Remember the two-speed economy?
For much of the three years prior to September 11, the service sector was booming in the UK while manufacturing was hitting recession. The sting, as every manufacturer is all too aware, was that interest rates stayed punishingly high in a bid to stave off inflationary pressures elsewhere in the economy.
With global slowdown threatening, recent hefty interest rate cuts have been aimed at stimulating the whole economy, in the face of slackening consumer demand. The spectre of runaway inflation has gone away, and the rate cuts have been to the immediate benefit of manufacturers. For the time, at least, the two-speed problem seems to have receded.
But within the manufacturing sector itself there is a whole new problem emerging. This is not at the macro level, but at plant and shop floor level. It is visible in boom times as much as during a slowdown. And its costs are immeasurable, but vast.
The problem is one of asset utilisation. Or, more to the point, under-utilisation. This is not so much the basic situation facing many companies today with machines standing idle because of a shortfall of orders. It is more to do with the way in which machines are run when the orders are flowing in. For many firms, even with what looks like busy plant and equipment running at full tilt, the assets are being used at a level way below their potential.
It is a problem that is linked with maintenance, scheduling, order intake, plant organisation, data collection, monitoring and control, statistical analysis, and basic common sense and engineering rigour.
Left as it is, the problem will leave a great swathe of UK industry with inefficiencies that, when business picks up, will lead to capacity constraints that can on the face of it only be solved by big-ticket investment in new manufacturing plant.
For many companies, though, this could be a big and expensive mistake, because low-cost changes to business processes or automation technology can unlock new manufacturing capacity where many plant bosses were unaware it existed.
That, then, is the challenge: giving the UK’s industrial assets the capacity to run faster and longer. Or in other words: cranking up what today looks like a half-speed economy.
Using plant effectively
The whole issue of asset management has become increasingly a boardroom issue. Firms offering software and consulting in some of the technologies involved are usually making their pitch at board level. ‘In one company the savings that could be realised from instigating an asset management strategy across the whole plant were more or less equivalent to the entire tax bill going to the Inland Revenue during the same period,’ says one industry supplier. ‘Not surprisingly, we got the ear of their finance director pretty soon.’
Part of the interest in this area stems from the financial indicators scrutinised by stock market analysts – and venture capital lenders. Return on net assets, and return on capital employed are both figures that feed into other composite measures of a company’s performance and prospects. There are of course ‘quick and dirty’ ways of boosting these indicators (for example simply by selling all your plant to a contract manufacturer).
But if you want to keep your manufacturing assets in-house, and most companies do, the only alternative is to use the plant you do have more effectively.
It is a source of competitive advantage. Most executives in the automation business are finding that the majority of plants are already ‘automated to death’, as one puts it. The trick now is to get more out of what’s already been automated.
This boardroom focus is forcing automation suppliers to train engineers to act more like consultants, making the business case rather than talking about technology. It has also meant that industrial firms have to create new links between their engineering departments and top board members. An executive at automation supplier Rockwell recalls a visit he made to a major manufacturingcustomer to discuss a project, where he was impressed by the fact that the chief executive, finance director, engineering director and IT director were all present at the meeting.
‘This is the first time I have been to a meeting where all these functions are represented in the same room,’ he told the chief executive after the meeting.’It’s the first time for us, too,’ the CEO admitted sheepishly.
Most in industry believe the problems of asset utilisation will become more intense as the recovery arrives and capacity utilisation creeps up. ‘It becomes a big deal,’ says Andy Peters, analyst at research organisation ARC, ‘because you are squeezing every last bit of productivity out of your equipment.’
Minimising downtime is obviously a key. Peters cites an example of a heavy piece of rotating equipment, such as a huge compressor for a gas liquefaction plant – a piece of equipment that on its own could be worth more than £10m. ‘So what’s your maintenance strategy? Do you take it down twice a year to change the bearings – which means you could lose 25% of your profitability from that plant? Or do you do nothing – in which case you could lose 40% of your profitability if it fails catastrophically?’
The first strategy of course is conventional preventive maintenance. The second is the risky, reactive approach, or what some observers call ‘fail and fix’. The cost of downtime and the speed of repair tends to push people into one or other of these camps.
However, as automated equipment becomes more ‘data rich’, it is easier to predict failure, using condition-based monitoring, to move to a more efficient ‘predict and prevent’ approach.
Basically, machines are wired up with sensors watching noise, vibration, heat output and so on, using the data to create an accurate model both of normal behaviour and what happens in the run-up to component failures. Every hour of monitoring of each machine allows more empirical data to be added into its predictive model, making it more accurate. If all this looks complex for a plant to manage on top of the ‘day job’ of manufacturing, then the information can be remotely monitored, possibly as a service provided by the automation supplier.
So if the machine is showing signs that it will run into trouble in, say, 60 days’ time, the operator can order the parts in good time and plan to take the equipment down when the production schedule is least pressured. It also allows the maintenance department to check it has the right technicians around at the time to do the job.’Having the right people lined up matters too,’ says Peters. ‘In the US, something simple like the hunting season can be a big problem. Often a machine needs maintenance and the guys are out in the woods.’
How far companies have advanced with this kind of ‘predict and prevent’ maintenance depends on the cost of downtime. But even in the automotive industry, where if a transfer line stops for an hour dozens of vehicle sales are lost, the record is not that impressive. In one study by a US magazine last year, General Motors admitted that around 50% of its maintenance was of the ‘fail and fix’ variety. It is now aiming to cut this down to less than 20%.
In other sectors, it is a lot worse. ‘In most metalworking industries, we are just waiting for failures to occur,’ says Professor Jay Lee, an expert on intelligent maintenance systems at the University of Wisconsin in Milwaukee. ‘But we are also spending time and money on redundant maintenance.’
Automation suppliers are working hard to encourage industry to embrace the technical solutions to these problems, which lie in components that can capture data about their performance and communicate it to monitoring and analysis software. The beauty of this is that for many companies, the whole thing can be built up incrementally by plant managers with a strategy of upgrading automation on a gradual basis with components that have the required level of connectivity.
Plant manager Steve Kullberg, from Unilever’s Raeford plant in NorthCarolina, did exactly this. ‘I didn’t have to go to the board and ask to invest in an integrated plant,’ he says. ‘In a sense, I did it by stealth.’
Optimising machine availability
Of course, all this data that becomes available about machine utilisation can be used for more than predicting when a plant is going to fail. Planning and scheduling operations can tap into this data too, so that machine availability is optimised in relation to order intake. Such systems can automatically juggle scheduling to take into account delivery dates, so that urgent orders can jump the queue – andautomatically generate earliest possible delivery dates and costs given more or less complete knowledge of a production unit’s capacity and inputs – depending on the level of upstream integration with the supply chain.
The good news for manufacturers is that most automation suppliers are now offering the components and expertise to get these kinds of systems in place. It is also encouraging that the savings such systems create are relatively high in proportion to the costs involved, so that quick payback times, sometimes over a period of months rather than years, are feasible.
Some suppliers claim that asset management strategies of this sort can generate up to 20% reductions in downtime and inventory costs, and 20% to 30% improvements in equipment productivity.
For some companies, of course, the savings go way beyond this.
Better asset utilisation boosts financial performance, which can create new opportunities to raise funds for investment and growth. Plus a higher level of assurance that those investments are not being wasted.