Environmental Engineering Reference
In-Depth Information
6 Conclusions
A key component in the success of industrial companies is their ability to sustain
innovativeness, i.e. the ability to continuously develop innovations. The rapid evo-
lution of technology, fast changing markets, environmental legislation and increas-
ingly demanding customers has led to a need to develop high quality new products
more efficiently and effectively, and according to green and sustainable manufac-
turing. Development of new and potentially successful products is a crucial pro-
cess in maintaining a company's competitive position. To increase the chance to
achieve a successful product, the identification of key success factors of innovation
is needed.
The presented approach aims to identify the key success factors of past prod-
ucts for improving the innovation of new products that are in the development
process. The data is collected from employees who evaluate the implementation
of eco-innovation, along with data retrieved from ERP systems, which are now
being used by more and more industrial enterprises. The proposed method takes
into account both qualitative and quantitative data, and uses the neural network
techniques and fuzzy logic approach to identify the factors that have a significant
impact on product success. These relationships are further used to forecast the suc-
cess of products that are in the development process.
This study offers insight into how decision-makers' can seek to manage prod-
uct development in a more sustainable way, by exploring how product-developing
companies can use an ERP system to identify the key factors of product success
and improve the process of development of innovation. Moreover, by identifying
seven areas (R&D, production, logistics, sales and marketing, eco-organization,
eco-process, eco-product) and several indicators in each area, this study contrib-
utes to the understanding of what areas/indicators are especially significant in
developing new products.
The proposed approach presents new insights and advances to literature on the
topic of measurement of the implementation of eco-innovation, using fuzzy num-
bers to describe subjective judgments and the artificial neural network to seek the
relationships between the implementation of eco-innovation and success of a prod-
uct. The benefits of the presented approach include the use of the sought relation-
ships to identify a set of the most promising products from within the development
process, and determine the most profitable proportion of eco-products in a compa-
ny's product range. Consequently, these actions enable an increase of company's
competitiveness.
The main limitation of the proposed approach is connected with considering
new products as modifications of previous products that belong to the same prod-
uct line. In the case of radical improvements in a new product variant, there can be
a lack of sufficient data to identify key success factors. Moreover, the presented
approach is based on data from ERP systems, which are mainly used in medium
and large companies, rather than in small businesses. Future research could be
focused on the development of the proposed approach towards a reduction of the
mentioned limitations.
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