Abstract
<p class="Abstract">Autonomous production control (APC) is able to deal with challenges, inter alia, high delivery accuracy, shorter planning horizons, increasing product and process complexity, and frequent changes. However, several state-of-the-art approaches do not consider maintenance factors contributing to operational and tactical decisions in production planning and control. The incomprehensiveness of the decision models and related decision support tools cause inefficiency in production planning and thus lead to a low acceptance in the manufacturing enterprises. To overcome this challenge, this paper presents a conceptual cost-based model for integrating different maintenance strategies in autonomous production control. The model provides relevant decision aspects and a cost function for different maintenance strategies using on a market-based approach. The present work thus makes a positive contribution to cope with the high demands on flexibility and response times in planning while at the same time ensuring high plant productivity.</p>
Highlights
In today’s competitive market, manufacturing enterprises are faced with the challenge of achieving high productivity, short delivery times and a high level of delivery capability despite evershorter planning horizons, a large number of external planning changes and increasing planning complexity [1], [2]
Considering the advancement towards Industry 4.0, new opportunities arise due to innovative technologies and approaches such as Industrial Internet of Things (IIoT) applications [7], horizontal and vertical communication within a production system by means of Open Platform Communications Unified Architecture (OPC UA) [8] or the use of artificial intelligence (AI) methods for data analysis, forecasting, optimization and planning [9]
A closer look at the results shows that some aspects are relevant for integration into Autonomous production control (APC), while other aspects may have a positive influence on the quality of decisions but are not absolutely necessary for integration purpose
Summary
In today’s competitive market, manufacturing enterprises are faced with the challenge of achieving high productivity, short delivery times and a high level of delivery capability despite evershorter planning horizons, a large number of external planning changes and increasing planning complexity [1], [2]. This high degree of complexity in planning is no longer effectively and affordably manageable for humans [3]. Current systems for production planning and control (PPC) neither incorporate technical innovations nor social requirements and are not able to meet the current challenges [5]. Considering the advancement towards Industry 4.0, new opportunities arise due to innovative technologies and approaches such as Industrial Internet of Things (IIoT) applications [7], horizontal and vertical communication within a production system by means of Open Platform Communications Unified Architecture (OPC UA) [8] or the use of artificial intelligence (AI) methods for data analysis, forecasting, optimization and planning [9]
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