With the advent of automation, AI, and machine learning, those working on or near the production line can literally see manufacturing changing before their eyes.
However, advances in technology also bring with them a growing complexity—particularly for CFOs. For example, CFOs need to understand the cost-effectiveness of new production processes, as well as rethink accounting systems for reporting and reshaping production models.
Effectiveness begins with understanding data, which is always easier said than done. Here are a few ways that CFOs can address the challenges manufacturing businesses face in an increasingly digital world, and how they can do more to leverage their data.
Digital manufacturing is data-rich. The hardware and software now available makes that possible. For example, integrating digital scanning provides easier and faster asset traceability, while CAD modelling and digital twinning ensure product precision in large or small batches, as required.
As a result, everything produces a digital footprint—from financial data to production speeds, supplier coordination, and asset management.
The potential insights that companies can uncover from this granular level of operational, production, and technical data are endless. But, unless that insight can be used to help make better business decisions, it is essentially going to waste.
This explains why so many manufacturing CFOs are increasing investment in AI and machine learning. Why? Because they’re the most effective ways of being able to handle big data sets at scale.
Essentially, they’re the crucial technologies needed to collate, review, extract, and analyse data—detecting patterns that can help manufacturers boost savings, optimise efficiency, and ensure a more agile operation.
According to a recent Grant Thornton survey, 83% of CFOs want technology purchases to be connected with quantifiable ROI. The big question is: how can CFOs demonstrate the need for investment when no precedent has been set—when they’re dealing with unknown or untested methods?
A mindset shift is needed. The finance function is renowned for risk aversion (as they should be!) but experimentation—in the form of a proof of concept or pilot programmes—can mitigate any significant costs. Looking at digital transformation as a continual improvement process is important too. The proliferation of connected IoT devices, for example, provides CFOs with plenty of case studies for getting their digital manufacturing infrastructure right.
However, an even more effective way to get the best use of your data is to partner with other companies. This doesn’t mean sharing insight with competitors; it means collaborating with suppliers, shipping companies, and other related service providers in order to identify best practice, such as using the insight gleaned from IoT networks.
In this way, CFOs can add greater depth to their data sets—potentially sharing the costs and even co-creating the industrial software and analytical approaches needed to run an end-to-end digital manufacturing business.
Having data readily available can help generate revenue. With the right tools and IT infrastructure, manufacturers can guarantee a fast, frictionless production line for their clients. And the right data can be used in other areas, such as quality control, to help assess and qualify what "good" looks like.
Ultimately, understanding data is at the heart of getting digital manufacturing right. Sure, CFOs can toe the line on key cost-saving initiatives like predictive maintenance and using IoT devices to monitor quality and productivity, but they can’t operate in isolation.
Transformation needs to be a strategic imperative for the entire operation so that everybody benefits. Done correctly, with the right tools, insights, and IT stack, digital manufacturers can continue to future-proof the way they do business.