In product-diverse, end-of-life (EoL) production lines the relevant markets, competitors and customer bases continuously change as new products are processed. The resale market itself changes with the influx of new products, as well as hardware and software discontinuations. Competitive business decision making is often performed by a human operator and may not be timely or fully informed. These are decisions such as whether to perform a high cost repair or recycle a product or whether to use a batch of parts in repair or sell them on can be used to optimise product life-cycle management (PLM) and profit margins. A real-time decision making capability can reduce the risk of performing non-profitable processing. The novel contribution of this work is an interoperable semantic decision support toolset that is necessary to enable a capability for timely EoL decisions based on complete knowledge on profitability, predicted pricing and cost-of-production. Many decision support systems have been proposed for the EoL domain, but a lack of interoperability and use of unstructured knowledge bases has led to decisions based on knowledge that is not up to date. Using formalised, semantic technologies offers sustainable decision making in this volatile and increasingly competitive domain.

Energy waste significantly contributes to increased costs in the automotive manufacturing industry, which is subject to energy usage restrictions and taxation from national and international policy makers and restrictions and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554p/kWh equates to 7.28% of a manufacturing business's energy bill based on an average total usage rate of 7.61p/kWh. Internet of Things (IoT) energy monitoring systems are being developed, however, there has been limited consideration of services for efficient energy-use and minimisation of production costs in industry. This paper presents the design, development and validation of a novel, adaptive Cyber-Physical Toolset to optimise cumulative plant energy consumption through characterisation and prediction of the active and reactive power of three-phase industrial machine processes. Extensive validation has been conducted in automotive manufacture production lines with industrial three-phase Hurco VM1 computer numerical control (CNC) machines.

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