Industry 4.0
The 4th Industrial Revolution (Industry 4.0) is rapidly transforming the manufacturing industry, driven by advancements in data and connectivity enabling manufacturing processes to be optimised. A key component of Industry 4.0, the Industrial Internet of Things (IIoT), has enabled manufacturing organisations to increase efficiency, reduce error, improve safety, and predict maintenance through the interconnectedness of sensors and devices. This interconnectedness results in vast amounts of big data, meaning manufacturing organisations now have more data than ever before to make decisions for a range of business needs, including competitive performance gains (Mikalef et al., 2018) improved decision making (Sheng et al., 2017), agile manufacturing practices (Gunasekaran et al., 2019), and supply chain sustainability (Jeble et al., 2018). Big data can increase market competitiveness, quality management, and enhance existing manufacturing systems and it is argued as being a pre-requisite for smart, or ‘intelligent’ manufacturing whereby continuous monitoring, simulation, and optimization of production activities can be achieved (Tao et al., 2018) with reduced costs (Wang et al., 2022). Further, smart manufacturing cannot be realized with traditional manufacturing software and technologies, due to dependencies on multiple vendors for data, and the lack of sensory information (Cui et al., 2020). In sum, big data offers the potential for manufacturing organisations to improve their overall performance capability and competitive advantage.
However, one of the biggest limiting factors of how effective big data can be is the lack of resource to accurately interpret the data and/or for these interpretations to result in decision-making. Whilst the concept of applying Artificial Intelligence (AI) to big data is well established, recent developments in AI have radically increased the capability of businesses to make accurate decisions based on big data at scale and in a cost-effective way. For instance, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees (Davenport, 2018). Further, AI has been shown to improve decision quality, efficiency, and a cultural change leading to competitive advantage (Ransbotham et al., 2021) and can facilitate the innovation of new models, means, and forms of intelligent manufacturing (Li et al., 2017). When overlaying AI with Internet of Things (IoT), Artificial Intelligence of Things (AIoT) provides the capability to analyse big data across multiple devices and sources simultaneously to make decisions and forecasts. For instance, AIoT can model potential outcomes based on different resource allocations, and proactively alert managers to potential project completions risks (El Khatib & Al Falasi, 2021). Fundamentally, AIoT arguably has the potential to offer a better return on investment due to enabling quicker, more accurate and more consistent decisions than employees.