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What is the Hard Systems Approach to problem solving?
Frequently asked questions.
The Hard Systems Approach (HSA) can be used to address both qualitative and quantitative problems. It involves a step-by-step procedure, which can be iterative, and the process should be revised if new information comes to light ora later stage in the process changes the situational perspective.
Stages in the HSA:
- Identify the problem or opportunity
- Describe the situation/system as it currently is (diagrams and open discussion can be helpful)
- Describe the situation/system as it would be ideally (objectives) and the constraints preventing the system operating in this way at present
- Identify metrics by which you will know if you have achieved your goal and generate ideas of possible routes to attain the ideal situation
- Evaluate how these routes identified will behave in practice - this may involve pilot studies, feasibility trials or tests
- Decide which of these routes should be pursued
- Follow this route and monitor and evaluate the outcomes.
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Hard and Soft Systems Thinking
Term paper, 2011, 33 pages, grade: distinction, phd candidate, mba, bba md. rajibul hasan (author), table of content.
2.Hard Systems Thinking
3.Soft Systems Thinking
4.Comparison of Hard and Soft Systems Thinking and the circumstances where the two System Thinking may overlap
5.Design and evaluation of a decision process by using the case study “CELTIC TIGER PR(CT-PR)”
7. Appendix 1
8. Appendix 2
9. Appendix 3
10. Appendix 4
11. Appendix 5
Checkland has been developing the system thinking and system developing for more than 30 years since 1970s. Undoubtedly, the greatest contribution of his work is his Soft System Methodology (SSM), which is playing a very significant role in the field of contemporary system practice. There is another system based methodology that can be used to apply system thinking to the resolution of the system. Checkland deﬁned this kind of system thinking as Hard System Thinking (HST)(Checkland,1981). The success that human landed on the moon in 1970s is a good example of Hard System Thinking. Checkland (1981) found that Hard System Thinking has the weakness of dealing with the diversity of human activity system, especially in case of the difference and conflict of world views and values within human organization. Finally, Checkland (1999) shows that SST and HST are two different stances in contemporary system practice (Zexian and Xuhui, 2010).
This paper focuses on discussing the Hard and Soft thinking and methodologies. The first section will present the definition and process descriptions of the two systems. Later, the second section will represent the comparison of the Hard and Soft System Thinking and it will also represent the circumstances where the two system thinking may overlap. Then, the third section will demonstrate a Rich Picture, a Flow Chart and evaluate the decision process by using the example of the case study “Celtic Tiger PR”.
Hard Systems Thinking:
According to Teale et al (2003, p. 137), “Hard System Analysis (HSA) enables us to look at parts of the system in greater depth.” HAS is linked closely with organization’s goals and objectives. It assumes that every system can be disaggregated into a number of subsystems.(Teale et al,2003). According to Kirk (1995), Hard system thinking considers system that has a clear purpose and well-defined goals and is useful for designing solutions that achieve those goals. This represents a model which has precise objective and these objectives can be expressed in quantitative terms allowing the development of mathematical models. It is assumed that the essence of the hard system approach consists of a number of subsystems and that the components of those subsystems can be identified and quantified to provide an explanation of the workings of those subsystems. Therefore, the whole system is the combination of all subsystems. A variety of tools like diagramming techniques, structured flowcharts of the processes involved, and mathematical representations based upon the application of management science techniques used to describe and analyse systems. An example of hard system is illustrated in the appendix 1. Hard system consists of various stages, and these stages include awareness and commitment, constraints, objectives and goals, generation of alternatives, assessing alternatives and model construction, evaluation and implementation. The awareness and commitment stage is to develop awareness of the problem situation. Later, agreement is reached concerning the purposes and the scope of the study and attempts are made to define the problem. Then, commitment is very important to implement a solution because a project without commitment will fail. In case of the constraints, objectives and goals stage, constraints and objectives that are relevant for the system are being studied to establish the nature and direction of the organization. The nature and direction of the organisation is established which can be expressed in the hierarchy of statements. The main purpose of the organization’s existence is its mission statement. These missions are the objectives of the firms for both long and medium terms. Moreover, a firm will set goals to meet the objectives. After establishing the objectives, in the generation of alternatives stage, a possible range of alternatives are explored to address the related issues and meet the objectives. If no alternatives exist, then the system, mission, and objectives are reassessed in order to create a review of the analysis. Later, in the assessing alternatives stage, we measure the alternatives against a set of criteria that allow us to make a value judgement as to the effectiveness of the proposed paths for objective attainment. Moreover, the measures of performance can be classified as the four Es: Effectiveness, Efficiency, Equity and Efficacy. Finally, in the model construction, evaluation and implementation stage, to model the system there need to be systematic description and evaluation in order to determine its credibility and evaluation of the alternative routes to the objectives (Jennings & Wattam, 1998).
Soft Systems Thinking:
“Soft systems methodology (SSM) was developed by Peter Checkland (1981) as a strategy for analysing complex problem situations and identifying acceptable improvements that could be made to those situations.” (Checkland, 1981, in Jennings & Wattam, 1998, p. 36) The aim of the SSM is to achieve improvement to the system; this is attained through a multistage process of information gathering, description, analysis and debate. (Jennings & Wattam, 1998). Appendix 2 represents an outline of Soft Systems Methodology and Appendix 3 represents the stages of SSM. The first stage in an SSM consisting of the careful observation of the problem situation with all its intricate details, and the recording of all that is perceived. This involves collecting qualitative data such as attitudes and opinions concerning the problem situation, including reactions to interference in matters as well as quantitative data, and recording this in the form of a ‘picture’ which is known as rich picture (Appendix 4). Then, models of these systems that are consistent with different viewpoints expressed within the descriptions are drawn. Finally, several comparisons are made of the models with the observations of the real world situation, which are used in a discussion with the problem owners to suggest systemically desirable and culturally feasible changes that are hoped will lead to improvements in the problem situation. (Checkland & Scholes, 1990) The stages of the Soft System Thinking will be described briefly during the evaluation of the decision process by using the example of the case study “Celtic Tiger PR”.
Comparison of Hard and Soft Systems Thinking and the circumstances where the two System Thinking may overlap:
It is necessary to know why Checkland developed two different systems. According to Checkland (1996, p. 190), “The main difference between ‘hard’ and ‘soft’ approaches is that where the former can start by asking ‘What system has to be engineered to solve this problem?’ or ‘What system will meet this need?’ and can take the problem or the need as given.” In case of Soft System methodology, there is a comparison stage, which has no equivalent in the Hard System methodology. In comparison stage, soft system thinking provides a structure for a debate about change which hopefully ensures superior quality as a result of the insight captured in the root definitions. On the other hand, hard system thinking is always busy preparing to implement the designed system. Moreover, Soft system thinking is considered for the general case and hard system thinking is considered for special cases. Soft system thinking improves the conceptual model using the formal system model and other systems thinking. On the other hand, hard system optimizes the design, using the defined performance criterion and select the alternative which best meets the need and is feasible. In case of soft system thinking, when problem is not clear, it requires an additional stage which uses system analysis as a mean of orchestrating debate about change. This additional stage is a reflection of the main characteristic of human activity system. However, the human activity can never be described in a single account which will be either generally acceptable or sufficient in case of hard system. Moreover, soft system implements the agreed system and hard system implement the designed system. (Copeland, 1996). The following table 1 compares the author’s approach with both the RAND corporation (1950) version of system analysis and the account given by Jenkins (1969) from which the hard system started.
illustration not visible in this excerpt
Source: Checkland P., (1996), Systems thinking, systems practice , Chichester: Wiley
According to Zexian and Xuhu (2010), the main difference lies between the hard system and soft system is the interpretation of the concept of system. Hard systems thinking consider the system as an objective part of the world. On the other hand, Soft Systems Thinking considers system as epistemological concept, which is subjectively constructed by people rather the objective entities in the world (appendix 5). Hard Systems Thinking presumes that a system should have a good structure and a specific goal. Soft Systems Thinking considers these difficulties and employs another strategy to deal with human affairs. Moreover, Hard system thinking assumes that there are problems and people have enough ability to deal with them. However, Soft system thinking believes that the interaction and interdependence between the observer and observed object build a problematic situation because the observer is involved in the observed situation (Zexian and Xuhui, 2010). After comparing the hard and soft system, “it is clear that SST achieves a paradigm shift which makes applied system thinking change from ‘hard’ approach to ‘soft’ approach” .(Zexian and Xuhui, 2010, p. 144)
Some of the differences between hard and soft system are given below in the table 2 to summarize the main differences:
Source: (cs.stir.ac.uk, Accessed on 5th July 2011)
There are some circumstances where the hard and soft system can overlap. The hard system is separated from the soft system is the traditional view. Applied system thinking leverages the combination of these two approaches. The blue arrow in the figure 1 represents the need for these two approaches to work together.
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Systems Thinking: How to Solve Problems So They Stay Solved
From production to customer service and marketing, organizations are made up of a series of interconnected parts. While each function may appear to operate efficiently on its own, a change in just one cog can throw the whole system out of whack. The problems that arise in interconnected organizations can be difficult to solve.
Systems thinking is problem-solving approach that examines the relationships between functions in an organization. Systems thinking is powerful because it enables you to predict the consequences of a potential change. This problem-solving method can also help you eliminate silos, see different viewpoints, and remain focused on the big picture.
Ultimately, systems thinking empowers you to solve problems so that they stay solved. Instead of offering quick-fix solutions that work only in the short term, systems thinking helps you make decisions that benefit your organization in the long run.
You will learn how to:
- Apply systems thinking in the workplace in ways that benefit you and your organization: encouraging innovation, learning from mistakes, and enhancing leadership and management skills.
- Apply the tools of systems thinking to solve a problem.
- Minimize the unintended consequences of major decisions.
$430 (includes instruction, seminar manual, refreshments, certificate of completion and parking)
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Root Cause Analysis Explained: Definition, Examples, and Methods
The easiest way to understand root cause analysis is to think about common problems. If we’re sick and throwing up at work, we’ll go to a doctor and ask them to find the root cause of our sickness. If our car stops working, we’ll ask a mechanic to find the root cause of the problem. If our business is underperforming (or overperforming) in a certain area, we’ll try to find out why. For each of these examples, we could just find a simple remedy for each symptom. To stop throwing up at work, we might stay home with a bucket. To get around without a car, we might take the bus and leave our broken car at home. But these solutions only consider the symptoms and do not consider the underlying causes of those symptoms—causes like a stomach infection that requires medicine or a busted car alternator that needs to be repaired. To solve or analyze a problem, we’ll need to perform a root cause analysis and find out exactly what the cause is and how to fix it.
In this article, we’ll cover the following:
- Definition of root cause analysis
Benefits and goals of root cause analysis
- How to conduct root cause analysis
- Tips for performing rot cause analysis
What is root cause analysis?
Root cause analysis (RCA) is the process of discovering the root causes of problems in order to identify appropriate solutions. RCA assumes that it is much more effective to systematically prevent and solve for underlying issues rather than just treating ad hoc symptoms and putting out fires. Root cause analysis can be performed with a collection of principles, techniques, and methodologies that can all be leveraged to identify the root causes of an event or trend. Looking beyond superficial cause and effect, RCA can show where processes or systems failed or caused an issue in the first place.
There are a few core principles that guide effective root cause analysis, some of which should already be apparent. Not only will these help the analysis quality, these will also help the analyst gain trust and buy-in from stakeholders, clients, or patients.
- Focus on correcting and remedying root causes rather than just symptoms.
- Don’t ignore the importance of treating symptoms for short term relief.
- Realize there can be, and often are, multiple root causes.
- Focus on HOW and WHY something happened, not WHO was responsible.
- Be methodical and find concrete cause-effect evidence to back up root cause claims.
- Provide enough information to inform a corrective course of action.
- Consider how a root cause can be prevented (or replicated) in the future.
As the above principles illustrate: when we analyze deep issues and causes, it’s important to take a comprehensive and holistic approach. In addition to discovering the root cause, we should strive to provide context and information that will result in an action or a decision. Remember: good analysis is actionable analysis.
The first goal of root cause analysis is to discover the root cause of a problem or event. The second goal is to fully understand how to fix, compensate, or learn from any underlying issues within the root cause. The third goal is to apply what we learn from this analysis to systematically prevent future issues or to repeat successes. Analysis is only as good as what we do with that analysis, so the third goal of RCA is important. We can use RCA to also modify core process and system issues in a way that prevents future problems. Instead of just treating the symptoms of a football player’s concussion, for example, root cause analysis might suggest wearing a helmet to reduce the risk of future concussions. Treating the individual symptoms may feel productive. Solving a large number of problems looks like something is getting done. But if we don’t actually diagnose the real root cause of a problem we’ll likely have the same exact problem over and over. Instead of a news editor just fixing every single omitted Oxford comma, she will prevent further issues by training her writers to use commas properly in all future assignments.
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How to conduct an effective root cause analysis: techniques and methods
There are a large number of techniques and strategies that we can use for root cause analysis, and this is by no means an exhaustive list. Below we’ll cover some of the most common and most widely useful techniques.
One of the more common techniques in performing a root cause analysis is the 5 Whys approach . We may also think of this as the annoying toddler approach. For every answer to a WHY question, follow it up with an additional, deeper “Ok, but WHY?” question. Children are surprisingly effective at root cause analysis. Common wisdom suggests that about five WHY questions can lead us to most root causes—but we could need as few as two or as many as 50 WHYs. Example: Let’s think back to our football concussion example. First, our player will present a problem: Why do I have such a bad headache? This is our first WHY. First answer: Because I can’t see straight. Second why: Why can’t you see straight? Second answer: Because I my head hit the ground. Third why: Why did your head hit the ground? Third answer: I got hit tackled to the ground and hit my head hard. Fourth why: Why did hitting the ground hurt so much? Fourth answer: Because I wasn’t wearing a helmet. Fifth why: Why weren’t you wearing a helmet? Fifth answer: Because we didn’t have enough helmets in our locker room. Aha. After these five questions, we discover that the root cause of the concussion was most likely from a lack of available helmets. In the future, we could reduce the risk of this type of concussion by making sure every football player has a helmet. (Of course, helmets don’t make us immune to concussions. Be safe!) The 5 Whys serve as a way to avoid assumptions. By finding detailed responses to incremental questions, answers become clearer and more concise each time. Ideally, the last WHY will lead to a process that failed, one which can then be fixed.
Change Analysis/Event Analysis
Another useful method of exploring root cause analysis is to carefully analyze the changes leading up to an event. This method is especially handy when there are a large number of potential causes. Instead of looking at the specific day or hour that something went wrong, we look at a longer period of time and gain a historical context. 1. First, we’d list out every potential cause leading up to an event. These should be any time a change occurred for better or worse or benign. Example: Let’s say the event we’re going to analyze is an uncharacteristically successful day of sales in New York City, and we wanted to know why it was so great so we can try to replicate it. First, we’d list out every touch point with each of the major customers, every event, every possibly relevant change. 2. Second, we’d categorize each change or event by how much influence we had over it. We can categorize as Internal/External, Owned/Unowned, or something similar. Example: In our great Sales day example, we’d start to sort out things like “Sales representative presented new slide deck on social impact” (Internal) and other events like “Last day of the quarter” (External) or “First day of Spring” (External). 3. Third, we’d go event by event and decide whether or not that event was an unrelated factor, a correlated factor, a contributing factor, or a likely root cause. This is where the bulk of the analysis happens and this is where other techniques like the 5 Whys can be used. Example: Within our analysis we discover that our fancy new Sales slide deck was actually an unrelated factor but the fact it was the end of the quarter was definitely a contributing factor. However, one factor was identified as the most likely root cause: the Sales Lead for the area moved to a new apartment with a shorter commute, meaning that she started showing up to meetings with clients 10 minutes earlier during the last week of the quarter. 4. Fourth, we look to see how we can replicate or remedy the root cause. Example: While not everyone can move to a new apartment, our organization decides that if Sales reps show up an extra 10 minutes earlier to client meetings in the final week of a quarter, they may be able to replicate this root cause success.
Cause and effect Fishbone diagram
Another common technique is creating a Fishbone diagram, also called an Ishikawa diagram , to visually map cause and effect. This can help identify possible causes for a problem by encouraging us to follow categorical branched paths to potential causes until we end up at the right one. It’s similar to the 5 Whys but much more visual. Typically we start with the problem in the middle of the diagram (the spine of the fish skeleton), then brainstorm several categories of causes, which are then placed in off-shooting branches from the main line (the rib bones of the fish skeleton). Categories are very broad and might include things like “People” or “Environment.” After grouping the categories, we break those down into the smaller parts. For example, under “People” we might consider potential root cause factors like “leadership,” “staffing,” or “training.” As we dig deeper into potential causes and sub-causes, questioning each branch, we get closer to the sources of the issue. We can use this method eliminate unrelated categories and identify correlated factors and likely root causes. For the sake of simplicity, carefully consider the categories before creating a diagram. Common categories to consider in a Fishbone diagram:
- Machine (equipment, technology)
- Method (process)
- Material (includes raw material, consumables, and information)
- Man/mind power (physical or knowledge work)
- Measurement (inspection)
- Mission (purpose, expectation)
- Management / money power (leadership)
- Product (or service)
- Promotion (marketing)
- Process (systems)
- People (personnel)
- Physical evidence
- Surroundings (place, environment)
Tips for performing effective root cause analysis
Ask questions to clarify information and bring us closer to answers. The more we can drill down and interrogate every potential cause, the more likely we are to find a root cause. Once we believe we have identified the root cause of the problem (and not just another symptom), we can ask even more questions: Why are we certain this is the root cause instead of that? How can we fix this root cause to prevent the issue from happening again? Use simple questions like “why?” “how?” and “so what does that mean here?” to carve a path towards understanding.
Work with a team and get fresh eyes
Whether it’s just a partner or a whole team of colleagues, any extra eyes will help us figure out solutions faster and also serve as a check against bias. Getting input from others will also offer additional points of view, helping us to challenge our assumptions.
Plan for future root cause analysis
As we perform a root cause analysis, it’s important to be aware of the process itself. Take notes. Ask questions about the analysis process itself. Find out if a certain technique or method works best for your specific business needs and environments.
Remember to perform root cause analysis for successes too
Root cause analysis is a great tool for figuring out where something went wrong. We typically use RCA as a way to diagnose problems but it can be equally as effective to find the root cause of a success. If we find the cause of a success or overachievement or early deadline, it’s rarely a bad idea to find out the root cause of why things are going well. This kind of analysis can help prioritize and preemptively protect key factors and we might be able to translate success in one area of business to success in another area.
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D aily, we are exposed to information from a multitude of sources: the media, newspapers, radio, T. V., and the Internet. Generally this kind of information reports events – what happened, where, when, how, who was involved, etc. This level of information is very shallow as it represents a snapshot of reality that only touches the surface of what actually happened. For example, the stock market information that is reported daily gives a snapshot of the day’s activities. It tells us whether stocks, on average, went up or down (often the index goes both up and down within the same day) and by how much. We also get information on the volume of shares traded, the dollar value of stocks traded (capital turnover), and much more. All of this information is at event level.
Commentaries about a news item or an issue allow one to examine trends and patterns of events and data. This provides a richer picture of reality and gives more insight into a “story.” In the case of stock market, this means looking at the trends over the past several months or years, observing the fluctuations in the market, and trying to explain “pulses” in the system – for example, news of a merger, a quarterly economic report, or a political scandal.
However, it is not common to read reports of how such trends and patterns relate to and affect one another. This represents a much deeper level of thinking that can show how the interplay of different factors brings about the outcomes that we observe. In the case of stocks, this means relating a number of factors that systemically cause the market fluctuations. These factors could be economic, social, political, or structural. The critical thing at this level of thinking is to understand how these factors interact.
There is yet another, deeper level of thinking that hardly ever comes to the surface. This is the “mental model” of individuals and organizations that influence why things work the way they do. Mental models reflect the beliefs, values, and assumptions that we personally hold, and they underlie our reasons for doing things the way we do. However, these generally remain “undiscussable,” according to noted educationalist Chris Argyris (Argyris, 1990).
The four levels of thinking described above are shown in “Four Levels of Thinking.” This figure uses the analogy of an iceberg, where the event level of thinking is only the tip and yet most of us are satisfied with this level. This is because events are the most visible part and often require immediate attention.
FOUR LEVELS OF THINKING
Systems Thinking and Modeling Methodology
The systems thinking and modeling methodology (ST&M) outlined here refers to a set of conceptual and analytical methods. The general approach is based on the system dynamics methodology that was initially developed by Jay Forrester and others at the Massachusetts Institute of Technology in the late 1950s, based on developments following World War II in:
- the theory of information feedback systems
- the understanding of decision-making processes
- the use of mathematical models to simulate complex systems
- the development of high-speed computing as a means of simulating mathematical models
There are several definitions of the system dynamics methodology. Wolstenholme (1997) offers the following description for system dynamics and its scope:
Why: For the purpose of solving problems and creating more robust designs, which minimize the likelihood of unpleasant surprises and unintended consequences.
How: By creating operational maps and simulation models that externalize mental models and capture the interrelationships of physical and behavioral processes, organizational boundaries, policies, information feedback, and time delays; and by using these architectures to test the holistic outcomes of alternative plans and ideas.
The Five-Phase ST&M Process The development of a systems thinking and modeling (ST&M) intervention involves five distinct but interrelated phases:
- problem structuring
- causal loop modeling
- dynamic modeling
- scenario planning and modeling
- implementation and organizational learning
These phases follow a process, each involving a number of steps, as outlined in “The Five-Phase Process of Systems Thinking and Modeling”. However, it must be emphasized that an ST&M intervention does not require all phases to be undertaken, nor does each phase require all the steps listed in the table. Rather, these phases and steps are presented as guidelines, and which phases and steps are included in a particular ST&M intervention depends on the issues or problems that have generated the systems inquiry and the degree of effort that the organization is prepared to commit to the intervention.
“Phases of the ST&M Methodology” shows the progression of the five phases above. As mentioned earlier, although these phases can be used separately/individually, their cumulative use adds more value and power to the investigation. These phases are described in the following sections.
PHASES OF THE ST&M METHODOLOGY
In this phase, the situation or issue at hand is defined and the scope and boundaries of the study are identified. This is the common first step in most problem-solving approaches. However, the importance of this step is generally underestimated as managers and decision makers often assume that they readily know what the real problem is while in reality they may think about the problem symptom. The problem structuring phase consists of the following steps:
- Identification of the problem area or policy issues of concern to management, and identification of main stakeholders and their interests. (The seminal book on stakeholder analysis has been written by Freeman, 1984. Examples of stakeholder analysis for systems thinking applications are provided by Elias et al., 2002, and Cavana, 2004.) This step requires that we clearly establish the objectives, taking into account multiple stakeholders and perspectives. This step is most effective when performed in consultation with other stakeholders in a manner that encourages openness to new ideas and generates commitment and collaboration from the start.
- Collection of preliminary information and data including media reports, historical and statistical records, policy documents, previous studies, and stakeholder interviews that justify the seriousness and clarify the scope and magnitude of the problem/issue identified.
- The conduct of group sessions for creative problem structuring. Following the identification of the main issue or problems/opportunities of concern to management, bring the main stakeholders together, or separately, for a group creative problem structuring and/or group modeling session using the “affinity diagram” or “hexagon clustering” approaches.
Causal Loop Modeling
During this phase, conceptual models of the problem, known as causal loop diagrams (CLDs), will be created. Causal loop modeling is the most commonly used phase of the systems thinking approach. The following steps are used in causal loop modeling:
- Identify main (key) variables.
- Draw behavior over time charts (or reference modes) for the main variables.
- Develop causal loop diagrams (influence diagrams) to illustrate the relationships among the variables.
- Discuss behavior over time of the dynamics implied by the causal loop diagrams.
- Identify system archetypes that would describe high-level causal patterns.
- Identify key leverage points.
- Develop intervention strategies.
This phase follows the causal loop modeling phase. Although it is possible to go into this phase directly after problem structuring, performing the causal loop modeling phase first will enhance the conceptual rigor and learning power of the systems approach. The completeness and wider insights of systems thinking are generally absent from other simulation modeling approaches, where causal loop modeling does not play a part.
The following steps are generally followed in the dynamic modeling phase:
- Develop a high-level map or systems diagram showing the main sectors of a potential simulation model, or a “rich picture” of the main variables and issues involved in the system of interest.
- Define variable types (e.g., stocks, flows, converters, etc.) and construct stock-flow diagrams for different sectors of the model.
- Collect detailed, relevant data including media reports, historical and statistical records, policy documents, previous studies, and stakeholder interviews.
- Construct a computer simulation model based on the causal loop diagrams or stock-flow diagrams. Identify the initial values for the stocks (levels), parameter values for the relationships, and the structural relationships between the variables using constants, graphical relationships, and mathematical functions where appropriate. This stage involves using specialized computer packages like STELLA, iThink, VENSIM, POWERSIM, DYNAMO, DYSMAP, COSMIC or Consideo.
- Simulate the model over time. Select the initial value for the beginning of the simulation run, specify the unit of time for the simulation (e.g., hour, day, week, month, year, etc.). Select the simulation interval (DT) (e.g., 0.25, 0.5, 1.0) and the time horizon for the simulation run (i.e., the length of the simulation). Simulate model stability by generating steady state conditions.
- Produce graphical and tabular output for the base case of the model. This can be produced using any of the computer packages mentioned above. Compare model behavior with historical trends or hypothesized reference modes (behavior over time charts).
- Verify model equations, parameters, and boundaries, and validate the model’s behavior over time. Carefully inspect the graphical and tabular output generated by the model.
- Perform sensitivity tests to gauge the sensitivity of model parameters and initial values. Identify areas of greatest improvement (key leverage points) in the system.
- Design and test policies with the model to address the issues of concern to management and to look for system improvement.
- Develop and test strategies (i.e., combinations of functional policies, for example operations, marketing, finance, human resources, etc.)
Scenario Planning and Modeling
In this phase, various policies and strategies are formulated and tested. Here “policy” refers to changes to a single internal variable such as hiring, quality, or price. Strategy is the combination of a set of polices and, as such, deals with internal or controllable changes. When these strategies are tested under varying external conditions, this is referred to as scenario modeling. This stage involves working closely with all major stakeholders.
- Develop general scope, timeframe, and boundaries of external environment for scenarios. Prepare stories of possible futures or theme scenarios.
- Identify key drivers of change, uncertainties, and factors that could have a significant impact on the decisions, policies, and strategies being evaluated. Determine ranges for external parameters and graphs.
- Construct forced scenarios by placing all the positive outcomes in an optimistic scenario and all the negative scenarios in a pessimistic scenario. Check the forced scenarios for internal consistency. Modify these scenarios as learning scenarios (this step is based on the method outlined by P. J. H. Schoemaker, 1995).
- Simulate the scenarios (either the individual scenarios varying the key uncertainties or the learning scenarios) with the model. Redesign scenarios if necessary.
- Evaluate the performance of the policies and strategies with the model for each scenario. Assess the performance against a range of relevant performance measures for overall robustness. Select the policies or strategies that meet management’s objectives for the investigation.
Implementation and Organizational Learning
One of the most beneficial and enduring outcomes of systems thinking and modeling is team and organizational learning. Once simulation models have been developed, they can be enhanced by extending them into a microworld. Microworlds (also known as management flight simulators) provide an interactive and user-friendly interface for managers to experiment with the model. The learning laboratory uses microworlds in a structured process, akin to a scientific environment, to test hypotheses and mental models designed to create individual and group learning. The following steps summarize this phase:
- Prepare a report and presentation to the management team and other stakeholders. This should document the background and development of the systems thinking project the challenges faced, and the lessons learned.
- Communicate results and insights of the study and the reasons for the proposed intervention to all stakeholders.
- Develop a microworld and design a learning lab for the simulation model. This involves adding necessary features (i.e., from computer software) to convert the simulation model into an interactive and user-friendly microworld. Then design a learning lab process for the microworld.
- Use the learning lab process to diffuse and facilitate learning in the organization and with clients, decision makers, and other main stakeholders.
Systems Thinking and Modeling Applications
Systems thinking and modeling has a wide range of general and specific applications. Most of these are within the “knowable” region of the sense-making framework Cynefin developed by Kurtz and Snowden (2003) and others at the Cynefin Center for Organizational Complexity at IBM Global Services. (The name “Cynefin” is a Welsh word whose literal translation into English is “habitat” or “place.”) This region is shown at the top right-hand side of “The ST&M Methodology and the Cynefin Framework.”
Kurtz and Snowden (2003) define the knowable domain of their Cynefin sense-making framework as:
“While stable cause and effect relationships exist in this domain, they may not be fully known, or they may be known only by a limited group of people. In general, relationships are separated over time and space in chains that are difficult to fully understand. Everything in this domain is capable of movement to the known domain. The only issue is whether we can afford the time and resources to move from the knowable to the known; in general, we cannot and instead rely on expert opinion, which in turn creates a key dependency on trust between expert advisor and decision maker. This is the domain of systems thinking, the learning organization, and the adaptive enterprise, all of which are too often confused with complexity theory (Stacey, 2001). In the knowable domain, experiment, expert opinion, fact-finding, and scenario-planning are appropriate. This is the domain of methodology, which seeks to identify cause-effect relationships through the study of properties which appear to be associated with qualities. For systems in which the patterns are relatively stable, this is both legitimate and desirable.
THE ST&M METHODOLOGY AND THE CYNEFIN FRAMEWORK
“Our decision model here is to sense incoming data, analyze that data, and then respond in accordance with expert advice or interpretation of that analysis. Structured techniques are desirable, but assumptions must be open to examination and challenge. This is the domain in which entrained patterns are at their most dangerous, as a simple error in an assumption can lead to a false conclusion that is difficult to isolate and may not be seen. It is important to note here that by known and knowable we do not refer to the knowledge of individuals. Rather, we refer to things that are known to society or the organization, whichever collective identity is of interest at the time.”
Examples of general applications of systems thinking and modeling are:
- design of new systems
- reengineering or improvement of existing systems
- prediction of behavior of complex systems under varying conditions
- understanding the interaction of component sub-systems
- strategy development and testing
- scenario modeling and testing
- group and organizational learning
The specific applications of the systems thinking and modeling methodology cover both strategic and functional aspects of business and organizations. Some of these are outlined below.
Strategy and Policy
Systems thinking and modeling is widely used for strategy formulation and testing. This occurs at the level of government and industry (e.g., healthcare, communication, regulation, etc.) as well as at the organizational level (e.g., marketing, production, human resources, finance, and their interfaces). Systems thinking highlights the following areas of strategy, which are often ignored or missed by other methodologies:
- internal contradictions in a strategy
- hidden strategic opportunities
- untapped strategic leverages
Operations and Design
Systems thinking and modeling also has widespread applications in operations and design. Traditionally, manufacturing systems have been a prominent area of application. Service industries such as healthcare, communications, and logistics are the upcoming areas that readily lend themselves to the application of systems thinking and modeling. Some of the specific applications are:
- new product and service development
- supply-chain management
- enterprise resource planning (ERP)
- network design and management
In addition to the areas mentioned above, the systems thinking and modeling methodology can be used to model functional areas such as finance, marketing, information technology, and human resource management.
Hard and Soft Modeling/Thinking
It is important to clarify the meaning of the terms model and modeling in this context. Model is defined as being a representation of the real world. Models can take on different forms – physical, analog, digital (computer), mathematical, and so on. This sense of the word model is the more traditional one and is sometimes referred to as quantitative or “hard.” More recently, the concept of soft modeling has been developed by Checkland and others (Checkland, 1981). Soft modeling refers to conceptual and contextual approaches that tend to be more realistic, pluralistic, and holistic than “hard” models. Hard and soft models are sometimes referred to as “quantitative” or positivist and “qualitative” or interpretivist, respectively (Cavana et al., 2001). The differences between the hard and soft approaches are summarized in “Hard Versus Soft Approaches”.
The methodologies presented cover both hard and soft approaches because we regard these approaches as complementary and mutually reinforcing. Systems thinking tends to fall in the category of soft approaches, while dynamic modeling gravitates toward the category of hard modeling.
In the following sections, two other approaches to systems thinking are outlined. These are soft systems methodology and cognitive mapping. While these approaches are most useful in the problem-structuring phase of systems methodology, their potential use is much wider.
Soft Systems Methodology
Another approach to systems thinking, known as soft systems methodology (SSM), originated in the U. K. (Checkland, 1981). Soft systems methodology is based on the notion that human and organizational factors cannot be separated from problem solving and decision making. Thus SSM takes a systems view of the organization (Pidd, 1996). Soft systems methodology consists of seven interrelated stages. These stages are listed below and shown in “Soft Systems Methodology”.
- The problem situation is unstructured.
- The problem situation is expressed.
- Root definitions of relevant systems are identified.
- Conceptual models are developed.
- The problem situation (stage 2) and the conceptual models (stage 4) are compared.
- Feasible and desirable changes are considered.
- Action is taken to improve the problem.
These stages are conceptually similar to the seven-step method or the plan-do-check-act (PDCA) process of quality management (Shiba et al., 1994). The focus of SSM on root definition is also analogous to the PDCA model’s root-cause analysis (i.e., the cause-and-effect or “fishbone” diagram). In essence, like quality management methods, SSM provides a powerful learning process for individuals as well as for groups and organizations.
A key feature of the second stage of the SSM process is the development of a “rich picture,” which is a “pictorial, cartoon-like representation of the problem situation that highlights significant and contentious aspects in a manner likely to lead to original thinking at stage 3 of SSM” (Jackson, 2003).
Cognitive Mapping and SODA
Cognitive mapping and strategic options development and analysis (SODA) were developed by Eden and his colleagues (Eden et al., 1983; Eden and Ackermann, 2001; Ackermann and Eden, 2001). This approach focuses on how individuals view their world and how they behave within the organization (Pidd, 1996), thus it is more individualistic than the SSM approach. The main premise of Eden’s approach is that desirable outcomes are the product of both content and process (i.e., the end and the means). This means that, in organizations, the effectiveness of policies and strategic plans, for example, depends not only on the plan itself or the apparent results, but also on how the plans are arrived at because this determines people’s commitment to organizational plans and decisions. Cognitive maps are tools for thinking and problem solving. They are intended for unraveling mental models and mapping how people think about a certain issue or problem. The main building blocks of cognitive maps are called “concepts.” The concepts are generated during an interview process using the words used by the interviewee (Pidd, 1996). These concepts or ideas are then linked together by arrows to form a cognitive map, as illustrated in this sustainable tourism example. Although cognitive maps and causal loop diagrams – one of the main tools of systems thinking – are somewhat similar visually, they are distinctly different both conceptually and methodologically (Richardson, 1999). In the first place, the “concepts” used in cognitive mapping are phrases that often contain comparative adjectives (e.g., better, bigger, fewer, less). On the other hand, the “variables” used in causal loops are nouns that have “quantities” associated with them (e.g., demand, supply, quality, motivation, etc.). In the second place, the linkages in cognitive maps are not “closed” and hence loops tend not to arise in cognitive maps. In causal loop diagrams, however, loops are the mainstay of the method, indicating dynamic and recurring patterns. When more than one individual is involved, the SODA methodology is used to create group commitment, especially with a focus on action. This is based on the premise that in order for people to work as a team and create a shared understanding, it is essential that they should be jointly involved in problem definition and the search for ways in which to solve problems (i.e., strategy formulation). SODA methodology moves people through a process of debate and negotiation towards a joint commitment to action (Pidd, 1996). While there are differences between SSM and cognitive mapping, “neither assumes that an organisation is a machine, which grinds on its way regardless of the people who compose it” (Pidd, 1996). The problem structuring phase of the five-phase ST&M process is consistent with and emphasizes this approach. Kambiz E. Maani is an international expert on systemic approaches to organizations and leadership. He is an author and inspirational speaker on systems thinking, complexity management, and organizational learning and leadership. Currently, Kambiz is an associate professor of management and systems sciences at the University of Auckland Business School, where he has held several leadership roles. Robert Y. Cavana is a reader in systems science with the Victoria Management School at the Victoria University of Wellington, New Zealand. He was previously a president of the Operational Research Society of New Zealand and a vice president of the International System Dynamics Society; he is currently a managing editor of the System Dynamics Review. This article is adapted with permission from Chapter 2 of Systems Thinking, System Dynamics: Managing Change and Complexity, Second Edition (Pearson Education New Zealand, 2007).
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