The effect of job similarity on forgetting in multi-task production

For many decades, research has been done on the effect of learning and forgetting for manual assembly operations. Due to the evolution towards mass customization, cycle time prediction becomes more and more complex. The frequent change of tasks for an operator results in a rapid alternation between learning and forgetting periods, since the production of one model is causing a forgetting phase for another model. a new mathematical model for learning and forgetting is proposed to predict the future cycle time of an operator depending on the product mix of his actual assembly schedule. A main factor for this model is the job similarity between the task that is being learned and is being forgotten. In our experimental study the impact of job similarity onto the forgetting effect is measured. Two groups of operators were submitted to an equal time schedule, with other tasks to perform. At first, both groups were asked to perform the same main task. In the subsequent phase, they were submitted to different assembly tasks, each with another job similarity towards the main task, before again executing that main task. After a period of inactivity, the main task was assembled again by every subject. Results confirm that a higher job similarity results in a lower forgetting effect for the main task

Learning-by-Doing and Cannibalization Effects at Multi-Vintage Firms: Evidence from the Semiconductor Industry

Previous studies on the measurement of learning-by-doing emphasize the importance of accounting for multi-vintage effects having an impact on firms’ production costs through economies of scope. This study shows that accounting for cannibalization effects on the demand side is equally important for the adequate measurement of learning. Since multi-vintage firms anticipate the demand-side cannibalization effects in their production optimization, a previously omitted incentive to decrease production is captured having an impact on the measurement of learning by doing. We derive an empirical model from a dynamic oligopoly game of learning-by-doing and allow cannibalization effects to enter from the demand side. Using quarterly firm-level data for the dynamic random access memory semiconductor industry, we find support for cannibalization effects entering firms’ pricing relations resulting in higher estimated learning effects.Dynamic Random Access Memory; Dynamics; Economies of Scale; Learning by Doing; Multiproduct Firms; Product Life Cycle; Semiconductors

Remembering as a mental action

Many philosophers consider that memory is just a passive information retention and retrieval capacity. Some information and experiences are encoded, stored, and subsequently retrieved in a passive way, without any control or intervention on the subject’s part. In this paper, we will defend an active account of memory according to which remembering is a mental action and not merely a passive mental event. According to the reconstructive account, memory is an imaginative reconstruction of past experience. A key feature of the reconstructive account is that given the imperfect character of memory outputs, some kind of control is needed. Metacognition is the control of mental processes and dispositions. Drawing from recent work on the normativity of automaticity and automatic control, we distinguish two kinds of metacognitive control: top-down, reflective control, on the one hand, and automatic, intuitive, feeling-based control on the other. Thus, we propose that whenever the mental process of remembering is controlled by means of intuitive or feeling-based metacognitive processes, it is an action

Goldilocks Forgetting in Cross-Situational Learning

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Purpose: The aim of this research is to develop a two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to those corresponding process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve. Design/methodology/approach: The research used system modeling approach by mathematically modeling the system consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers sides. Findings: This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve. Research limitations/implications: This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model we also does not differentiate two types of learning, namely autonomous and induced learning. Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI. Originality/value: This paper combines two models, which in previous research the models are discussed separately. The inclusions of learning and forgetting also gives a new perspective in quality investment decision.Peer Reviewe

On a volume flexible production policy in a family production context

A mathematical model for a volume flexible manufacturing system is developed in a family production context, assuming that there exists a dedicated production facility as well as a separate management unit for each of the items. The possibility of machine breakdowns resulting in idle times of the respective management units is taken into account. The production rates are treated as decision variables. It is also assumed that there is a limitation on the capital available for total production. An optimal production policy is derived with maximization of profit as the criterion of optimality. The results are illustrated with a numerical example. Sensitivity of the optimal solution to changes in the values of some key parameters is also studied

Are Routines Reducible or Mere Cognitive Automatisms? Some contributions from cognitive science to help shed light on change in routines

The aim of this article is to understand permanence and changes inside organizational routines. For this purpose, it seems important to explain how individual and collective memorisation occurs, so as to grasp how knowledge can be converted into routines. Although memorisation mechanisms imply a degree of durability, our procedural and declarative knowledge, and our memorisation processes, evolve so that individuals and organisations can project themselves into the future and innovate. Some authors highlight the necessity of dreaming and forgetting (Bergson 1896); others believe that emotions play a role in our memorisation processes (Damasio 1994). These dimensions are not only important at the individual level but also in an organisational context (Lazaric and Denis 2005; Reynaud 2005; Pentland and Feldman 2005).I review the individual dimension of these memorisation processes, with the Anderson’s distinction between procedural knowledge and declarative knowledge. I discuss the notion of cognitive automatisms in order to show why routines should be investigated beyond their first literal assumption (Bargh, 1997). This leads to a clear understanding of the micro level that underpins organisational flexibility and adaptation (notably the motivational triggers). Within organisations, the memorisation mechanisms are at once similar and diverse. Indeed, organisations use their own filters and mechanisms to generate organisational coordination. Organizational memory has its own dimension as it does not merely consist of the sum of individual knowledge and must be able to survive when individuals leave. Routines depend on the organisational memory implemented and on the procedural knowledge and representations of it (individual and collective representations).Knowledge; memorisation; organizations; individuals