“The war in Ukraine, lockdowns in China, supply-chain disruptions, and the risk of stagflation are hammering growth,” said David Malpass, president at the World Bank, in June. “For many countries, recession will be hard to avoid.”
It’s the harsh reality. A sign of the times that means businesses are already facing some tough decisions.
Looking for efficiencies and better ways of working are natural reactions to any economic threat, but the challenge is balancing this with increased productivity. How do companies not just keep the lights on but also remain competitive and grow the business?
One key area is frontline service. We saw how integral service engineers were during the pandemic and how the need for touchless service led to an increase in self and remote services. Companies found efficiencies in reduced journeys and customers often enjoyed quicker fixes, at least for smaller problems.
It is a transformation that has continued and one that can enable companies to improve fix times but also reduce costs.
Fundamental to this is IoT and AI. Aside from the economic pressure, automation is increasingly important given the ageing, retiring workforce and the shift in customer demands and expectations.
There is a maturity to automation now that is delivering measurable benefit. The technology is driving capabilities, such as predictive analysis that can determine the real-time state of machines and devices and even warn of potential issues and propose solutions.
Predictive maintenance has come a long way since the early days of purely sensor-based alarms. Today, systems are more holistic. They don’t rely on a set of pre-defined criteria to watch and report on. This means a clearer, more manageable warning system, across entire operations that offers detailed analysis of equipment status, while also proposing part replacement and repair options.
It’s understandable that this can drive efficiencies in how engineers manage jobs. Less journey time. More accurate part ordering. Reduced customer downtime and increased customer satisfaction. But getting there demands two key things – connected systems and change management within the workforce.
As Deloitte suggested late last year in its report Next Generation Customer Service: The
Future of Field Service, to transform to next generation field service, businesses need a 360-
degree view of customers and assets. They also need “new processes and mind-sets to
embed the changes,” according to McKinsey’s Prediction at scale: How industry can get
more value from maintenance.
While data silos remain a problem – we recently completed a study that revealed 34 per cent of companies say that, while asset data is being shared across the business, its use is limited by disparate systems of record – so do data skills and training. It’s a two-pronged transformation challenge, one that businesses will need to address to meet customer expectations.
“Everything in manufacturing now depends on service,” says Mark Hessinger, VP of global customer services at 3D Systems, a leading additive manufacturing solutions company.
3D Systems uses AI and ServiceMax to power its predictive maintenance service that helps the company meet uptime demands while empowering engineers on the job. “Customers want guaranteed uptime,” adds Hessinger.
This is the point. Customers will be increasingly under pressure to ensure they have no downtime. Productivity is key to dealing with any economic downturn, so ensuring there is no break in production is the challenge faced by all machine makers and OEMs.
So, how does Hessinger guarantee uptime? “All our new products are connected,” he says. “We are using that data to proactively know what is going on. The better the data is, the better the printing system is. Now we can maintain uptime and resolve customer issues much more quickly.”
Not everyone is so switched on. The problem many businesses face in deploying predictive analytics is that machine data is not connected enough, or the data quality is insufficient.
Many companies also face issues around legacy technologies or piecemeal solutions that overlap. Inconsistencies can creep in but how do you spot them and improve the design of the system?
Organisations need skilled data engineers and data scientists to do that, to build advanced analytical models.
This is the transformation. The barriers to predictive analytics especially in terms of costs, are coming down.
The tools themselves, particularly for frontline service, are more intelligent than ever before and provide companies with an opportunity, to not just ride out any economic storm coming our way but actually thrive within it. Anticipating faults, failures and service requirements is directly linked to mastering productivity – in good times and bad.