Pitfall #1: Cybersecurity Risks
Security breaches continue to make major headlines due to the serious impact they can have on business. A breach not only risks a loss of sensitive information but also disruption, downtime and performance issues, as well as serious reputation damage. This highlights the importance for businesses to improve their data management processes and invest in their IT infrastructure.
Predictive maintenance support can help manufacturers avoid such issues by automatically monitoring for unusual patterns and immediately identify potential signs of data theft or network intrusion. They also require a comprehensive approach to security that includes policies and procedures and provides layers of defence around people, processes and technology risks.
Pitfall #2: Having Too Much Data
Businesses are generating huge volumes of data that, when utilised correctly, can be an immensely valuable asset. However, many manufacturing organisations don’t know how to make the best use of their data and, as a result, don’t optimise their workflows or production processes in a way that enables them to gather the best insights and results.
Being able to understand massive amounts of data is key to solving the biggest challenges facing organisations. But the skills and capabilities required to do it are rarely part of a business’ core competencies. It’s therefore important to partner with a trusted data expert that can collect the right information, store it and present it in a way that enables them to make the most effective business decisions.
Pitfall #3: Poor Management of Data
Businesses are amassing more data than ever, but simply having huge amounts of data does not suffice. They need tools that help them better harness their data and understand the information they have.
The true value of automation lies in the IP that businesses hold on their customers, processes and product designs. Leveraging AI and machine learning enables them to analyse huge amounts of information, hypothesise and create significant data patterns, and train learning models to discover the unknown. Furthermore, data teams will be able to try more use cases in significantly reduced times, which will help them to make huge strides in understanding their data.
The potential of these advances in AI is highlighted by McKinsey analysis that found the most advanced deep learning techniques could account for up to $5.8 trillion in annual value. In two-thirds of the 400 use cases it tested, AI improved performance beyond that enabled by other analytics techniques. Without this ability to collect huge amounts of data from multiple platforms and action it effectively, manufacturers will continue to struggle to draw effective conclusions on changes and productivity within their plants.