Process Selection Using Variation and Cost Relations

Cogent Engineering, 2016

Vehicle license number plate production in Nigeria faces high variability in terms of process times and inter-arrival times, resulting in poor production schedule reliability. This study aims to clarify the level of such variation and to provide process improvement strategies within plate production. The specific objectives herein include identifying assignable variables, estimating variability indices and minimizing variation by developing solutions to improve system performance. This study explores the variability pooling method in assessing potential cost-effective process improvements and a case study is conducted on four Nigerian vehicle license number plate production plants in order to demonstrate the applicability of the proposed technique. Structured questionnaires were circulated to plant workers and data collected from plant production records from 2012 to 2015 in seven production lines were analyzed. A preliminary study on the production lines revealed the coefficient of variation (CV) for the Awka, Gwagwalada, Lagos and Lagos State Plants, showing measured variability levels of 0.62, 0.67, 0.60 and 0.78, respectively. Comparatively, the results obtained after the variability pooling showed a significant improvement in performance characteristics, such as low CV levels, enabling a 68% increase in net annual income for each plant, as well as enhanced machine utilization.

International Journal of Flexible Manufacturing Systems, 2004

Frequency of model change and the vast amounts of time and cost required to make a changeover, also called time-based competition, has become a characteristic feature of modern manufacturing and new product development in automotive, aerospace, and other industries. This paper discusses the concept of time-based competition in manufacturing and design based on a review of on-going research related to stream-of-variation (SOVA or SoV) methodology. The SOVA methodology focuses on the development of modeling, analysis, and control of dimensional variation in complex multistage assembly processes (MAP) such as the automotive, aerospace, appliance, and electronics industries. The presented methodology can help in eliminating costly trial-and-error fine-tuning of new-product assembly processes attributable to unforeseen dimensional errors throughout the assembly process from design through ramp-up and production. Implemented during the product design phase, the method will produce math-based predictions of potential downstream assembly problems, based on evaluations of the design and a large array of process variables. By integrating product and process design in a pre-production simulation, SOVA can head off individual assembly errors that contribute to an accumulating set of dimensional variations, which ultimately result in out-of-tolerance parts and products. Once in the ramp-up stage of production, SOVA will be able to compare predicted misalignments with actual measurements to determine the degree of mismatch in the assemblies, diagnose the root causes of errors, isolate the sources from other assembly steps, and then, on the basis of the SOVA model and product measurements, recommend solutions.

Modeling of variation propagation in multistation assembly processes is crucial in predicting product dimensional quality and general performance of manufacturing systems. Based on the state space modeling, this paper develops a variation propagation model, which can be applied for analysis of various tolerances such as size tolerance, bonus tolerance, floating tolerance, etc. The nonstationary tolerance/variation (varying variance) caused by bonus tolerance and floating tolerance is properly handled by the proposed method. Thus, by using the developed variation propagation model, the variations on key product characteristics (KPCs) can be accurately predicted. This enables broad applications of the proposed method in actual manufacturing processes. The results of the case study also validated the proposed model.

Proceedings of the …, 2001

Reducing variation in the assembly of a vehicle body is a critical element in improving its quality. Assembly plants have dimensional-management teams, who monitor variation, respond to" variation crises," and continually reduce variation when there are no ...

Volume 3: 6th Design for Manufacturing Conference

Yielded cost is defined as cost divided by yield and can be used as a metric for representing an effective cost per good (non-defective) assembly for a manufacturing process. Although yielded cost is not a new concept, it has no consistent definition in engineering literature, and several different formulations and interpretations exist in the context of manufacturing and assembly. In manufacturing, yield is the probability that an assembly is non-defective. To find the effective cost per good assembly that is invested in the manufacturing or assembly process, cost is accumulated and divided by yield. This paper reviews and correlates existing yielded cost formulations and presents a new method that enables consistent measurement of sequential process flows. This new method views the yielded cost associated with an individual process step (step yielded cost) as the change in the process’s yielded cost when the step is removed from the process. This approach is preferred because it i...

Integrated Design and Manufacturing in Mechanical Engineering ’98, 1999

Manufacturing process selection involves two steps. The first involves the screening of all available processes to determine whether they are technically capable of making the design; the second involves the ranking of those which are successful, using economic criteria. The ranking step requires techniques of cost-estimation. In seeking to achieve this, two problems are encountered. The first: that the design is still in an early stage at which little information is available; the second: that conventional cost-estimation techniques require detailed information and cannot easily be applied to widely diverse processes. A simple approach for this problem is presented. It is based on the idea of a resource-consumption cost model, applicable early in the design process. The system is described and a case study is used to demonstrate how it works. RESUME La sélection d'un procédé de fabrication comprend deux étapes. La première consiste a évaluer les différents procédés afin de vérifier qu'ils permettent, d'un point de vue technique, d'obtenir un produit en accord avec les spécifications. La seconde étape consiste a classer les procédés retenus selon des critères économiques. Cette phase fait intervenir des techniques simples d'estimation de coût. Cependant, on rencontre au cours de cette sélection deux types de problèmes. Le premier d'entre eux est lié au fait qu'elle intervient alors que la conception du produit est peu avancée, ce qui limite la quantité d'informations disponibles. D'autre part, les techniques précises d'estimation de coût, qui existent pour certains procédés de fabrication, nécessitent des renseignements détaillés concernant le procédé. L'objet de la presentation est d'aborder ce probleme par une approche simple, basée sur l'idée d'un modèle d'évaluation des coûts liés à la consommation des ressources, et applicable au début de la démarche de conception. Le système sera décrit et une étude de cas sera utilisée afin de montrer comment il fonctionne. * The current selector divides materials into 9 classes and shapes into 24 categories. Processes are also divided into classes: primary, secondary, and tertiary; with subclasses under each [ESA.97].

Proceedings of the …, 2009

Small to medium manufacturing companies are coming to realise the increasing importance of performing fast and accurate cost estimates at the early stages of projects to address customers' requests for quotation. However, they cannot afford the implementation of a ...

IFIP Advances in Information and Communication Technology, 2012

Assembly processes are undergoing frequent changes as a result of the current drive for agility and rapid product solutions. These changes induce complexities and dynamics in the survival of most Manufacturing Enterprises (MEs). To remain competitive, MEs have to continuously and flexibly adjust through the redesign and organisation of their manufacturing and assembly processes as well as resource elements, with the aim to improve 'cost' and 'values' generated. Cost and values are part of key performance indicators necessary for determining the economic viability of assembly processes. The paper therefore presents a methodology capable of capturing, modelling and using information related to cost and value generation for in-depth assembly process analysis. This form of analysis can help determine assembly process efficiency and therefore support the selection or redesign of assembly processes for maximum value realisation at minimal cost.

RESUME La sélection d'un procédé de fabrication comprend deux étapes. La première consiste a évaluer les différents procédés afin de vérifier qu'ils permettent, d'un point de vue technique, d'obtenir un produit en accord avec les spécifications. La seconde étape consiste a classer les procédés retenus selon des critères économiques. Cette phase fait intervenir des techniques simples d'estimation de coût. Cependant, on rencontre au cours de cette sélection deux types de problèmes.