Engineering the way to better strategic decisions

Peter Hatfield is a Director at Vendigital. He recently shared his insights with The Engineer.

A perfect storm of factors such as high inflation, record energy prices and the increased cost of raw materials has left 49 per cent of C-suite executives in the UK feeling as if their recent decisions have been skewed in favour of short-term considerations over the past year. With technology, including Artificial Intelligence (AI), advancing at a faster pace than ever before, businesses must have strategies in place to utilise it effectively or risk falling behind.

With one in five (22 per cent) respondents feeling as if they have been stuck in ‘fire-fighting’ mode over the past year, returning to long-term planning could seem daunting, even to the most experienced of Boards. However, it is vital to begin thinking about long-term strategies now to save frustration and unnecessary cost in the future, and data-led cost engineering could help businesses to achieve their goals.

Industrial manufacturers have been hit particularly hard by factors that have encouraged short-term thinking over recent years. Rising energy prices have had an enormous impact on individual businesses and across the supply chain, with countries such as China implementing energy caps, putting further limits on manufacturing capacity. This is just one example of the challenges that Boards have had to react to, and though things are beginning to look up, breaking away from this short-term, reactive thinking could be harder than anticipated.

Most Boards have limited appetite for managing change, and short-term objectives, such as meeting KPIs, can often take priority. The best way to instil a long-term mindset is to ensure that strategy setting doesn’t rest with a few individuals but is rather built into the structure of the organisation. With long-term strategies such as reshoring and nearshoring gaining popularity as manufacturers look for ways to mitigate future supply chain issues, access to comprehensive and trustworthy data to help guide decision making has become vital.

Meaningful data can have a positive effect on businesses, providing a lens of pragmatism through which opportunities to reduce cost or improve operational efficiency can be scoped. It can also help to guide decisions about how and when to invest. Recent hikes in interest rates have increased the cost of borrowing significantly, which means that the return on any capital expenditure needs to be much higher to justify the outlay. Data-led cost engineering can equip businesses to take such decisions with confidence, even in a changeable and complex environment.

However, Boards should be aware that the capabilities of data-based systems are advancing quickly, something that can be seen in the rapid take-up of predictive maintenance in the engineering sector. While many businesses still rely on planned maintenance schedules, advances in AI and machine learning mean that such systems could soon do a better job at identifying maintenance issues than human operatives.

Previously, too much data could confuse decision makers, but nowadays external data sets have become much more accessible, and many platforms are able to process large amounts of data quickly and present their message clearly. AI-powered systems are also able to process multiple variables at once. For example, food manufacturers may seek to optimise efficiency by considering the weather, the moisture of the grain, the performance of the machine, the temperature inside the factory and more. An individual worker simply wouldn’t be able to process this amount of data simultaneously without vast experience, and Boards should take advantage of the technology available by training systems to make future strategic decisions based on historical data.

While the technological know-how already exists, Boards may have to ensure ‘buy-in’ internally to ensure that employees trust the data enough to listen to it. Shutting down a multi-million pound plant for a day is no small decision, and workers will have to trust that the AI is making the right call when it flags a potential issue.

A further use of AI could come in the form of centralised control centres. With some businesses having multiple sites across the globe, a central control centre could streamline the maintenance process and remove the requirement for multiple site operators and onsite engineers as maintenance status is reported from across the world to one central location. Remote monitoring could also drive efficiency for global businesses, allowing multiple sites to be run from any location.

The truth is that things are changing so quickly in this sphere that it’s no longer enough to think in small steps. The most successful businesses will be those that examine the future of their industries and work backwards from that vision to create a long-term strategy. For example, as centralised databases become more common, Boards should invest in equipment that will be able to connect their plants to these databases, rather than filling the gap with short-term solutions that will do ‘for now’. Short-term or temporary solutions could end up costing more in the long run as they will become obsolete and need to be replaced.

Boards should also examine their internal teams to ensure that a business has the right level of capability and expertise to keep up with the rapid changes to technology. As it continues to advance, there is a high probability that individuals will not have the expertise to make informed decisions, as previous experience may not lead to the right choices. It is therefore essential that businesses look to develop this capability externally to mitigate the risk of project delays or falling behind competitors.

By investing in data and using it to guide decision-making on equipment and technology, Boards can ensure that their businesses keep up with the market and achieve growth where there is opportunity to do so.