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Editorial: Novel applications of learning curves in production planning and logistics
... Resaltaron que en la mayoría de las publicaciones se analizaron partes de la CS como la gestión de inventarios, producción y aprovisionamiento. Ello fue confirmado en otra publicación más reciente [16]. ...
Learning curves have beenfrequently applied inproduction/operations management and various logistics processes in many manufacturing and service organizations. However, studies on their integral use in the supply chain are recent. This paper contributes to filling this knowledge gap by measuring the impact of learning on lead time in logistics management systems. The empirical study was used as a methodological tool to demonstrate this. The logarithmic-linear models, with their terminology and calculation equations, were applied to three case studies representatives of the logistics systems proposed by the Supply Chain Operations Reference (SCOR) model: make-to-order, make-to-stock, and engineer-to-order. As a result, the first two were adjusted to the Stanford model and the third to De Jong's model. Their learning curve, mathematical equations, and a sensitivity analysis were determined. This approach demonstrated its relevance and difference compared to previous publications, which mainly analyze links or parts of the CS.
... In this direction, future research in this field could further investigate the differences between the two systems by considering also different assembly times, with FW being faster since generally more focused on fewer assembly tasks. This could be modeled through the consideration of the learning curves and of the learning and forgetting principles [69]. Moreover, it would be interesting to integrate also the availability of the operators according to their different energy expenditures [70] as well as to include ergonomics evaluations both in the line balancing and in the comparison model, for example using motion analysis systems [71]. ...
Recently, the need for assembly systems of better following the market demand has led to the spread of alternative solutions with respect to the traditional fixed worker strategy. Walking worker systems, where operators move across the line to produce the finished product, are gaining always more interest. In this paper, a comparison between the fixed worker (FW) and the walking worker (WW) assembly strategies is provided, by taking into account their differences both in terms of performed activities during assembly and of material exposure at the assembly workstations. The evaluation is carried out through the proposal of a mathematical model, to understand the impact on assembly time and on operators’ energy expenditure. The behavior of the model is investigated both with a parametric analysis and a real case study. The results show that the FW is the best one when the system is working at its maximum throughput, while the WW strategy turns out to be preferable for the lower values of market demand, both from a time and an energy expenditure perspective.
... Therefore, learning curve has little studies on processes of the supply chain and specifically in the lead-time. In [15] it different learning curve models be carried out in light of new technologies and emerging industries, giving the example in the fourth industrial revolution. In [16] it is suggested that with the fourth industrial revolution, the separation between materials and information will disappear, because information will be an intrinsic part of the products. ...
This paper is a continuation of the study of learning in the supply chain, specifically in its lead-time taking into account the logistics management approaches offered by the SCOR model: make to order, make to stock and engineer to order. To this end, empirical data was collected from three companies, which respond to these approaches. In these companies, the effect of learning was analyzed and it was modelled to improve the accuracy of forecasts and production planning. The selected sample was adjusted to the learning curves recognized in the literature with the help of MATLAB software. It was estimated and it was investigated which curve approximates the actual behavior of the points. For this purpose, the values of the sum of squares, the determination coefficient and the errors of the equation generated by the deviation of the mean square root were analyzed. The analysis confirm that learning effects occur in the lead-time. The smallest error is found in the representations of the learning curves are made by log-linear models. The best log-linear models to represent the curve in the selected companies are the Stanford-B and Jong model. The learning rates varied between 61% and 93%. As a result, it can be assumed that the length of the lead-time decreases as the number of orders processed increases. This increase encourages the creation of work styles and the consultation of stable suppliers and customers.
... Therefore, learning curve has little studies on processes of the supply chain and specifically in the lead-time. In [24] is suggested the realization of different models of learning curve in the light of new technologies and emerging industries, giving the example in the fourth industrial revolution. Considering that lead-time is an activity that includes provisioning, production and commercialization as a result of repetitive investigations, it can be assumed that lead time reduces with learning over time. ...
Knowledge is a strategically valuable resource in business management. Activity repetition is known to help learning and improve the ability to do certain job with better quality, lower time consumption and consequently, at lower production cost. This learning ability allowed the development of a prediction technique known as the "learning curve". This technique has been applied in different production processes for planning and to consider the impact on productivity, quality of work. This research is based on the possibility to extrapolate the concept of the learning curve to the supply chain and more specifically to its lead-time. Lead-time is understood as the period of time between the request of an order by the customer and the time when it is satisfied. Therefore, this paper describes a methodology to extrapolate the concept of the learning curve to the supply chain. Firstly, the logistics network is described. Then a pilot sample of orders is determined, for which the lead-time is estimated according to the logistic management approaches (make to order and make to stock) in two Cuban companies. Subsequently, the sample size is verified by statistical regression. Conversion factors for the "learning curve" are determined, and the expected lead-time is adjusted in future orders. Consequently, the essential objective of this paper is to realize the learning curve to measure the lead-time of the orders, for the MTO and MTS logistics management approaches.
Although technological innovation has enabled a new wave of warehouse automation, human involvement remains necessary for most order picking operations in grocery retailing. This has spawned new forms of interaction between humans, machines, and intelligent software, that is, cyber‐sociotechnical systems. However, scant empirical field‐based research has been conducted on how this transition impacts human learning and the perception of work characteristics. Considering that humans are an essential element of these systems, it is fundamentally important to quantify the impact of these transformations when aspiring to improve performance, quality, and workers' well‐being as primary outcomes of order picking systems. This study utilized a mixed‐methods design, developing and applying parametric and non‐parametric approaches to operationalize learning progress, and semi‐structured interviews were conducted to examine perceived work characteristics. The findings indicate that the perception–cognition–motor–action cycle for learning by doing tasks can be accelerated through real‐time feedback provided by the order picking system. Furthermore, perceived work autonomy and feedback from the picking system are constant or perceived as greater when human decisions are accepted. The results have valuable implications for logistics practitioners, emphasizing the need for human‐centered work system design.
Market equilibrium models are often specified and solved as mixed complementarity problems (MCPs). These formulations combine the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problems faced by multiple strategic players with market-clearing conditions, such that the solution to this system provides the Nash equilibrium prices and quantities. MCPs are widely applied to energy markets including those for electricity, oil, and natural gas. While researchers have made substantial progress on expanding the model features included in MCPs and on solving these problems, a limitation of existing MCPs is that they treat costs as exogenous input parameters. Therefore, MCPs have not been able to capture learning-by-doing (LBD), the empirically observed phenomenon whereby production costs tend to decline as a function of cumulative production experience. In this paper, we demonstrate the incorporation of LBD into a mixed complementarity equilibrium model. We consider two closely related, but nevertheless distinct, LBD formulations: one with discrete changes in cost from period to period, and another where cost declines continuously. Through theoretical analysis and numerical exploration, we establish the conditions under which these LBD formulations lead to convex optimization problems. Confirming convexity is important because it guarantees that their KKT conditions are sufficient for optimality. Then, we demonstrate the practical application of a mixed complementarity equilibrium model with LBD using the North American natural gas market as an example. When LBD is incorporated into the cost of liquefaction, North America exports more liquefied natural gas, which raises prices and reduces domestic consumption.
Evidences indicate that around 50% of productivity increase originates from organizational learning and learning by doing. The speed of implementing productivity knowledge increase is a key component of competitiveness and for the case when the implementation is instantaneous, an optimal control model is developed. The analysis of this model indicates that, under certain circumstances, the optimal production level does not change over time, and utilizing this property, a closed form solution is provided to determine the optimal production level. When the implementation process happens step by step, a discrete time model is offered, and we found that using the same assumptions, the production dynamics will be decreasing. To determine the optimal production volumes, a backward dynamic programming procedure is presented.
Market equilibrium models are often specified and solved as mixed complementarity problems (MCPs). These formulations combine the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problems faced by multiple strategic players with market-clearing conditions, such that the solution to this system provides the Nash equilibrium prices and quantities. MCPs are widely applied to energy markets including those for electricity, oil, and natural gas. While researchers have made substantial progress on expanding the model features included in MCPs and on solving these problems, a limitation of existing MCPs is that they treat costs as exogenous input parameters. Therefore, MCPs have not been able to capture learning-by-doing (LBD), the empirically observed phenomenon whereby production costs tend to decline as a function of cumulative production experience. In this paper, we demonstrate the incorporation of LBD into a mixed complementarity equilibrium model. We consider two closely related, but nevertheless distinct, LBD formulations: one with discrete changes in cost from period to period, and another where cost declines continuously. Through theoretical analysis and numerical exploration, we establish the conditions under which these LBD formulations lead to convex optimization problems. Confirming convexity is important because it guarantees that their KKT conditions are sufficient for optimality. Then, we demonstrate the practical application of a mixed complementarity equilibrium model with LBD using the North American natural gas market as an example. When LBD is incorporated into the cost of liquefaction, North America exports more liquefied natural gas, which raises prices and reduces domestic consumption.
This paper considers a manufacturer–retailer closed-loop supply chain where the manufacturer’s production process is subject to both learning and forgetting, and inventories of the manufacturer and the retailer are managed by consignment stock policy. The demand of the finished product is linearly dependent on the retail price. The return rate of used product is random and returned used items are inspected to select those which qualify for remanufacturing. The proposed model is demonstrated with a numerical example. A sensitivity analysis is performed to identify the parameters which have significant impacts on the optimal decisions of the supply chain system. Some managerial insights of the model leading to improved strategies beneficial for firms and environment are also discussed.
The occurrence and characteristics of human learning and forgetting are extensively researched in many fields. Thus, the literature on learning curves is abundant. Within production and operations management, learning curves can describe the performance improvement of workers due to repetitions or experience, which makes them a useful tool for managerial decision making. Human learning is very relevant to manufacturing firms that are labor-intensive, especially where labor is costly. Also, technology and organization learning frequently occur in many organizations, even when manufacturing is not labor-intensive. It is not surprising, therefore, that, despite the plethora of prior work in learning curves, research in this important area continues to appear in the production and operations management literature. A comprehensive, systematic review of the literature, however, has never been conducted.
This article aims at giving a general overview of learning curves in production and operations management. It maps this specific research area, synthesizes its research findings, highlights its key application areas and explores future research directions. This work achieves this goal by presenting the results of a systematic literature review on the applications of learning curves in production and operations management. First, a framework that includes typical learning curve models is developed. The fundamental characteristics of learning curves and their applications in production and operations management are then identified. This framework is used to categorize the literature. This study also presents a discussion of the most important and informative articles in each of the major categories, as well as highlighting future research opportunities.
For almost a century, researchers and practitioners have studied learning curves in production economics. Learning, in this context, refers to performance improvements of individuals, groups or organizations over time as a result of accumulated experience. Various learning curves, which model this phenomenon, have been developed and applied in the area of production economics in the past. When developing planning models in production economics, the question arises which learning curve should be used to best describe the learning process. In the past, the focus of the literature has been on empirical studies that investigated learning processes in laboratory settings or in practice, but no effort has been undertaken so far to compare existing learning curves on a large set of empirical data to assess which learning curve should be used for which application. This study systematically collected empirical data on learning curves, which resulted in a large database of empirical data on learning. First, the data contained in the database is categorized with the help of meta-tags along different characteristics of the studies the data was taken from. Second, a selection of well-known learning curves is fitted to the empirical datasets and analyzed with regard to goodness of fit and data characteristics. We identify a set of data/task characteristics that are important for selecting an appropriate learning curve. The results of the paper may be used in production economics to assist researchers to select the right learning curve for their modeling efforts.
In this paper we examine the “global war for talent,” the factors that impact it, and organizations' responses to it. Using a comprehensive search of more than 400 contemporary academic and business press articles, the paper reviews relevant research and reassesses the “talent war.” We posit that the dominant approaches to the “talent war” based on a scarcity state of mind and action, often characterized by a tactical and exclusive top talent or “star” focus, are being challenged by the emergence of a more evolutionary paradigm. This new paradigm adopts more strategic, innovative, cooperative and generative approaches which we describe as creative ‘talent solutions.’ The paper also highlights implications for future research, teaching and development in the field.
The use of the learning curve has been receiving increasing attention in recent years. Much of this increase has been due to learning curve applications other than in the traditional learning curve areas. A comprehensive survey of developments in the learning curve area has never been published. The closest thing to a survey was by Asher in 1956. His study focused exclusively on military applications during and immediately after World War II. This paper summarizes the learning curve literature from World War II to the present, emphasizing developments since the study by Asher. Particular emphasis is given to identifying the new directions into which the learning curve has made recent inroads and identifying fruitful areas for future research.