Wyniki wyszukiwania - BazTech

1

The innovation chain in rail transport

Dinges W., Pieriegud J.

Acta Innovations

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2016

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no. 18

23--32

EN

The paper proposes a classification of innovations in rail transport by compiling a catalog of innovations and introduces the notions of innovation diffusion and innovation chain. It also provides examples of innovative products and technology solutions developed by Polish manufacturers.

2

Innovation Diffusion in the Supply Chain

Laskowska-Rutkowska A.

Research in Logistics & Production

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2015

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Vol. 5, No. 3

287--297

EN

Professional literature has not devoted too much attention to innovation diffusion in the supply chain. Specificity of innovation in supply chains also lacks relevant classification. The aim of this paper is to fill this gap by suggesting classification of innovation adjusted to specificity of supply chains and to describe the model of innovation diffusion in the supply chain. In order to verify the model of innovation diffusion in the supply chain, the following research methods were used: literature research, case description and instrumental case study. These are qualitative methods, often applied in theories of management. There were presented two instrumental descriptions of cases, being an effect of literature research and four descriptions of cases, being based on descriptions of innovative solutions in the logistics, submitted to the Polish contest titled "Innovative Solutions for the Logistics" in 2011. The research hypothesis, which is the model of innovation diffusion in the supply chain, has only been partly validated. As a result, the model of innovation diffusion in the supply chain requires further research, with the aim of the confirmation or the falsification.

3

Dynamics of innovation diffusion with two step decision process

Szymczyk M., Kamiński B.

Foundations of Computing and Decision Sciences

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2014

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Vol. 39, No. 1

39--53

EN

The paper discusses the dynamics of innovation diffusion among heterogeneous consumers. We assume that customers’ decision making process is divided into two steps: testing the innovation and later potential adopting. Such a model setup is designed to imitate the mobile applications market. An innovation provider, to some extent, can control the innovation diffusion by two parameters: product quality and marketing activity. Using the multi-agent approach we identify factors influencing the saturation level and the speed of innovation adaptation in the artificial population. The results show that the expected level of innovation adoption among customer’s friends and relative product quality and marketing campaign intensity are crucial factors explaining them. It has to be stressed that the product quality is more important for innovation saturation level and marketing campaign has bigger influence on the speed of diffusion. The topology of social network between customers is found important, but within investigated parameter range it has lover impact on innovation diffusion dynamics than the above mentioned factors.

4

Measuring commercial software operational reliability: an interdisciplinary modelling approach

Shatnawi O.

Eksploatacja i Niezawodność

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2014

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Vol. 16, no. 4

585--594

EN

In the software reliability engineering (SRE) literature, few attempts have been made to model the failure phenomenon of commercial software during its operational use. One of the reasons can be attributed to the inability of software engineers to measure the growth in usage of commercial software while it is in the market. It is unlike the testing phase where resources follow a definite pattern. In this paper, an attempt has been made to model the software reliability growth linking it to the number of users. Since the number of instructions executed depends on the number of users. The number of users is estimated through an innovation diffusion model of marketing. Once the estimated value is known, the rate at which instructions are executed can be found. The intensity with which failures would be reported depends upon this value. To model the failure observation or defect removal phenomena, a non-homogenous Poisson process (NHPP) based software reliability models developed in the literature have been employed. Software reliability models are most often used for reliability projection when development work is complete and before the software is shipped to customers. They can also be used to model the failure pattern or the defect arrival pattern in the field and thereby provide valuable input to maintenance planning. Numerical example with real software field reliability data is presented to illustrate descriptive and predictive performance as well as to show practical applications of the proposed models.

PL

Literatura dotycząca inżynierii niezawodności oprogramowania, podejmuje zaledwie nieliczne próby modelowania zjawiska uszkodzenia oprogramowania komercyjnego w trakcie jego eksploatacji. Jednym z powodów może być to, iż programiści nie są w stanie zmierzyć wzrostu użytkowania oprogramowania komercyjnego w trakcie obrotu handlowego tego typu oprogramowaniem. Etap ten różni się bowiem od fazy testowania, gdzie zasoby funkcjonują według określonego wzorca. W niniejszej pracy podjęto próbę stworzenia modelu wzrostu niezawodności oprogramowania łącząc to pojęcie z pojęciem liczby użytkowników, jako że liczba wykonywanych poleceń zależy właśnie od liczby użytkowników. Liczbę użytkowników szacuje się na podstawie modelu marketingu opartego na dyfuzji innowacji. Gdy szacowana wartość jest już znana, można określić częstość wykonywania poleceń. Intensywność zgłaszania uszkodzeń zależy od tej wartości. Do modelowania zjawisk zaobserwowania uszkodzenia lub usunięcia usterki zastosowano opracowane wcześniej w literaturze modele niezawodności oprogramowania oparte na niejednorodnym procesie Poissona (NHPP). Modele niezawodności oprogramowania są najczęściej wykorzystywane do projektowania niezawodności już po zakończeniu prac rozwojowych, ale zanim jeszcze oprogramowanie dotrze do klientów. Mogą być również stosowane do modelowania wzorców uszkodzeń lub wzorców występowania usterek w trakcie eksploatacji, stanowiąc tym samym cenny wkład do planowania czynności konserwacyjnych. Przykład liczbowy uwzględniający dane z eksploatacji rzeczywistego oprogramowania ilustruje opisowe i predykcyjne możliwości proponowanych modeli, jak również pokazuje, jak można je stosować w praktyce.