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REASON® Root Cause Analysis |
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REASON® 6
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Lessons Learned and REASONQuick Links What Lessons Learned Will Do for Your Organization Tactical Uses for Lessons Learned How the REASON System is Different from Conventional Lessons Learned Systems REASON Lessons Learned: The Future
Trending and Tracking: A
Gardener’s Perspective
“Those who cannot
remember the past are condemned to repeat it.” Certainly, most of us can agree on the wisdom of learning from past mistakes. When an organization reaches the point where a Lessons Learned program is being planned and budgeted, the manager has several important responsibilities. He understands the full significance of implementing a system upon which critical decisions will be based. This brief is intended to provide background information that will assist managers in the design and implementation of a computerized Lessons Learned system during the planning stages. From an executive management perspective, there can be only one criterion for assessing value from a Lessons Learned program. The system can be measured for value only on the basis of the unique prevention benefits it provides to the organization. Lessons Learned is not just an additional source for rules, instruction booklets, TQM/Safety meetings, or special bulletins and newsletters. The users of a Lessons Learned system are the employees, supervisors and managers on the job, who have an immediate need for specific information to plan and accomplish their daily tasks. When a problem on the job occurs, they need information on how to deal with the situation now if their chances for mistake and injury are to be reduced. This is the prevention role that Lessons Learned should fill. Before the advent of REASON, major organizations established Lessons Learned programs. While the administrators of the programs spoke of the success of the system, the users of the system did not give the program high marks. They complained, "The Lessons never seem to apply to me" and "There is too much information to keep up with." These complaints carry important messages for executive managers who are responsible for the investment in Lessons Learned. They tell us that conventional Lessons Learned approaches do not adequately target data to the user's needs. Why not? One reason is rooted in the way our concept of what a Lessons Learned System is. Too often, those who establish and maintain the system perceive its success in terms of how many users receive their prepared, packaged and distributed stories with a lesson. This concept of Lessons Learned will not translate to effectiveness in the workplace. It is too out of touch with what the actual users of the system perceive as useful. This is one of those areas of concern where an informed executive manager can provide the vision and set the criteria for success. The answer to establishing a successful and cost-effective program is to conceptualize Lessons Learned as a system designed to facilitate access to a relational data base of “situational” data. Situational data record a counter-quality problem as a series of causal interactions occurring within the organization. Instead of a collection of lengthy reports, the system holds electronic memories and models of the exact processes and interactions that produced the problems in the past. With this new concept, our vision of how the system works changes dramatically. There are new criteria and new expectations. In this vision, users contribute to the data resource every time they solve one of their own problems. There is no longer a labor intensive process just to get the data into the system. Now the data not only provides a traditional report of the individual occurrences and recommended solutions, but also provides the user with the ability to visit and explore the data and find relevant lessons quickly. There will be a growing volume of resource data to broadly represent the base of experience of the organization. There will be no artificial boundaries around the data. The resource will be accessible and responsive to the immediate needs of the user. Is such capability really possible? Yes it is, but even today there can be obstacles if old approaches or inappropriate methods are chosen. In the past, the obstacles were the ponderous authoring and publishing processes that made timely lessons impossible. Now in today's world, the problem can be too much information – or more precisely – too much information in the wrong form. Planners and designers need to understand that conventional methods of accessing computerized data cannot accommodate high volumes of resource data without overloading the users. If we understand how these unnecessary obstacles can be avoided, rapid and cost-effective implementation of a successful Lessons Learned system is possible.
What the REASON Lessons Learned System Will Do for Your Organization
Knowledge BroadcastingUnencumbered Knowledge SharingThe Lessons Learned system of a larger organization must not rely on the use of a person’s time to extract lessons from events occurring at other facilities or on a person’s time to communicate those lessons. The demand for time and attention to keep the system going would become an insurmountable hurdle to the lessons learned system’s success. Therefore, a successful RCA/LL system must use the organization’s investment of time on investigation and analysis to feed an automatic system that targets and broadcasts the lessons to the organization. Without an automatic mechanism, the system would place time obstacles between decision markers and vital information. A Lessons Learned system that will meet the demands and needs of a large organization must automatically broadcast lessons learned knowledge out to the people who need it in all facilities without bureaucratic obstacles.
With REASON, managers, supervisors and workers can set up a search profile for Lessons Learned, which will specify tools, equipment, materials, process, or any other factors that affect their operations and jobs. Then, when a problem is released to Lessons Learned anywhere in the organization containing any of the factors specified in the profiles, the users will receive an e-mail with an abstract of the case.
Knowledge MiningExpedite Problem ResolutionProblem solvers can tap into knowledge acquired in the past in order to expedite problem resolution in the present and into the future. Currently, as the workforce ages and decision makers leave the industry, their knowledge, experience and understanding of operations are lost. Your organization needs to hold on to yesterday’s lessons and use them to promote efficient problem solving, to assure proper design of future systems, and to create future policies and procedures based upon known and understood risks.
Provide an Early Warning System to ChangeNo industry is static. What worked yesterday to make business happen does not necessarily help business get the job done today. REASON Lessons Learned deployed organization wide is an incredible oversight tool for tracking and trending new industry patterns, trends and changes. This is an important capability for any industry, but especially for industries that are in constant modes of change. The ability to see emerging patterns and issues within the problems experienced by your organization allows you to take proactive, preventative steps before those issues make you pay a bigger price later as a consequence of not remembering past lessons. Access to unbiased and reliable knowledge and metrics concerning what has produced problems in the past provides an invaluable resource in support of policy creation and/or modification. This oversight ability allows new trends to be discovered early and dealt with through new or adjusted processes, before these new issues can combine to spawn larger and more serious events/issues.
Provide metrics to measure effectiveness of policiesIn many industries, the nature of the processes, hardware and interaction among facilities makes it essential that the organization-wide lessons learned system provide a standard for metrics that can be used reliably to represent, calculate, anticipate and compare. REASON Lessons Learned gives the organization a solid base of reference beyond assessments and opinions. Metrics provide visibility of the effectiveness of policies.
Tactical Uses for REASON Lessons Learned
Investigators of current problems can search the Lessons Learned database for problems similar to his or for factors similar to those contributing to his problem.
Workers beginning a job or task that they are not familiar with can do a quick search of Lessons Learned to acquaint themselves with potential problems involving the tools, equipment, materials and processes they will be using in the new task.
When evaluating the pros and cons of acting on the various Corrective Opportunities uncovered in REASON investigation, Managers can run key terms or strings from Root Cause statements through Lessons Learned to see if similar breakdowns in control have been causing problems more widespread than the one which contributed to the current investigation.
Managers, supervisors and workers can set up a profile and receive an e-mail when any case worldwide is submitted to Lessons Learned involving tools, equipment, materials and process that affect their operations and jobs.
How the REASON System Supercedes Conventional Lessons Learned SystemsEveryone who has searched for something on the Internet knows that if you search for anything in a large database, you get thousands of "hits." Often, only a small number of these "hits" are even loosely associated with the information you were seeking. A simple search within a database is what constitutes most Lessons Learned systems worldwide. Again, if you’ve searched the Internet you know the results are often not relevant to your search. As an organization’s knowledge base grows, people are forced to look at irrelevant information with increased frequency. This dynamic grows in an organization until it subverts the effectiveness of the system. Simply put, when people realize that they cannot get useful or relevant information out of a knowledge base quickly, they stop putting it in and they stop trying to get it out. REASON works differently. A REASON search is much more accurate than any other Lessons Learned, knowledge-base searching method available. Due to the fact that the data is ordered upon its entry into the system, REASON's Lessons Learned system can look for causal interaction between search terms. The main obstacle for any knowledge base is the availability of meaningful information to those who need it. REASON is not merely looking for the presence of key words in a document. REASON's Lessons Learned system is looking for actual interaction between the key words relevant to a current investigation. The end result is a knowledge base searching system that is by far more accurate and faster than any other method. It gives those who seek it ready access to relevant information. Simply put, only REASON "orders" causal data as part of its process. "Causally ordered" information is searchable information. The better the information is organized, the more able you are to accurately search for relevant information. REASON is the only system that takes advantage of causally ordered data in a Lessons Learned system. This is the key to REASON's performance superiority. Finding the LessonsKey word searches, upon which computerized Lessons Learned systems have traditionally relied, provide fast access to records, but create an obstacle to usage by producing lengthy lists of hits. When these conventional word searches are employed, they may produce useful information, but to find the information, the user has to read through all of the reports located by the search in the hope that one of them will relate to his interests. Often this task will take hours. The screening effort required of the user is a disincentive to use the system. REASON solves the user's dilemma by providing (1) a data-targeting method that allows users to better target their searches and (2) a means to significantly simplify the data screening process for the user. 1. Better TargetingDocument Search
Traditional word searches set their sights upon the whole document as the
target. If the words appear anywhere in the document, it is listed as a
"hit." If the document contains the statement, "This report is definitely
not about "chlorinated solvents," a search for the words
"chlorinated" and "solvent" will find that report, and require
the client to read it before he knows that it contains no help for him. “Situational,” Causal-Interaction Search
Instead of using the entire report document as a target for the word
search, REASON narrows the search to only the factors which interacted
causally. For example, in a REASON search, if the user searches for the
words "chlorinated" and "solvent," the files found will be
only those in which these two factors causally interacted in some way. The
kind of causal interaction can be specified in the search criteria entered
by the user and the search results will reflect the degree to which there
was causal interaction. Because the REASON search engine seeks out an
interaction of causes instead of just looking for key words in a document,
irrelevant cases that would have been included in conventional key word
searches are weeded out. The result is smaller yields with richer content.
Narrowing the search target produces fewer files to screen for information, and each of those files is more likely to contain information relevant to the user’s needs. This eliminates one shortcoming of conventional Lessons Learned searches. Now, let's see how REASON deals with the other. When a REASON user explores the cases found in his causal data search, the REASON Lessons Learned system displays a brief abstract of the case report. The abstract contains the factors containing the user’s search terms and their causal relationships to the event. The abstract concentrates upon the concerns of the user, while masking the other areas in the model. If the abstract seems relevant to the user's current interests, he can expand it to display more details, all the way up to displaying the entire narrative, and ultimately the complete case report. This feature significantly reduces the time required for data screening of cases. REASON Lessons Learned: The FutureBefore REASON technology, Lessons Learned was limited to lessons that were based upon a relatively small sample of exceptional cases. Lengthy reports were distributed or posted. Lessons were used either to make long-range planning decisions or to prevent the recurrence of previous losses. The process was valuable, but ponderous and limited in its applications. Now with REASON, the qualities of timeliness and immediacy have been added to the data. REASON has released the full potential of Lessons Learned. We will see line supervisors going to their Lessons Learned system a few minutes before setting up a job to examine the problematic experiences that others have had in similar operations. Employees will solve on-the-job problems quickly and contribute to the organization's base of knowledge each time they identify a solution. As a result, supervisors and safety professionals will have critical information about operations at their fingertips. Quality professionals, with a new ability to see how causes interact within the environment, will devise new ways to improve operations. Human factor engineers can know exactly how and why personnel act or fail to act in specific and similar situations. Maintenance Directors will examine where and why equipment failures contribute to counter-quality problems. This is the future for safety and operations improvement, and REASON has opened it up. The organization has the wisdom – and now the tools – to benefit from its past mistakes. Trending and Tracking: A Gardener’s PerspectiveA friend once told me that he could look out across any land in Texas and tell how alkaline the soil was within a few seconds without taking any soil samples for testing. He explained that anyone who could recognize a few weeds and grasses could learn this trick in minutes. It seems that certain weeds and grasses grow only in alkaline soils, some in slightly alkaline and some in strongly alkaline. The grasses that indicated strongly alkaline soil – which in turn indicated poor production – he called “indicator grasses.” In business and industry, some of the most common indicator grasses are the things that we call quality problems: recurring scrap, rework, missed schedules, accidents, angry customers, downtime, lost sales, and missed goals. So why is it that we are continually dealing with counter-quality in our organizations? It is certainly not because no one can recognize when we have scrap product or an angry customer. The gardener attending a lush and productive garden is someone who sees the “big picture” of what is going on in the garden. Instead of trying to impose unnatural demands upon the plants and environment, he is noticing the trends in the weather and the effects of his new bug spray on the plants. He is committed to keeping an eye on the trends and indicators of healthy growth. He is so attuned to his garden that the slightest trend of decline prompts him to take action. It is no secret to any management team that they too should be looking intently at their industry’s “indicator grasses” and be ready to take action to protect their business. But sometimes the sheer size or design of their “garden” obscures management’s ability to see the trends and indicators. It’s sometimes difficult to see the trees for the forest. REASON’s Trending and Tracking Lesson Learned system is a state of the art system that gives management the ability to monitor the trends and health of their organization. With REASON’s Tracking and Trending , management has an early warning system that alerts them when bad business weather is approaching, or when a bad business process is causing some of their plants to wither. Just as the gardener sees that winter cold is approaching and starts covering his cold-susceptible plants, your organization can have the ability to anticipate the approach of cold weather so that you can take pre-emptive action to minimize or even prevent loss. |
