Daily, 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. Show
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. TEAM TIPWhether your organization is more inclined to take a “hard” or “soft” approach to systems, use some of the processes outlined in this article to begin to shift from “event thinking” to looking at patterns, structures, and mental models. 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 THINKINGSystems Thinking and Modeling MethodologyThe 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:
There are several definitions of the system dynamics methodology. Wolstenholme (1997) offers the following description for system dynamics and its scope:
What: A rigorous way to help thinking, visualizing, sharing, and communication of the future evolution of complex organizations and issues over time. 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. Within: A framework that respects and fosters the needs and values of awareness, openness, responsibility, and equality of individuals and teams. The Five-Phase ST&M Process The development of a systems thinking and modeling (ST&M) intervention involves five distinct but interrelated phases:
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 METHODOLOGYProblem Structuring 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:
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:
Dynamic Modeling 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:
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.
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:
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:
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:
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:
Functional Modeling 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/ThinkingIt 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. 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”.
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 SODACognitive 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. What are the six steps in evaluating decision making process?Overview of the 6-Step Process. Step 1: Define Desired Outcomes and Actions. ... . Step 2: Endorse the Process. ... . Step 4: Develop Alternatives or Options. ... . Step 5: Evaluate, Select, and Refine Alternative or Option. ... . Step 6: Finalize Documentation and Evaluate the Process.. Which of the following are the four common types of effectiveness MIS metrics?Mid Term 1. What refers to the level of detail in the model or the decision making process digital dashboard granularity visualization all of the above?Granularity refers to the level of detail in the model or the decision-making process.
How does a DSS typically differ from an EIS?A DSS typically uses external sources, and EIS use internal sources to support decisions. An EIS requires data from external sources to support unstructured decisions, where a DSS typically uses internal sources to support semistructured decisions.
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