Nonlinearity

Another difference between complicated and complex systems is the nature of interactions among their subsystems. The former have only linear while the latter have both linear and nonlinear interactions. Nonlinearity means that causes (inputs) are not directly proportional to their effects (outputs). Big changes can have little or no effect while small changes can have drastic effects over a period of time.
Interactions in complex systems are also sensitive to their initial or starting conditions, their current context, and the history of the subsystems.

By analogy, nonlinear side effects of decisions (strategies) and actions (operations) at LTH should be taken into account. For example, small investment on research areas / core competencies as well as interactions among them over a period of time can lead to huge ROI (return on investment) in the future. The effect of increasing ROI can be seen when, for example, more patents are registered; new jobs are created; and the knowledge is exported.

There are also research-based evidences of nonlinearities in the supply chains. ‘Bullwhip effect’ is a good example of such nonlinearities. The bullwhip effect describes how tiny initial shifts downstream in supply chains (like customer demand or order quantity) can result in upstream chaotic and extreme events via dynamical nonlinear processes.
Another example is the tremendous reduction of transport and traffic intensity and as a result negative environmental impacts by small changes in dimensions and materials of packages. A further example is nonlinear relation between vehicles’ speed and fuel consumption in transportation.

The first lesson for governing transformative transition of supply chains and logistics towards sustainability is to understand the nonlinear effects of governing rules and policies. Some todays’ decisions and actions may have high consequences on future decisions and actions. For example, decision about investment on logistical infrastructure or design of supply chains may have long-term effects on future of supply chains operations. Although study of all nonlinear consequences (effects) of today’s sustainability oriented decisions and actions (causes) might be difficult, some tools like scenario analysis and agent-based modeling can be helpful.
The second lesson is to implement rules and policies that may have a larger consequence than others. For example, it is expected that several new clean technologies needed for greening the transport, infrastructures, production, and base industry be introduced by 2020 (in the transformation phase of growth cycle). After 2020 (in the rationalization phase of growth cycle), governing rules and policies should be defined which encourage implementation of those clean technologies that may have the largest consequence beyond greening (i.e. job creation, safety and security, etc.).