•To address the main concepts of production planning and control (PPC) systems.•To review reference architectures for PPC.•To identify the impact of Industry 4.0 technologies on a smart PPC ...system.•To propose a conceptual framework of a smart PPC system dubbed as SPPC4.0.
This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.
One of the main objectives of Material Flow Control (MFC) is to ensure delivery performance. Traditional MFC realizes this through independent decisions at two levels: order release and production ...authorization on the shop floor. This hierarchical decision-making can be improved by integration because these decisions are interconnected. This study introduces a new reinforcement learning method that combines, and jointly optimizes various MFC decisions. It enhances the delivery performance of an agent by enabling it to interact with the environment and to learn the parameters of the decision model. Results from a make-to-order pure job shop simulation model demonstrate that the new approach outperforms exiting MFC methods in most cases. This extends existing literature on MFC, which remains entrenched in traditional decision methods, and existing literature on reinforcement learning in the context of production planning and control, which remains largely focused on production scheduling. It has important implications for the future design of production planning and control systems and practice, specifically in contexts where data is readily available or a digital shadow can be obtained.
•Transcends hierarchical material flow control.•Integrates release, authorization and dispatching decision.•Outlines a new material flow control mechanism that uses reinforcement learning.•Demonstrates that the new mechanism can outperform the state-of-the-art.
To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an ...appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it has been argued that novel information processing technologies – as part of Industry 4.0 – result in PPC frameworks with decentral structures. This conflicts with traditional works arguing for hierarchical or central structures. Therefore, we review the PPC design literature to create a comprehensive overview and summarize design proposals. Based on our review, we come to the intermediate conclusion that PPC frameworks continue to have a hierarchical structure, although decision-making is shifted more to decentral levels compared to traditional hierarchies. Our analysis suggests that the effect of a decentralization shift has potentially strong and poorly understood implications, both from a decision-making and organizational perspective.
•Review the Production Planning and Control (PPC) design and structure literature.•Argues that PPC frameworks should be structured hierarchically.•Reasons that Industry 4.0 technology can improve intra-level coordination.•Discusses key design challenges in hierarchical decision processes.•Summarizes several design propositions on how to design a PPC framework.
Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the ...Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.
Despite an increase in the availability of digital solutions, planners’ remain at the center of the production planning and control (PPC) decision-making process and have to make numerous decisions ...involving different information. Due to the inherent complexities, planners’ are supported by PPC systems to fulfil their tasks. Research has shown that humans are influenced by cognitive bias when making decisions, especially in uncertain and complex environments and when disruptions such as machine failures occur. Researchers have therefore proposed debiasing methods to reduce human errors in decision-making. However, there is currently no environment within this research to test these proposed methods. In this study, we aimed to take the first step toward closing this research gap. We combined existing PPC research with a systematic literature review on disruptions in production as well as perspectives from practice through interviews with 12 experts. We investigated the interactions between planners’ and PPC systems with consideration of different cognitive biases and disturbances to derive relevant simulation uses cases.
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.
The absence of efficient optimization methods combined with Artificial Intelligence concepts has led to inefficiencies and high costs in the production planning of organizations. Thus, this study ...aims to optimize production planning in an electronic equipment company, using Linear Programming and Machine Learning to support assertive and efficient decisions. The methodological process comprises seven stages: Literature review; Collection and analysis of production data; Application of Machine Learning methods for modelling; Selection of the best model; Development and application of the Linear Programming model; Analysis of results; Validation with stakeholders. The approach resulted in optimized production planning, capable of reducing operating costs and assisting in the daily decision-making of the organization. The Machine Learning forecasting technique achieved an average error of 9%, demonstrating its accuracy in forecasting future demand. This study evidences a robust and promising approach to improve efficiency and effectiveness in production planning operations. In this context, the union between Operations Research and Machine Learning emerges as a response to existing gaps and a driving direction for continuously optimizing these crucial processes.
•Iterating between production planning and scheduling level is a promising technique.•This has been applied especially for order release planning.•Iterative order release mechanisms often fail to ...converge.•The paper identifies the reason for this failure.•This is done by analyzing the theoretical underpinning of this iterative procedure.
Planning future order releases for complex manufacturing systems with substantial lead times must consider limited capacities and the resulting production smoothing problem as well as the highly nonlinear relationship between capacity utilization, work-in-process and flow times. An important solution approach that has been proposed for this problem is to iterate between an order release model with fixed lead times and a lead time estimation (usually simulation) model that estimates the flow times for given order releases, providing the lead times for the next iteration. However, the convergence of this iterative procedure is highly unpredictable, limiting its practical use.
The iterative mechanism is analyzed analytically for simplified formulations of the order release and lead time estimation model. We show that an order release procedure of this type that iterates on the lead times is a dual (price) coordination mechanism whose design does not meet the theoretical requirements, and there is no straightforward way to overcome this. The analysis also provides insights into the results of several numerical studies from the literature and suggests a possible research direction to improve the method.
Order release mechanisms of this type are a special case of a broader class of production planning methods that iterate between the production planning and the production scheduling level in order to provide realistic values for lead times and planned capacities. Providing theoretical underpinning for this type of production planning methods is an important research objective, and the paper pursues this direction for the special case of order release.
Rapid advances in Industry 4.0 have the potential to transform production planning and control (PPC) through the emerging concept of smart PPC. This paper provides a visionary perspective by ...addressing the gap in research on how the characteristics of a company's planning environment impact on the need for, and potential benefit of, smart PPC. The paper posits that the potential of smart PPC to improve PPC performance increases with the complexity of the planning environment. A set of propositions is developed for how 12 product, market, and process variables impact on the need for smart PPC. These are operationalized into a conceptual framework that can be used as a tool by practitioners and academics to assess a company's need for smart PPC. A case study from the food sector illustrates the applicability of the framework and describes three potential applications for how four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning) can be used to address key PPC challenges and open new opportunities for improving PPC. Future research should strengthen the validity and applicability of the proposed framework through additional cases across industrial sectors and carry out case studies, surveys, and structural equation modeling to investigate the specific relationship between planning environment characteristics, smart technologies, and the elements of smart PPC.
The case helps students to understand the emerging concept of linear and circular economies. It facilitates to examine the implications of circular business models such as remanufacturing on ...operations management decisions. It also introduces them to the concept of total cost of ownership and impact of remanufacturing on reducing total cost of ownership. The cases help students to evaluate the challenges and opportunities of remanufacturing business in emerging economy like India. This case is among the first few cases on the application of circular economy principles in context of heavy-duty and off-road sector and the impact of these principles on product design and production planning and control decisions.
PDF
