A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information

Scheduling and shop floor control in commercial airplane manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.Includes bibliographical references (p. 73-75).Boeing is the premier manufacturer of commercial jetliners and a leader in defense and space systems. Competition in commercial aircraft production is increasing and in order to retain their competitive position, Boeing must strive to improve their operations by reducing costs. Boeing factories today still schedule and monitor the shop floor much as they have for the past 100 years. This thesis compares and contrasts several different methods for shop floor control and scheduling including Boeing's barcharts, Toyota production system, critical chain, and dynamic scheduling. Each system is will be analyzed with respect to how it handles variability in labor output required and how that affects which products are typically made under each system. In additional to qualitative comparisons, discrete event simulations comparing the various strategies will be presented. Areas for future simulation study are also discussed. The recommended approach for commercial airplane assembly is critical chain. A suggested implementation plan is presented along with methods to ease acceptance.by Vikram Neal Sahney.S.M.M.B.A

Performance adaptive manufacturing processes

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 296-301.Energy efficiency is of increasing importance towards sustainable manufacturing in the automotive industry, in particular due to growing environment regulations and rising electricity costs. Approaches within the manufacturing planning phase are insufficient to address dynamic influences during run-time (e.g., electricity tariffs or workload). Additionally, conventional production monitoring and control systems consider the ‘Overall Equipment Effectiveness‘ of manufacturing systems, but do not include related energy efficiency. This paper introduces a novel approach that combines these both aspects and provides more effectiveness based on socalled production variants. The latter are designed during the planning phase and used to adapt manufacturing behavior when facing dynamically changes during run-time. A simulation shows how dynamic adjustments of cycle times lead to a high reduction of energy costs while maintaining high throughputs

Analysing the implementation of a material requirements planning (MRP) system into an engineer-to-order (ETO) company ; the case of National Oilwell Varco Norway (NOVN)

Masteroppgave økonomi og administrasjon- Universitetet i Agder, 2014A material requirements planning (MRP) system is a computer-based planning and control system whose main objectives are to provide the right part at the right time, and to meet the schedules for completed products. The development of these systems revolutionised the manufacturing industry, and lead to it being adopted by many companies. The expectations of the systems were high, both from academia and industry in the subject area of production planning and control. However, the widespread use of the system has uncovered several failures, mainly because the systems are implemented under the assumption that “one-size-fits-all”, and thus do not differentiate between various operations strategies. Prior research has already identified MRP systems as successful production planning and control systems in several operations strategies. Despite its importance, the previous research on MRP systems has not thoroughly addressed the systems strategic fit with an engineer-to-order (ETO) operations strategy. This thesis therefore focuses on the use of an MRP system in an ETO environment, and the overall objective is to investigate if implementation of an MRP system supports the operations strategy of an ETO company. To help investigate the overall objective, a literature review and a case study has been conducted. The literature review was carried out to provide a theoretical base for the research and a foundation for the future work of the research. A case study was conducted to help get a better understanding of an MRP system’s strategic fit in an ETO company to draw parallels between theory and practice. Numerical data has been collected to conduct statistical analysis. The case study company is a large ETO company that is about to implement an MRP system and that previously have used a similar system in some of its departments. Qualitative data from the case study have mainly been conducted through interviews and informal conversations with key informants employed in the case study company. The result of this research shows that there is a clear misalignment between the decision support provided by an MRP system and the decision support required by an ETO company. The product-, market- and process characteristics of an ETO company are too much of a constraining factor for the MRP system, which may lead to reduced competitiveness. Furthermore, the research suggests that organisational factors, such as education level of employees, company size and culture have significant impacts on implementation of an MRP system. The results gathered from the research have a foundation from relevant theory, which strengthens the quality of the thesis. The thesis has therefore contributed with increased knowledge and provides a better understanding of the use of an MRP system in an ETO company. In particular the definitions in the thesis, the identified variables, and the frameworks should be of interest for researchers, management, and consultant in the area of production planning and control (PPC). The research also has important implications for top management and policy makers in implementing an MRP system, as these stakeholders need to communicate effectively with their organisation about their MRP adoption intentions. Case study findings suggest that MRP systems are not suitable for ETO products, and that MRP implementation is influenced by, but not necessarily bound by, existing national and organisational factors. The findings of this study aid the management of organisations that are implementing MRP systems to gain a better understanding of the likely challenges they may face and enables them to put in place appropriate measures to mitigate the risk of implementation failures

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed