Abstract
ABSTRACTCyber-physical production systems (CPPS), as an emerging Industry 4.0’s technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined.
Highlights
IntroductionA recent market report forecasts a Compound Annual Growth Rate (CAGR) of 39% for predictive maintenance investments within the period 2016–2022 (IoT Analytics 2017)
It reveals that predictive and prescriptive maintenance of production systems including equipment, machineries and physical assets will be the most important application area of Industrial Analytics within the upcoming three years (79%) (Lueth et al 2016). In line with this fact, approximately 60% of the respondents emphasise on developing knowledgebased decision-support systems to improve efficiency and effectiveness of industrial processes (Lueth et al 2016). It is well-known from production management theories and empirical studies that efficient and effective maintenance is an integral part of the production strategy and is a critical factor for overall production system stability (Wireman 2014)
The undertaking paradigm shift known as Industry 4.0 and smart manufacturing technologies leads to evolution and transformation of Knowledge-Based Maintenance’ (KBM) strategies and models from diagnostic to predictive and prescriptive
Summary
A recent market report forecasts a Compound Annual Growth Rate (CAGR) of 39% for predictive maintenance investments within the period 2016–2022 (IoT Analytics 2017) This corresponds with the key findings of an in-depth industry survey including 151 analytics professionals and decision-makers from industrial companies, in particular, Original Equipment Manufacturers (OEMs), product manufacturers and service providers (Lueth et al 2016). It reveals that predictive and prescriptive maintenance of production systems including equipment, machineries and physical assets will be the most important application area of Industrial Analytics within the upcoming three years (79%) (Lueth et al 2016) In line with this fact, approximately 60% of the respondents emphasise on developing knowledgebased decision-support systems to improve efficiency and effectiveness of industrial processes (i.e. using data from operation to automate maintenance planning decisions) (Lueth et al 2016). It is well-known from production management theories and empirical studies that efficient and effective maintenance is an integral part of the production strategy and is a critical factor for overall production system stability (Wireman 2014)
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