This dissertation presents dynamic system models of continuous manufacturing systems based on analogies with electrical systems. The developed modeling technique has the capability to explicitly specify production control schemes including control points, material and information flow paths, and logical operations. The model provides standard graphical representations and governing equations to describe both the steady state and transient responses of continuous manufacturing systems. For deterministic systems, these equations can be solved to get closed-form solutions. For stochastic systems, numerical solutions can be obtained for any probabilistic distribution. The electrical analogs provide an excellent tool to model control signals and logical operations. This is especially important for pull control schemes where qualitative descriptions often found in the literature can be ambiguous. The proposed technique is demonstrated by modeling push and a variety of pull systems. The developed models are used to study the relative performance of push, CONWIP, and kanban control systems. The results show that the card count distribution significantly affects the performance of a kanban system, and that drawing conclusions on kanban performance requires card count optimization. The results also show that the relative performance of push and CONWIP systems varies with operational factors. The factors studied in this dissertation are the bottleneck utilization, line balance, demand rate variability, and processing rate variability. At some combinations of these factors, the push system is superior to the CONWIP system, with other combinations, the CONWIP system is the superior system. In this work, push systems tend to have better relative performance (compared to CONWIP) at high variability levels in the processing rates and low variability levels in the demand rate, while CONWIP systems tend to have better relative performance (compared to push) at high variability levels in the demand rate and low variability levels in the processing rates. CONWIP systems tend to have higher relative performance at high utilization levels and in lines where a distinct bottleneck exists.

The development of Supply Chain Management has occurred gradually over the latter half of the last century, and in this century will continue to evolve in response to the continual changes in the business environment. As organizations exhaust opportunities for internal breakthrough improvements, they will increasingly turn toward the supply chain for an additional source of untapped improvements. Manufacturers in particular can benefit from this increased focus on the chain, but the gains realized will vary by the type of supply chain. By applying basic production control principles to the chain, and effectively using tools already common at the production line level, organizations address important supply chain considerations. Both the Theory of Constraints and the factory physics principles behind the Constant WIP concepts focus on the system constraint with the aim of controlling inventory. Each can be extrapolated to focus on a system whose boundaries span the entire supply chain.

Работа посвящена эмпирическому исследованию проблемам повышения эффективности деятельности промышленных предприятий. Считается, что ключевым направлением развития любого предприятия является его производственное планирование. С учетом тенденций и особенностей современного рынка, предполагается, что наиболее перспективным решением является внедрение гибридной системы производственного планирования, которая объединяет в себе элементы наиболее результативных методологий. В работе разработан методический подход к управлению производственным планированием на предприятии, базирующийся на самодиагностике состояния системы оперативного планирования и алгоритме по выбору вида производственного планирования, в основе которого лежит оценка функционирования системы с помощью показателей эффективности предприятия, имеющих как прямое, так и косвенное влияние, что позволит повысить эффективность управления и изменить состав и приоритеты основных ресурсов предприятия. / The research is devoted to an empirical study of the problems of improving the efficiency of industrial enterprises. It is hypothesized that the key direction of development of any enterprise is its production planning. Taking into account the trends and features of the modern market, it is assumed that the most promising solution is the introduction of a hybrid production planning system that combines elements of the most effective methodologies. There is developed a methodological approach to the management of production planning at the enterprise. It is based on 2 steps: the first step is self-diagnosis of the state of the operational planning system and the second on algorithm for choosing the type of production planning, which is based on the assessment of the system functioning using the enterprise performance indicators that have both direct and indirect influence, which will increase the management efficiency and change the composition and priorities of the main resources of the enterprise.

This Master’s thesis has been carried out within the subject area of Production Development and Management and aims to reduce internal lead times in make-to-order (MTO) and job-shop production environments with the use of identified theoretical methods. The reason this particular production environment was chosen was the flexibility and satisfaction it provides its customers. Today, customers expect customised products, a situation which causes problems for manufacturers as they are unable to produce such products in large amounts. In order to investigate problems with these kinds of environments and the causes for long lead times, we have conducted a literature study where we identified the problems these particular kinds of production environments experience regarding production planning and control which are related to the immense amount of time consumed by changeovers because of high demand variance and high requirements for customisation. To affirm the theoretical findings, we opted to undertake a case study and chose Talent Plastics Gislaved AB as our case, because this company utilises an MTO and job-shop production environment for its production of highly customised products with high demand variance. In the analysis of our case, we found that the wastes in the organisation were similar to those identified in the theoretical findings. The current planning system and the current state of the manufacturing lead time system were evaluated and a theoretical framework using a combination of lean production, work load control and constant work-in-process theories was suggested. We claim that concentrating on the reduction of setup times can lead the job-shop towards drastically decreased lead times and a much more effective use of time throughout the organisation. Because the organisation will continue to face problems due to the ever-increasing demand variance and requirements for customisation, there are plenty of opportunities for further research in these kinds of production environments. Emerging theories, such as quick response manufacturing, may also be tested to construct an efficient framework.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The contribution of the metalworking sector to the gross domestic product is decreased by 8.6% in the 2017–2018 period because of problems such as high rejection rates, lead times, and raw material losses. Consequently, the sector’s production was reduced by PEN 1,200,000 while demand remained flat. Thus, this article proposes a production control model using 5S, Single-Minute Exchange of Dies, and tools such as Kanban and Constant Work-in-Progress (CONWIP) to increase the productivity of a sand casting line in a Peruvian metalworking enterprise that manufactures parts for mining on request. The improvement proposal involves the implementation of lean manufacturing tools and analyzes low productivity effects. The problems identified are typical of the Peruvian metalworking sector; thus, this study may contribute to finding possible solutions for issues faced by other enterprises. / Revisión por pares

De nos jours, de nombreux systèmes de production sont gérés en flux « tirés » et utilisent des méthodes basées sur des « cartes », comme : Kanban, ConWIP, COBACABANA, etc. Malgré leur simplicité et leur efficacité, ces méthodes ne sont pas adaptées lorsque la production n’est pas stable et que la demande du client varie. Dans de tels cas, les systèmes de production doivent donc adapter la tension de leur flux tout au long du processus de fabrication. Pour ce faire, il faut déterminer comment ajuster dynamiquement le nombre de cartes (ou de ‘e-card’) en fonction du contexte. Malheureusement, ces décisions sont complexes et difficiles à prendre en temps réel. De plus, dans certains cas, changer trop souvent le nombre de cartes kanban peut perturber la production et engendrer un problème de nervosité. Les opportunités offertes par l’industrie 4.0 peuvent être exploitées pour définir des stratégies intelligentes de pilotage de flux permettant d’adapter dynamiquement ce nombre de cartes kanban.Dans cette thèse, nous proposons, dans un premier temps, une approche adaptative basée sur la simulation et l'optimisation multi-objectif, capable de prendre en considération le problème de la nervosité et de décider de manière autonome (ou d'aider les gestionnaires)  quand et où ajouter ou retirer des cartes Kanban. Dans un deuxième temps, nous proposons une nouvelle approche adaptative et intelligente basée sur un réseau de neurones dont l’apprentissage est d’abord réalisé hors ligne à l’aide d’un modèle numérique jumeau (simulation), exploité par une optimisation multi-objectif. Après l’apprentissage, le réseau de neurones permet de décider en temps réel, quand et à quelle étape de fabrication il est pertinent de changer le nombre de cartes kanban. Des comparaisons faites avec les meilleures méthodes publiées dans la littérature montrent de meilleurs résultats avec des changements moins fréquents. / Today, many production systems are managed in "pull" control system and used "card-based" methods such as: Kanban, ConWIP, COBACABANA, etc. Despite their simplicity and efficiency, these methods are not suitable when production is not stable and customer demand varies. In such cases, the production systems must therefore adapt the “tightness” of their production flow throughout the manufacturing process. To do this, we must determine how to dynamically adjust the number of cards (or e-card) depending on the context. Unfortunately, these decisions are complex and difficult to make in real time. In addition, in some cases, changing too often the number of kanban cards can disrupt production and cause a nervousness problem. The opportunities offered by Industry 4.0 can be exploited to define smart flow control strategies to dynamically adapt this number of kanban cards.In this thesis, we propose, firstly, an adaptive approach based on simulation and multi-objective optimization technique, able to take into account the problem of nervousness and to decide autonomously (or to help managers) when and where adding or removing Kanban cards. Then, we propose a new adaptive and intelligent approach based on a neural network whose learning is first realized offline using a twin digital model (simulation) and exploited by a multi-objective optimization method. Then, the neural network could be able to decide in real time, when and at which manufacturing stage it is relevant to change the number of kanban cards. Comparisons made with the best methods published in the literature show better results with less frequent changes.

A CONWIP (Constant Work-In-Progress) system is basically a hybrid system with a PUSH-PULL interface at the first machine in the line. This research addresses the most general case of a cyclic CONWIP system by incorporating two additional constraints over earlier studies namely; stochastic processing times and limited intermediate storage. One of the main issues in the design of a CONWIP system is the WIP level 'M', to be maintained. This research proposes an iterative procedure to determine this optimal level. The second main issue is the optimization of the line by determining an appropriate job sequence. This research assumes a 'permutational' scheduling policy and proposes an iterative approach to find the best sequence. The approach utilizes a controlled enumerative approach called the Fast Insertion Heuristic (FIH) coupled with a method to appraise the quality of every enumeration at each iteration. This is done by using a modified version of the Floyd's algorithm, to determine the cycle time (or Flow time) of a partial/full solution. The performance measures considered are the Flow time and the Interdeparture time (inverse of throughput). Finally, both the methods suggested for the two subproblems, are tested through computer implementations to reveal their proficiency. / Master of Science