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Purpose -In this paper the degree of understanding of traceability concept, drivers, systems' characteristics, benefits and barriers, and frameworks is tested with focus on UK small and medium businesses (SMEs) that operate in the food supply chain (FSC). Design/methodology/approach -This study employs a survey strategy by means of a questionnaire that was sent to food and drink companies operating in the FSC. 164 SMEs answered the questionnaire. Answers were analysed by means of frequency distributions, contingency tables, coding, and pattern matching. Findings -UK SMEs appear to have a moderate understanding of the definition of traceability. The main drivers for traceability implementation are product safety and quality, even more than regulation. It is also found that SMEs do not consider technology as driver to implement traceability. In term of frameworks employed, about half of the SMEs stated that they were regulatory compliant, and followed industry standards. Furthermore, in term of traceability systems' characteristics, one out of three companies have a basic system in place (only regulatory compliant), while two out of three have a more sophisticated system, with many companies voluntarily tracing the material during the production process, while chain traceability appears not to be widely implemented. Finally, it is felt that the benefits of traceability outweigh the barriers/disadvantages, with the main benefits found in the area of crisis management. Nonetheless, it appears that many benefits are still unknown to SMEs, especially in relation to the firm's operations/strategy. Some implications for government and managers are suggested. Originality/value -This study fills the gap found in the literature where few recent academic papers focused attention on SMEs awareness of traceability in the FSC.

The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics. Keywords Similarity-based RUL calculation • C-MAPPS datasets • Neural networks • Data-driven prognostics

Complex systems are expected to play a key role in the progress of Prognostics Health Management but the breadth of technologies that will highlight gaps in the dynamic regimes are expected to become more prominent and likely more challenging in the future. The design and implementation of sophisticated computational algorithms have become a critical aspect to solve problems in many prognostic applications for multiple regimes. In addition to a wide variety of conventional computational and cognitive paradigms such as machine learning and data mining fields, specific applications in prognostics have led to a wealth of newly proposed methods and techniques. This paper reviews practices for modeling prognostics and remaining useful life applications in complex systems working under multiple operational regimes. An analysis is provided to compare and combine the findings of previously published studies in the literature, and it assesses the effectiveness of techniques for different stages of prognostic development. The paper concludes with some speculations on the likely advances in fusion of advanced methods for case specific modeling.