In Part I, we noted that today’s business leaders are operating in an era when forces such as technological change and the historic rebalancing of global economic activity from developed to emerging markets have made the problems increasingly complex, the tempo faster, the markets more volatile, and the stakes higher. As a result, there is an ever-greater premium on developing innovative, unique solutions than ever before as organizations have to swiftly move from one temporary advantage to another to remain relevant in many constantly-changing industries. This is requiring these organizations to essentially innovate on demand.
However, innovation on demand is hard. Really hard. There is no one approach guaranteed to work for any particular problem, and given the wide range of problems facing today’s organization, there might not be any approach with a chance of success that exceeds the your chance of winning the lottery. We have the situation where some problems are so challenging that your odds of success are only favourable if you simultaneously attack the problem from multiple angles with multiple approaches. This is where flexons come in. Flexons, which are essentially problem solving languages derived from the social and natural sciences to accomodate the world of business problems, used in concert, provide a way for you to tackle a wide range of very difficult problems that will arise in the course of normal operations. They might not solve every problem, but, properly applied, have a good chance of solving a decent number of problems and are a good addition to your toolkit.
Flexons come in five flavours:
The network flexon helps decompose a situation into a series of linked problems of prediction (how will ties evolve?) and optimization (how can we maximize the relational advantage of a given agent?) by presenting relationships among entities. A properly constructed influence network can be used to determine who can best influence the adoption of a certain viewpoint, product, or solution; a properly constructed transportation network can help you optimize deliveries and shipments through various intermodal channels; and a properly constructed organizational map can help you determine which organizations your organization could partner with to quickly create a new servitized product offering.
Based on the concept of an evolutionary algorithm, which uses randomness (to generate diversity) and parallelization (to generate multiple possible solutions) to quickly identify unworkable and/or sub-optimal solutions in a directed search process to get to a workable and/or near-optimal solution, the evolutionary flexon embraces a model where a series of low-cost, small-scale experiments involving product variants pitched to a few well-chosen market segments are preferred to a large all-in strategy. With every turn of the evolutionary-selection crank, where losers are discarded and winners are evolved into a pool of potentially better solutions, the company’s predictions will improve and the distance to the marketable solution will decrease.
Based on the definition of social behaviour as the outcome of interactions among individuals, each of whom tries to select the best possible means of achieving his or her ends, the decision-agent flexon represents teams, firms, and industries as a series of competitive and cooperative interactions among agents. The basic process is to determine the right level of analysis (such as the firm), ascribe to each agent at this level the beliefs and motives that represent their actions, consider how their payoffs change as a result of the actions of others, determine the combinations of strategies they might collectively use, and seek an equilibrium where no agent can unilaterally deviate from the strategy without becoming worse off.
Making the relations between variables of a system, along with the causes and effects of decisions, more explicit allows you to understand their likely impact over time and using a system dynamics lens that shows the world in terms of flows and accumulations of money, matter, energy, or information serves to shed light on a complex system by helping you develop a map of the causal relationships among key variables. This helps you identify the impacts of different decisions and is a great tool for identifying potential risks, and mitigations, in a system.
This flexon focuses attention on what information is used, the cost of computation, and how efficiently the computational device solves certain kinds of problems with the goal of determining how to transform information in an intelligent way into an insight that a decision can be made on.
The approaches contained in these flexons can prove quite insightful in a wide range of industries. In their article on five routes to more innovative problem solving, the authors provide examples in biofuel manufacturing and telecommunications where the biofuel manufacturer wants to improve researcher productivity and the telco wants to predict future usage and customer desires. SI highly recommends reviewing the case studies if you believe that flexons would be a good tool in your innovation toolkit, which SI believes they will be for most organizations.