Industry 4.0

Multiple disruptive forces are impacting global economies. Increasing market volatility, customer expectations and technology impacts are driving businesses everywhere towards transformation. Initiatives to digitise assets, machines, devices, workflows and processes are being launched under different banners such as Digital Business, the Industrial Internet of Things (IIoT) and Industry 4.0.

As industry commentators debate the semantics, the reality is that all such initiatives are similarly targeted at increasing automation, operational intelligence and connectedness. The endgame is to maximise business agility and the flow of customer value at optimum cost. The method by which companies are doing this is to develop a digital twin of their business that surfaces the information they need to improve decision making, de-risk planning and delight customers with new experiences and business models.

A Data & Analytics Challenge

This demanding digital agenda creates significant business challenges. At a time when many managers are struggling to process existing data volumes, new waves of big data will be arriving exponentially. In addition customer demand is more volatile than ever before, with rear-view business intelligence becoming inadequate for making decisions for an evolving business. To support this need for increased decision-making agility, predictive analytics methods, providing forward-looking business intelligence, are becoming necessary. Businesses will find it challenging to implement all these changes given the technology, financial and people integration barriers. The skills and capabilities needed to manage this scale and complexity of change are in short supply.

The Digital Opportunity

While companies respond by announcing plans to increase levels of device connectivity, machine intelligence, data science and analytical capabilities – successful firms do not approach these in isolation. A strategy of collecting as much data as possible, then considering how to best analyse the information to uncover customer value is flawed. Instead success is achieved through deploying techniques to unleash the power of data across end-to-end processes in ways that drive the creation and execution of new business models and customer experiences.

The Value of Predictive Simulation

Predictive simulation enables smarter business models by providing a digital twin of the business facilitating better understanding of complex dynamic processes, improved business data visualization and insight. This level of management understanding is critical when considering radical transformation options which can incur major business risk.

Predictive digital twins enable business scenarios to be simulated and outcomes analyzed and compared before deciding on the best course of action. Having secured decision clarity in a virtual world, optimum decisions can then be passed for real-world execution.