Abstract
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. 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. 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. 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. 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. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.
Highlights
Production planning and control (PPC) refers to the activities of loading, scheduling, sequencing, monitoring, and controlling the use of resources and materials during production
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)
There are gaps, of architectural designs and concepts, and more importantly about how to translate the system requirements and attributes to the lower level elements – of data structures, of class definitions, of entity-relationship diagrams, of matching appropriate algorithms, etc. – in a way that supports the development of smart PPC systems that fit the near- and long- term requirements of a production system
Summary
Production planning and control (PPC) refers to the activities of loading, scheduling, sequencing, monitoring, and controlling the use of resources and materials during production. It is expensive to integrate additional software (called ‘add-ons’) with the large, monolithic systems, often making it difficult to adapt to changing business needs and leading many manufacturing managers and planners to build simpler, easier to manage, but disparate tools outside their PPC systems (Carvalho et al, 2014; Shaikh et al, 2011) Another stream of research has looked at the development of complementary decision support systems for addressing some of the challenges being faced by companies implementing ERP, APS and MES systems. This question determines whether a solution, even if well executed, will deliver any real and lasting value to a manufacturing operation They emphasize on the need for research within development of intelligent decision support systems, frameworks and architectures that can advance smart PPC.
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