Browsing by Author "Peltokorpi, Jaakko"

Filter results by typing the first few letters
Now showing 1 - 20 of 62
  • 3P (Production Preparation Process) lean-tuotannossa
    (2014-04-29) Tenkula, Aleksi
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Adjustment for cognitive interference enhances the predictability of the power learning curve
    (2021-04) Jaber, M. Y.; Peltokorpi, Jaakko; Glock, C. H.; Grosse, E. H.; Pusic, M.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Learning curves, which express performance as a function of the cumulative number of repetitions when performing a given task, have a long tradition of supporting managerial decisions in production and operations management. Performance generally improves as the number of repetitions of a given task increases, with the latter being a primary proxy to reflect experience. A learning curve is usually a maximum-likelihood trend-line that best fits raw data points by splitting them to above and below it. However, its curvature does not always accurately capture the scatter around it, which reduces its accuracy. This paper advocates for an improved learning curve, one that accounts for the variable degree of cognitive interference that occurs while learning when moving from one repetition to the next. To capture this phenomenon, this paper accounts for memory traces of repetitions to measure the residual (interference-adjusted), not the nominal (maximum), cumulative experience. Two alternative learning curve models were developed. The first model aggregates the residual cumulative experience for each repetition while fitting the data. The second model is an approximate expression and, as a continuous model, much easier to implement. The models were tested against data from different learning environments (such as production and assembly), alongside a more traditional power (log-linear) form of the learning curve and its plateau version. The results show that the interference-adjusted models fit the data very well, such that they can serve as valuable tools in production and operations management.
  • Agile manufacturing
    (2014-04-28) Ylänen, Joonas
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Analysis of the effects of group size and learning on manual assembly performance
    (2019) Peltokorpi, Jaakko; Niemi, Esko
     A4 Artikkeli konferenssijulkaisussa
    On the basis of a prior experimental study, this paper performs a further analysis of the effects of group size (one to four workers) and learning (up to four repetitions per group) on manual assembly performance. More specifically, this paper aims to investigate the factors and the extent to which they affect reduced assembly time as a function of repetitions and reduced productivity per worker as a function of increasing group size. The ultimate aim of this study is to increase the understanding of how working in groups of different sizes develops through repetitions when workers are free to organize their work themselves. The results from the video-based analysis show that with a new, relatively complex product, instructions play a crucial role in learning and the losses caused by the inexperience of workers decrease rapidly through repetitions. Unequal temporal workloads between workers in larger groups increase idleness and cause a significant loss of productivity. The findings presented in this paper give insights for industrial managers when assigning workers to products in variable assembly production of highly customized products.
  • Automaatiomahdollisuudet massaräätälöidyn tuotteen loppukokoonpanossa
    (2011) Ekelund, Jonas
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Comparing Worker Coordination Policies in Parallel Station Systems with Different Worker Skill Level Distributions
    (2015) Peltokorpi, Jaakko; Niemi, Esko; Tokola, Henri
     A4 Artikkeli konferenssijulkaisussa
  • Comparison of Balancing Policies in Multi-Item Assembly
    (2012) Peltokorpi, Jaakko; Tokola, Henri; Niemi, Esko
     A4 Artikkeli konferenssijulkaisussa
  • Lastuamisvoimien mittauslaitteiston kehittäminen
    (2010) Peltokorpi, Jaakko
     School of Engineering | Master's thesis
    The aim of this master's thesis was to evaluate the suitability of cutting force measurement hardware for teaching and research use by the Production Engineering Laboratory at Aalto University. Obtaining measurement results for this hardware enabled students to gain an understanding of the cause - effect relations involved in a cutting process, i.e., drilling in this case, while also becoming acquainted with the hardware and experimental set-up. The thesis presents the subsystems of the hardware from a technical and procurement perspective. The text deals with the generation of electric charge in the piezoelectric sensor as a result of external forces. This work also explains the signal flow and processing in the hardware, as well as describes how the cutting force signals would be displayed in the measurement program. This study found that the purchasing process for new hardware involved first reviewing the alternatives offered by suppliers as well as the hardware used elsewhere in the university research environment. It was noted that the prices of hardware vary depending on the technical specifications. Based on the hardware specifications, two new cutting force measurement devices were purchased for the laboratory. In order to ensure the usability of the new hardware, a users' manual was written for the hardware. A further aim of this thesis was to determine the performance and compatibility of the devices. The hardware was installed as a subsystem of the measurement system. Calibrating the devices permitted equations to be created between the voltage readings of the hardware and mechanical units. Tests carried out with a radial drilling machine were used to estimate the effects of the drilling parameters on the cutting forces. The measurements were also tested to ensure their repeatability. The signal generated in the drilling tests was found to include considerable variation due to the discontinuous character of the cutting process. An error analysis performed on the measurement system indicated that the error sources can be modelled mathematically and minimized through test design.
  • DFA - Design for Assembly
    (2011) Renvall, Jani
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • DFMA
    (2017-04-28) Koivu, Kasper
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Differences between worker pairs in manual assembly: a case study
    (2018-07-25) Peltokorpi, Jaakko; Niemi, Esko
     A4 Artikkeli konferenssijulkaisussa
    Working in pairs is common and often necessary to carry out industrial manual assembly tasks. This paper studies differences in performance that can occur between pairs of workers. Within a case assembly product, activity analysis for each worker in a total of ten pairs and up to four repetitions (learning) is conducted on the basis of video evidence. The results show significant variation in assembly time between the pairs. Repetitions reduce the relative variation, while the ranking of the pairs remains mostly unchanged. In general, the time used for installing parts explain most of the variation between the pairs.
  • Economic order/production quantity (EOQ/EPQ) models with product recovery : A review of mathematical modeling (1967–2022)
    (2024-05) Jaber, Mohamad Y.; Peltokorpi, Jaakko
     A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    This paper reviews the mathematics of the economic order/production quantity (EOQ/EPQ)-based models in the reverse logistics (RL) literature. It starts with the seminal work of Schrady (A deterministic inventory model for reparable items. Naval Research Logistics Quarterly, 14(3), 1967, 391–398) up until December 31, 2022. It provides researchers with a road map of how the mathematics has evolved since the work of Schrady and saves them time and effort by avoiding the need to review the entire RL literature. As it unifies the notations for all reviewed models, it also provides some research pointers for promising topics to be pursued.
  • Effective relationships of factors in a manual assembly line environment
    (2014) Peltokorpi, Jaakko; Lohtander, Mika; Laakso, Sampsa
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
  • Effects of group size and learning on manual assembly performance: an experimental study
    (2019-01-17) Peltokorpi, Jaakko; Niemi, Esko
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In variable manual assembly production of highly customised products, effective allocation of workers to products is required. To support decision making here, industrial managers should be aware of the performance effects of the number of workers and learning within individual products. Evidence on such fundamental effects requires laboratory studies with products similar to those in real assembly industries. Because of the lack of such studies, this paper studies experimentally the effects of group size (one to four workers) and learning (up to four repetitions per group) on the performance of product assembly. The product, built for the purpose of the present study, consists of representative elements from real products in the mechanical engineering industry. A total of 68 undergraduate students participated in the experiments. The results from the experiments are in line with the hypotheses that the mean assembly time decreases at a decelerating rate as a function of both group size and repetitions, and that productivity per worker decreases as a function of group size. The results are explained in more detail through the experiences of the participants. Managerial implications and aspects for future research are also discussed.
  • The effects of learning in production and group size on the lot-sizing problem
    (2020-05-01) Jaber, M. Y.; Peltokorpi, Jaakko
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    One of the lot-sizing problem extensions that received noticeable attention in the literature is the one that investigated the effects of learning in production. The studies along this line of research assumed learning to improve with the number of repetitions following a power form. There is evidence also that the group size, i.e., the number of workers learning in a group affects performance (time per unit). This note revisits the problem and modifies it by incorporating the group size, along with cumulative production, as a proxy for measuring performance. Numerical examples are provided to illustrate the behavior of the modified model. The results of the two models are also compared to draw some meaningful insights and conclusions. Although the results favor using a simple univariate learning curve, considering group size when modeling lot-sizing problems can significantly affect the unit production cost.
  • EU carbon border adjustment mechanism and production location
    (2023-06-12) Ahmed, Tanzin
     Insinööritieteiden korkeakoulu | Master's thesis
    As part of the European green deal European Union (EU) has proposed Carbon Border adjustment mechanism (CBAM). Its purpose is to compensate for emission taxes within EU to avoid carbon leakage to non-taxing countries. Among all possible options, border tariffs for products imported from non-taxing countries to EU countries are set equal to emission taxes. This appears fair, but the economies of scale effects are not considered. This paper examines the effects of realistic cost functions on the resulting production allocation. We experiment with single product, four-country, equilibrium model where three countries are potential producers, and all four countries are markets. In our model, demand and supply (producer cost) function parameter values and transportation cost are equal, and total producer profit is maximized. Our cost function is a power curve entailing economies of scale. We primarily examine the effects of producer tax and border tariff levels on production and profit. We also consider the effect of consumer tax during experimentation. The results show that the amount of economies of scale strongly affects the profit-maximizing production allocation. It appears that under economies of scale effects the EU border tariff levels are far too low to create a fair marketplace. Low border tariffs under economies of scale lead to centralized production by a non-taxed producer using polluting technology, whereas sufficiently large border tariffs decentralize production to different countries, and the amount of polluting production is reduced significantly. The effects of short-run capacity constraints and market size on the production allocation are also examined.
  • Greenhouse Gas Accounting for Manufacturers
    (2024) Peltokorpi, Jaakko; Vatanen, Saija; Glock, Christoph H.
     A4 Artikkeli konferenssijulkaisussa
    Legislation and stakeholders increasingly urge European original equipment manufacturers (OEM) to disclose and reduce greenhouse gas (GHG) emissions of their operations and supply chains. However, a sector-specific reporting standard is not yet available for manufacturing industry. This study takes a stepwise approach to identify the GHG emission sources and intensities in further detail. The primary data of a case company, a Finnish gear manufacturer, was supplemented with secondary data from the literature. Using the proposed approach, an OEM share of the total emissions was estimated at 15%. Evaluating emissions of specific suppliers is more difficult as there is little access to the suppliers’ data. The study gives insights for the manufacturers on how to get started with GHG emissions accounting in the factories and supply chains and to show their responsible actions in the climate change mitigation.
  • A group learning curve model with motor, cognitive and waste elements
    (2020-08) Peltokorpi, Jaakko; Jaber, M. Y.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Nowadays, workers, individually or in groups, are continually learning new tasks. The speed at which they learn directly contributes to the success of their firms in competitive markets. Learning curve research has been either on the individual or organizational level. A few papers have developed learning curve models for a group of workers, even fewer that used empirical data for that purpose. However, none of the existing models comprises measurable elements from real industrial tasks. This paper aims to fill this gap in the literature by proposing a bivariate group learning curve model, an aggregation of three learning curves where the number of workers in a group and the number of repetitions are the independent variables. The dependent variable is the unit assembly time. The three learning curves represent motor, cognitive, and waste per unit assembled. The aggregated learning curve was fitted to experimental data consisting of different group sizes (1 to 4 students/workers), each performing four repetitions, and later compared to two log-linear learning curves, with and without plateauing. The results showed that the aggregated model represented the data the best and that segmenting waste into sub-elements (job familiarization, errors, and group coordination) improved the performance of the model. The parameter values affected by group sizes and repetitions for each task element provided insights that managers could use to improve the performance of their workforce.
  • Henry Fordin ideat menetelmien ja tuotannon kehittämiseen
    (2014-04-04) Loiskekoski, Tuomas
     Insinööritieteiden korkeakoulu | Bachelor's thesis
  • Interference-adjusted power learning curve model with forgetting
    (2022-03) Peltokorpi, Jaakko; Jaber, Mohamad Y.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Researchers in production and operations management have studied the effect of worker learning and forgetting on system performance for decades. It remains an active research topic. Those studies have assumed that production interruptions (or production breaks) cause forgetting, which deteriorates performance. Research on human working memory provides enough evidence that continuous forgetting, precisely cognitive interference, results from overloading the memory with information. Despite the evidence, few studies have incorporated it into learning curve models. This paper presents an enhanced version of the power learning curve that accounts for a variable degree of interference when moving from a production cycle to the next. It adopts the concept of memory trace decay to measure the residual (interference-adjusted), not the nominal (maximum) cumulative experience. We test the developed model against learning data from manual assembly and inspection tasks, with varying numbers of repetitions and breaks. We also test three alternative power-form learning and forgetting curve models from the literature. The results show that the interference-adjusted model fits the data very well. The proposed learning and forgetting model and its individualized cumulative metrics can help identify struggling workers early and release precocious learners earlier than expected. As such, the model gives insights for managers on the occurrence of interference to enable individual learning support.