Digital Twinning : Predictive Foresight, Creative Hindsight

With the UK economy struggling to keep up with rapid technological advances, I ask myself where do we, as a Nation, find the dynamic systems and people capable of contributing to continuous business improvement processes and predicting the future, without adversely affecting business culture and ongoing commitments?

Irrespective of the industry or sector we operate in, we are acutely aware as manufacturers that the most difficult task within today’s global manufacturing environment is the successful delivery of commitments made to stakeholders and customers on-time-in-full, to-quality, at cost.  And all of this, coupled with driving the strategic plan forward, financially predicting the future path and mitigating risks and gaps that may appear.

We need the ability to look forward with confidence and provide detailed insights to implement a robust continuous improvement process using tools such as digital twinning as part of our lean toolkit.

Digital twinning powered by simulation software that utilises your OWN data is a tool for continuous improvement that compliments the tools we have spent years learning and using. It does not supersede, it does not solve all business problems, it compliments what we already have available to us. In 2007 visionaries from academia and industry across the world predicted that ERP linked predictive simulation would be the future of sales, operational planning and business intelligence, so we ask ourselves the question, why is adoption still weak?  The simple answer is, your business data is a valuable part of your toolkit and must be the source data used to make your simulations truly connected and powerful.

 

A simulated digital twin of your business systems, people and processes can generate a powerful future wellbeing of your business and help demystify much of the analytical process. It can also do this by providing rich interactive 2D/3D visualisations of your data to enable quick and easy interpretation of internal and external factors which will enable you to predict future business performance. This technique can easily reflect the process rules, randomness and variability that affects the behaviour of real-life systems and complex operating environments. These variables include people and skills, supply chain data, demand signals, new product introduction, machine breakdowns, holidays, preventative maintenance, overall equipment effectiveness, probability of defects, late deliveries etc, movements and logistics; the list goes on.

Digital transformation is the phrase we are now hearing to get manufacturing businesses ready for the adoption of new digital technologies through business improvement.  It is being driven by the exploitation of new digital innovations, such as digital twinning, aimed at reducing long-term expenditure in order to improve efficiency and productivity.

We’re looking forward to sharing our experiences and case study examples at the SMAS Conference of real-life active projects that are embracing this digital journey.