Modelling production cost with the effects of learning and forgetting

Defining a dynamic model for calculating production cost is a challenging goal that requires a good fitting ability with real data over time. A novel cost curve is proposed here with the aim of incorporating both the learning and the forgetting phenomenon during both the production phases and the reworking operations. A single-product cost model is thus obtained, and a procedure for fitting the curve with real data is also introduced. Finally, this proposal is validated on a benchmark dataset in terms of mean square error

An almost learning curve model for manual assembly performance improvement

In this paper, an almost learning curve (ALC) model is presented. This provides a more accurate approximation of the production data than the traditional log-linear learning curve model. The proposed ALC model is based on the solution of differential equations and still has all the necessary log-linear learning curve function properties. The ALC model was tested on the wiring harness manufacturer production data. Findings suggest that the ALC model approximates data accurately and is superior to the classical learning curve (CLC) for various manufacturing situations. Moreover, the use of the ALC showed an additional insight into the analysis of learning and skill development

Evaluating manufacturing flexibility driven by learning

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Page 126 blank. Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 110-115).A defining feature of modern industry is operating in a context of nearly continuous technological change. Nevertheless, industrial decision-makers must select technologies and implement production strategies even in the face of known-to-be-incomplete information and environmental uncertainties. Further complicating the picture, the performance, including the economic performance, associated with novel technology options is likely to change over time. To address this problem, two approaches are possible: improving the quality of currently available information, and implementing flexible production strategies. The present work characterizes how the former approach impacts the valuation of the latter. First, a dynamic approach integrating learning curves and process-based cost modeling is used to examine learning in manufacturing, thus allowing decision-makers to incorporate information about expected technology evolution into their economic evaluations of technology. The approach is applied to an automotive assembly process, and quantifies the cost impacts of learning improvements in manufacturing time, downtime, and defect rates. Analysis can be used to focus learning activities on primary learning operational drivers, and to forecast cost improvements for a novel process. Flexibility strategies are often focused on capital-intensive processes, while labor-intensive processes are thought to be inherently flexible. The existence of learning effects, however, implies that labor flexibility has costs and, potentially, benefits in the context of uncertainty. A simple automotive assembly case is used here to illustrate the impact of manufacturing learning on labor flexibility and its economic value. A framework using cash-flow and decision tree models is introduced to quantify the costs and benefits of acquiring worker flexibility, and improve information available for strategic decision-making in labor-intensive systems. The front-end characterization of the technical drivers of learning provides insight into how the value of flexibility can be impacted at the operational level, enabling managers to prioritize improvements and minimize the costs of flexibility.by Marie-Claude Nadeau.S.M.in Technology and Polic

Modeling learning when alternative technologies are learning & resource constrained : cases In semiconductor & advanced automotive manufacturing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 173-179).When making technology choice decisions, firms must consider technology costs over time. In many industries, technology costs have been shown to decrease over time due to (a) improvements in production efficiency and the accumulation of worker experience accompanying production, known as "learning-by-doing," and (b) firm investments in research and development, worker training and other process improvement activities, known as "learning-by-investing." Rapid technological progress may mean that new technologies become available while existing technologies still exhibit learning-related cost reductions. In these cases, switching to a new technology means giving up these ongoing benefits while also incurring new technology introduction costs. Additionally, In some industries, high switching costs, regulatory compliance and/or the risks associated with new technologies may require firms to continue allocating production volume and investments to an existing technology whether or not a new technology is introduced. In these cases, firms must decide how to allocate finite production volume and investment resources between technologies. Learning is driven by resource allocation. Therefore, sharing finite resources among multiple learning technologies may reduce the learning-related benefits associated with each. This may lead firms to underestimate technology costs, leading to sub-optimal technology choice and resource allocation decisions. A methodology is presented which couples technology costs over time via capacity and investment resource allocation to characterize the impacts of (1) learning in an incumbent technology, and (2) resource allocation constraints, on technology choice and resource allocation decisions. Case studies in the semiconductor and automotive industries are examined using this method in combination with process based cost modeling. We find that (1) when the existing technology is still learning, diverting resources to a new technology results in an opportunity cost in both technologies which diminishes the benefits of switching technologies; (2) this effect can persist over a wide range of learning rates and technology costs; (3) capacity allocation constraints can significantly change the conditions under which the firm should choose a new technology, and (4) cumulative production volume and investment based learning differentially impact technology costs, leading to different cost-minimizing resource allocation decisions.by Thomas Rand-Nash.Ph.D

Exergy Cost Assessment in Global Mining

El desarrollo económico, social y tecnológico de la sociedad actual está fuertemente ligado a la extracción de recursos minerales. Una sociedad en constante crecimiento que consume estos recursos rápida e ilimitadamente. El continuo incremento de la demanda mundial de recursos minerales se debe en gran medida al crecimiento económico de China y otros países asiáticos, que demandan una gran cantidad de materias primas en los sectores de la construcción, la infraestructura y la manufactura. El agotamiento de los recursos naturales no renovables es la consecuencia de este progreso y constituye el mayor reto al que se enfrentará la industria minera. De ahí que la disponibilidad futura de los recursos minerales está adquiriendo importancia en los planes estratégicos de los gobiernos. Una vez que los minerales han sido extraídos, una serie de procesos que consumen grandes cantidades de energía son necesarios para producir materias primas utilizables. El requerimiento energético de la extracción de minerales y en su posterior procesamiento depende principalmente de la calidad y composición del mineral. Considerando la disminución en la ley mineral a nivel global, los consumos energéticos y los impactos ambientales se han venido incrementando continuamente. Adicionalmente, es necesario procesar más material para obtener una cantidad equivalente de metal. En este sentido, uno de los factores críticos que la industria minera tendrá que afrontar será la disponibilidad de energía para la extracción y el procesamiento de los minerales. Por lo anterior, es de suma importancia analizar y entender los procesos de la industria minera para determinar las posibles mejoras cuando se tiene en cuenta el factor de escasez de las materias primas. La primera actividad puede realizarse a través de un enfoque termoeconómico. La Termoeconomía ha sido utilizada tradicionalmente para la optimización de plantas termoeléctricas haciendo uso de la exergía como unidad de medida. En esta tesis doctoral, el análisis termoeconómico es adaptado y modificado, teniendo en cuenta la complejidad de los procesos mineros y metalúrgicos, en los cuales se presentan flujos de materias primas y energía. Cuando se considera el factor de escasez de los recursos minerales en este tipo de análisis, es necesario incluir una variable adicional. Esto se lleva a cabo a través del enfoque Exergoecológico propuesto por Valero et al. (2003). Conceptualmente, el metódo Exergoecológico permite realizar una evaluación de los recursos minerales utilizando los costos exergéticos de reposición, los cuales representan la exergía requerida para restituir los minerales que han sido totalmente dispersados en la corteza terrestre una vez que su vida útil ha terminado, al estado inicial de composición y concentración en el que se encuentran en las minas. De ahí que esta tesis tiene como objetivo principal adaptar y aplicar metodologías termoeconómicas que permitan realizar un Análisis de Ciclo de Vida absoluto de los recursos minerales: un análisis convencional de la “cuna” a la puerta de entrada (producción de las materias primas refinadas) y un análisis adicional de la “tumba” a la “cuna”, en el cual se cuantifique el factor de escasez de los minerales. El análisis exergético de los recursos minerales y los procesos metalúrgicos de la industria de la minería realizados en esta tesis, requirió el establecimiento de una serie de objetivos. El primero de ellos fue realizar un estudio detallado de las tecnologías y los consumos energéticos asociados a la industria minera y metalúrgica. Un segundo objetivo fue analizar la influencia del aprendizaje tecnológico y la disminución de la ley mineral en la disponibilidad de los recursos minerales, con el objetivo de conocer si la adquisición de experiencia a través del tiempo, ha sido capaz de evitar el aumento en la demanda de energía que presentan los procesos extractivos y de metalurgia. Los resultados obtenidos de las dos actividades anteriores, permitieron una importante mejora del método Exergoecológico: los costos exergéticos de reposición que tradicionalmente habían sido evaluados de manera estática, pudieron ser actualizados considerando la tendencia del decremento de la ley mineral. Una mejora adicional presentada en esta tesis fue resolver el problema de asignación de costos entre productos, subproductos y residuos que comúnmente aparecen en la industria minera y metalúrgica. Considerando los nuevos costos exergéticos de reposición obtenidos, se propuso un nuevo procedimiento de asignación de costos que será utilizado en el análisis termoeconómico aplicado a los procesos mineros y metalúrgicos. Otro objetivo de esta tesis, consistió en la integración del análisis termoeconómico realizado a través del Costo Termoecológico desarrollado por el grupo del ITC de la Silesian University of Technology, para combinar las ventajas de ambos enfoques para el análisis de la industria minera. Finalmente, cada objetivo descrito anteriormente fue aplicado a diferentes casos de estudio

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Learning curves for imperfect production processes with reworks and process restoration interruptions

Many production processes are not defect free, and reworks are unavoidable. This makes the assumption of the Wright's learning curve [Wright, T., 1936. Factors affecting the cost of airplanes. Journal of Aeronautical Science 3 (2), 122-128] that all units produced are of acceptable quality impractical, suggesting a linkage between quality and learning to be inevitable. The quality learning curve (QLC) developed by Jaber and Guiffrida [Jaber, M.Y., Guiffrida, A.L., 2004. Learning curves for processes generating defects requiring reworks. European Journal of Operational Research 159 (3), 663-672] is a modification of the Wright's learning curve for processes that generate defects that can be reworked. This paper investigates the QLC for the assumption that the production process is interrupted for quality maintenance to bring the process in control again. New learning curves are developed with numerical examples provided and results discussed.

The impact of adopting Additive Manufacturing on responsive supply chain performance

Additive Manufacturing (AM), or 3D printing as it is frequently known, is an umbrella term for a collection of manufacturing technologies that enables products to be manufactured layer-by-layer from three-dimensional digital data. While the costs associated with AM represents a barrier to its wider adoption, its benefits outweigh its costs when considered in some contexts. Few studies have investigated the costs and benefits of this technology from a supply chain perspective, particularly in market environments characterized by demand uncertainty. In this type of scenario, it becomes necessary to adopt higher levels of internal competencies, find the optimal way to manage inventories and flexibly respond to sudden market requirements. This thesis therefore aims to address this gap by examining three key aspects: the learning effects offered by AM, the impact of AM on inventory-related costs and the impact of AM on the critical capability of flexibility. To assess learning in AM, this thesis focuses on the experimental measurement of AM operator time and improvement in operator effectiveness as a result of learning. Learning is thus assessed by measuring the reduction of labour time through operator learning within a series of build repetitions and estimates a progress ratio which captures the learning effect within this series. To assess the impact of AM on inventory-related costs, this thesis develops a conceptual model that matches possible AM scenarios with demand volume level and severity of stockout penalty. It also conducts a case study to obtain insights into the resulting model which has been developed. In this case study, an interprocess comparison is undertaken by simulating a supply chain based on data collected from a plastic products manufacturing company that produces pipe fittings using Injection Moulding (IM) technology. The simulation model produced has been built using the Arena software package for three distinct scenarios: the current configuration with IM only, iii a proposed configuration with AM only, and a proposed configuration that combines AM with IM. To evaluate the impact of AM on flexibility, a conceptual model has also been constructed that maps certain AM characteristics relevant to flexibility to key market disruption scenarios faced by managers. This aspect is also highlighted through the case study which assesses the impact of AM on four distinct supply chain flexibility types: volume, delivery, mix and new product using metrics obtained from the literature. The results obtained on learning in AM suggest that AM exhibits a learning effect for both the novice and the expert operator with progress ratios of 67.73% and 80.42% respectively. Further, results on the impact of AM on inventory-related costs revealed that utilizing IM alone showed the lowest supply chain unit cost (€0.90) compared to utilizing AM as a stand-alone (€2.72) or in a combined approach (€0.94). With regards to AM’s impact on flexibility, the supply chain employing IM showed greater volume and delivery flexibility levels (i.e. 65.68% and 92.8% for IM compared to 58.70% and 75.35% for AM, respectively). However, AM showed higher mix and new product introduction flexibility level, indicated by the lower changeover time and cost of new product introduction to the system (i.e. 0.33 hrs and €0 for AM compared to 4.91 hrs and €30,000 for IM, respectively). It is anticipated that these results can be used to inform practitioners and scholars on various contexts where AM can create value and the appropriate and timely investments needed to unlock that value

The implications of judgemental interventions into an inventory System

Physical inventories constitute a considerable proportion of companies’ investments in today’s competitive environment. The trade-off between customer service levels and inventory investments is addressed in practice by formal quantitative inventory management (stock control) solutions. Given the tremendous number of Stock Keeping Units (SKUs) that contemporary organisations deal with, such solutions need to be fully automated. However, managers very often judgementally adjust the output of statistical software (such as the demand forecasts and/or the replenishment decisions) to reflect qualitative information that they possess. In this research we are concerned with the value being added (or not) when statistical/quantitative output is judgementally adjusted by managers. Our work aims to investigate the effects of incorporating human judgement into such inventory related decisions and it is the first study to do so empirically. First, a set of relevant research questions is developed based on a critical review of the literature. Then, an extended database of approximately 1,800 SKUs from an electronics company is analysed for the purpose of addressing the research questions. In addition to empirical exploratory analysis, a simulation experiment is performed in order to evaluate in a dynamic fashion what are the effects of adjustments on the performance of a stock control system. The results on the simulation experiment reveal that judgementally adjusted replenishment orders may improve inventory performance in terms of reduced inventory investments (costs). However, adjustments do not seem to contribute towards the increase of the cycle service level (CSL) and fill rate. Since there have been no studies addressing similar issues to date, this research should be of considerable value in advancing the current state of knowledge in the area of inventory management. From a practitioner’s perspective, the findings of this research may guide managers in adjusting order-up-to levels for the purpose of achieving better inventory performance. Further, the results may also contribute towards the development of better functionality of inventory support systems (ISS)