![]() This brings one to the concept of smart manufacturing or Industry 4.0, which presents a new frontier for the advancement of manufacturing planning and control for its potential realization, spurred by the concurrent maturation of emerging ‘smart’ technologies such as cloud computing, internet of things (IoT), big-data analytics (BDA) and machine learning (ML) for improving the PPC system and processes (Bueno et al., 2020 Cadavid et al., 2020 Oluyisola et al., 2020).įor smart manufacturing (and the associated terms of industry 4.0), while many authors have addressed the potential impact, the expectations, industry implementation experiences, strategies for adoption, there is currently no clear methodological guide towards the design and development of a smart PPC system – the valley that separates conceptual literature from implementation reality. Additionally, production systems are generating increasingly large volumes of data and the potential for enhancement of planning systems to use this data for performance improvements has been widely promoted in industry and academia, but with limited adoption (Chavez et al., 2017 Fatorachian & Kazemi, 2020 Nagy et al., 2018). From time immemorial, notable studies have highlighted the aforementioned challenges as the impetus for increased efficiency in production systems and have devised strategies that could be used to achieve this aim – including those that involve the use of information technology (IT) and lean manufacturing techniques (Chan, 2005 Hong et al., 2010 Skinner, 1974). While APS systems have been associated with various potential benefits, including support for real-time decision-making, the challenges associated with their implementation and integration with ERP systems render these benefits far from achievable in practice (Lupeikiene et al., 2014).Ĭurrently, the business environment is typified by increasing market and supply chain complexity, globalization and global competition, waves of protectionism, and customer expectations of more sophisticated products. Furthermore, manufacturing execution systems (MES) and advance planning and scheduling (APS) systems have also been developed in the last two decades to address some of these weaknesses of ERP systems (Öztürk & Ornek, 2014). However, ERP systems are typically unwieldy and do not support real-time decision-making that today’s market environments demand. Commonly, these activities of PPC are carried out and coordinated using enterprise resource planning (ERP) systems (Arnold et al., 2011) and spreadsheet solutions (de Man & Strandhagen, 2018). Loading concerns how much to do scheduling concerns when to do things sequencing concerns in what order to do things and monitoring and control is concerned with whether activities are going to plan, and corrective actions needed to bring activities within plan (Slack et al., 2013). ![]() Production planning and control (PPC) refers to the activities of loading, scheduling, sequencing, monitoring, and controlling the use of resources and materials during production. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system.
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