Quantitative Benchmarks for the Manufacturing Industry

Manufacturers play in a sector that can be considered traditionally data abundant, with batch data from existing sources such as traditional IT systems – inventory, production, sales, etc. – but also new streams of data – with the increasing importance of IIoT (Industrial Internet of Things).

The manufacturing sector, despite being wide and embracing a large variety of activities, identifies the same common needs: which are supply chain optimisation – a topic that has never been more relevant than today, when during the early days of the pandemic worldwide supply chains collapsed -, predictive maintenance, and product development. The first two use cases can be both assessed by strong cost reduction, but they also deliver relevant increased profits. In optimising the supply chain, in broadening and consolidating partnerships, and in strengthening the ecosystem, manufacturers are able to promptly respond to unexpected crises, maintaining resiliency and production up to speed. In avoiding disruptions of the supply chain, organisations can maintain unaffected the production level and hence keep profitability and high margins. Predictive maintenance – having a platform that forecasts the exact moment when a machinery needs to be stopped for maintenance, and extending this concept also predictive failure – helps organisations to properly allocate maintenance hours with little impacts on the production schedules and lines. The ability to maintain production up to speed helps organisations in delivering higher margins and profits. The third most important use case is new product development and it is assessed with profit increase. The use of big data to understand customers' needs and desires and translating them into new products or upgrade of existing lines of production strengthen the organisations possibility to achieve larger profits. Summarising on the qualitative benchmarks, there is no specific benchmark that is valued as more relevant than the others. However, business model innovation is perceived less important as benchmark in BDA activities. This is because right now big data is fairly exploited for other – more relevant activities – and some business model innovation activities (e.g. data monetisation) are still currently under evaluation by manufacturers.