrument should increase the survey metrics significantly. This approach should be considered a required step in piloting the survey instrument. Using the TP to increase the rigor of qualitative research could provide the solution to addressing the rigor versus relevance issue and ultimately make management more scientific.

Footnotes

Appendix A

Using the categories of legitimate reservation to build a logical current reality tree

A dialog between the diagram developer (DD) and diagram scrutinizer (DS) is used to illustrate the CLR. The DS always starts the inquiry at Level 1 Clarity, proceeding to the specific challenge at level 3 to develop and explain the diagram more fully.

Note: Each entry point in the diagram is read separately. One can apply the clarity and entity existence reservations if needed at this point. 10 Farmers have bad bug problems. Suppose the DS questions whether this entity exists: he would ask for clarity first and if the clarity does not address this concern he might challenge entity 10 using the entity existence reservation. The DD might respond that this diagram is a historical account (1950 – 1972) of the use of DDT as a pesticide and the plight of the bald eagle (among other birds and animals). Until the banning of the use of DDT on crops in 1972 this was the current reality. The use of DDT was first seen as a major advancement in agriculture (Paul Hermann Müller was awarded the Nobel Peace Prize for the discovery of DDT in 1948.) and later as a major cause of the decline of bald eagles. If no reservation is given by the DS then entity 10 is accepted as existing. Going back to the bottom of the CRT, let’s read and scrutinize each segment of the diagram. The DD’s and DS’s reading of the diagram is provided (from Fig. 6) for the first few entries only. Afterwards it is assumed the reader knows how to read the diagram. The DS’s reservations are also provided on the first few figures. See Fig. A1.

Fig. A1a. DD: If 10 Farmers have bad bug problems then 30 Poison gets in streams and lakes.

DS: Please clarify. I do not agree with the causality between 10 and 30. How can 10 Farmers have bug problems by itself cause 30 Poison gets in streams and lakes?

DD: Farmers plant crops to provide food for people and animals and Farmers use poison (pesticides) to kill the bugs.

DS: Thanks that makes much more sense. Let’s add these two entities to solve the (L3) cause insufficiency. The sharp cornered rectangles indicate actions taken in contrast to the rounded corner rectangles which indicate causes and effects. The line across the incoming arrows indicates an if-and-then relationship (“conceptual and” connector). The CRB is on Fig. A1b.

DD: The diagram now reads: If 5 Farmers plant crops to provide food for people and animals and 10 Farmers have bad bug problems and 15 Farmers use poison (pesticides) to kill the bugs then 30 Poison gets in streams and lakes.

DS: I now agree with this part BUT... I still have Clarity and Causality Existence Reservations. I do not see how 5, 10 and 15 cause 30. How does the poison get from the crops to the streams and lakes? How does 10 cause 30?

DD: I see. The poison gets into the soil and the rains carry the poison to the streams and lakes. The CRB is missing an entity in the causality chain (Causality Existence) and suffers from Cause Insufficiency on how the poison gets from the soil to the streams and rivers. The corrected CRB is shown as Fig. A1c. Please read.

DD: If 5 and 10 and 15 then 20. If 20 and 25 then 30. That makes much more sense.

Note: to this point clarity is used first to have the responder provide additional explanation on how the lower entity (10) caused the upper entity (30). Generally the response is to Cause Insufficiency. Even after the cause insufficiency reservation had eliminated some ambiguity, the DS still doesn’t see the causal linkage between two entities (10 causing 30). The DS then used Causality Existence stating that she did not agree that 10 caused 30. This reservation forced the DD to build the intermediate step or linkage (20) between the twoentities.

This segment provides an example of moving from Level 1 Clarity Reservation, through Level 2 Causality Existence to the specific reservation: Level 3 Cause Insufficiency Reservation. Recall–when scrutinizing a diagram always start at Level 1 then Level 2 then Level 3 proceeding to the next level only when the question of clarity still remains unresolved. The DS should know the specific problem (L3) with the logic but starts with L1 then L2 looking for the explanation.

Let’s now analyze the logic from entity 30 building upward to 40.

See Fig. A2a. DS: Clarity: 40 and Causality Existence: 30 to 40.

DD: If Poison gets in the streams and lakes then Many fish are poisoned. The eagles routinely eat the poisoned fish and drink the poisoned water.

See Fig. A2b. DS: Clarity: How are fish involved from 30 to 40? Additional Cause?

DD: 40 has been clarified. 40 involves eagles both eating fish and drinking water but entity 30 only gets poison into the water. Once there 35 Many fish are poisoned. If 35 then 40. Two causes produce 40; one related to eating poisoned fish and one related to drinking poisoned water. Both independently provide poison to the eagle. Read the diagram.

DS: If 30 then 35. If 35 then 40. If 30 then 40. Note: two causes for entity 40, both 30 and 35, have been surfaced. There may be additional causes for the eagles getting poisoned such as eating other animals that consumed the poison. This could also be added to the CRT as an additional cause for entity 40 if it is thought a major contributor to the problem above entity 40.

See Fig. A2c. The DS might also use the Causality Existence Reservation to question the linkage between 35 and 40 if they knew little about eagles. At Level 2 the DS might challenge with Cause Insufficiency questioning the significance of fish being poisoned and eagles frequently eating fish. The cause insufficiency reservation might elicit 37 Fish are a major food source for eagles. The diagram would then be as shown.

Fig. A2d. Let’s now check the causal logic from entity 40 to entity 60.

DS: Clarity? If 40 then 60. Do you mean die prematurely? Are there other major causes for the decline of eagles?

DD: Prematurely, yes, and fewer eagles are born because poisoned male eagles are sterile. Additionally eagle eggs frequently are soft and crack easily before hatching. Let’s add these additional major causes.

DS: Continued poisoning causes these effects also?

DD: Yes.

See Fig. A2e. DD: The resulting diagram with clarity on 60 (prematurely added), causality existence and cause insufficiency is presented. If 40 and 55 then 60. Is this logical?

DS: The addition is: 55 Continued poisonings are harmful to eagles but what causes 55?

See Fig. A3a. DD: The causes are: 18 and 15. They should be connected with an arrow. If 15 and 18 then 55.

DS: Yes, now let’s address the Additional Cause Reservations which identified 65 and 70. Both were a result of 18.

DS: Cause insufficiency on 40 to 65. To surface the condition use: if 40 then 65 because____.

DD: Because 45 The poison causes the eagles’ egg shells to be soft and weak. 55 also.

DS: If 40 and 55 and 45 then 65. That clarifies the situation. Now what causes entity 45?

DD: Entities 15 and 18. If 15 and 18 then 45. Is this logical? Read Fig. A3a.

DS: Great! Now for the last puzzle pieces: Both entities 60 and 80 independently cause 85. By using Additional Cause two major causes of 85 were identified. See Fig. A3b.

Let’s now examine the logic of the full CRT provided on Fig. 5b. Each entry point in the CRT should be examined with the clarity and entity existence reservations. Let’s assume clarity. Please read the entities from Fig. 5 as you read the discussion below.

Entity 5. Does entity 5 exist (entity existence)? Yes.

Entity 10. Does entity 10 exist (entity existence)? Yes.

Entity 15. Does entity 15 exist (entity existence)? Yes.

If 5 and 10 and 15 then 20. Do entities 5, 10 and 15 cause entity 20 (causality existence)? Yes.

Entity 25. Does entity 25 exist (entity existence)? Yes.

If 20 and 25 then 30. Do entities 20 and 25 cause entity 30 (causality existence)? Yes.

If 30 then 35. Does entity 30 cause entity 35 (causality existence)? Yes.

Entity 37. Does entity 37 exist (entity existence)? Yes.

If 35 and 37 then 40. Do entities 35 and 37 cause entity 40 (causality existence)? Yes.

If 30 then 40. Does entity 30 cause entity 40 (causality existence)? Yes.

Entity 18. Does entity 18 exist (entity existence)? Yes.

If 15 and 18 then 45. Do entities 15 and 18 cause entity 45? (causality existence)? Yes.

If 15 and 18 then 55. Do entities 15 and 18 cause entity 55? (causality existence)? Yes.

If 40 and 45 and 55 then 65. Do entities 40, 45 and 55 cause entity 65 (causality existence)? Yes.

If 65 then 80. Does entity 65 cause entity 80 (causality existence)? Yes.

If 40 and 55 then 70. Do entities 40 and 55 cause entity 70 (causality existence)? Yes.

If 70 then 80. Does entity 70 cause entity 80 (causality existence)? Yes.

If 80 then 85. Does entity 80 cause entity 85 (causality existence)? Yes.

If 40 and 55 then 60. Do entities 40 and 55 cause entity 60 (causality existence)? Yes.

If 60 then 85. Does entity 60 cause entity 85 (causality existence)? Yes.

The CRT presented on Fig. 5 is logically sound.

Other factors such as hunters killing eagles (against the law in many states), harsh winters, injuries, etc. exist but until the US banned the use of DDT this diagram was the stark reality.

This example was analyzed to illustrate the use of the CLR in strengthening the logic of the observation presented. Most diagrams are far more logical at the starting point than this example particularly if the DD uses the CLR to strengthen her argument before having someone scrutinize the tree. Emily’s description of this situation was far better than this starting point but a crude diagram (Fig. 6) was needed to illustrate the various errors addressed by the CLR. Please go back to the figure in the text to read the completed argument for how using pesticides (DDT) endangered eagles in the 1950-70’s. Many scientists and others blame DDT as the primary cause of the decline of the bald eagles during this period. The pesticides problem still exists today even with the 1973 enactment of regulations governing pesticide use. (For an overview see the Virginia Cooperative Extension website: https://pubs.ext.vt.edu/420/420-013/420-013.html)

The purpose here was to surface the unspoken assumptions in this situation so that one might study the causes of serious mistakes. Note both the shifting system boundaries over time and the delay from the first use of DD till the recognition of its harmful effect on eagles (and other wildlife) and the time to enact legislation. The initial scope of using DDT focused on its positive aspects and not until much later (a significant time delay) was the scope increased and frame changed to identify the negative unintended consequences of its use.

Acknowledgments

The authors would like to acknowledge the assistance of Victoria J. Mabin, Eli Schragenheim and Shoshir Reiter on earlier versions of this paper.

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