The future of manufacturing: resilient when data-enabled

“Tesla’s not really a car company, it’s a tech company on wheels. That’s what keeps confusing people” said Jim Cramer, the host of CNBC’s Mad Money programme, in January 2020 after the value of Tesla shares rose above the US$100 billion mark for the first time in the company’s history. When it came to making investments, it made more sense to compare Tesla to semiconductor companies such as Nvidia and Advanced Micro Devices, Cramer argued, than to Ford and General Motors.

The valuation, and Cramer’s comments, were the latest salvoes in a debate that had already been exercising Wall Street, Silicon Valley, and the internet for some time and which continued throughout 2020.

The following month Joel Feder, Internet Brands Automotive Group’s Interactive Content Manager, who counts Barak Obama amongst his Twitter followers, disagreed when he joined in with this tweet: “Reminder: Tesla is a car company. Tesla is not a tech company. It builds cars.”

The long-time Tesla watcher and award-winning journalist, Matthew DeBord, then agreed when he argued that not only was Tesla not a tech company but that it should aspire to be more like Honda.

Finally, in October 2020, the company’s founder and CEO, Elon Musk, attempted to draw a line under the issue when he also took to Twitter. “Tesla should really be thought of as roughly a dozen technology start-ups, many of which have little to no correlation with traditional automotive companies,” Musk tweeted, the day before the company released yet another set of headline-grabbing financial results.

The ambiguity that fuelled this debate is particularly telling. In the new advanced industrial landscape that’s being forged by the confluence of digitalisation, automation, machine learning and artificial intelligence (AI), it’s proving increasingly difficult, across a whole range of advanced manufacturing sectors, to distinguish between firms that make widgets and firms that make tech.

Stuart Hughes has seen this shift accelerate throughout his career in ways that have merged the personal with the professional. Hughes is Chief Information and Digital Officer at Rolls-Royce, where he leads the digital teams in the firm’s civil aerospace division that support its IntelligentEngine Vision, a merging of AI, digital twin technology, and robotics that is helping to design, simulate, test, and deliver a new generation of less materially-intensive, more energy-efficient engines.

What is surprising, however, are the similarities that Hughes sees between his current job and his earlier roles with consumer-focused dot.com companies such as LateRooms and moneysupermarket.com where the platform was the product.

“At moneysupermarket.com we would go to 120 car insurers in real time, obtain a quote, and display that on a web page. That’s really hard to do when you think of the number of people who are all doing that at once. At LateRooms, the hotel price data that was coming in contained 200 price changes a second from hotel groups. Processing those while conducting searches and providing quotes for a hotel room is really difficult to do,” Hughes remembers.

“That’s almost identical to what we did at Rolls-Royce. We capture extremely large amounts of data [from our engines], that is extremely volatile because it changes all of the time, and we process this in real time and use it to make data-driven judgements while also conducting analyses of batches with much larger data sets.”

For Hughes, the other underlying point of similarity between Rolls-Royce’s latest manufacturing techniques and his earlier work is a move towards personalisation.

For Sasa Petrovic, a solutions strategist responsible for strategic accounts and long-term strategy with the global software company Citrix, the use of AI and digital twins are two of the key drivers of change in the advanced manufacturing sector.

“At the moment, some manufacturers have embarked upon the process of transition, but this is a process that will never end,” he says. “Change is happening so quickly that manufacturers need to become more agile so that they’re able to react at speed.”

For Petrovic, the global pandemic posed particular problems for manufacturers who, regardless of the extent of their automation and digitalisation, struggled with business continuity at a time when remote working was officially mandated.

“In contrast to other industries, manufacturing was especially vulnerable because a part of its workforce is employed in on-site jobs that cannot be done remotely,” the strategist explains. “While employee safety was the top priority for manufacturers, the operational model for the on-site and off-site workers had to be adjusted in order to keep the lights on.”

Nitin Paranjpe, Chief Operating Officer (COO) with Unilever, believes that the global pandemic has not only prompted unprecedented levels of digital transformation, but has also delivered profound lessons about the need for organisational resilience and agility, not just in managing raw materials and supply chains but in forecasting and managing demand.

“The adoption of e-commerce has been staggering. In a 10-week period, the penetration of e-commerce in the US increased as much as it had in the previous ten years. From 6 to 10 per cent in a 10-year period, and then from 16-27 per cent in the next 10 weeks. That’s the scale of the shift that we’ve been dealing with,” says Paranjpe.

“To deal with this sort of shift in consumer behaviour, the demands of the supply chain in terms of agility and responsiveness have had to increase by orders of magnitude, something that would not be possible without technology.”

The challenge facing Unilever was considerable.

Due to its global, agile and resilient supply chain, Unilever still managed to operate at 98 percent capacity.

For Paranjpe one of the most immediate examples of the kind of technologically-driven, institutional change that resulted was the transformation in the manufacturer’s sales and operational planning meetings (S&OP).

“We used to have sales and operational planning meetings where we do forecasting once a month and we used to operate on a 13-week rolling cycle. During the early days of the pandemic, we started to have weekly meetings and then after a while they became daily,” the COO remembers.

“This involved a sort of agility that we could not have imagined before which was only made possible by a tremendous increase in computing power, machine learning, and AI. It was an extreme situation,” Paranjpe admits, “but the world is unlikely to have less change or be less volatile in future, so the premium that we place on agility going forwards must be much higher than it was in the past.”