Abstract: The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.
Abstract: Knowledge is considered as an important asset which
can help organizations to create competitive advantage. The necessity
of taking care of these assets is more important in these days – in
days of turbulent changes in business environment. Knowledge could
facilitate adaption to constant changes. The aim of this paper is to
describe how the knowledge sharing can be supported in the
manufacturing companies. The methods of case studies and grounded
theory were used to present information gained by carrying out semistructured
interviews. Results show that knowledge sharing is
supported in very similar ways in respondent companies.
Abstract: The purpose of this paper is to propose a text mining
approach to evaluate companies- practices on affective management.
Affective management argues that it is critical to take stakeholders-
affects into consideration during decision-making process, along with
the traditional numerical and rational indices. CSR reports published
by companies were collected as source information. Indices were
proposed based on the frequency and collocation of words relevant to
affective management concept using text mining approach to analyze
the text information of CSR reports. In addition, the relationships
between the results obtained using proposed indices and traditional
indicators of business performance were investigated using
correlation analysis. Those correlations were also compared between
manufacturing and non-manufacturing companies. The results of this
study revealed the possibility to evaluate affective management
practices of companies based on publicly available text documents.
Abstract: The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.
Abstract: Small and Medium Sized Enterprises (SMEs) play an important role in many economies. In New Zealand, for example, 97% of all manufacturing companies employ less than 100 staff, and generate the predominant part of this industry sector-s economic output. Manufacturing SMEs as a group also have a significant impact on the environment. This situation is similar in many developed economies, including the European Union. Sustainable economic development therefore needs to strongly consider the role of manufacturing SMEs, who generally find it challenging to move towards more environmentally friendly business practices. This paper presents a systems thinking approach to modelling and understanding the factors which have an influence on the successful uptake of environmental practices in small and medium sized manufacturing companies. It presents a number of causal loop diagrams which have been developed based on primary action research, and a thorough understanding of the literature in this area. The systems thinking model provides the basis for further development of a strategic framework for the successful uptake of environmental innovation in manufacturing SMEs.
Abstract: Capital structure is one of the most important financial
decisions in corporate financing strategy. It involves the choice of
debt and equity level in financing a company-s operations. This study
aims to investigate whether the capital structure choice of Malaysian
electrical and electronic manufacturing companies that are listed in
the Bursa Malaysia can be explained by factors that have been found
by most studies as dominant determinants of capital structure
(company size, profitability, asset tangibility, liquidity and growth).
Using debt ratio as the proxy for capital structure and applying
pooled ordinary least square multiple regression estimation, the
results showed that on average, Malaysian electrical and electronic
manufacturing companies used less debt in funding their business
operations. The findings also showed that size and asset tangibility
has a significant positive relationship with debt level, while liquidity
has a negative significant relationship with leverage.