Manufacturers are experiencing a tidal wave of data, and mastering it isn’t straightforward. Off-the-shelf data solutions are often not responsive enough to real-time events. Tailored solutions can present exciting and valuable opportunities, such as unlocking real-time and predictive intelligence.
In this Manufacturer Directors Forum virtual roundtable in partnership with global innovation consultancy PA Consulting (PA), the importance of harnessing data to make businesses more agile was discussed in a session with senior executives from leading UK manufacturers and data experts from PA. The session was inspired by PA recently building a world-leading and award-winning predictive intelligence tool for Unilever that enabled the company to monitor, predict and respond to COVID-19 trends across their global supply chain throughout the pandemic.
This group of manufacturers shared their pain points, experiences and insights into how to effectively harness data that is growing exponentially in volume and complexity, and how data analytics can transform operational agility and decision-making. Attitudes have shifted from ‘data is terrifying and I’d rather not go anywhere near it’ to having to consider and value data in an increasingly digital and technology-driven world. Circumstances have forced manufacturers to understand the topic better, however there’s still a limited understanding of applied data. People are much happier being involved in data conversations than they were even five years ago, but work still needs to be done to nurture a genuine understanding of the practical business value that data can unlock for organisations and how this can be best achieved.
Pain points of manufacturers included:
“My interest in the topic is in driving digitisation and data management – particularly around scientific, technical, analytical and microbiology data. We want to try and get the business to a more mature level globally, and get more than a single use out of data.”
“We’re very interested in data in this field. The challenge is trying to push and pull a global company that is currently very disparate and full of independent businesses – it’s about pulling them together to show them the benefits of collaboration.”
“We’re constantly looking to see how we use data to optimise manufacturing processes, and then subsequently roll those out as quickly as we can across the world.”
“My interest in this subject area comes from currently working in a number of critical technology projects to better understand how to use data to improve efficiency of our manufacturing system.”
“We want to get actionable insight from data which we can then use to improve quality across our regions in a standard way, by making sure that our 19 facilities are using the same approach.”
Navigating the wave of data – what challenges are manufacturers facing?
Manufacturers in this session reflected on the difficulty of navigating data. They were eager to hear the biggest digital challenges from around the virtual room. What approaches work best? All manner of problems occur when companies adopt new systems and this is equally true for digital technologies which are evolving at pace. By the time a new system is rolled out there’s a ‘better or at least a more shiny’ solution on the table. How do businesses convince people that data is the door to a more informed evidence-based approach for making decisions? How do they get them to treat it like an asset? Not all data is equal, as a concept this is sometimes a challenge – lack of awareness of that fact can lead organisations to get tied up in knots as they attempt to manage it all equally.
Insights from PA and the group on this point included:
“This is a conversation that has moved at pace and will continue to do so. There are a lot of practical challenges that stop organisations moving forward as effectively as they might want to. Number one is recognising the key enablers have little to do with data, tech or digital – it’s about people, and the individual perception of value created either at a business level or at a personal one. Recognising that in any change or transformation initiative, being clear about what you’re doing and then describing that purpose in the form of a compelling and consistent story is so important. One of the early learnings in delivering strategies in data and digital programmes was to make sure you have a storyteller in the room, who can convert data-driven ambition into language which resonates and that people understand.”
“Additionally, some quite established organisations with traditional architectures and technologies are tempted to try and increase their digital and data maturity not by changing the way that they work, but by just overlaying new technology. What we find in fact is it’s similar to a big oil tanker roaming around the seas – they take an age to turn around. Part of becoming digital is becoming agile – able to move quickly and at certain points being able to address and change some of the fundamental ways of operating. It’s not just about augmenting existing processes with new tech, but a more fundamental reimagining of the way that you work, placing digital first and by design at the core.”
“And lastly, it’s recognising that disruption is difficult – people don’t typically want to be disrupted. Again, it comes back to the people element, but this time it is oriented around value. How do you demonstrate the value of that reimagined end state in a way which is not just compelling but is visible and delivered quickly? How do you use that to secure sponsorship in the right places? You need champions who will help you spread the word but also keep that delivery momentum positive. Because ultimately, maintaining positive momentum in any kind of digital change programme is critical.”
How do you pinpoint and access the right data to answer your business questions?
One manufacturer was keen to understand best practice around evaluating monetary benefits from technology investments. Benefits are often measured in terms of operational processes, such as improvements in productivity, efficiency, speed of delivery to customers and so on. When you’re dealing with the operational parts of the organisation, they each have their own areas of focus. Executive stakeholders then come looking for succinct summaries. That bridge between improved operations converting into financial metrics such as profits or sales is not always a linear one.
Insights from the group on this question included:
“There’s a challenge with traditional thinking around ROI when it comes to digital investment. There is a whole thought process about how to create, capture, calculate and articulate value, which is not as easily quantified. There is a whole element of that which I feel is still being evaluated and worked through. Some traditional financial metrics won’t allow you to capture and communicate value. I think there is a greater recognition across organisations that this is restricting investments and a number of companies are reflecting on this and changing how value is measured.”
“Additionally, if you identify where you believe value is created, you can start capturing that information and proving it. A lot of that is through hypothesis, and there are many occasions where the hypothesis is correct. But sometimes, the hypothesis is incorrect and the investment hasn’t created value. And rolling out an investment that doesn’t create value isn’t great for any business.”
“One of the biggest challenges we’re finding is that the IT function holds the purse strings and for every proposal they’re saying, ‘what’s the return on investment?’ And as previously mentioned, it’s really difficult to articulate that return on investment. I think we’re seeing that a lot of the proposals for investments are at the foundation stages, where those investments themselves won’t give that return on investment. Our team has been making quite a few proposals to invest in a number of activities. The majority of the answers are ‘no, because as individual investments they haven’t demonstrated the return.’”
Getting ahead of the curve – using data to unlock real-time and predictive intelligence
Manufacturers get excited about technologies like AI and machine learning. But what’s the most effective way of exploring this technology in a business? There are very few experts who understand what it is and what it’s capable of. Just trying to create the “sparks of experimentation” is extremely difficult, according to a consumer goods manufacturer involved in this session.
“When it comes to recording decisions, we’re brilliant. We intuitively want to record data from instruments, telemetry and systems. But often we don’t record the result of all that data and the decision that the data actually drove.”
Insights from the group included:
“AI and machine learning has got a bit of an image. These days it’s seen as the all-enabling representation of sophisticated data solutions. Unfortunately, the problem is that it’s not an understood science most of the time. It’s often seen as a bit ‘plug and play’, without any customisation. What we skip over all too many times is the need to deliberately plan a strategy where value through AI can actually be delivered.”
“You have to give AI a purpose. Setting a clear question or hypothesis that you want a data-led solution to deliver is important. Because that informs what exactly you’re doing – what kind of algorithm, model, technique or technology are you going to put into practice, given the objective that you have for the exercise? Having that up front is often overlooked, but it’s fundamental.”
“It’s about small-scoping. You need to identify where the value is to be delivered and cut to the chase. The route of bringing robotic process automation (RPA) into the AI space is a journey. I think it’s about the team that you build around it. If you’ve got two or three people in your organisation with the skills to do it, then how do you harness that neurodiversity across the organisation? You’ll need the coders and the analysts to be brought together by the business process owners, so they can understand the opportunities, sell it and tell people what to do with it.”
The leadership challenge – do leaders now need to be ‘data people’?
The session ended with a discussion on what makes “data people”. Does every department of a business need to understand data? Is it imperative for leaders to be data people? Are manufacturers recruiting individuals with the correct skills in their manufacturing sites, and are they able to interpret data and take appropriate action? What are the core skills needed?
Insights from the group:
“This could be a question of personality. I think data people have a slightly different personality to a lot of others. I look at the diffusion of technology curve and where people naturally sit, either as early adopters or as people who don’t really buy into technology. I think it takes a certain type of person to see numbers, put them in a pattern and expect to get an outcome.”
“As data people, we are all slightly unique beasts. There’s certainly a personality element to being a data person. The challenges that I’ve seen in interpreting, acting on and understanding data effectively don’t often come at the analyst level, it’s not those who are producing the insights who are necessarily misinterpreting or misrepresenting, it’s at leadership level.”
“I think it goes back to the interpretation of data. Developing a solution where not everyone has to be a data scientist to understand it is really critical. You want to simplify it – dumb it down without taking the insight away. Provide it in a way that people can respond to; have a central capability to build, a core that can be scaled, and an appreciation of where you build the skills within a team to ensure that you’re best in class.”
As the wave of data flows through organisations, manufacturers are sharing common pain points. There has to be early assessment to distinguish important data from unimportant data, and to determine how data are used and managed. Conversations often centre around capturing bits of information, while not necessarily capturing the results, outputs and the learnings from it. Companies should think about where to start the data journey, and determine what they want to understand. If certain information isn’t available, then put plans in place to begin collecting that information.
Measuring, creating and communicating the value of data through the business is key. Sustainability and environmental control is becoming more important and legislation around it will increase. Whether it’s ROI or regulation and legislation, there will be a need to capture, report and value data in a different and more rigorous way.
Lastly, having people with data skills – and storytellers to communicate the value and purpose of data – is key. It’s about the insight that the data provide and how people use and respond to it. It’s about understanding who the audience is when presenting data and ensuring that it helps them solve a problem. Data should simplify the way businesses operate, rather than adding complexity. Without communicating that benefit, you won’t achieve widespread adoption of the importance of data, and without that adoption you won’t get the benefits that people are seeking across the business.
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