How to implement digitization and automation in antiquated sectors like logistics
AI’s role in logistics >
AI has been making headlines for some time now and can deliver the predictive capabilities that make digital twins even more valuable. It’s clear that AI and its incarnation, machine learning (ML), will be able to revolutionize the world of logistics through decision support and automation.
However, ML is a difficult function to integrate and adopt, requiring extensive training and expertise within an organization that wants to incorporate it in its suite of tools. In addition, one of the key elements for the success of ML models is the quality of the datasets used to train them, as well as having the right people in a team to manipulate them.
Digital twinning can combine the intricacies of the real world with the power of AI. By improving the quality of datasets used as input, one can vastly improve the utility of ML, potentially resulting in unprecedented inventory optimization, carbon footprint and cost reduction. This can also enhance employee and customer satisfaction.
Ultimately, these technologies are set to move supply chain management from being reactive and incredibly stressful to more proactive and competitive.
