Log-linear learning model for predicting a steady-state manual assembly time

This paper presents the method for estimating the parameters of a two parameter learning curve (LC). Different values of parameters and different sample sizes are used for this estimation. Based on the experimental data an adequate mathematically grounded LC model is proposed for a manual assembly process of automotive wiring harness. The model enables us to determine the LC parameters αε (slope coefficient) and the learning rate stabilization point xc, i.e. to completely restore LC and predict the production process. The propositions that ground the model application correctness are proved. The model adequacy is estimated, based on concrete production process monitoring data. The criterion that determines production process without stabilized learning rate is proposed

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

A novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: case study in beverage industry

The present study proposes a novel decision model to aggregate production planning (APP) decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of current study, are discussed

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

Measuring the energy innovation process : an indicator framework and a case study of wind energy in China

Whilst a well-established literature on metrics to assess innovation performance exists, relatively little work has linked it to the energy technology innovation process. This paper systematically brings together indicator sets and derives an indicator framework for measuring energy innovation, offering an important step forward in the quantitative evaluation of energy innovation performance. It incorporates input, output and outcome metrics that relate to different stages along the energy technology innovation chain, namely research, development, demonstration, market formation and diffusion. To test its efficacy, the indicator framework is applied to the case of wind energy in China, drawing comparisons against global market leaders such as Denmark, Germany and the USA. The paper finds that the framework enables a more rigorous comparative analysis of energy innovation between countries than currently offered by either the application of piecemeal indicators and complements contextually rich qualitative case studies. The empirical analysis shows that China has begun to lead across a range of innovation inputs (e.g. R&D expenditure) and outputs (e.g. publications) but lags considerably behind international competitors against other output and outcome indicators such as patents, revenue and exports

Quality Control Along Supply Chain

Product quality is the core competitiveness of a brand, prompting brand-owner continuously to pursue. Learning curve is the tool to improve product quality by variance reduction. At the same time, knowledge depreciation with its negative effects attracts attention. Thus, a comprehensive learning curve is introduced in this dissertation. This research focus to explore the quality improvement along the supply chain. There are three contributions in this dissertation: 1) it provides the supply chain’s optimal distribution of quality improvement to manufacture and its suppliers; 2) it shows the benefit from coordinated quality improvement among the supply chain; 3) it illustrates the benefit from increased demand and decreased cost from quality improvement. Three models are presented consecutively in this dissertation. The first model assists the supply chain to coordinate all suppliers for quality investment. Based on the traditional learning curve, the comprehensive learning model is introduced in order to better understand the knowledge accumulation effect. Autonomous learning, induced learning and their respective knowledge depreciation effects are considered in this model. The product quality is measured from several aspects, and each aspect linearly depends on the component quality. Therefore, suppliers’ quality improvement contribute to the end product quality. The second model further considers each outsourced components have interaction effects. To better understand knowledge forgetting effect, it adopt Weibull distribution to simulate producing disruption. What’s more, it considers the optimal quality investment for the whole producing system and a suboptimal quality investment when there is no coordination in the system (Dyadic Supply Chain). Without coordination, every supplier is trying to save her own quality cost, but the total quality cost is higher than the coordinated system. Thus, incentives are necessary to these suppliers to cooperate in quality improvement. In addition, the second model provides existence proof of the optimal solution. Since the previous models using variance reduction to save cost, it increases demand as well. The third model starts to consider demand increasing and quality saving simultaneously. Similar to model 2, the third model compares the optimal quality efforts under the coordinated system and the sub-optimal quality efforts under un-coordinated system. Generally, the coordinated system is more efficient. With coordination, 1) marginality is eliminated; 2) cost is lower; 3) demand is higher from higher quality. Since supplier invest more with cooperation, incentives is required to all suppliers

THE DEVELOPMENT OF A PREDICTIVE PROBABILITY MODEL FOR EFFECTIVE CONTINUOUS LEARNING AND IMPROVEMENT

It is important for organizations to understand the factors responsible for establishing sustainable continuous improvement (CI) capabilities. This study uses learning curves as the basis to examine learning obtained by team members doing work with and without the application of fundamental aspects of the Toyota Production System. The results are used to develop an effective model to guide organizational activities towards achieving the ability to continuous improve in a sustainable fashion. This research examines the effect of standardization and waste elimination activities supported by systematic problem solving on team member learning at the work interface and system performance. The results indicate the application of Standard Work principles and elimination of formally defined waste using the systematic 8-step problem solving process positively impacts team member learning and performance, providing the foundation for continuous improvement Compared to their untreated counterparts, treated teams exhibited increased, more uniformly distributed, and more sustained learning rates as well as improved productivity as defined by decreased total throughput time and wait time. This was accompanied by reduced defect rates and a significant decrease in mental and physical team member burden. A major outcome of this research has been the creation of a predictive probability model to guide sustainable CI development using a simplified assessment tool aimed at identifying essential organizational states required to support sustainable CI development

Feature Papers of Forecasting

Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented

Integrierte Leistungsprogrammzuordnung und Kapazitätsplanung im Krankenhausverbund

Ruwwe-Glösenkamp K. Integrierte Leistungsprogrammzuordnung und Kapazitätsplanung im Krankenhausverbund. Bielefeld: Universitätsbibliothek Bielefeld; 2014