Rockwell Automation: remote monitoring & cloud analytics
Marc Baret, Director, EMEA industrial services, Rockwell Automation discusses the future of automation for remote monitoring and cloud analytics
Automation is rapidly changing the jobs we do, the places we work, and even how we define work. Forrester has predicted that one million knowledge-work jobs will be replaced by chatbots, software robotics, RPA and virtual agents in 2020 alone. This may sound like an ominous forewarning, but the report also estimates 331,500 jobs will be added to US workforces alone, uplifted by roles requiring empathy, intuition and mental and physical agility.
As automation adoption increases it will bolster connectivity and reliability and help businesses make data, systems and processes more accessible and available. However, many manufacturing firms are finding their route to automation and digital transformation strategies being slowed down by productivity levels and the simultaneous risk of downtime.
Addressing this is possible through tools such as predictive analytics and maintenance that act as a virtual extension of their teams. However, implementing these technologies successfully requires additional external expertise. To explore this further, here are the main five pitfalls we see businesses face as they approach digital transformation and suggestions for how to address them.
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.
Pitfall #4: Not Keeping up with the Pace of Technology
The amount of buzzwords surrounding digital transformation can often be overwhelming and even irritating for businesses that simply want technology to work. Many providers also demand vast investments upfront, which can be a daunting prospect and can put businesses off when a project doesn’t work out. Furthermore, getting locked into one vendor or deployment can result in companies get left behind by their competitors.
It’s therefore important to work with providers that offer a pilot or prototype in advance of any deployment that represents a huge technological shift. This will provide a step-by-step vision of how the process will work, provide milestones, and help the business understand how it will work and what their expected ROI will be. Trusted technology partners need to be extensions of a team if they are to help businesses realise their goals and KPIs.
Pitfall #5: Lacking Expertise
Even with the right automation technologies in place, businesses often still require external support from people with appropriate experience and expertise. This can now be achieved by using augmented reality to provide remote application support and overlay information for engineers to follow.
As with any technology deployment, it needs to fit in with the business’ culture and what works best for their specific needs. However, businesses that are slow to move on these types of emerging technology run the real risk of being left behind.
Embrace the Future of Automation
Meeting the key challenges of better productivity and reduced downtime is possible with the right technology practices and the right technology partner in place. By understanding the common pitfalls outlined above, manufacturing firms can better navigate their path to the future of automation. However, digitalisation is not something they can achieve alone.
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