Why Manufacturers Need a Phased Approach to Digital Transformation

Those that succeed with this difficult work break it into three stages, each with its own guiding metrics.

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Alex Nabaum/theispot.com

As complex as digital transformation can be for manufacturers, leaders in that sector tend to view it as a single process, the success of which can be demonstrated via a single metric: return on investment.

Such a simplistic view belies what’s actually involved: investing in and mastering new operational technologies (OTs), reskilling workers, keeping capabilities in sync with external supply-chain partner infrastructures, and enabling new digital ecosystems for partners and customers. Digital transformation for manufacturing differs substantially from transforming IT services or implementing e-commerce, because it requires combining the staged integration of physical assets with digital technologies. For these and other reasons, many manufacturers struggle to adopt transformative tech and end up misaligning and wasting their investments or misdirecting their scarce specialized resources. As a consequence, their digital investments generally fail to enable the business transformation they seek.

Our studies find that manufacturers’ digital transformation journeys involve three distinct stages, each with its own set of appropriate metrics. The companies that correctly identify where they are in this work and then apply the right metrics are more likely to achieve the kinds of innovation they seek. Given the huge investments manufacturers are making in digital innovation and transformation, the stakes are extremely high. A failure can waste enormous capital investments, put competitiveness at risk, and cost many people their jobs. That might be why some leaders fall back on traditional ROI measures. However, those metrics provide a false sense of security — and they do nothing to help managers reach the advanced stages of digital transformation that promise benefits far more strategically valuable than mere cost savings and productivity gains.

Applying the wrong metrics — that is, those more appropriate for a different stage — can be a costly mistake. One glaring example is Predix, General Electric’s industrial internet-of-things edge-to-cloud platform for digital applications, which was designed to collect both OT and IT data and then transfer it to the cloud.


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