Abstract

Introduction

Theoretical background

Production planning and control theory and the limitations of classical PPC systems

Emerging uses of smart technologies for smarter PPC

Design and development considerations

Choosing an appropriate machine learning algorithm

Data architecture considerations

Systems architecture considerations

A methodology for designing and developing a smart PPC Solution

Case study

Determine objectives and priorities in ftting with the planning environment variables

System requirements specifcation: operations’ problems and performance indicators

Requirements specifcation

Performance indicators

Insights from the case study and implications for research and practice

Conclusions, limitations, and further research

Appendix

References

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.

Introduction

     Production planning and control (PPC) refers to the activities of loading, scheduling, sequencing, monitoring, and controlling the use of resources and materials during production. 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). 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). However, ERP systems are typically unwieldy and do not support real-time decision-making that today’s market environments demand. 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). 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).

Conclusions

     The question of how a smart PPC system should be designed and developed for an environment has been addressed in this paper through a five-step methodology. The steps of the methodology have been formulated and structured with the consideration that the resulting PPC system should fit the characteristics of the environment in question. Furthermore, the importance of contextual fit in algorithm selection, solution scalability and amenability of the smart PPC system to address future demands have been highlighted. In summary, the principles and considerations that guide the design in a smart PPC system are as follows:

     The design of the smart PPC system should fit the characteristics of planning environment. This highlights an issue that has been observed in numerous ERP and APS implementation case studies – expensive monolithic systems forcing managers to modify the production system to fit an inflexible PPC system. The proposed methodology can guide the design and development of such a fitting smart PPC system.