Discover the practical, real-world advantages of the Oliver Wight master planning and scheduling methodology. The newly revised Fourth Edition of Master Planning and Scheduling: An Essential Guide to ...Competitive Manufacturing delivers a masterful exploration of today's master planning and scheduling techniques, as well as an insightful discussion of the future of the master planning and scheduling processes and profession. Written in the context of an ever-evolving digital environment and augmented with new and critical information required to implement best practices, the book is a guide for practitioners and leaders on the principles of master planning and scheduling and its application in modern and future work environments. In this book, readers will learn: Insights regarding top-down, bottom-up, and side-to-side integration of business practices in support of a company's strategic direction and tactical deployment The critical link between time-phased integrated business planning, master planning, master scheduling, capacity planning, and material planning "How-to" details and examples to support master planning and scheduling implementation and enhancements within the company's demand and supply organizations Master Planning and Scheduling is an indispensable guide for supply chain professionals, planners and schedulers in all functional domains of a business. It also belongs on the bookshelves of any executive or manager who seeks to improve their understanding of best practice planning and scheduling processes and how those processes enable a business to outperform the competition through alignment, integration and synchronization across all functions in an organization.
Production Systems Engineering (PSE) is an emerging branch of Engineering intended to uncover fundamental principles of production systems and utilize them for analysis, continuous improvement, and ...design. This volume is the first ever textbook devoted exclusively to PSE. It is intended for senior undergraduate and first year graduate students interested in manufacturing. The development is first principle-based rather than recipe-based. The only prerequisite is elementary Probability Theory, however, all necessary probability facts are reviewed in an introductory chapter. Using a system-theoretic approach, this textbook provides analytical solutions for the following problems: mathematical modeling of production systems, performance analysis, constrained improvability, bottleneck identification and elimination, lean buffer design, product quality, customer demand satisfaction, transient behavior, and system-theoretic properties. Numerous case studies are presented. In addition, the so-called PSE Toolbox, which implements the algorithms developed, is described. The volume includes numerous case studies and problems for homework assignment.
Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional ...enterprise information systems such as ERP and MES. However, SMEs find themselves ill-equipped to face these new possibilities regarding their production planning and control functions. This paper presents a literature review of existing applied research covering different Industry 4.0 issues with regard to SMEs. Papers are classified according to a new framework which allows identification of the targeted performance objectives, the required managerial capacities and the selected group of technologies for each selected case. Our results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things. Likewise, SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still absence of real applications in the field of production planning. Finally, our literature review shows that reported Industry 4.0 projects in SMEs remained cost-driven initiatives and there in still no evidence of real business model transformation at this time.
Problem Definition
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Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production ...rate, implying that the equipment’s deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront.
Academic/Practical Relevance
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Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision.
Methodology
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We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes.
Results
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The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output.
Managerial Implications
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Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration.
•A framework of fault diagnosis with multiple validation of results is proposed.•A method for improving the performance of minor faults through digital twin models is proposed.•A digital twin model ...that can reflect the fault state of physical system is proposed.
A subsea production system is essential for the subsea production of oil and gas. Real-time monitoring can ensure safe production. A subsea production control system is the core of the subsea production system and the top priority to be monitored. Minor faults refer to faults with weak characteristics and are difficult to be found. They are common and pose significant risks in subsea production system. Safe operation of the system can be guaranteed to the greatest extent with a timely detection and handling of minor faults. Digital Twin driven fault diagnosis is an effective method to monitor the subsea production control system. However, the combination of digital twin and fault diagnosis is not comprehensive, especially in data interaction. It leads to the fact that digital twin cannot improve fault diagnosis accuracy significantly, mainly when a minor fault occurs. A cross-validation enhanced digital twin-driven fault diagnosis methodology for minor faults of the subsea production control system is proposed to achieve high accuracy. Control, loss and fault parameters are presented and used for building a digital twin model. Bayesian Networks are used to construct a fault diagnosis model. Single and cumulative errors are used to measure the difference between digital twin models and physical systems. A verification feedback method is used to check the diagnosis results. Data of four days of a subsea production system in the South Sea of China is used to demonstrate the proposed methodology. The results show that the method can improve the diagnosis accuracy for minor faults effectively.
Accelerating the agility of production control systems in today's dynamic production environment is one of the challenges that many types of research have been conducted using multi-agent systems to ...improve it. The current models of these systems have shortcomings such as limited predictability, low reliability in the decision-making process, poor ability to understand and interpret the current state of the system, control with many limitations, and generally the existence of error-prone systems. In order to solve these problems, the current research presents a new methodology for multi-agent production control based on integration with ERP, which improves the capabilities of the system in the face of the above deficiencies. The research method employed in this study is qualitative, and developmental-applicative, aiming to enhance the integration of multi-agent production control systems with ERP. The objective is to improve the flow of material, production, and the quality of semi-finished products on the production line by considering the parameters that influence them. The key accomplishment of this research is the development of a reliable production control methodology that encompasses three components: a data exchange framework, tools, and implementation. These components are derived from existing ERP information systems that are functionally mature and designed based on best practices with a focus on maintenance, modification, and performance, aiming to minimize errors. The developed methodology offers a practical and agile solution for enhancing production control using an ERP system, with a lower implementation cost than the implementation of a commercial ERP system with a separate multi-agent system.
Traditionally, production control on construction sites has been challenging, and still remains challenging. The ad-hoc production control methods that are usually used, most of which are informal, ...foster uncertainty that prevents smooth production flow. Lean construction methods such as the Last Planner System have partially tackled this problem by involving site teams into the decision making process and having them report back to the production management system. However, such systems have relatively long “lookahead” planning cycles to respond to the dynamic production requirements of construction, where daily, if not hourly control is needed. New solutions have been proposed such as VisiLean, KanBIM, etc., but again these types of construction management systems require the proximity and availability of computer devices to workers. Through this paper, the authors investigate how the communication framework underlying such construction management systems can be further improved so as to fully or partially automate various communication functions across the construction project lifecycle (e.g., to enable lean and close to real-time reporting of production control information). To this end, the present paper provides evidences of how the Internet of Things (IoT) and related standards can contribute to such an improvement. The paper then provides first insights – through various construction scenarios – into how the proposed communication framework can be beneficial for various actors and core business perspectives, from lean construction management to the management of the entire building lifecycle.
•IoT standards are considered to enhance lean construction management systems.•The proposed framework helps to improve information flow throughout the construction project, and beyond•Major opportunities and challenges of developing such a framework are detailed•First proofs-of-concept of how the developed framework can be implemented in construction projects are provided.
Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable ...high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimisation of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyse safety aspects and demonstrate reliability under prevailing conditions.
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