Organizations today operate in a dynamic continuum where the rules of competition and operation are constantly evolving. No industry is immune to the effects of their organizational ecology to properly understand the mechanisms of different manufacturing industries. We cannot just compare the resiliency and responses of segments of the same industry.

Smart factories of the future driven by robots, computers and AI, are no longer the stuff of science fiction. We currently exist in the overlap of Industry 4.0 and the Digital Ecosystem and with it brings technologies and strategies that are fast changing how we conduct business – from day-to-day operations to high-level decision making.

“What a business needs most for its decisions – especially strategic ones, is data about what goes on outside it. Only outside a business are there results, opportunities and threats.”
— Peter Drucker

The pressures of cost, competition and a more demanding consumer continue to drive the digital transformation of industries. It pays to know how best to invest in the right technology for your sector and business type. While benchmarking exercises have long been essential to business success, digitalization requires that companies remain particularly vigilant, lest they find themselves on the wrong side of a digital divide. Positioning an organization in an era of complexity can create differentiated advantages.

The first industrial revolution in the late 18^th century brought with it mechanisation and steam power.  Mass production, assembly lines and electricity followed with the second a century later. Almost another century hence, computers and automation hailed the third. We had witnessed the proliferation of scientific management systems and with it brought the rise of efficiency driven attributes.

We live, and work in a world, where Big Data management tools, affordable cloud computing and storage, and AI machine-learning algorithms, have converged to provide us with a constant stream of analytical information. The challenge we face is decoding the data’s messages, and finding effective ways to put the learnings to good use.