The complexity dilemma: Three tips for dealing with complexity in organizations

Today storm-tossed markets call managers to take a stand on the rising up of external complexity. Organisations are constantly facing a crossroad (complexity dilemma): To accept and nurture complexity, or to avoid and reduce it. The first option can be traced back to Ashby\u2019s Law of Requisite Variety, 1. while the second comes from Luhmann\u2019s Complexity Reduction, 2. Both Ashby and Luhmann theories are valid due to an inverted U-shaped relation between complexity and firm\u2019s performance, called \u201ccomplexity curve\u201d. Once fixed the amount of external complexity, performance increase as internal complexity increase, till reaching a tipping point; after that point, an overburden of complexity sinks performance. To solve Ashby-Luhmann trade-off on complexity, and moving over the complexity curve, we suggest that complex organizing may be facilitated by a simple design through (i) modularity, (ii) simple rules, and (iii) organisational capabilities

Structural tendencies - Effects of adaptive evolution of complex (chaotic) systems

We describe systems using Kauffman and similar networks. They are directed funct ioning networks consisting of finite number of nodes with finite number of discr ete states evaluated in synchronous mode of discrete time. In this paper we introduce the notion and phenomenon of `structural tendencies'. Along the way we expand Kauffman networks, which were a synonym of Boolean netw orks, to more than two signal variants and we find a phenomenon during network g rowth which we interpret as `complexity threshold'. For simulation we define a simplified algorithm which allows us to omit the problem of periodic attractors. We estimate that living and human designed systems are chaotic (in Kauffman sens e) which can be named - complex. Such systems grow in adaptive evolution. These two simple assumptions lead to certain statistical effects i.e. structural tendencies observed in classic biology but still not explained and not investigated on theoretical way. E.g. terminal modifications or terminal predominance of additions where terminal means: near system outputs. We introduce more than two equally probable variants of signal, therefore our networks generally are not Boolean networks. T hey grow randomly by additions and removals of nodes imposed on Darwinian elimination. Fitness is defined on external outputs of system. During growth of the system we observe a phase transition to chaos (threshold of complexity) in damage spreading. Above this threshold we identify mechanisms of structural tendencies which we investigate in simulation for a few different networks types, including scale-free BA networks.Comment: 20 pages with fugures, to be published in Int. J. Mod. Phys.

Discovering The Classification Of Manufacturing Complexity From Malaysian Industry Perspective

Nowadays, manufacturing complexity (MC) is considered as a major challenge in manufacturing industry. MC covers a very wide area in manufacturing practices either within firm's control or out of control, either directly or indirectly with manufacturing routines. As the technology and globalization getting better, the challenges born by MC are also getting tougher. This scenario experienced by worldwide manufacturing firms including Malaysian manufacturing industry. In order to face this challenges, it is essential to manage MC accordingly. Although some researchers expressed MC negatively, it is believed that managing MC in correct manners will be beneficial to manufacturing firms. The first step towards managing MC accordingly is knowing MC itself in every angle. Generally, MC is divided into two division which are internal MC (IM) and external MC (EM). Initially, both division have several elements which the numbers are 30 and 22 elements for IM and EM, respectively. A set of questionnaire survey consisting of these elements has been distributed to representative of manufacturing firms across Malaysia to gather the information and through factorial analysis using Statistical software (SPSS), these elements are classified into smaller number of classification to facilitate towards the better MC management