To overcome the challenges posed by increasing competition, many traditional manufacturing companies are moving from the mere production of manufacturing goods to the integration of services that are more or less integrated into the product, which is also due to the constant development of the industry. Moreover, many manufacturing companies offer products that use smart technologies. This paper focuses on the importance of smart service provision for cooperation and innovation flexibility, innovation performance and business performance in small and medium manufacturing companies. The paper aims to find out if smart service manufacturing providers are different in cooperation and innovation flexibility and innovation and business performance from non-smart service manufacturing providers. To better understand the issue, research was undertaken in 112 small and medium manufacturing companies of the Czech Republic. The problems of smart service provision were investigated in the first empirical research held among the electric engineering companies (CZ-NACE 26 and CZ-NACE 27) in the Czech Republic. The findings show that smart service manufacturing providers are better in internal cooperation flexibility, innovation flexibility related to product and to accompanying services and in business performance than non-smart service manufacturing providers. Theoretical implication contributes in two specific ways: first, in the presentation of the interconnection of smart services and cooperation flexibility, innovation flexibility, innovation performance and business performance; and second, in the identification of the impact of smart services in manufacturing SMEs and in finding out which areas affect the provision of smart services. The findings can have a positive influence in several areas; therefore, they can be important factors for many manufacturing companies which still need some persuasion to offer smart services.
The article aims to provide a theoretical basis for the assessment of the institutional impact on oil production. The availability of fuel is the key driver of the functioning national economy, which determines the strategic and tactical landmarks of socioeconomic development and vectors of the country's foreign economic course. Such tendencies are represented in the results of the provided correlation analysis of the fluctuation between oil-production volumes and greenhouse gas emissions, the use of alternative energy sources, the number of patents for oil production, and unemployment. The provided bibliometric analysis, which was made using VOSviewer, has shown the content of interconnections between the categories of oil production and institutional determinants. The authors hypothesised that changes in the institutional environment and their interconnectedness formed a chain “oil production and oil rents → the level of corruption → the efficiency of public governance”. The hypothesis was confirmed by constructing a system of dynamic models and using the Generalised Method of Moments. The calculations confirmed that oil rents were associated with corruption and were a direct threat to the stability of public institutions. An increasing level of corruption was associated with an increase in the level of rent payments and occurred only when the quality of democratic institutions was below the threshold level. The current level of efficiency in public administration did not have a significant impact on national oil production. Of all indicators, only the level of political stability had a statistically significant impact on oil production. The identified interconnections provide the basis for creating an efficient state policy aimed at effectively functioning state institutions, which promote the development of the oil industry, and the reduction of the country's energy dependence as well as strengthen the resilience of the national economy.
This study aims to explore the predominant critical success factors (CSFs) for the implementation of lean manufacturing (LM) in small and medium-sized enterprises (SMEs) producing machinery and equipment (M&E). The convergent parallel mixed-methods (qualitative and quantitative) were employed in three Malaysian M&E manufacturing SMEs. The study identified four predominant CSFs that significantly impact on the LM application in M&E manufacturing SMEs, namely, leadership and commitment of the top management, training to upgrade skills and expertise, employee involvement and empowerment, and the development of LM implementation framework for SMEs. This study can assist the M&E manufacturing SMEs in prioritising these predominant CSFs so that the management teams can work on the improvement strategy and achieve a higher level of lean sustainability. It offers valuable insights into the LM implementation that could provide a practical reference guide to other industrial companies.
Engineering management and engineering projects are subject to greater levels of uncertainty and complexity as part of the current dynamic and competitive industrial environment. Engineering managers need to navigate the arising challenges and consequently gain access to effective decision-making processes. Engineering education has a clear role to play here. However, formal education in quantitative methods is only part of the solution — engineers and engineering managers should also have access to a broader set of skills and knowledge to be effective in the industrial landscape. Therefore, we now need a new paradigm for engineering management and the decision-making process. This article draws on supporting material from the literature and the insights gained from a series of industrial cases using the participatory action research method and a process of inductive reasoning to allow synthesis of generalised propositions that are linked to the industrial cases and antecedent factors from the literature. The findings lead to a set of areas that require further development to support engineering managers to be more effective when dealing with increasing levels of uncertainty and complexity. This includes a number of areas, which are as follow: the need for engineering managers to have enhanced professional skills and knowledge; the importance of experience-based judgement; effective knowledge management; supportive leadership and overall organisational culture; and a holistic approach to decision-making. The research study has practical relevance to engineering management practitioners working in industrial companies to support self-evaluation and professional development. The findings are also pertinent to academic researchers seeking to evaluate decision-making models as part of extending the current understanding of the field of engineering management in technology-based organisations.
The purpose of the article is to present the method for forecasting one of the three categories of exploitation costs, i.e., operational costs. The article analyses the available subject literature discussing the methods of measuring operational costs used in the LCC analysis. The presented method for forecasting operational costs of technical objects applies econometric modelling, probability distributions and certain elements of descriptive and mathematical statistics. The statistical data analysis was performed using the functions and commands available in Microsoft Excel. Weibull++ application was also used for constructing probability distributions for random variables and verifying hypotheses. The method was tested on eight single-mode railbuses, operated by one of the regional railway companies providing passenger transport. An ex-post relative forecast error was used to measure the level of accuracy of the operational cost forecast. The analysis of the compliance between forecasted cost value and the actual costs showed extensive convergence as evidenced by the level of estimated relative errors. In forecasting the operational costs of railbuses, the average error was approx. 2.9%. The presented method can, therefore, constitute the basis for the estimation of both operational costs and exploitation costs, which represent an important cost component considered when assessing the profitability of purchasing one of the several competing technical objects offered by the industry.
Although the occurrence of road accidents and the number of road accident casualties in almost all Polish voivodeships has decreased over the last few years, the rate of this change varies considerably from region to region. To provide a better understanding of such a tendency, panel data regression models are proposed to conduct this pilot research which evaluates the relative performance of Polish regions in terms of their road traffic safety. Panel data are multi-dimensional data which involve measurements over time. In the research, a voivodeship is a unit analysed at a group level, whereas a year is a unit analysed at a time level. A two-way error component regression model has been applied to survey the impact of regressors, the group effects, and time effects on a dependent variable. The analysis has been conducted using data acquired from the Statistics Poland Local Data Bank website, as well as from the General Directorate for National Roads and Motorways. The panel data from 16 regions in Poland and the 2012–2018 period have been investigated. The examined models refer to road traffic safety indices defined based on the following characteristics: the number of road accidents, the number road fatalities, and the number of people injured. The results of all the three models indicate a negative effect as regards the GDP per capita, (car) motorisation rate, the indicator of government expenditure for current maintenance of national roads, and the road length per capita. A positive association has been found between the truck motorisation rate and the indicator of local government expenditure on roads. The impact of the region's urbanisation indicators on road safety is ambiguous as, on the one hand, its increase causes a reduction in the road accident and accident injury indices, but, on the other hand, it produces a rise in the accident fatality index. In the models, the significance of time effects has been identified; a decreasing time trend suggests a general improvement in road safety from year to year. Most of the group effects have turned out to be highly significant. However, the effects differ as regards both the road accident and the accident injury indices in magnitude and direction.
The article aims to present practical methods for prioritising the activities of maintenance departments based on the Pareto analysis and the failure risk analysis. Based on the collected data on the number of observed failures and their removal times, commonly known reliability indicators were determined, which were then used to estimate the probabilities and consequences of failures in terms of the risk of loss of production continuity. Based on commonly collected failure data, the developed methods allow proposing to the maintenance departments the sequence of maintenance and repair work to be undertaken in terms of minimising the risk of failure. Risk analysis is somewhat commonly used in the practice of maintenance departments (e.g. RBI, FMEA, ETA, FTE, HIRA). The added value of this work is the use of reliability indicators for estimating the values of risk components, i.e., probability and consequences. The method was developed on the basis of operational data collected in one of the plants of the dairy cooperative and, after assessing the effects of its implementation, it was implemented in other enterprises of the cooperative.
Warehouses are crucial infrastructures in supply chains. As a strategic task that would potentially impact various long-term agenda, warehouse location selection becomes an important decision-making process. Due to quantitative and qualitative multiple criteria in selecting alternative warehouse locations, the task becomes a multiple criteria decision-making problem. Current literature offers several approaches to addressing the domain problem. However, the number of factors or criteria considered in the previous works is limited and does not reflect real-life decision-making. In addition, such a problem requires a group decision, with decision-makers having different motivations and value systems. Analysing the varying importance of experts comprising the group would provide insights into how these variations influence the final decision regarding the location. Thus, in this work, we adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to address a warehouse location decision problem under a significant number of decision criteria in a group decision-making environment. To elucidate the proposed approach, a case study in a product distribution firm was carried out. Findings show that decision-makers in this industry emphasise criteria that maintain the distribution networks more efficiently at minimum cost. Results also reveal that varying priorities of the decision-makers have little impact on the group decision, which implies that their degree of knowledge and expertise is comparable to a certain extent. With the efficiency and tractability of the required computations, the TOPSIS method, as demonstrated in this work, provides a useful, practical tool for decision-makers with limited technical computational expertise in addressing the warehouse location problem.