The manufacturers that have proven to be the most successful are the ones who can leverage their data and make it part of their own fabric.
On 18 April, almost 100 senior decision-makers representing UK manufacturers of every size and sector gathered in London to discuss ways to unlock and take advantage of the value their data contains.
Hosted by The Manufacturer, Industrial Data Summit 2018 featured an innovative format focused around a series of intimate roundtable sessions – allowing attendees to sit next to experts and have their concerns addressed first-hand.
These roundtables were built around three streams – data collection, data insight, and data management – and in total, helped to facilitate 45 30-minute conversations.
Over the course of the day, delegates heard how digital technologies and data-driven insights could help improve productivity, increase asset utilisation, enable preventative (and even predictive) maintenance, foster deeper customer relationships, enhance supply chain transparency, and create new revenue streams.
What has become starkly apparent is that technology development is taking place at an exponential pace. The growth opportunities afforded by achieving just some of these examples – let alone all of them in unison – are vast.
Yet, organisations can’t change overnight, and there are many historic challenges manufacturers must overcome before embarking on their own business-specific digital initiatives.
One of the Industrial Data Summit streams – data collection – was hosted by Dell EMC.
You can catch up on all the key takeaways from the day here.
In the afternoon, Paul Brook, data analytics director for EMEA at Dell EMC, outlined ‘The Digital Future: fueled by data and valuable returns for business’.
You can read an overview of his keynote here.
To learn how these challenges can be addressed, The Manufacturer spoke with Nuno Antonio, chief technology officer for analytics, IoT and big data at Dell EMC for Europe, Middle East and Africa (EMEA).
One of the most commonly heard challenges at Industrial Data Summit was the existence of internal siloes between people, departments or sites. How can these be broken down?
Nuno Antonio: Siloes exist for many different reasons. My first piece of advice is to understand what siloes exist within your organisation, why they are there and what is causing them. Typically, siloes exist because of human issues or idiosyncrasies, rather than technological ones.
Companies where siloes don’t exist usually have a flexible environment where responsibilities are shared across different teams or departments, avoiding the knowledge or ownership of the data being concentrated in a single point.
They also enable their workforce to spend 80% of their time analysing data and 20% on overcoming infrastructure problems, rather than the opposite.
The key to that is identifying and addressing any infrastructure problems so that your highly paid engineers and technicians can focus their time on extracting insights and value from data.
How can organisations change their view of IT so it is seen more as an enabler of business growth, rather than an onerous but necessary business cost?
I’m a very strong believer in businesses having to unite technology and analytics with common sense and creativity. There is so much hype surrounding big data and artificial intelligence (AI) currently; you’ve got to take a step back, view the opportunities objectively and focus wholly on what it is you actually want to achieve.
IT not only provides access to data, it also provides ways of extracting value from data and translating that directly into business growth through greater agility, flexibility, responsiveness, or whatever the objective happens to be.
There is considerable value hidden inside data that is inaccessible with human capabilities, even when augmented with business intelligence tools. The only way to access it is through technology such as advanced analytics and AI – in particular, machine learning.
Most problems need to be addressed in a completely unbiased way without any prior assumptions. Inherent human bias can influence our analysis and lead us down roads which aren’t necessarily correct. Data analysis helps to overcome the human issue of confirmation or cultural bias.
More importantly, data allows us to not only know and understand the root causes of a problem, it also helps forecast the future and move a business from being purely reactive to preventative and eventually predictive.
Data allows us to learn from the past and to anticipate problems or find opportunities before they occur. That provides greater business efficiency, increased revenue and margins, greater product quality and improvements in human thriving.
Advanced analytics and edge computing are playing an ever-more important role in helping manufacturers cope with the huge volume of data at their disposal. Can you offer a real-world example of a business using them to tangible benefit?
One of the companies Dell EMC supports is a major global industrial manufacturer that makes an extensive use of large pumps. The business oversees thousands of assets in the field, many of which have been operating for decades. The pumps are typically located in very harsh, remote environments and consequently there are huge costs associated with servicing them.
Prior to deploying edge analytics, technicians could sometimes arrive on site without the correct tools or parts. Now, by collecting sensor-fed data, the business has created a historical repository which it uses to build various models of different pumps operating under a wide variety of conditions. These models are then used to answer specific questions, such as, ‘How likely is a breakdown to occur in the next ‘X’ hours?’
The predictions of these models enable the creation of business rules, for example: ‘If that likelihood is greater than 75%, than dispatch a technician with the correct tools and parts to avoid a potential stoppage’.
This method of edge analytics-driven predicative maintenance has helped the business increase efficiencies, optimise asset utilisation, reduce costs and monitor everything that is happening in their global operation in real-time.
The opportunities data-driven insights can provide are clear, but do UK manufacturers have the skills in-house to take full advantage?
We face a very real shortage of skills. As such these skills are highly sought after and therefore highly paid. Even if your organisation has access to these skills, they still represent a very expensive resource and must be utilised as such.
Good news comes on two fronts. First, there is a clear trend for the democratisation of the knowledge that is extracted from data. We saw hardware and software become commodities, and data is increasingly heading that way.
Second, when it comes to data science, businesses believe that they require very high levels of expertise. Yes, eventually, you will need to hire someone with a PhD or MSc in data science, but the need today is someone capable of ‘solving the problem until Friday’. These are people who don’t need to be exceptionally highly educated, they just need to have common sense and a good understanding of how to architect a problem.
The tools available today hide all the complexity surrounding data analytics and enable so-called ‘citizen’ data scientists to concentrate on extracting insight and value from your data. Citizen data scientists are less expensive to hire and more easily found, even from within your own organisation.
Many businesses have embarked on their ‘digital journey’, but there are still those who are reliant on paper and pen processes. What advice do you have for these late adopters?
Put simply: think big, start small and scale fast. When does a company achieve a competitive edge? What causes a ‘black swan’ event to happen – i.e. one that is highly unexpected and has a major knock-on effect, such as the rise of Facebook or the collapse of the Soviet Union?
Leverage the resources you have and create an environment in your business that maximises the chances of a black swan taking place.
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