Managing complexity

Whether an organization prospers or merely survives, depends on how it manages complexity (Ahlemeyer, 2001;Ashmos et al., 2000;Beard, 1978;Berniker & Wolf, 2001;Klatzky, 1970;Meyer &

Trice, 1979;Stacey, 1996;Stengers, 2004;Van Uden, 2005). Complexity can transgress into chaos if it is not kept within a certain range (Bennet & Bennet, 2004). Since complexity refers primarily to the number of decisions that need to be made and the number of factors that need to be considered when making these decisions, this calls for a tremendous effort. According to Cannon (1995, p. 96), as the cognitive capabilities of most people are limited to handle only a limited number – working memory capacity is said to be five plus or minus two – this poses a problem to dealing with complexity. However, successful organizations seem to accomplish a manageable complexity-level. For this, they have a set of strategies. They are dealt with next.

Requisite variety

Requisite variety refers to Ashby’s (1962) argument that it takes internal complexity to cope with external complexity. In this sense, a complexity absorption response involves holding multiple and conflicting portrayals of the variety in the environment (Boisot & Child, 1999, p. 238).

“Managerial responses to complexity from the absorption perspective would include the development of multiple and sometimes conflicting goals, the importance of a variety of strategic activities, more informal and decentralized structural / decision making patterns, and a wide variety of interactions and connections for decision making” (Ashmos et al., 2000, p. 581).¹⁶ Absorbing complexity means they “hold multiple and sometimes conflicting representations of environmental variety, retaining in their behavioral repertoire a range of responses, each of which operates at a lower level of specificity'' (Boisot & Child, 1999, p. 238).

The notion of requisite variety – despite its high face validity – is not without problem. For instance, it is obvious that this need of requisite variety should be balanced with the limited cognitive capabilities (supra) and the need of dealing with tight coupling. It has been reported in military settings (Dov & Gil, 2003, p. 853) that is done by using a shared a meta-script system that resulted in similar representations of the environment, i.e., conceptual convergence. This reduces requisite variety to cognitively manageable proportions.¹⁷ Another caveat concerns the contradiction between requisite variety and trust. “[I]t is argued that the management of requisite variety must be accompanied by attention to power relations and the development of practical wisdom, since diversity inevitably means differences in belief and opinion within groups and potential increases in anxiety, which while necessary for innovation may often work against the building of high levels of trust and shared understanding that may be required for such innovation” (Moss, 2001, p. 11/20).

Simplification: codification and abstraction

At first sight in contradiction with the techniques of increasing requisite variety, are the set of techniques that aim at streamlining the observed reality. They do so by codification (specifying categories to which data are assigned) and abstraction (limiting the number of categories that need to be considered in the first place) (Ashmos et al., 2000, p. 581). Popular tools include Balanced Scorecards and Key Reliability Indicators. The opposing phenomena of Requisite Variety and Simplification tend to occur together in organizations and as such, they are a nice example of paradox. This a priori so, because both phenomena exhibit dynamic characteristics.

Organizations, for instance, tend to become simpler over time (Miller, 1993) and have a propensity to increase variety. We refer here to organization growth models like the one of Greiner (1972) where organizational variety increases in the wake of the solutions for organizational crises.¹⁸

Sinks

The presence of ‘sinks’ that absorb external impacts and buffer subsystems from change will make the system less complex (Longstaff, 2003): “For example, when the price of an input to a product goes up but the firm can immediately pass this on to consumers this acts as a sink protecting the firm from the impact of the price increase and makes it unnecessary for it to build a complex system for response” (Longstaff, 2003, p. 17).

Shortening feedback loops

“Positive and negative feedback (and feed forwards) loops of different lengths. Long feedback loops, with communication going through many agents or subsystems tend to be more complex”

(Longstaff, 2003, p. 16-17).

Also the connectivity, ‘the extent to which agents or units are all connected or are connected through hubs’ will increase the complexity of the system” (Longstaff, 2003, p. 16-17).

A self-creating paradox – Acknowledge the boundaries

At several occasions in this dissertation, we stress the importance of looking ‘beyond the boundary’. Paradoxically enough, this does not imply that boundaries have to be omitted. On the contrary, they are needed: how else can one look behind them? However, there is more: in a field that is as complex as reliability, taking a broad perspective is a merit, but it is also a vice if there is no partitioning of knowledge. “We also realize, however, that some partitioning of

knowledge is necessary and some boundaries to this knowledge must be set if meaningful progress in summarizing and classifying pertinent knowledge is ever to be made” (Koontz, 1980, p. 183).

Hence, our plea to acknowledge the boundaries, even though the aim of our approach – and especially its systems thinking component – is to cut across the boundary perspective of organizations. Boundaries are and remain vitally important for organizational structure. ‘You can’t live with them, you can’t live without them’ In search of a balance, the key would seem to be to recognize the boundaries, but to make them as transparent as possible. “To see them as necessary ‘net curtains’, not ‘heavy drapes’” (Wolstenholme, 2003, p. 9).

Complexity and variety

Referring once again to the models of organizational growth (Greiner, 1972), we believe that over time organizations/systems are capable of handling more and more complexity (Boisot &

Child, 1999, p. 238). They “*…+ differentiate internally, acquiring sophisticated data-processing capabilities as they do so.” *…+ “With growth and specialization comes an ability to handle an ever wider and varied range of internal representations of the external environment” (Boisot &

Child, 1999, p. 238). ¹⁹Ashby’s Law states that systems exhibiting a high degree of variety will be better in dealing with complexity. The variety of control measures must match the variety of disturbances (Gazendam & Jorna, 1998, p. 5).

Complexity , resilience and structure

If a complex system exhibits resilience it will bounce back from changes and is more likely to be stable in the long term (Longstaff, 2003, p. 16-17). Lin and Carley (2001, p. 34) state they have found empirical evidence that in response to crises, organizations should move to complex structures with greater resource access.

Complexity is a young field searching for laws, theories, and principles that can be used to build a structure and a discipline (Bennet & Bennet, 2004, p. 285). Nevertheless, sufficient knowledge is present to understand that if an organization wants to prosper, or merely survive, such depends on how it manages complexity. The inconvenience is that complexity can transgress into chaos if it is not kept within a certain range (Figure 1.6). This calls for a tremendous effort and the application of a strategy to accomplish a manageable complexity-level: pursuing strategic chunking, sequential elaboration, organizational specialization/coordination and intermediate measures of reliability (Cannon, 1995). It is our opinion however that these strategies seem to aim at a reduction of complexity, and not its management.

Figure 1.6 - Systems Space

Simple Complicated Complex Complex Adaptive Chaotic

Little change

Table 1.2 - Properties of System Dimensions (Bennet & Bennet, 2004)

The future is truly unknowable and therefore we must learn to live and deal with uncertainty, surprise, paradox, and complexity (Bennet & Bennet, 2004, p. 297-298). In this dissertation we therefore do not aim at reducing complexity – like suggested by Cannon (1995), but at keeping it as high as possibly acceptable without crossing the thin line between complexity and chaos (Table 1.2) and as suggested in the Cynefin framework (Kurtz & Snowden, 2003).

5 Coupling

5.1 Decomposing coupling

Interdependence or coupling is the degree to which organization components – whether they be applications, functions, departments or individuals – depend on each other. Loose-coupling means that the components can operate independently from one another. Tight-coupling means a continuous interchange of information, goods or services. Coupling has many dimensions.

Thompson (1967), for instance, in his framework of departmental interdependence, classifies in three types of interdependence: pooled, sequential and reciprocal interdependence. Another dimensional framework is to distinguish between piece, batch and continuous production.

However, no matter what framework is used to define tight or loose coupling, essential is the question about availability of buffers, resources, time, and information that can enable recovery from failures. The looser components are coupled, the more possible recovery paths exist. The tighter they are coupled, the less an organization disposes of ways to recover (Berniker & Wolf, 2001). Table 1.3 shows the differences between tight and loose coupling from a technical perspective.

Tight-coupling characteristics Loose-coupling characteristics

Time-dependent processes which cannot wait

Processing delays are possible

Rigidly ordered processes (as in sequence A must follow B)

Order of sequence can be changed

Only one path to a successful outcome

Substitution is available

Little slack (requiring precise quantities of specific resources for successful operation).

Slack in resources is possible, buffers and redundancies available

Table 1.3 - Tightly vs. loosely-coupled processes (Perrow, 1999, p. 92-93)

From the above one might conclude that tight-coupling is not a choice as it is the consequence of requirements of the production or service delivery context. This may be true in part, but it is also true that poorly run, financially less successful organizations will have fewer resources available for e.g. preventative maintenance, replacement of aging equipment or modernization and less slack operating resources (Berniker & Wolf, 2001). Besides, as Wolf (2001) notes, such organizations may be forced to operate with tighter coupling as a result of cost cutting

measures, meaning that tight-coupling may be a managerial decision based upon budgetary stress or profit targets.

We do not suggest a categorical approach to loose coupling, independent of the interactive complexity of an organization’s context. Stronger even, we acknowledge that an exclusive focus on reducing tight-coupling (e.g. by increasing redundancy) may lead to normal accidents if this is done in a setting that merely addresses single loop learning (Bain, 1999).