Network science will be a critical enabler of advanced enterprise management in the 21st century.
Major advances are already being made in applying the principles of network science to social, technological and business systems and it will be vital for the future enterprise to weave sophisticated network optimisation principles into all aspects of its operations.
Network science essentially involves analysing and managing the properties and dynamics of interconnected complex systems such as social groups, the Web, power grids, supply chains, markets, ecosystems and the brain.
Such networked systems are based largely on scale-free topologies. This is the natural architecture most relevant to the world around us and is modelled on structures with a relatively small number of hubs or nodes, each with a large number of connections and a much larger number of nodes with a relatively small number of links- broadly obeying a mathematical power law.
Knowledge of network topology and dynamics allows for optimisation and prediction of the behaviour of complex system processes and is becoming increasingly vital in managing major business activities, via information systems that control vast numbers of interlinked transactions, resources, agents and events.
Failure in a tightly coupled network such as a power grid or market system of a single node may force the failure of other nodes, resulting in cascades of failures, eventually triggering a catastrophic breakdown of the whole system, as in the recent collapse of the global economy.
Examples of potential applications of network science principles include-
Economies and Markets- reducing the risk of global failure by ensuring economic networks are more robust; by closely monitoring market signals and adjusting the topology of nodes and links to reduce the risk of runaway feedback loops and conflicts between local interests and global efficiency.
Ecologies and Biodiversity- improving the sustainability of ecological systems in a period of global warming with the capacity to provide timely warning of species and resource collapse. The network model provides a powerful representation of ecological interactions among species and highlights global ecological interdependencies, which can then be re-modelled to manage risk.
Business and Finance- improving the capacity to make quality decisions regarding markets and product development, to avoid the future collapse of companies such as General Motors and Lehman Brothers. In these instances, poor decision-making was amplified by the systemic risk of runaway cascading financial asset dependencies, due to overloaded coupling strengths between nodes and indeterminate feedback loops in the myriad interconnected customer and supply networks.
The relevance of network science for the future enterprise is therefore threefold-
Firstly, many of the systems involved in business may be modelled in the future by scale-free networks, such as supply chain, investment, infrastructure, production and customer systems; with nodes representing suppliers, assets, products, consumers and customer groups.
Secondly, an organisation’s systems may be modelled by networks, with nodes representing process and activity decisions and the links represented by the dynamic flows of information feeding them.
Thirdly, the architecture of the enterprise itself may be viewed as a network of control flows between decision-makers and operational agents. As processes become more complex and time critical they will be increasingly automated, but the architecture- the information and decision-making structures and channels, will still need to be continuously optimised.
As forecast in previous posts, the enterprise of the future will be driven by networked architectures- patterns of linked decision processes- constantly morphing, reforming and adapting to a continuous flux of a changing global environment.
Today’s traditional hierarchical or even flat management models will be incapable of supporting tomorrow’s vastly more complex and competitive techno-social environment.
Such techno-social systems composed of technological layers operating within the larger social and physical environment that drives process application and development will need a more integrated, adaptive and intelligent framework for achieving sound management capability, underpinned by network science.
Most real world transportation, manufacturing, computing and power infrastructure networks will be linked and monitored by sensors and tags embedded in largely autonomous networked societies; constantly adapting to global evolutionary dynamics.
Network science algorithms will be developed to monitor and engineer optimal decision topologies, critical thresholds and non-linear outcomes. These will combine with AI technologies to manage complex enterprise operational and management processes.
These algorithms will apply adaptive defence mechanisms, often providing counterintuitive approaches to the engineering and control of complex techno-social systems. Such techniques will be based on the manipulation of key nodes, links and pathways to induce intentional network behavioural changes- mitigating for example potentially catastrophic outcomes.
This will represent the new Network Science Management Paradigm of the 21st century.